How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

The Study of Man. By Michael Polanyi. Price, $1.75. Pp. 102. University of Chicago Press, 5750 Ellis Ave., Chicago 37, 1959. One subtitle to Polanyi's challenging and fascinating book might be The Evolution and Natural History of Error , for Polanyi is at pains to expunge what he believes to be the false notion contained in the contemporary view of science which treats it as an object and basically impersonal discipline. According to Polanyi not only is this a radical and important error, but it is harmful to the objectives of science itself. Another subtitle could be Farewell to Detachment , for in place of cold objectivity he develops the idea that science is necessarily intensely personal. It is a human endeavor and human point of view which cannot be divorced from nor uprooted out of the human matrix from which it arises and in which it works. For a good while

Personal Knowledge: Towards a Post-Critical Philosophy

체험마케팅(Experiential Marketing)의 오해와 진실

CROMBACH, Lee J., GLESER, Goldine C., NANDA, Harinder & RAJARATNAM, Nageswar. The dependability of behavioral measurements: theory of generalizability for scores and profiles. New York, John Wiley and Sons, 1972.

The dependability of behavioral measurements: theory of generalizability for scores and profiles

Information management for the intelligent organization: The art of scanning the environment

The conference “SIIE” aims to develop the dialogue between experts and researchers from public and
private sectors, to acquire basic and experimental on Information Systems (IS) and Economic Intelligence
(EI) (or Competitive Intelligence in English acceptance and terminology). This promotes, in a risk
environment, technologies related to economic intelligence. The dynamic of EI depends on the control of
knowledge and requires competences to design the best strategies and ensure that decision-­makers take
the right decisions. The international conference SIIE will be held in its seventh (7th.) edition in Marrakech
in April 2017, after the six successful editions. This edition is organized by CIEMS and IEEE Technology &
Engineering Management Society (TEMS), and sponsored by the Universities of Maghreb and Europe
countries. The theme of SIIE is « Trends in Technology Management and Economic Intelligence ». Since
2008, the six proceedings and editions have allowed academic researchers and economic actors to achieve
completed projects. The goal of SIIE is to continue in this way by creating opportunities, ideas and
innovative ways to enhance projects, and build connections between universities and industries on both
sides of the Mediterranean Sea. SIIE'2017 includes keynotes, tutorials, authors’ sessions and industrial
panels, animated by experts, to identify new approaches and knowledge in economic intelligence, applied
research and feedback. This will allow the emergence of new clusters in competitive intelligence. Within a
convivial and comfortable framework, as Morocco knows so well how to offer such a framework, the SIIE
conference has always been thought to promote the weaving of trust networks between actors in academia,
industry and politics, thus contributing to the training of the SIIE scientific community. The expert
recommendations and advices will help the SIIE community to find solutions to their many questions and
problems.

7th. Information Systems and Economic Intelligence, SIIE 2017 : Proceedings

Email marketing is an effective channel in marketing strategies not only as a tool to increase brand visibility and brand awareness, but also as an excellent tool to help promote and sell. It will continue to be a critical channel for content marketers. Even with the advent of social media and networking platforms, email marketing still remains the most preferred channel for generating leads, informing and influencing customers. In this paper, we present our experiences using a learning model on predicting the "click" and "conversion" of email-marketing. We present a comparative study on the most popular machine learning methods applied to the challenging problem of email marketing personalization. Subject and sender lines have a strong influence on click rates of the emails, as the customers often open and click emails based on the subject and the sender. We propose a method to aid the marketers by predicting subject-line click rates by learning from history of subject lines. In the first step of our experiences, all models were applied and evaluated by cross-validation. In the second step, the improvement of the performance offered by the boosting has been studied. In order to determine the most efficient parameter combinations we performed a series of simulations for each method and for a wide range of parameters. Our results demonstrate that it is possible to predict the rate for a targeted marketing email to be clicked or not.

Data-driven marketing: how machine learning will improve decision-making for marketers

Nowadays, digital marketing has become an important tool to increase both activities and revenue of each enterprise. Therefore, many enterprises from different fields have integrated big data and data mining utilities, in order to well classify and target the marketing offers according to member's specific and individual needs taking advantage of member's navigational behavior. Personalized Email Marketing is the process of delivering right offer, at the right time to the right person based on the customer's profile. The main objective of Personalized Email Marketing is to identify the needs of a customer and offer products and services that appeal to that particular customer. In this perspective of research, a comparative study concerning several classification algorithms that can be handle this kind of problems, will be discussed in this paper.

Classification and Prediction Based Data Mining algorithms to Predict Email Marketing Campaigns

Personalization and relevance have become more and more critical factors for all kind of media used in digital marketing. Email marketing which is the most used media in the field can be more effective if emails can reach the right customers at the right time. However, the use of data analytics technics and machine learning algorithms can help marketers to make a good decision about how to plan a successful campaign strategy based on legitimate predictive intelligence. In this paper, our research team will present several experiences in how to make a learning model, to predict the clicks of the targeted emails. The proposed model will be based on several features extracted from different campaigns and email recipients profiles. Then it will be established by using five Machine learning algorithms for classification including Decision tree, Bagging classifier, adaptive Boosting, Neural Network, Random Forest to choose which one of them is suitable to predict clicks rates according to several criteria like subject-lines, from-lines, device, offers and vertical.

Machine Learning as an Efficient Tool to Support Marketing Decision-Making

Patents have become a potentially powerful data sources and a wealth of information for companies and organizations that tend to analyze and exploit them for a variety of purposes and interests. However, with the ever-increasing volume of patents filed year after year and the multiplicity of patent global data bases, the task of patent research and analysis is becoming increasingly complicated and traditional analytical approaches have shown their limitations and have become costly in terms of time and labor. Thus, various techniques and approaches have been proposed to help specialists in their tasks of patent collection, analysis and results visualization. Document Clustering is one of these common technics widely used in patent analysis. Over time, several powerful clustering techniques and algorithms have emerged, but they often require modifications and adaptations depending on the fields of application and the target data. In view of this, we propose in this paper a methodology for obtaining an efficient clustering for patent documents based on the k-means, k-means ++ algorithm and various data-mining and text-mining techniques. Commons issues often faced during the analysis of patents such as the manipulation and representation of textual data or the curse of dimension will also be addressed in this study.

Anass El Haddadi

Papers

7th. Information Systems and Economic Intelligence, SIIE 2017 : Proceedings

Data-driven marketing: how machine learning will improve decision-making for marketers

Classification and Prediction Based Data Mining algorithms to Predict Email Marketing Campaigns

Machine Learning as an Efficient Tool to Support Marketing Decision-Making

Automatics Tools and Methods for Patents Analysis: Efficient Methodology for Patent Document Clustering