There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Basics of qualitative research: Grounded theory procedures and techniques

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

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

物件導向軟體之架構(Object-Oriented Software Construction)探討

Smart city services are supported by information and communication technologies (ICT) referred to as digital technologies which increasingly promise huge opportunities for growth but are faced with system alignment and data integration issues when providing digital services. Therefore, this study aims to use enterprise architecture (EA) in digital transformation of cities by developing an architecture to address system alignment and data integration in digital transformation of cities.,Qualitative method is applied to evaluate the presented architecture based on electric-mobility (e-mobility) scenario, and data was collected using case study via interviews from a municipality in Norway to validate the applicability of EA for digital transformation of city services.,Findings from the interviews were represented in ArchiMate language to model the digital transformation of e-mobility in smart cities. Findings suggest that the architecture serves as a guide to recommend urban administrators of the potential of EA and digital transformation in addressing system alignment and data integration issues in smart cities.,Data used in this study is from a single case, hence there is a need to evaluate the application of EA for digital transformation of city services with data collected from multi-cases.,This study adopts enterprise architecture approach to support city transformation as it has been widely applied by institutions to align business and ICT components.,This study provides implication on how municipalities can use EA and digital transformations towards a sustainable smart city.,An architecture is presented that can be used as a guide to help urban developers and designers in deploying sustainable transport policies for smart cities. Additionally, EA is used to foster digitalization towards achieving system alignment and data integration in cities to support urban environment as they digitally transform services provided to citizens.

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Digital transformation with enterprise architecture for smarter cities: a qualitative research approach

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online.

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Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda

The cost and value of data quality have been discussed in numerous articles; however, suitable and rigor cost measures and approaches to estimate the value are rare and indeed difficult to develop. At the same time, as a critical concern to the success of organizations, the cost and value of data quality become important. Numerous business initiatives have been delayed or even cancelled, citing poor-quality data as the main concern. Previous research and practice have indicated that understanding the cost and value of data quality is a critical step to the success of information systems. This chapter provides an overview of cost and value issues related to data quality. This includes data quality cost and value identification, classification, taxonomy, and evaluation framework, as well as analysis model. Furthermore, this chapter provides a guideline for cost and value analysis related to data quality.

Cost and Value Management for Data Quality

The number of people looking for health information on the Internet is constantly growing. When searching for health information, different types of users, such as patients, clinicians or medical researchers, have different needs and should easily find the information they are looking for based on their specific requirements. However, generic search engines do not make any distinction among the users and, often, overload them with the provided amount of information. On the other hand, specific search engines mostly work on medical literature and specialized web sites are often not free and contain focused information built by hand. This paper presents a method to facilitate the search of health information on the web so that users can easily and quickly find information based on their specific requirements. In particular, it allows different types of users to find health web pages with required language complexity levels. To this end, we first use the structured data contained in the web to classify health web pages based on different audience types such as, patients, clinicians and medical researchers. Next, we evaluate the language complexity levels of the different web pages. Finally, we propose a mapping between the language complexity levels and the different audience types that allows us to provide different types of users, e.g., experts and non-experts with tailored web pages in terms of language complexity.

Facilitating access to health web pages with different language complexity levels

In order to use external knowledge sources for innovation activities in organizations, recently Crowdsourcing platforms have been increasingly suggested and used. Critical success factors for such platforms include user motivation and participation, however the effect of those factors is still little understood. The aim of this paper is to analyze the extent to which selected Crowdsourcing platforms consider motivating and incentive factors from a human computer interaction perspective. Motivated by Malone’s principles for designing enjoyable user interfaces we employed this framework as reference to conduct a participatory heuristic evaluation. The results of this paper demonstrate that there are several areas of improvement. At present intrinsically motivating factors in regard to the user interface are only addressed to a limited extent.

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Markus Helfert

Papers

Digital transformation with enterprise architecture for smarter cities: a qualitative research approach

Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda

Cost and Value Management for Data Quality

Facilitating access to health web pages with different language complexity levels

Principles of Human Computer Interaction in Crowdsourcing to Foster Motivation in the Context of Open Innovation