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

Knowledge Management

Over the past decade, marketers have been challenged by proponents of individual marketing (e.g., one-to-one marketing, mass customization, personalization) to shift from a focus on market segments to making individually customized offers. Specifically, marketers should develop "learning relationships" with their customers, be able to predict the customers' wants, and tailor their offerings to those preferences. Building on current knowledge regarding the manner in which customer preferences are constructed, the present research examines the basic premise of these new approaches and, specifically, the manner in which customers evaluate and respond to offers that are customized to their wants. A conceptual framework and a series of research propositions are presented regarding key determinants of customers' responses to customized offers, including (a) the characteristics of customers' preferences, (b) cues provided by the offer regarding its fit with the customers' preferences, (c) customers' receptivity to offers seen as customized to their preferences, and (d) the specific customized offer's content and format. The theoretical and practical implications of the framework and research propositions are discussed.

Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions

Location-based check-in services in various social media applications have enabled individuals to share their activity-related choices providing a new source of human activity data. Although geo-location data has the potential to infer multi-day patterns of individual activities, appropriate methodological approaches are needed. This paper presents a technique to analyze large-scale geo-location data from social media to infer individual activity patterns. A data-driven modeling approach, based on topic modeling, is proposed to classify patterns in individual activity choices. The model provides an activity generation mechanism which when combined with the data from traditional surveys is potentially a useful component of an activity-travel simulator. Using the model, aggregate patterns of users’ weekly activities are extracted from the data. The model is extended to also find user-specific activity patterns. We extend the model to account for missing activities (a major limitation of social media data) and demonstrate how information from activity-based diaries can be complemented with longitudinal geo-location information. This work provides foundational tools that can be used when geo-location data is available to predict disaggregate activity patterns.

Urban activity pattern classification using topic models from online geo-location data

Because two decades have almost passed since the introduction of activity-based models of travel demand, this seems the right time to evaluate progress made in the development and application of these models. This invited paper seeks to discuss the initial promises of activity-based models as an alternative to four-step and tour-based models, summarize progress made and identify still unsolved issues that require further research.

Activity-based models of travel demand : promises, progress and prospects

Knowledge management and e-learning in professional development

The rapidly growing use of technology in education is changing the way in which knowledge is produced, stored and distributed. Online education has already been accepted as the way of the future; knowledge may be distributed across both time and space. Knowledge Management (KM) techniques can be used to capture, organise and deliver this knowledge and management systems can be used to quickly identify the most relevant information and distribute it to meet specific needs. In this paper, light is shed on the basic concepts of KM and e-learning. A discussion on how KM and e-learning can be integrated and leveraged for effective online education and training is presented.

The role of knowledge management and e-learning in professional development

Electronic learning (e-learning) is seen as a good solution for organisations that deal with fast-changing knowledge and for reducing the cost of training. E-learning is a good opportunity for companies but it needs to be well prepared because it often takes high investment costs. That is why it is important for a company to know if it is e-ready. E-readiness is already well covered in the literature and several models are suggested. We used these models to develop an e-learning readiness measurement instrument and questionnaire. We used our instrument to check whether the Flemish hospitals were e-ready for e-learning.

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E-learning Readiness Analysis (ERA): an e-health case study of e-learning readiness

2 3 The aim of this paper is to achieve a better understanding of rule-based activity-based 4 models, by proposing a new level of validation on the process model level in the 5 ALBATROSS model. To that effect, the work activity process model, which includes six 6 different decision steps, is investigated. Each decision step is evaluated during the prediction 7 of individuals’ schedules. The comportment of execution in the process model contains 8 activation dependency. This branches the execution and evaluation of each agent under 9 examination. And yields a sequence of decisions for each agent, where the Sequence 10 Alignment Method (SAM) is employed to evaluate how similar/dissimilar predicted with 11 observed decision sequences are. SAM utterly fits for assessing the analysis of decision 12 sequences on this level. The original CHAID decision trees at each decision step utilized in 13 ALBATROSS are compared with other well known induction methods chosen to appraise 14 the purpose of the analyses. Additionally, the performance of the models is compared at three 15 existing validation levels: the classifier or decision step level using confusion matrix 16 statistics. The work activity trips Origin-Destination (OD) matrix level and time of day work 17 activity start time level, using the correlation coefficient. The results of validation on the 18 proposed process model level show conformance to those already existing, with additional 19 information to help in better understanding the process model’s behaviour. 20 21

The usefulness of the sequence alignment methods in

Fraudsters can manipulate telecom regulatory systems to their advantage, and to the disadvantage of the telecom operator, in ways that are difficult to detect, trace, and prosecute. A subscriber whose network has been compromised will often refuse to pay large fraudulent charges, leaving the operator to cover the bill resulting in revenue losses. Moreover, attacks frequently happen over holidays and weekends, when networks are often less monitored closely. This calls for an intelligent automated solution for telecom fraud detection. Wangiri (Japanese term) telecom fraud also referred to as “one ring and cut fraud” relies on this single ring method for a quick way to make money. Missed calls from unknown callers entice subscribers to call back unknowingly premium numbers where they are deceived to stay on the line for as long as possible in an effort to inflate their bill. This paper proposes the use of varied ensemble classifiers to overcome the highly biased dataset and help make a more precise classification in an efficient and effective manner. Extreme Gradient Boosting algorithm was found to have the best results in terms of correctness and performance.

George Sammour

Papers

Knowledge management and e-learning in professional development

The role of knowledge management and e-learning in professional development

E-learning Readiness Analysis (ERA): an e-health case study of e-learning readiness

The usefulness of the sequence alignment methods in

Detection of Wangiri Telecommunication Fraud Using Ensemble Learning