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Using a portfolio comprising liquid global stocks and bonds, this study aims to limit absolute risk to that of a standardised benchmark and determine whether this has a significant impact on expected return in both high volatility period (HV) and low volatility period (LV).

Using a traditional benchmark comprising 40% equity and 60% bonds, a constant tracking error (TE) frontier was constructed and implemented. Portfolio performance for different TE constraints and different economic periods (expansion and contraction) was explored.

Results indicate that during HV, replicating benchmark portfolio risk produces portfolios that outperform both the maximum return (MR) portfolio and the benchmark. MR portfolios outperform those with the same risk as that of the benchmark in LV. The MR portfolio weights assets to obtain the highest return on the TE frontier. During HV, the benchmark replicated risk portfolio obtained a higher absolute risk value than that of the MR portfolio because of an inefficient benchmark. In HV, the benchmark replicated risk portfolio favoured intermediate maturity treasury bills.

There is a dearth of literature exploring the performance of active portfolios subject to TE constraints. This work addresses this gap and demonstrates, for the first time, the relative portfolio performance of several standard portfolio choices on the frontier.

This paper aims to investigate the mechanisms for managing coordinated benchmarking projects and the outcomes achieved from such coordination. While there have been many independent benchmarking studies comparing the practices and performance of public sector organisations, there has been little research on initiatives that involve coordinating multiple benchmarking projects within public sector organisations or report on the practices implemented and results from benchmarking projects. This research will be of interest to centralised authorities wishing to encourage and assist multiple organisations in undertaking benchmarking projects.

The study adopts a case study methodology. Data were collected on the coordinating mechanisms and the experiences of the individual organisations over a one-year period.

The findings show successful results (financial and non-financial) across all 13 benchmarking projects, thus indicating the success of a coordinated approach to managing multiple projects. The study concluded by recommending a six-stage process for coordinating multiple benchmarking projects.

This research gives new insights into the application and benefits from benchmarking because of the open access the research team had to the “Dubai We Learn” initiative. To the authors’ knowledge the research was unique in being able to report accurately on the outcome of 13 benchmarking projects with all projects using the TRADE benchmarking methodology.

This paper aims to understand the issue of interest rate benchmarking in Islamic financial institutions (IFIs) from a macro-economic perspective and assessing the relevance of creating a Sharīʿah-compliant profit rate benchmark to solve this issue. This paper also aims at suggesting an Islamic alternative that will handle both the negative economic impact on IFIs as well as on their financial performance.

The paper is based on literature review of conventional finance and Islamic finance theories to construct a theoretical model to assess the impact of interest rate benchmarking on the ability of IFIs to achieve the objectives of the Islamic economy.

The macro-economic perspective concludes that conceiving a profit rate benchmark for the Islamic finance industry is not relevant to raising the Sharīʿah credibility of the industry. Indeed, several adjustments need to be introduced in terms of the business model.

The recommendations of this paper require the involvement of financial authorities and governments for their implementation. Indeed, the adjustments require a macro-economic review.

The paper considers a profit rate benchmark irrelevant and inefficient. Instead, it suggests the necessary adjustments in terms of business model and economic approach for IFIs to achieve their objectives.

The paper considers zakat implementation and the adjustment of IFIs as the real path to implement a fair wealth distribution in the society.

The creation of a profit rate benchmark has always been the only solution for the pricing issue in IFIs. This paper challenges this idea and tries to give a deeper understanding of the situation.

This study aims to analyze how the interplay between hard and soft elements of total quality management (TQM) produces the conditions for sustaining success in the quest for quality.

A qualitative analysis (Gioia method) was carried out on an original dataset collected through both direct and indirect methods (i.e. archival sources, interviews and observations) to generate a new interpretive framework.

The interpretative framework identifies four categories of elements: trigger elements create the starting conditions for a quality virtuous cycle; benchmarking tools set the standards of performance; improvement tools enable exploration of the space of possible alternative practices and finally, catalytic forces allow the institutionalization of effective techniques discovered in this search process into new standards.

The findings the authors present in this paper are derived by a single case study, limiting the generalizability of our results in other settings.

This study has three implications: first, the design of trigger elements is critical for the success of any TQM initiative; second, the interplay of improvement and benchmarking tools at several levels should be coherent and third, to exploit the potential of TQM, efforts should be devoted to the dissemination of new effective practices by means of catalyzing elements.

The model provides a more specific understanding of the nature and purpose of the hard and soft elements of TQM and the dynamic interaction between the two classes of elements over time.

While all recoveries are good, some are better than others with regard to their speed and/or magnitude. Many revenue-related key performance indicators (KPIs), such as comparisons to budgets and forecasts that were designed pre-pandemic to assess a hotel's or destination's performance are no longer valid. Therefore, the primary purpose of this conceptual paper is to highlight the need to peg financial-related KPIs relative to competitors' performance during and following a radical market disruption. The secondary purpose of this paper is to summarize advances reported in the literature and in the industry related to competitor benchmarking and accurately identifying competitor sets.

This conceptual paper synthesizes research from disparate sources to offer a series of recommendations to the industry regarding best practices for developing and monitoring revenue-related KPIs during pandemic recovery. Such KPIs will be different based upon hospitality or tourism sector but must be largely founded upon benchmarking off comparable operations.

Industry disruptions triggered by COVID-19 underscore the need (1) to increasingly utilize competitor-based revenue KPI benchmarks; (2) to have reliable competitor benchmarking data more readily available for use by hotels and destination marketing organizations (DMOs) and (3) for both hotels and DMOs to more accurately identify their competitive sets.

The recommendations offered in this paper are anchored with appropriate theories and empirical research; and as a consequence, offer guidance for the industry for KPI formulation during and following the pandemic.

Inverse problems in electromagnetism, namely, the recovery of sources (currents or charges) or system data from measured effects, are usually ill-posed or, in the numerical formulation, ill-conditioned and require suitable regularization to provide meaningful results. To test new regularization methods, there is the need of benchmark problems, which numerical properties and solutions should be well known. Hence, this study aims to define a benchmark problem, suitable to test new regularization approaches and solves with different methods.

To assess reliability and performance of different solving strategies for inverse source problems, a benchmark problem of current synthesis is defined and solved by means of several regularization methods in a comparative way; subsequently, an approach in terms of an artificial neural network (ANN) is considered as a viable alternative to classical regularization schemes. The solution of the underlying forward problem is based on a finite element analysis.

The paper provides a very detailed analysis of the proposed inverse problem in terms of numerical properties of the lead field matrix. The solutions found by different regularization approaches and an ANN method are provided, showing the performance of the applied methods and the numerical issues of the benchmark problem.

The value of the paper is to provide the numerical characteristics and issues of the proposed benchmark problem in a comprehensive way, by means of a wide variety of regularization methods and an ANN approach.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

Exposure to unhealthy lifestyle behaviours before pregnancy affects the health of mothers and their (unborn) children. A social marketing strategy could empower prospective parents to actively prepare for pregnancy through preconception care (PCC). This study aims to describe the development of a PCC social marketing strategy based on the eight-point benchmark criteria for effective social marketing and to clarify the concept of using social marketing for health promotion purposes.

An extensive literature search was carried out regarding the needs of the target population and PCC behavioural goals, leading to the development of a bottom-up, ambassador-driven, communication concept.

In-depth insights of all benchmarks were analysed and incorporated during the development process of a new PCC social marketing strategy, with a special focus on the application of the “Health Belief Model” (Benchmark 3) and “the Four-P framework” (Benchmark 8). Evidence-based preconceptional health information is our product, for a low price as the information is freely attainable, promoting a message of overall women‘s health and online or through a consult with a health-care provider as the appropriate place. This formative research resulted in the development of the Woke Women® strategy, empowering women to actively prepare for pregnancy.

Developing a social marketing strategy to enhance actively preparing for pregnancy shows potential to encourage prospective parents to adopt healthier preconceptional lifestyle behaviours and can therefore improve the health of future generations.

This study investigates the performance distribution of passive funds in the Korean market and compares it with the performance distribution of active funds. The key findings are as follows, first, the performance distribution of passive funds has a thicker tail compared to that of active funds. There are passive funds that achieve outstanding performance, and both the false discovery rate (FDR) analysis and simulation analysis suggest that their outperformance is driven by managerial skill rather than luck. Second, passive fund performance is more persistent compared to active fund performance. Third, investors are less responsive to passive fund performance compared to active fund performance. The fund flow-performance relationship is significantly positive for active funds but not for passive funds. This implies that investors may not recognize the managerial skills of passive funds.

Stirred by scant regard for market phases in portfolio performance assessments, the current paper investigates the active versus passive investment strategies under the bull and bear market conditions in emerging markets focusing on South Africa as a case study.

Methodologically, the measures of Jensen's alpha and Treynor index are applied to the monthly returns of 20 funds from January 2010 to June 2022.

The results are enlightening; though they contradict developed market evidence, they are consistent with emerging market trends. The findings show that actively managed funds outperform the market benchmark and passive investing style under bear and normal market conditions. Passive investment strategy outperforms both market benchmark and actively investing style under bull market conditions.

In the face of improved market efficiency, increased liquidity and recent technological impact, the findings of this study have practical application. The study outcomes should inform and update global investors, especially asset managers interested in emerging markets; however, the limitations of the study should also be considered.

While limited studies consider market conditions when comparing and contrasting the performance of passive versus active investing, such consideration is lacking in emerging markets. The current study corrects this literature imbalance.