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Companies coping with global business are called more and more to lead their transformation into intelligent organizations, i.e. organizations able to leverage their intellectual capital to achieve their business objectives and envision new development paths. This paper, assuming that the organizational intellectual capital corresponds to the knowledge assets attributed to a company, aims to discuss the organizational value, the processes, the approaches and the evaluation architectures of the knowledge asset assessment strategies.

On the basis of a literature review, first the sub‐processes of the assessment are discussed, highlighting the main features of the assessment of knowledge assets as well as the twofold nature of the knowledge asset assessment, distinguishing between stock and flow perspective of the evaluation. Second, the managerial reasons at the basis of the implementation of measurement systems for disclosing organizational knowledge resources are discussed. Finally, according to a distinction between the assessment approaches and the evaluation architectures, four main knowledge asset assessment strategies are defined.

Despite a number of models to assess intellectual capital being proposed, they highlighted a need for clarification of their strategic focus. With the aim of filling this gap, the main finding of the paper is to clarify relevant issues providing an organic, literature review‐based and coherent framework about the knowledge assets assessment strategies.

The identification of these strategies not only contributes to advance the theory of knowledge asset assessment, but also provides insights to managers to better define and implement models and approaches for measuring organizational knowledge resources. Especially they support managers in choosing and arranging critical information for defining the most suitable decisions and actions concerning the effective management and development of a company's intellectual capital.

The purpose of this paper is to address the problem of designing a performance measurement system (PMS) for research and development (R&D) activities; in particular, it investigates if and how different objectives for the PMS use influence the design of its constitutive elements.

A literature review was first conducted, aimed at identifying the constitutive elements of a PMS for R&D and the major purposes for its use. Then, a multiple case study involving four Italian technology‐intensive firms was undertaken for investigating the relationship between PMS constitutive elements and measurement purposes.

Different PMS objectives imply significantly different design choices for constitutive elements. Even when companies are very different in terms of size and sector of activity, similar objectives lead to very similar PMSs.

The research is qualitative. Further research should aim to increase the rationality and objectivity of the proposed relationships and explore the joint effects of the measurement objectives and other contextual factors on the measurement system design.

R&D managers who plan to design a PMS for their departments can follow the guidelines suggested in the paper to tune the basic features of the PMS to the real objectives they mean to pursue.

It is the first attempt, to the best knowledge of the authors, that explicitly and practically suggests how to tailor the design of each PMS's constitutive element according to the objectives that are pursued.

To provide an overview of self‐validating sensor technology for researchers and engineers which can help them understand the concept and recent developments of this research area.

The concept of self‐validating (SEVA) sensors, including definition, output parameters and requirement of SEVA sensors are introduced. The differences between SEVA sensors and traditional sensors are given from which we can see many advantages of SEVA sensors. The principium of SEVA sensors is presented by the functional architecture. The research development of SEVA sensors is introduced in two aspects: research development of sensor fault diagnosis and signal reconstruction and research development of SEVA sensor hardware.

Summarizes the methods for sensor fault diagnosis and signal reconstruction in the research of SEVA sensors, and the development steps of SEVA sensor hardware. Indicates the shortages and problems of current research and gives our research and ideas to solve these problems.

This paper provides a detailed description and research information of self‐validating sensor technology for those who want to know and research on this area.

The purpose of this paper is to present a model for measuring enterprise architecture (EA) readiness in organizations.

This paper reviews the literature of enterprise architecture, defines and extracts factors and indicators of enterprise architecture readiness with factor weighting and indicators. Thereafter the authors propose a model to measure organization EA readiness in practice.

In this paper nine factors and 34 indicators that affect enterprise architecture are defined and an enterprise architecture readiness model is constructed. The model of research is practical, because it specifies readiness of organization in enterprise architecture and helps to define current state of organization via factors and indicators, and from priorities of factors and indicators, alerts organization of an optimization strategy.

In order to increase the utility of the model for use in other organizations, further studies can help to refine the model.

The essence of extracting and choosing factors and indicators in this study is from enterprise architecture processes and key success and failure factors of enterprise architecture in organizations. In addition selected factors and indicators related to organization infrastructure not related only to enterprise architecture project planning and executing are examined through the use of a questionnaire. The model derived from this study can be utilized as a multi‐dimensional and practical model because of its complexity and examination of organizational infrastructures.

The purpose of this paper is to introduce a layered, comprehensive model of quality of service (QoS) for local eGovernment, and discuss its feasibility on a regional eGovernment case study. The eGovernment online services are becoming a key infrastructure for advanced countries. They allow significant efficiency gains in different sectors of society, offering benefits for individual citizens and for the community as a whole. The deployment of online services alone is not sufficient in order to qualify an eGovernment strategy. The intrinsic and perceived quality of services offered, as well as the actual impact of new functionalities, should be properly measured and taken into account.

This paper presents an applied research study for a quality‐focused evolution of a service‐oriented architecture for local eGovernment portals. This investigation was based on three main layers: the perceived quality and effective impact of services (G2C layer), the effectiveness of the deployed processes (WFM layer) and finally, the system‐level efficiency (G2G layer).

The measurement of quality with respect to eGovernment services is a complex task which requires appropriate tools to tackle the different aspects of the problem. Specifically, active and passive tools (respectively surveys and usage analysis) should be used to evaluate the quality perceived by the users as well as the utility of the service itself. The efficiency of the back office workflow must be estimated measuring statistical and dynamical indicators. Finally, technical measures should be used to monitor the responsiveness and scalability of software implementations and deployment systems.

A better knowledge regarding (e‐)Government service delivery processes, their QoS and their impact on the society can empower both citizens and local administrators, and can help them to better improve the effectiveness of local government.

The multi‐layered quality measurement architecture proposed in this paper offers local governments the capability to systematically monitor and analyse the quality of their online services. The business process management technologies allow citizens to get a better knowledge of the service delivery processes; the QoS measurements allow to improve control on them; and the eGovernment Intelligence model allows to better quantify their actual social impact.

Art and architecture are multidisciplinary fields with complicated assessments that, similar to the teaching and the learning process, are inconsistent with common assessment models. During submission, the presence of jurors, who have different approaches and standards for evaluating design projects, makes this issue more complicated. Numerous assessment and grading models are being used in architecture departments around the world. Some of these methods are based on the experiences of instructors from their lives as students, whereas some are based on university systems. Grading policies seem to be clear in most fields, but when the object of grading is an artistic product with different levels to be assessed and graded, the issue becomes more complex. This observation is especially true in the case in which quality is given a numerical grade.

The nature of skills that students are expected to develop and that are going to be assessed is often subjective. Such skills include invention, problem solving, and presentation. Problems of reliability, personal criterion, and unique perceptions lend difficulty in assessing such skills using traditional methods.

This research suggests that a criteria-based assessment and grading model is a more effective model in promoting student learning, making assessment and grading less complex and more explicit. The effectiveness and reliability of the proposed and implemented criteria-based grading system has been tested at Eastern Mediterranean University using distributed questionnaires and a Rasch measurement system.

Architecture influences people and the environment from the past, present and the future. Nevertheless architecture and design quality is viewed as subjective, and benchmarks to achieve consensus are necessary for design or evaluation of buildings. This paper establishes architectural design criteria for design quality of multi-storey housing buildings. A set of the criteria was established with literature review, an operational definition and survey on qualified persons or architects in the professional practice of architecture. The literature reviews identified seven concepts for architecture and design quality, and the operational definition translated this architectural design quality to measurable and observable cases and variables. The survey collected these variable data from a purposive sample of 95 respondents, and these data were examined by statistical analysis. The results of the descriptive statistics, inferential t-tests (p ≤ 0.05) and positive hypothesis testing verified that respondents in general agreed to these seven design concepts as architectural design criteria for design quality. These results established the first ever set of seven architectural design criteria which were ranked in descending order of significance as function, socio-culture, site context, cost, aesthetic of art, sustainability, and Feng Shui. These architectural design criteria can be applied to the design or evaluation of multi-storey housing buildings for the good of people and the environment.

The relationship between the built environment and the human body is rarely considered explicitly in contemporary architecture. In case architects do take the body into account, they tend to derive mathematical proportions or functional dimensions from it, without explicit attention for the bodily experience of a building. In this article, we analyse the built environment in a way less common in architecture, by attending to how a particular person experiences it. Instead of relating the human body to architecture in a mathematical way, we establish a new relationship between architecture and the body—or a body—by demonstrating that our bodies are more involved in the experience of the built environment than we presume. The article focuses on persons with a sensory or physical impairment as they are able to detect building qualities architects may not be attuned to. By accompanying them during a visit to a museum building, we examine how their experiences relate to the architect's intentions. In attending to the bodily experiences of these disabled persons, we provide evidence that architecture is not only seen, but experienced by all senses, and that aesthetics may acquire a broader meaning. Senses can be disconnected or reinforced by nature. Sensory experiences can be consciously or unconsciously eliminated or emphasized by the museum design and use. Architects can have specific intentions in mind, but users (with an impairment) may not experience them. Attending to the experiences of disabled persons, and combining these with the architect's objectives, provides an interesting view of a building. Our analysis does not intend to criticize the one using the other; rather the combination of both views, each present in the building, makes for a richer understanding of what architecture is.

Past studies have paid extensive attention to investigate learning issues in individual, organizational and team contexts. Learning activities in the workplace, however, often occur in the interdisciplinary dyadic context. Hence, the purpose of this paper is to clarify what factors make interdisciplinary dyads lead to better learning effects. The authors attempted to clarify two major agendas: What knowledge factors (intellectual capitals) owned by the parties of the dyads can induce better learning effects? What contextual factors (learning tasks) can make better learning effects during the dyadic learning process?

To examine the previous agendas, the authors first conducted in-depth interviews and an exploratory survey so that a four-element dyadic intellectual capital (DIC) architecture was circumscribed: knowledge interdependency, expertise similarity, collaborative routines and mutual trust. Dyadic learning tasks were classified as exploitative and exploratory learning. The authors then sampled 248 respondents for the formal empirical survey to examine the relationship between DIC, dyadic learning tasks and knowledge sharing.

The statistical evidences confirmed the positive relationships between DIC and knowledge sharing, and the results also signified the previous relationships with a mediating effect from exploratory learning, while the effect of exploitative learning was not supported. Thus, only dyads (the two heads) with knowledge interdependency and mutual trust can make the exploratory task better than that of one individual (the one head).

This study provides a new insight into the learning issue with an interdisciplinary dyadic perspective to supplement the existing gap between academic efforts and learning practices in the workplace.

Environmental issues are rapidly emerging as one of the most important topics in strategic manufacturing decisions. Growing public awareness and increasing government interest in the environment have induced many companies to adopt programmes aimed at improving the environmental performance of their operations. State of the art literature has proposed many models to support executives in the assessment of a company’s environmental performance. Unfortunately, none of these identifies operating guidelines on how the systems should be adapted to support the deployment of different types of “green” manufacturing strategies. The present paper seeks to illustrate techniques and architecture for performance measurement systems (PMSs) to support the implementation of feasible “green” manufacturing strategies.