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

Context Systematic mapping studies are used to structure a research area, while systematic reviews are focused on gathering and synthesizing evidence. The most recent guidelines for systematic mapping are from 2008. Since that time, many suggestions have been made of how to improve systematic literature reviews (SLRs). There is a need to evaluate how researchers conduct the process of systematic mapping and identify how the guidelines should be updated based on the lessons learned from the existing systematic maps and SLR guidelines. Objective To identify how the systematic mapping process is conducted (including search, study selection, analysis and presentation of data, etc.); to identify improvement potentials in conducting the systematic mapping process and updating the guidelines accordingly. Method We conducted a systematic mapping study of systematic maps, considering some practices of systematic review guidelines as well (in particular in relation to defining the search and to conduct a quality assessment). Results In a large number of studies multiple guidelines are used and combined, which leads to different ways in conducting mapping studies. The reason for combining guidelines was that they differed in the recommendations given. Conclusion The most frequently followed guidelines are not sufficient alone. Hence, there was a need to provide an update of how to conduct systematic mapping studies. New guidelines have been proposed consolidating existing findings.

Guidelines for conducting systematic mapping studies in software engineering : An update

Dorothy Leonard-Barton warns readers upfront that her new book, "Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation," is not a quick read. Indeed it is not, though the author, a Harvard Business School professor, tries hard to soften a knot of strategy research with true-life examples from several technology companies. Unfortunately, business books tend to be like Arnold Schwarzenegger movies: the special effects and twists are a lot more important than the basic plot, which doesn't change much.

"Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation"

The corporation of the 1990s: Information technology and organizational transformation

Cloud computing is a new way of delivering computing resources and services. Many managers and experts believe that it can improve health care services, benefit health care research, and change the face of health information technology. However, as with any innovation, cloud computing should be rigorously evaluated before its widespread adoption. This paper discusses the concept and its current place in health care, and uses 4 aspects (management, technology, security, and legal) to evaluate the opportunities and challenges of this computing model. Strategic planning that could be used by a health organization to determine its direction, strategy, and resource allocation when it has decided to migrate from traditional to cloud-based health services is also discussed.

Opportunities and challenges of cloud computing to improve health care services.

Cloud consumers have a variety of deployment related techniques, such as auto-scaling policies and recovery strategies, for dealing with the uncertainties in the cloud. Uncertainties can be characterized as stochastic (such as failures, disasters, and workload spikes) and subjective (such as choice among various deployment options). Cloud consumers must consider both stochastic and subjective uncertainties. Analytic support for consumers in selecting appropriate techniques and setting the required parameters in the face of different types of uncertainty is currently limited. In this paper, we propose a set of application availability analysis models that capture subjective uncertainties in addition to stochastic uncertainties. We built and validated the models by using industry best practices on deployment, and actual commercial products for disaster recovery and live migration. Our results show that the models permit more informed and quantitative availability analysis than industry best practices under a wide range of scenarios.

Incorporating Uncertainty into In-Cloud Application Deployment Decisions for Availability

This paper reports research-in-progress that considers standardization as an enabler in business ecosystems Based on issues encountered, we propose that: business model design is also critical for standards bodies; standards can separate competition from cooperation; standards employ rule-centric designs; and requirements specification and compliance checking are essential The propositions are illustrated using a standards body with which we have been working

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Standardization as a Business Ecosystem Enabler

A basic assumption underlying any process improvement initiative is that it will have a positive impact on the organization. Therefore, it can become easy to assume that process change will in fact deliver benefits to business. This paper takes a practice-based look at some fundamental assumptions about process improvement that can be as fallacious as they can be true. The argument is supported by case scenarios. Also, some ways are suggested to manage around these fallacies to achieve net benefits rather than no impact or negative impacts on the business. Four basic fallacies are considered: that process improvement leads to business improvement; that process change equates to process improvement; that software processes are non-lethal; and the vision of the enterprise as an automated process. The paper concludes that the future success of process improvement as a management strategy is dependent upon the capability of organizations to capture material gains.

Capturing business benefits from process improvement: four fallacies and what to do about them

Deployment choices are critical in determining the availability of applications running in a cloud. But choosing good deployment for various software application components into virtual machines is a challenging task because of potential sharing of components among applications and potential interference from multi-tenancy. This paper presents an approach for improving the availability guarantee of software applications by optimizing the availability, performance and monetary cost trade-offs of different deployment choices. Our approach explicitly considers different classes of application requests during the decision process. The results of our experimental evaluation show that the approach can effectively improve the availability guarantees with little or negligible increase in the performance and monetary cost of the deployment choice.

Improving Availability of Cloud-Based Applications through Deployment Choices

A common view is that good project management is necessary but not enough to ensure project success. This begs the question: Why not? This paper examines current factor-, process-, and capability-based explanations of project performance and finds gaps in their explanatory power. An alternative explanation is developed from organizational learning and capability theory that integrates drivers for project success and drivers for project failure in one model. Project performance is modeled as the contested outcome of the two opposing drivers, enabling unpredictable variations in project outcomes. A key contribution of the model is the inclusion of drivers of underperformance and failure. These comprise common learning barrier conditions that can reduce or negate the performance value of organizational capabilities applied to projects. Two types of conditions are described: liabilities of incumbency and liabilities of newness. Together, these negative drivers can offset or overwhelm the positive effects of organizational learning on capability accumulation, creating a net performance liability, leading to underperformance or failure— even in well-managed projects. The performance model is illustrated using a longitudinal case study. The model improves our understanding of project performance as well as our ability to explain industry data on project outcomes and anomalous inconsistencies in performance in successive projects. Implications of the model are discussed, including how it might be used in practice. The paper argues from an information systems project perspective, but the model also has relevance to other application domains.

Paul L. Bannerman

Papers

Incorporating Uncertainty into In-Cloud Application Deployment Decisions for Availability

Standardization as a Business Ecosystem Enabler

Capturing business benefits from process improvement: four fallacies and what to do about them

Improving Availability of Cloud-Based Applications through Deployment Choices

Why Good Project Management Is Not Enough: Liabilities of Incumbency and Newness