Miguel Ángel Manzanedo

Predisposition of Workers to Share Knowledge: An Empirical Study

Our objective is to determine the factors and obstacles that either contribute or complicate knowledge exchange between workers. This research area is new in Spain, as only three references have been found for firms in China and North America. In our approach to this topic, we apply concepts of a sociological, psychological, and motivational nature, which affect knowledge exchange and sharing and allow us to justify the theoretical basis of this study, as well as its purpose and its organizational benefits. A detailed survey was prepared for the empirical research with 21 questions given to a sample of 557 workers from firms in Burgos. Among the positive factors that contribute to knowledge sharing, the results highlight recognition and an appreciation of the worker’s contribution, the work environment and reciprocity. The most important barriers are the poor quality of employment contracts, fellow workers that do not wish to learn, and unfair and disloyal behaviour.

A hybrid proposal for cross-sectoral analysis of knowledge management

At present time, although many theoretical formulations have been successfully proposed, there is a lack of ICT-based tools to support practical deployment of knowledge management (KM) in real settings. To bridge this gap, a hybrid artificial intelligence system is proposed in present study, aimed at gaining deeper knowledge about KM practices in four different economic sectors. By means of soft computing, companies are diagnosed according to their status regarding KM and subsequent explanations about crucial KM practices and perspectives are generated. Interesting conclusions are then derived from these explanations, allowing KM managers to optimise their decisions and obtain better results. Experimental results of real-life data from Spanish companies associated with different economic sectors validate the proposed combination of techniques.

Analysis of Knowledge Management in Industrial Sectors by Means of Neural Models

It is required for an organization, before successfully applying a Knowledge Management (KM) methodology, to develop and implement a knowledge infrastructure, consisting of people, organizational and technological systems. Up to now, few approaches have been proposed for such technological systems supporting KM in organizations. Present paper advances previous work by proposing neural projection models for the analysis of the KM status of companies from two different industrial sectors. Exploratory methods are applied to real-life case studies to know and understand the structure of KM data. Subsequently, the application of such models generates meaningful conclusions that allow experts to diagnose KM from two different points of view: companies on the one hand and industrial sectors on the other hand.

Hybrid Visualization for Deep Insight into Knowledge Retention in Firms

Neural projection models are applied in this study to the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. More precisely, data projections are combined with the glyph metaphor to analyse KM data and to gain deeper insight into patterns of knowledge retention. Following a preliminary study, the retention of specialized employees in hi-tech companies is investigated, by applying the configurational approach of Strategic HR Management. The combination of these two aforementioned techniques generates meaningful conclusions and the proposal is validated by means of an empirical study on a real case study related to the Spanish hi-tech sector.

Studying Road Transportation Demand in the Spanish Industrial Sector Through k-Means Clustering

Transportation is the economic activity that is the most tightly coupled with the other ones. As a result, knowledge about transportation in general, and market demand in particular, is key for an economic analyisis of a sector. In present paper, the official data about the industrial sector, coming from the Ministry of Public Works and Transport in Spain, is analysed. In order to do that, k-means clustering technique is applied to find groupings or patterns in the dataset that contains data from a whole year (2015). Samples allocation to clusters and silhouette values are used to characterize the demand of the industrial transportation. Useful insights into the analysed sector are obtained by means of the clustering technique, that has been applied with 4 different criteria.

The Characteristics of Workers as Mediators in Knowledge Sharing Within a Company

The importance of knowledge sharing to a company is such that it can, to a large extent, determine the success or failure of the competitiveness and survival of the organisation. One of the problems facing the management of knowledge, to ensure its application in the company, is the existence of barriers or impediments to effective exchange. Some of these impediments are due to the personality and personal circumstances of the employee, as well as certain management decisions, which have an influence on the willingness of workers to share their knowledge. The aim of this paper is to look at individual strategies that people employ so as not to share knowledge, as well as to identify certain management decisions and behaviour which may hamper this exchange. As far as the empirical work is concerned, a survey has been prepared, aimed at employees, unemployed workers and students in their final year at university, which was completed by a total of 1088 people. Some of the most pertinent results point to initial proactive attitudes towards knowledge exchange that weaken, depending on a person’s age, their professional situation, type of contract, their salary, and their level of motivation. Similarly, the management decisions that contribute most to the success of knowledge sharing are ensuring good organization, a suitable organizational culture and climate, compatible leadership styles and fair recruitment, pay, and promotion policies in the eyes of employees.

Applications of the Lagrangian Relaxation Method to Operations Scheduling

Lagrangian Relaxation is a combinatorial optimization method which is mainly used as decomposition method. A complex problem can be divided into smaller and easier problems. Lagrangian Relaxation method has been applied to solve scheduling problems in diverse manufacturing environments such as single machine, parallel machine, flow shop, job shop or even in complex real-world environments. We highlight the two key issues on the application of the method: the first one is the resolution of the dual problem and the second one is the choice which constraints should be relaxed. We present the main characteristics of these approaches and survey the existing works in this area.

An Empirical Analysis of the Most Influential Components of an Income Statement

This paper presents an empirical analysis of income statements in 1,000 Spanish companies. Firstly, the relation between the components of an income statement is studied, focusing on the operative profit/loss. Then, the most influential components in the operative profit/loss are identified and their effects measured. Finally, we conclude that they provide business groups with higher predictable profits.