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Libraries are closing or reducing opening hours in the UK due to budgetary cuts. Library provision for children is consequently diminishing and libraries have to justify their existence. Therefore a reliable methodology for assessing the importance of libraries is vital to demonstrate their value to children’s literacy. Two methodologies were combined to study children visiting children’s mobile libraries (CMLs). The purpose of this paper is to consider whether the combined, qualitative methodology was the correct choice.

Aspects of each methodology are examined for their appropriateness for researching children. The compatibility of their philosophical stance and the validity of combining ethnography and grounded theory is explored and questioned.

It is found that grounded theory and ethnography were the optimum combination to form a powerful research tool that allows children to be active participants in research. The combined methodology was successful because the ethnographic elements allowed the researcher to enter to the children’s world, whereas the grounded theory elements provided a structural framework, exploration into a novel research topic and ensured that a valid conclusion was drawn.

It is unusual for qualitative methodologies such as grounded theory and ethnography to be combined in order to study learning in a non-pedagogic, library environment. This paper is valuable reading for librarians, or educationalists wishing to examine how libraries aid literacy because it verifies the benefits of the combined methodology of grounded theory and ethnography and provides a template which can be used by other researchers.

To combine the forecasting by single method using influence information fully, other than regular combined methods only focusing on historical forecasting errors.

To combine the single methods based on the analysis of improved gray correlation, with more related information being considered to enhance the price forecasting precision, such as the trend of the prices, the historical forecasting errors, and the temporal influence factors on prices.

A case of PJM market of USA shows that the proposed method has better performance than any other combined methods, and all single models as well.

The combined performance depends on the forecasting precision of single methods, and the correlation between the single methods, as well as the number of single method that to be combined.

It is a novel idea for combined method to forecasting the time series data, such as electricity prices, electric power loads.

The proposed method considers all the following factors: the similarity between the trends of the single forecasting, the errors of the single models and the temporal influence.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

This paper aims to present a methodology for activity‐based costing, which combines simulation modeling and association rule mining, one of the core data‐mining techniques. The objective of the proposed methodology is to deal with the problem of defining cost drivers.

Activity‐based costing uses the output produced by the simulation of cost drivers as inputs. As opposed to the integration of the ABC technique with simulation modeling, the possibility of estimating an empirical distribution of the simulated cost drivers does not exist in the proposed methodology. This is achieved with the use of data‐mining techniques and is based on the proposition that, if an association is found between a cost driver, whose estimation or calculation is time‐consuming, and another cost driver, which can easily be estimated or calculated, then the latter can lead to the estimation or calculation of the former.

The extracted association rules correspond to existing dependencies between the cost drivers.

The paper presents a combined methodology to deal with the problem of defining cost drivers in activity‐based costing. An example of the proposed methodology in healthcare is also presented.

The aim of this research is to improve the responsiveness of the healthcare network of a large city to a major earthquake, by applying a combined methodology to reduce human suffering and death.

Scenario analysis, a non-linear programming (NLP) model, and the analytical network process are sequentially applied to find the “best location pattern”.

When considering the occurrence of major earthquakes in cities with high population density, as a rule of thumb, the location of healthcare facilities should prioritize areas characteristically overcrowded and/or that were built based on poor standards of seismic resistance.

The proposed research design does not include a cost criterion in the set of decision variables involved. Furthermore, the results derived from the NLP-model are restricted by the input simulation data.

The performance of the “best location pattern” is compared with the current location of healthcare facilities in terms of their distances to the affected zones. Metropolis areas worldwide with similar conditions to the city under consideration could be benefited from applying the general methodology for relocation of healthcare facilities described in this research.

This research implements a diverse combination of methodologies to examine the problem of relocating of healthcare facilities in a large city in the wake of an assumed earthquake. In addition, to the best of authors' knowledge, this is the first study of its kind that proposes improvements in the responsiveness of the healthcare facilities' network in the city in question.

Defining and describing research methodologies is difficult. Methodologies have similarities and resonances, and overlapping characteristics. Familiar labels of case study, action research and ethnography may not be adequate to describe new and creative approaches to qualitative research. If we simply transfer old ways to new contexts, we risk limiting our understanding of the complexities of real life settings. The call to set aside old dualisms and devise new methodological approaches has been sounded. Accordingly, this article sets out to describe a fledgling new methodological approach, and how it was operationalized in a small‐scale study of digitally‐mediated classroom learning.

The methodology combines elements of action research and case study with an ethnographic approach. It was devised for a study of the use of Facebook as an educational resource by five dyslexic students at a sixth form college in north‐west England. Its flexibility and attention to detail enabled multiple data collection methods. This range of methods enabled meticulous analysis of many of the group's online and offline interactions with each other and with Facebook as they co‐constructed their group Facebook page.

Reflexively combining elements of case study, action research and ethnography thus helped capture the “connected complexities” (Davies) of this contemporary classroom setting. This is necessary if researchers are to obtain any meaningful understanding of how learning happens in such contexts.

The author hopes to contribute to the discourse on qualitative methodology and invites other researchers studying similar contexts to consider a similar approach.

The objective of this work is to demonstrate how the use of a business process management (BPM) methodology reinforced with the use of lean manufacturing (LM) tools and practices enhances information and documentation flows.

For this purpose, a case study on a large wind blade manufacturing company is described, in which BPM and LM were combined to improve information and documentation flows associated to the process of quality inspections and quality controls.

The joint use of BPM and LM strongly contributed to the improvement of information and documentation flows. The BPM lifecycle can be used to guide the entire improvement process, while LM tools can be used to act at specific points with an emphasis in the process analysis and implementation phases. Thus, LM complements a BPM approach leading to significant process improvements.

The results show that LM can be used to support some phases of the BPM lifecycle. Furthermore, LM can contribute to identify lean waste in information and documentation flows associated to quality management processes and help in the selection of methods and tools to support process improvements.

This study is one of the first reporting the use of LM tools and practices as complementary to the BPM methodology to support the improvement of information and documentation flows associated to quality management in a large manufacturing company. This research enriches the literature by presenting empirical evidence that these two continuous improvement approaches are not incompatible in their objectives and visions and can complement each other.

In this article, a practical design methodology is proposed for discrete sizing optimization of high-rise concrete buildings with a focus on large-scale and real-life structures.

This framework relies on a computationally efficient approximation of the constraint and objective functions using a radial basis function model with a linear tail, also called the combined response surface methodology (RSM) in this article. Considering both the code-stipulated constraints and other construction requirements, three sub-optimization problems were constructed based on the relaxation model of the original problem, and then the structural weight could be automatically minimized under multiple constraints and loading scenarios. After modulization, the obtained results could meet the discretization requirements. By integrating the commercially available ETABS, a dedicated optimization software program with an independent interface was developed and details for practical software development were also presented in this paper.

The proposed framework was used to optimize different high-rise concrete buildings, and case studies showed that material usage could be saved by up to 12.8% compared to the conventional design, and the over-limit constraints could be adjusted, which proved the feasibility and effectiveness.

This methodology can therefore be applied by engineers to explore the optimal distribution of dimensions for high-rise buildings and to reduce material usage for a more sustainable design.

Multiple attribute decision making (MADM) is a conceptual agenda used for evaluation and selection of optimal nanofluid to assure best performance of heat exchanger. Most of the studies focus on nanofluids focus on individual ability at one time. Relatively, not even a single study is available for selection of nanofluid for heat exchanger using concurrent design and MADM approach. The purpose of this paper is to propose a concurrent design methodology using MADM approach to assist improved design of heat exchanger concurrently for all the x-abilities in an integrated manner.

A combined methodology of applying MADM approach using concurrent design for x-abilities is called CE-MADM approach. Implementation of nanofluid to improve thermal performance of heat exchanger entails thorough evaluation of nanofluids in various x-abilities (performance, maintenance, thermophysical properties and modelisation) to make exhaustive management decision. Sensitivity analysis is also proposed to study the behaviour of height of variation of density, heat capacity, thermal expansion and thermal conductivity with varying particle volume fraction and variation of relative closeness of available alternates from ideally best possible solution.

MADM approach considering various x-abilities concurrently provide an approach for relative ranking of available nanofluids for optimum performance. Fishbone diagrams of all x-abilities are constructed to identify all the attributes and converge large number of attributes into single numerical index that are concurrently responsible for the cause thus saving time for easy evaluation, comparison and ranking by decision makers. Sensitivity analysis to demonstration height of variation of pertinent attributes with varying particle volume fraction. A MATLAB programming is established to execute calculations involved in the procedure.

This paper comprises a predictable and effective mathematical approach to improve design of heat exchanger with nanofluid bearing in mind all the required x-abilities concurrently. This combined approach of CE-MADM is never applied before in the field of nanofluid to predict best possible results in feasible conditions considering all the x-abilities. Sensitivity analysis is also presented from the assumed mathematical equations of thermophysical properties.

In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide between members of the relevant family of econometric models and demonstrates, under quadratic loss, the nonoptimality of the conventional pretest estimator. First-order asymptotic properties of the combined estimator are demonstrated. A sampling study is used to illustrate finite sample performance over a range of econometric model sampling designs that includes performance relative to a Hausman-type model selection pretest estimator. An important empirical problem from the causal effects literature is analyzed to indicate the applicability and econometric implications of the methodology. This combining estimation and inference framework can be extended to a range of models and corresponding estimators. The combining estimator is novel in that it provides directly minimum quadratic loss solutions.