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This paper aims to present an automated system that measures the volume of excavated soil in earthmoving operations. It focuses primarily on presenting the use of the radio frequency identification – real time location technology and image analysis techniques in isolation of stockpiles from their noisy backgrounds, extraction of their geometrical attributes and determination of their location.

An extensive literature review of image analysis techniques is performed to identify the ones most applicable in developing the proposed automated system. A set of techniques are selected and experimented to evaluate their effectiveness in the proposed application. A review of the state‐of‐the‐art distance measurement technologies is also conducted to identify the most suitable system to be used in the proposed system.

A set of image analysis techniques that positively contribute to the accuracy of the proposed system is identified. Geometrical attributes of stockpiles are automatically extracted for volume measurement purposes. The use of an affordable and reliable automated distance measurement tool is also suggested.

Although the proposed methodology is believed to be applicable to most configurations of stockpiles, it was tested on conical ones only.

The current practice of measuring the volume of excavated soil is time consuming and costly. Furthermore, it does not facilitate monitoring of excavation activities on close time intervals. The proposed system overcomes the problems associated with the current practice and provides an automated strategy that can be easily used by field personnel and/or home office management staff to closely monitor the progress of their earthmoving operations without physical human intervention.

The purpose of this paper is to present an artificial intelligent (AI) system for estimating the construction cost of water and sewer rehabilitation projects.

To develop the proposed system, data pertaining to 54 sewer and water rehabilitation projects was collected. The collected data were analyzed using Pareto analysis technique to identify the most important factors that contribute positively to the cost estimation process. These factors were then utilized to develop a neural network (NN) model that estimates the construction cost of this class of projects.

The study reveals a set of 23 factors that highly impact the construction cost of water and sewer network rehabilitation projects and presents a NN model that predicts the cost of these projects with high accuracy.

The proposed system was developed using information obtained from the city of San Diego, California, USA. The cost of these projects ranged from $800,000 to $7 million. The diameter of pipes installed in these projects ranged from 1 in. to 36 in. and their length was up to about 2.7 miles.

The developed system saves time, improves the accuracy of the estimates and prevents problems that are usually associated with inaccurate estimates. The system will not only help funding authorities to ensure maximum utilization of resources, but will also help cities to manage their expenditures in a manner that assures satisfactory performance of their buried assets. Furthermore, the developed system is also believed to assist cities in comparing alternatives and the go/no‐go decision making process.

The purpose of this paper is to explore an identified medication error using a root cause analysis and a clinical case study.

In this paper the authors explore a medication error through the completion of a root cause analysis and case study in an aged care facility.

Research indicates that medication errors are highly prevalent in aged care and 40 per cent of nursing home patients are regularly receiving at least one potentially inappropriate medicine (Hamilton, 2009; Raban et al., 2014; Shehab et al., 2016). Insufficient patient information, delays in continuing medications, poor communication, the absence of an up-to-date medication chart and missed or significantly delayed doses are all linked to medication errors (Dwyer et al., 2014). Strategies to improve medication management across hospitalisation to medication administration include utilisation of a computerised medication prescription and management system, pharmacist review, direct communication of discharge medication documentation to community pharmacists and staff education and support (Dolanski et al., 2013).

Discussion of the factors impacting on medication errors within aged care facilities may explain why they are prevalent and serve as a basis for strategies to improve medication management and facilitate further research on this topic.

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms.

Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm).

Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it.

As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

The purpose of this article is to examine the factors that influence turnover intentions by examining the effect of congruent internal marketing on turnover intentions among restaurant employees in North Cyprus. The study also integrates work immersion variables to factor in the reduction of turnover intentions of employees.

The quantitative study employed questionnaires distributed to 150 participants at various restaurants throughout Northern Cyprus. The study's results were utilized to examine the effect of several components of congruent internal marketing on turnover intention, including work engagement, job satisfaction, coaching and employee development structural equation modelling.

The study confirmed a positive and significant relationship between congruent internal marketing and turnover intentions, work engagement, job satisfaction and management coaching. It is evident that congruent internal marketing is contingent on elements that minimize employees' turnover intentions. Given the increasing pace of voluntary and involuntary turnover, it is critical to provide concise research for both present and future analysis.

The data collection for this study was limited to the cities of Northern Cyprus, as it focuses on small business operations. Additionally, the researchers were able to collect data from only 30 establishments during the pandemic. Quintessential implication of the current study posits how internal marketing efforts coupled with coaching and engagement practises helps to reduce turnover.

Recently, turnover intention has emerged as a major source of concern for business and organizational survivors, particularly during this COVID-19 pandemic. This had a detrimental effect on local and international enterprises, health, education, hotels and tourism. In essence, the current crisis has had a profound effect on both internal marketing and employee turnover intentions. Drawn from the POS and SET theories, the current study develops and tests parsimonious model in fostering avenues to reduce turnover.

The purpose of this paper is to utilize critical success factors (CSF) and identify items Green Belt (GB) practitioners note as barriers to completion of Six Sigma (SS) projects in a major manufacturer setting.

The design of this paper is a descriptive study of a single location of a global manufacturer’s internal data and survey of accredited GBs who have completed an SS project for company accreditation utilizing company focus on CSFs.

The results demonstrate the GB practitioners have competing priorities, have time constraints and lack project management skills that reduce timely completion of SS projects. Top management responsibility for SS GB projects are defined through the CSFs of leadership, project management and project selection.

This study pertains to the single manufacturing location of a major, multinational company. The survey of SS GBs is limited to those individuals who have become accredited to company requirements, in the initial stages of strategic implementation, resulting in a small sample size. All GB projects follow the DMAIC (Define, Measure, Analyze, Improve and Control) methodology. The implications may be reproduced in similar environments where GBs conduct SS projects to test the robustness of the study.

This study underscores the importance of proper coaching and mentoring of SS practitioners, especially those who are expected to contribute as GBs in a part-time manner. Implementation of SS goes beyond initial deployment and requires active mentoring of GB practitioners to make sure that SS projects get proper focus. The results are relevant to both researchers and practitioners.

This paper examines SS projects with a GB perspective, an important contribution to SS but lacking in the literature. While GBs are important to SS implementation, and serve as a pathway to fulltime SS personnel, there are few studies that note this work. This study will support practitioners in the importance of wider SS deployment through active support of GBs, where top management responsibility for GB success is defined through CSFs for improvement.

This paper aims to present a process to generate physics-based trade-off curves (ToCs) to facilitate lean product development processes by enabling two key activities of set-based concurrent engineering (SBCE) process model that are comparing alternative design solutions and narrowing down the design set. The developed process of generating physics-based ToCs has been demonstrated via an industrial case study which is a research project.

The adapted research approach for this paper consists of three phases: a review of the related literature, developing the process of generating physics-based ToCs in the concept of lean product development, implementing the developed process in an industrial case study for validation through the SBCE process model.

Findings of this application showed that physics-based ToC is an effective tool to enable SBCE activities, as well as to save time and provide the required knowledge environment for the designers to support their decision-making.

Authors expect that this paper will guide companies, which are implementing SBCE processes throughout their lean product development journey. Physics-based ToCs will facilitate accurate decision-making in comparing and narrowing down the design-set through the provision of the right knowledge environment.

SBCE is a useful approach to develop a new product. It is essential to provide the right knowledge environment in a quick and visual manner which has been addressed by demonstrating physics knowledge in ToCs. Therefore, a systematic process has been developed and presented in this paper. The research found that physics-based ToCs could help to identify different physics characteristics of the product in the form of design parameters and visualise in a single graph for all stakeholders to understand without a need for an extensive engineering background and for designers to make a decision faster.

This study seeks to empirically examine the influence of corporate governance on corporate performance in Ghana.

The study employs data from 30 listed firms spanning from 2008 to 2018 and applies the generalized method of moments technique. The authors use economic value added, shareholder value added (SVA) and economic margin (EM) as measures of corporate performance.

The findings reveal that the presence of both inside directors and outside (nonexecutive) directors significantly improves corporate performance, lending credence to both the stewardship theory and the agency theory. The inclusion of women on the corporate boards and frequent meetings of the board reduce the economic profits of firms. The authors find that CEO duality impedes corporate performance, supporting the presumption of the agency theory. The study further reveals that audit committee size and ownership concentration positively drive the performance of quoted firms in Ghana.

Prior studies on corporate governance and firm performance nexus have chiefly adopted traditional accounting-based performance measures such as return on assets and return on equity to evaluate firm performance. However, these indicators are critiqued for being historic and fail to consider firms' cost of equity. In light of the shortcomings of the accounting-based proxies, this study takes a unique direction by using value-based metrics, which are considered superior measures of performance. Besides, to the best of the authors' knowledge, this study provides a first attempt to investigate the link between corporate governance and firm performance using SVA and EM as performance indicators.

The purpose of this paper is to review the existing literature on the critical success factors (CSFs) of various continuous improvement (CI) like total quality management (TQM), Lean, Six Sigma and Lean Six Sigma (LSS), and perform a content analysis (CA) leading to an agenda for future research.

CA is based on literature review of 41 papers published in peer-reviewed scholarly journals. A four-stage methodology is used by the authors, with Stage 1 featuring relevant material collection; Stage 2 presenting a descriptive analysis; Stage 3 outlining the category selection; and Stage 4 featuring the material evaluation using clustering and Pareto analysis.

The CA revealed various research implications and directions for the future. The number of studies in the literature regarding CSF assessment is found to be far from substantial while compared to CI implementation. Assessment methodologies developed have been ranging from qualitative to quantitative models. Key gaps were identified and directions for CSF assessment are proposed which requires further validation.

Only articles that appeared in peer-reviewed journals were analyzed; conference papers, reports, manuals and white papers from practice were excluded. Another limitation of the paper would be the scope of CA limited to four CI constructs – TQM, Lean, Six Sigma and LSS. Research implications indicate that the future studies in this domain would be intensive toward analyzing the vital CSFs for enterprise organizational excellence and not merely process excellence.

This review study can serve as a resource for both researchers and practitioners to understand the importance of CSFs and positioning CSFs for the successful implementation of CI programs.

The CA on the existing literature on CSFs for CI presents a unique, systematic effort to identify an agenda for future research. This study is the apparently the first of its kind on CSF assessment.