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The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

The incidents of hotel theft constitute a significant cause of concern for the hotel industry. Such incidents result in substantial financial implications, inventory management issues and complications in finding exact replacements. This study, therefore, investigates customer stealing practices in Indian hotel industry.

A structured email interview was conducted with twenty-five senior hotel managers. The data were analyzed by adopting a coding technique using inductive content analysis.

The results were discussed under seven headings-extent of such stealing incidents, financial consequences of such practices, strategies to prevent such instances and, finally, how to deal with customers when caught stealing.

This study highlighted themes related to the profile of customers involved in stealing practices, motives, consequences and strategies required to manage such customers. Future studies can focus on restaurants, airlines, airports and tourist attractions to identify strategies and best practices for preventing such practices.

The study's findings have tremendous potential for the hospitality industry. Dealing with customers’ stealing practices is a very sensitive issue. Furthermore, hotels bear lots of expenses if the policy for tackling the problem still needs to be implemented. Furthermore, if not handled professionally, a lack of effective guidelines can cause embarrassment for managers.

The study has the potential to raise awareness among the members of society to be more responsible toward hotel property.

This study extends to the limited literature on hotel guests' theft. The comprehensive analysis of the themes generated from an interview with the senior managers of the hotel industry provides a unique insight into the nature of the problem. The solutions offered in this study are an original contribution to the literature.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

This study aims to investigate key aspects of public ownership of land, expropriation and compensation laws and practices in Ethiopia with special reference to Burayu Town.

A mixed research technique of descriptive and analytic approach is applied in the research. This study used a purposive sampling technique to select case study counties and a systematic method for sampling households. Questionnaire surveys, focus group discussions, interviews and observations were used to collect empirical data. Average, percentage and paired-sample t-test analyses are used for quantitative data analysis.

Significant discrepancies exist between the expropriation laws and how property valuation and compensation are practiced in Ethiopia. The findings include the arbitrariness in designating public interest status to projects; unfair property valuation practice that neglects location factor to determine market value due to a skewed understanding of public ownership of land; and the assignment of property valuators who have no valuation expertise and proper knowledge of expropriation related laws. Findings revealed the socio-economic status of expropriated households has deteriorated due to the expropriation of their landholding.

It was difficult to locate the relocated persons as they were resettled in different localities. Furthermore, the town officers were not forthcoming to provide complete information on the expropriation and compensation procedures they followed. However, this study overcame the limitations through persistent requests and availing time for the data gathering.

The findings indicated the need to redefine relationships between public ownership of land, public interest and expropriation of landholding. A proper understanding of the triad will pave the way for better expropriation practice in Ethiopia and in countries where land is under public ownership.

The social implication of the study revealed that the socio-economic situation of relocated persons was adversely affected due to the poor implementation of laws.

The disparity between public ownership of land and the rights of citizens on landholding is misunderstood by policymakers. Research has shown for the first time the root cause for the discontent of expropriated persons in Ethiopia.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

Construction industry faces challenges in making objective decisions due to monetary value attached to quality. Among various quality management techniques available, cost of quality (COQ) is one such method used to address the concern. However, the absence of measurable COQ factors to monitor quality costs hampers the implementation of COQ framework in the construction industry. Therefore, this study aims to identify COQ factors focused on visible factors (VF) and hidden factors (HF) and the current requirements to achieve it.

This study is based on Preferred Reporting Items for Systematic Review and Meta-Analyses protocol guidelines. The present study identified 57 articles published between 1992 and 2023 in peer-reviewed journals.

The findings reveal 22 factors, which are grouped into four categories based on COQ. Through systematic review, the authors observed limited methodological and theoretical diversity. In fact, there are no quantitative frameworks to calculate COQ. The study, therefore, developed a framework comprising four major routes/paths of COQ factors within the framework.

The COQ routes developed through this study will enable the practitioners to meticulously categorise VF and HF, facilitating quantifying of quality throughout the lifecycle of project, which is currently absent from the existing quality assurance/quality control (QA/QC) approach. In addition, these COQ routes stand as essential construction strategies, significantly enhancing outcomes related to time, cost, quality, sustainability and fostering closer relationships within project frameworks.

The current study contributes significantly to the existing body of knowledge by developing various COQ routes and proposing future research directions to address gaps in the literature.

The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.

A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.

Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.

The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.

The United Nations has demonstrated a commitment to preserving the ecosystem through its 2030 sustainable development goals agenda. One crucial objective of these goals is to promote a healthy ecosystem and discourage practices that harm it. Building materials production significantly contributes to the emissions of greenhouse gases. This poses a threat to the ecosystem and prompts a growing demand for sustainable building materials (SBMs). The purpose of this study is to investigate SBMs to determine their utilization in construction operations and the potential impact their application could have on construction productivity.

A systematic review of the existing literature in the field of SBMs was conducted for the study. The search strings used were “sustainable” AND (“building” OR “construction”) AND “materials” AND “productivity”. A total of 146 articles were obtained from the Scopus database and reviewed.

Bio-based, cementitious and phase change materials were the main categories of SBMs. Materials in these categories have the potential to substantially contribute to sustainability in the construction sector. However, challenges such as availability, cost, expertise, awareness, social acceptance and resistance to innovation must be addressed to promote the increased utilization of SBMs and enhance construction productivity.

Many studies have explored SBMs, but there is a dearth of studies that address productivity in the context of SBMs, which leaves a gap in understanding. This study addresses this gap by drawing on existing studies to determine the potential implications that using SBMs could have on construction productivity.

Poor indoor air quality (IAQ) contributing to occupants’ health symptoms is a universal, typically ventilation-related, problem in schools. In cold climates, low-cost strategies to improve IAQ in a naturally ventilated school are rare since conventional methods, such as window opening, are often inappropriate. This paper aims to present an investigation of strategies to relieve health symptoms among school occupants in naturally ventilated school in Finland.

A case study approach is adopted to thoroughly investigate the process of generating the alternatives of ventilation redesign in a naturally ventilated school where there have been complaints of health symptoms. First, the potential sources of the occupants’ symptoms are identified. Then, the strategies aiming to reduce the symptoms are compared and evaluated.

In a naturally ventilated school, health symptoms that are significantly caused by insufficient ventilation can be potentially reduced by implementing a supply and exhaust ventilation system. Alternatively, it is possible to retain the natural ventilation with reduced number of occupants. The selected strategy would depend considerably on the desired number of users, the budget and the possibilities to combine the redesign of ventilation with other refurbishment actions. Furthermore, the risk of poorer indoor air caused by the refurbishment actions must also be addressed and considered.

This study may assist municipal authorities and school directors in decisions concerning improvement of classroom IAQ and elimination of building-related symptoms. This research provides economic aspects of alternative strategies and points out the risks related to major refurbishment actions.

Since this study presents a set of features related to indoor air that contribute to occupants’ health as well as matters to be considered when aiming to decrease occupants’ symptoms, it may be of assistance to municipal authorities and practitioners in providing a healthier indoor environment for pupils and teachers.

Earlier studies on employee turnover have invested enormous scholarly mileage to understand and address human resource challenges. Considering the substantial evidence on the negative and non-linear relationship between employee turnover and firms’ performance, the purpose of this study is to investigate the effects of employee turnover on the social outreach (e.g. breadth of outreach) of microfinance institutions (MFIs), also known as the financial inclusion agenda of the Sustainable Development Goals.

To achieve the study objective, the authors collected unbalanced panel data of 1,391 MFIs, covering a total of 96 economies and a period of 2010–2018. The organizational and macroeconomic data were obtained from the World Bank’s Mix Market and World Development Indicators databases, respectively, and subsequently analysed using the pooled ordinary least squares, random effects model, fixed effects model and generalized method of moments.

Overall, the authors found that employee turnover has a positive impact on the social outreach of MFIs, which suggests that employee turnover reduces organizational blindness and groupthink, potentiates efficiency gains and minimizes retention costs. On the contrary, this study does not find evidence of a non-linear effect of employee turnover on the outreach objectives of MFIs. Meanwhile, these effects were observed to vary depending on the proxy, sub-samples and techniques used in the analysis.

Motivated by the paucity of literature, the study has uniquely investigated the effect of employee turnover on the social outreach objective of MFIs by using relatively recent and global-level data. The study findings can help managers and the human resource departments to make optimum decisions about employee turnover management.