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To define availability importance measures in order to calculate the criticality of each component or subsystem from the availability point of view and also to demonstrate the application of such importance measures for achieving optimal resource allocation to arrive at the best possible availability.

In this study the availability importance measures of a component are defined as a partial derivative of the system availability with respect to the component availability, failure rate, and repair rate. Analyses of these measures for a crushing plant are performed and the results are presented. Furthermore, a methodology aimed at improving the availability of a system using the concept of importance measures is identified and demonstrated by use of a numerical example.

The availability importance measure of a component/subsystem is an index which shows how far an individual component contributes to the overall system availability. The research study indicates that the availability importance measures could be applied in developing a strategy for availability improvement. The subsystem/component with the largest value of importance measure has the greatest effect on the system availability.

The result of availability improvement strategy is demonstrated using only a hypothetical example.

Using availability importance measures will help managers and engineers to identify weaknesses and indicate modifications which will improve the system availability.

This paper presents the concept of availability importance measure for a component/subsystem. It also introduces some availability importance measures based on failure rate, mean time between failures (MTBF), and repair rate/mean time to repair (MTTR) of a component /subsystem. The concept of importance measures is used to prioritise the components or subsystems for the availability improvement process.

The purpose of this paper is to propose new importance measures for degrading components based on Shapley value, which can provide answers about how important players are to the whole cooperative game and what payoff each player can reasonably expect.

The proposed importance measure characterizes how a specific degrading component contributes to the degradation of system reliability by using Shapley value. Degradation models are also introduced to assess the reliability of degrading components. The reliability of system consisting independent degrading components is obtained by using structure functions, while reliability of system comprising correlated degrading components is evaluated with a multivariate distribution.

The ranking of degrading components according to the newly developed importance measure depends on the degradation parameters of components, system structure and parameters characterizing the association of components.

Considering the fact that reliability degradation of engineering systems and equipment are often attributed to the degradation of a particular or set of components that are characterized by degrading features. This paper proposes new importance measures for degrading components based on Shapley value to reflect the responsibility of each degrading component for the deterioration of system reliability. The results are also able to give timely feedback of the expected contribution of each degrading component to system reliability degradation.

This study aims to consider how the consumer ascribes importance to attributes that define a given product or service consumption experience relative to the extent that they contribute to satisfaction. Two separate dimensions of attribute importance are conceptualized, contrary to the more common view that there exists only a single form of importance. A hypothesis of how each type of importance changes throughout a consumption event is constructed in order to illustrate the nature of each importance dimension and how they operate in tandem.

Survey data taken from tourists who were asked to evaluate attributes describing a travel destination are used to illustrate how each type of importance changes.

Changes in each type of attribute importance occurred as predicted. Evidence indicates that the differences between the two types of attribute importance diminish as the consumption experience unfolds.

In addition to validating the theoretical underpinnings of this conceptualization of attribute importance, the study illustrates the usefulness of a framework developed to measure each type of importance.

Importance performance analyses, commonly used by organizations to evaluate and improve product and service performance, should recognize and incorporate these two separate types of attribute importance.

The study provides a new conceptualization of attribute importance.

There is an emerging consensus that systemically important banks should face stricter regulations and systemic surcharges. To make this latter principle operational the regulator will need to quantify the systemic importance of individual banks. The purpose of this paper is to review the proposed measures of systemic importance from the research community and discuss their merits relative to how a regulator would ideally wish to calibrate surcharges on systemically important banks, and to evaluate how useful proposed measures of the systemic importance of financial institutions will be to regulators.

The author reviews the main contributions to the research literature and discusses their relevance for the problem faced by regulators.

There are five main caveats that make the proposed measures of systemic importance less useful for regulators.

The proposed measures may help identify relevant aspects of systemic importance, but the regulators will need to construct their own measures for practical use.

The paper provides a critical review of a research literature that could potentially have large practical implications.

The paper seeks to show that the strategic types of service marketers (e.g. Prospectors, Defenders or Analyzers) match the types of success measures that they use to evaluate new services.

A theory is developed to show why service marketers of different strategic types use different success measures for the evaluation of new services. Using responses from 202 financial services marketers, strategic types are shown to relate in theoretically expected ways with the importance ratings of the categorized success measures.

Notable relationships among strategic types and their success measure are identified. Prospectors, for example, attach greater importance to growth performance measures, consistent with the growth orientation of their service development programs. Defenders, on the other hand, attach more importance than Prospectors to efficiency performance measures, which relate to their programs' efficiency orientation. Analyzers, interestingly, place more emphasis on objectives‐based performance measures, including strategic fit, than Prospectors.

The sampling frame purposely contains only US financial services firms; as such, future research may build upon this single‐industry, single‐country study.

Academic success literature generally disregards the strategic types of respondents in measuring the success of service development programs. Practitioners, however, seek performance measures that are consistent with their firm's business strategy. This study provides a categorization of the most important success measures as appropriate to different strategic types.

The service success literature has often dealt with the question of “what causes success?” and has rarely confronted, head‐on, the question of “what is success?”. This paper addresses this critical research gap.

The purpose of this paper is to analyze the changes in its importance due to the maintenance and repair of components.

In this paper, a concept of time-varying importance measure is proposed to solve the problem of component importance change caused by maintenance. When the system is broken-down, the probability difference between the component works well after repairing and the component break down before repairing is solved, this difference is measured as an index of time-varying importance method. Then, the approach has been verified by the CNC machine tool.

The paper provides a method to analyze the importance of changes of components in the system due to maintenance. The time-varying importance measure can evaluate the component importance anytime during its whole life span, and it has the ability to find out the most responsible component for a system failure in the actual production process. What is more, it provides guidance for the next maintenance work.

The proposed method can guide the next maintenance time according to the change of component performance caused by each maintenance activity of the manufacturing system, and avoid the waste of resources caused by repeated maintenance.

The purpose of this paper is to investigate the effects of audit client importance on future bank risk and systemic risk in US-listed commercial banks.

The authors use archival research method.

The authors mainly find that client importance is negatively related with future bank-specific crash risk and distress risk, and also with sector-wide systemic crash risk and systemic distress risk in the future. The authors also report some evidence that these relations become more pronounced during the crisis period than during the non-crisis period. Moreover, the effect of client importance on systemic risk is found to strengthen in banks audited by Big-N auditors, by auditors without clients who restate earnings, and by auditors with more industry expertise.

These findings contribute to the auditing and systemic risk literature.

This study has implications for regulating the banking industry.

This study provides original evidence on how client importance affects bank-specific risk and systemic risk of the banking industry.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

Connectivism has been proposed to explain the impact of new technologies on learning. According to this approach, learning may occur even outside the individual within an organization or a system. Learning objectives are not defined in advance and learning requires the ability to form connections and use networks to find the required knowledge. The connections by which individuals can learn are more important than what they currently know. The purpose of this paper is to investigate if a measure, rating the importance of concepts, can be derived from a network representation of the learning domain and if highly connected concepts – with high importance value – can describe whether information is explored in such ways as assumed by connectivism.

The authors empirically examined if the proposed measure can provide insight on the role of connections in learning and explain the reasons behind passing certain parts of a test using a linear regression model.

The results are twofold. First, an implementation of the information exploration principle of connectivism has been introduced, applying semantic technologies and the importance measure. Second, although no significant effects could be isolated, trends in performance improvement concerning highly important concepts were identified.

However, connectivism has been known since 2005, it is still lacking for successful implementations. The presented approach of a concept importance measure is a promising starting point by providing means of connected learning, enabling individuals to effectively improve their personal abilities to better fit job demand.

The purpose of this paper is to propose a new measure for criticality analysis of different components of a production plant, called cost‐effective importance measure (CEIM) that considers the component's performance, system structure and economic aspects.

In this work, an explorative literature study covering the concept of importance measure and criticality analysis has been carried out on contemporary literature. The literature study shows that the commonly used importance measures consider the probability of failure of a component and systems structure, and ignore the effect or severity of failures, which is an important factor in engineering decision making. It is not clear how to use the concept of importance measure in combination with cost parameter. Hence, a cost‐effective importance measure (CEIM) is defined and a case study is presented, to demonstrate the application of the proposed importance measure.

The paper indicates that CEIM useful for the analysis of production plants where reliability and cost of break down are of paramount importance.

The concept of CEIM is demonstrated using only a case study of a belt conveyor system in the underground mine of Singareni Coal Company Ltd. However, the concept of CEIM can be used in other area.

The concept of CEIM can be a handy and effective tool for scheduling of inspection, maintenance and fault diagnosis and these activities can be carried out as per the rank of the components to maximize the benefits in skilled manpower crunch. It also indicates that the upgradation of the production plant's performance can also be done by improving performance of components with relatively large CEIM values.

In this paper, the concept of importance measure is extended to include the effect of severity of failures and cost parameter in the criticality analysis.