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Many process capability indices have been proposed to measure process performance. In this paper, we first review C_p, C_pk, C_pm and C_pmk, and their generalizations, C_Np, C_Npk, C_Npm and C_Npmk, and then propose a new index S_pmk for any underlying distribution, which takes into account process variability, departure of the process mean from the target value, and proportion of nonconformity. Proportion of nonconformity can be exactly reflected by S_pmk. Its superiority over C_Npmk, a recently developed index, also taking into account process variability and departure from the target value, is demonstrated with several non‐normal processes. A method is proposed to estimate S_pmk, with illustrations.

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

This issue is a selected bibliography covering the subject of leadership.

When the process probability distribution is non‐normal or is unknown, the process mean and standard deviation may not properly describe the distribution’s shape. Consequently, the traditional process capability indices (PCI) C_p, C_pk, C_pm and C_pmk cannot express the actual process capability. This paper presents a procedure to construct lower confidence limits for PCIs when the process distribution is unknown. First, the order statistics are utilized to find the estimators of C_p, C_pk, C_pm and C_pmk. Bootstrap simulation method is then utilized to construct the lower confidence limits of PCIs, thereby allowing the process’s capability to be evaluated. A numerical example demonstrates the effectiveness of the proposed procedure.

The PRISM SaveScreen facility and function keys provide alternatives in producing data for budget planning and PRISM search cost estimation, and also facilitating PRISM search training. By combining the SaveScreen function key with frequently used PRISM function keys such as 〈SEND〉, 〈DISPLAY RECORD SEND〉, 〈FOR〉, and 〈BAC〉, the activities of a PRISM user can be recorded and stored in a predefined data file for later analyses. For example, from the results of the analyses, the usage levels of OCLC workstations and the approximate monthly PRISM search cost of each workstation were obtained. In addition, based on the recorded search activities, a retraining in PRISM search can be planned. Certainly, by familiarizing oneself with PRISM search commands and search structures, a user can perform cost‐effective and efficient searches. Toward that end, also presents methods of performing cost‐effective PRISM searches.

This paper provides a quantitative review of the literature on the repercussions of idiosyncratic information on firms’ cost of equity (CoE) capital. In total, I review the results of 113 unique studies examining the CoE effects of information Quantity, Precision and Asymmetry. My results suggest that the association between firm-specific information and CoE is subject to moderate effects. First, the link between Quantity and CoE is moderated by disclosure types and country-level factors in that firms in comparatively weakly regulated countries tend to enjoy up to four times greater CoE benefits from more expansive disclosure—depending on the type of disclosure—than firms in strongly regulated markets. Second, a negative relationship between Precision and CoE is only significant in studies using non-accrual quality proxies for Precision and risk factor-based (RFB)/valuation model-based (VMB) proxies for CoE. Third, almost all VMB studies confirm the positive association between Asymmetry and CoE, but there is notable variation in the conclusions reached when ex post CoE measurers are used.

The purpose of this research is to develop a new key performance index (KPI) and its interval estimation for measuring the service quality from customers' perceptions, since most service quality data follow non‐normal distribution.

Based on the non‐normal process capability indices used in manufacturing industries, a new KPI suitable for measuring service quality is developed using Parasuraman's 5th Gap between customers' expectation and perception. Moreover, the confidence interval of the proposed KPI is established using the bootstrapping method.

The quantitative method for measuring the service quality through the new KPI and its interval estimation is illustrated by a realistic example. The results show that the new KPI allows practising managers to evaluate the actual service quality level delivered within each of five SERVQUAL categories and prioritize the possible improvement projects from customers' perspectives. Moreover, compared with the traditional method of sample size determination, a substantial amount of cost savings can be expected by using the suggested sample sizes.

The paper presents a structured approach of opportunity assessment for improving service quality from a strategic alignment perspective, particularly in the five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The new approach provides practising managers with a decision‐making tool for measuring service quality, detecting problematic situations and selecting the most urgent improvement project. Once the existing service problems are identified and improvement projects are prioritized, it can lead to the direction of continuous improvement for any service industry.

Given a managerial target on any desired service level as well as customers' perceptions and expectations, the new KPI could be applied to any non‐normal service quality and other survey data. Thus, the corporate performance in terms of key factors of business success can also be measured by the new KPI, which may lead to managing complexities and enhancing sustainability in service industries.

The purpose of this paper is to develop an yield-based process capability index (PCI), C _py , to overcome the shortcomings of existing PCIs that limit their use and lead to inaccurate measures of quality conformance under a variety of common conditions.

–C _py is developed conceptually to flexibly and accurately reflect conformance and then used to numerically measure inaccuracies of C _pk .

–C _py overcomes many of the problems associated with existing PCIs, including C _pk . The degree of process distribution non-normality, level of quality (the sigma level), and whether the process is centered or shifted left or right affect the direction and size of process capability error produced by C _pk . The accuracy of C _pk can be greatly affected by process data that deviate even slightly from normality.

–C _py offers numerous advantages compared to existing PCIs. It accurately reflects process conformance regardless of the process distribution. It is applicable even if the process has multiple characteristics and with both variable and attribute data. Its calculation is relatively simple and the necessary data for it are likely already captured by most organizations.

The main contributions are the development of a new PCI, C _py ; a conceptual analysis of its advantages; and a numerical analysis of the improved accuracy of C _py as compared to C _pk for shifted and non-shifted process means for normal, nearly normal, and highly non-normal distributions over a range of process variability levels.