In today’s context (competition and knowledge economy), ML and KM on the supply chain level have received increased attention aiming to determine long and short-term success of many companies. However, demand forecasting with maximum accuracy is absolutely critical to invest in various fields, which places the knowledge extract process in high demand. In this paper, we propose a hybrid approach of prediction into a demand forecasting process in supply chain based on the one hand, on the processes analysis for best professional knowledge for required competencies. And on the other hand, the use of different data sources by supervised learning to improve the process of acquiring explicit knowledge, maximizing the efficiency of the demand forecasting, and comparing the obtained efficiency results. Therefore, the results reveal that the practices of KM should be considered as the most important factors affecting the demand forecasting process in supply chain. The classifier performance is examined by using a confusion matrix based on their Accuracy and Kappa value.

Destination management organisations currently operate in an environment where online media greatly influences travellers' decision-making. In this digital environment, electronic word of mouth (eWOM) is considered an important source of information affecting tourist's behaviour and destination image formation. Destination image is also recognised as major element influencing holiday destination choice, intention to revisit a destination and the willingness to recommend it to others. The Theory of Reasoned Action (TRA) in turn, offers a relevant conceptual framework to analyse tourists’ behaviour. This paper aims to (1) incorporate eWOM and destination image as exogenous variables into the TRA model, then evaluate the ability of this extension to predict tourists’ behavioural intention (2) examine both eWOM and destination image impact on intention to visit an emerging destination (3) inspect eWOM role in destination image formation (4) evaluate the ability of the TRA’s core constructs (i.e., attitude and subjective norms) to predict intention. A quantitative approach based on structural equation modelling conducted this study in order to test the extended model, by analysing data collected from 234 potential foreign tourists, selected using a convenience sampling method. Results revealed that the extended model had a good predictive ability for tourists’ intentions to visit an emerging destination. Besides, attitude, subjective norms and destination image were significant predictors of visit intention, and eWOM significantly influenced the image. The study outcomes may help to develop a more efficient and successful tourism marketing strategy.

The motivations for a company to adopt Knowledge Management (KM) and Customer Relationship Management (CRM) systems can be very varied, which brings an additional complexity to the adoption decision. It is important to understand the main drivers of KM and CRM adoption so that companies can better target their performance indicators. However, the influence of KM on organization performance in hotel industry has received less attention. In addition, the relationship between CRM technology and hotel performance is still ambiguous. In the right context, this paper tends to bring attention to the influences of KM and CRM to improve hotels performance. It presents and discusses the main findings of a study undertaken among a sample of large hotels Algerian, identifying and discussing the main motivations for KM and CRM systems utilization and adoption in order to improve hotels performance. A survey of 80 hotels executives was conducted on hotels operating in the Algeria west territory. In short, the results of this study may help these businesses to effectively use KM and CRM, so as to improve hotels performance and subsequently create a sustainable competitive advantage.

In the context current industrial, the companies consider the knowledge as an important resource and strategic for innovation. For it, a good understanding of intellectual patrimony of the company and its environment promotes the emergence of the new ideas. In this paper, we present a new approach to support innovation which builds on the one hand, on the critical knowledge mapping, respecting the principle of the method MASK, and on the other hand on the exploitation of these capitalized knowledge (mapped) for innovate the production processes using the method TRIZ.

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