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The purpose of this paper is to develop a test for data centres, repositories and archives to determine OAIS functional model conformance. The test developed was carried out among the World Data Centre (WDC) member data centres. The method used to develop the OAIS functional model conformance test is discussed, along with the test results.

To conduct the OAIS functional model conformance test, a quantitative approach in the format of an online survey was used. This was part of a mixed methods research project.

The test developed did produce a means for quantifying OAIS functional model conformance. The mean score for the 26 WDC member data centres that completed the test was 62.08 out of a possible 92. The highest scoring WDC member data centre obtained a score of 90, while the lowest score obtained was 27.

This test was only conducted among a relatively small sample, making it difficult to generalise the results obtained and determine how effectively the OAIS functional model conformance test measured conformance. It remains a challenge to quantify data curation practices with regard to the OAIS functional model.

The OAIS functional model conformance test is the first attempt at quantifying OAIS functional model compliance.

Preparing digital mock-ups (DMUs) for finite element analyses (FEAs) is currently a long and tedious task requiring many interactive CAD model transformations. Functional information about components appears to be very useful to speed this preparation process. The purpose of this paper is to shows how DMU components can be automatically enriched with some functional information.

DMUs are widespread and stand as reference model for product description. However, DMUs produced by industrial CAD systems essentially contain geometric models, which lead to tedious preparation of finite element Models (FEMs). Analysis and reasoning approaches are developed to automatically enrich DMUs with functional and kinematic properties. Indeed, geometric interfaces between components form a key starting point to analyze their behaviors under reference states. This is a first stage in a reasoning process to progressively identify mechanical, kinematic as well as functional properties of components.

Inferred semantics adds up to the pure geometric representation provided by a DMU and produce also geometrically structured components. Functional information connected to a structured geometric model of a component significantly improves FEM preparation and increases its robustness because idealizations can take place using components’ functions and components’ structure helps defining sub-domains of FEMs.

Future research will carry on improving algorithms for geometric interfaces identification, processing a wider range of component functions, which will contribute to a formalization of the concept of functional consistency of a DMU.

Simulation engineers benefit from this automated enrichment of DMUs with functional information to speed up the preparation of FEAs of large assemblies.

Examines the effects of service quality on customer satisfaction from two distinct methodological perspectives. Specifically, a study utilizing a sample of international private banking customers is conducted wherein service quality is operationalized via two distinct and well‐known measures – SERVQUAL and Technical/Functional Quality. These two service quality measures are subsequently compared and contrasted as to their ability to predict customer satisfaction. To further assess the validity of these findings, two moderators of the service‐quality/customer‐satisfaction relationship are introduced and evaluated. Finally, this research examines the potential utility of employing separate measures for customer satisfaction from the perspectives of both technical and functional aspects of the service delivery process. Overall, our findings are of importance to service managers as they strive to identify efficient and effective approaches for improving quality. The paper explores the theoretical and practical insights of the findings, including potential strengths and limitations of current service quality models with regard to their ability to define and explain the quality/satisfaction relationship.

Three-dimensional computer-aided design (CAD) assembly model has become important resource for design reuse in enterprises, which implicates plenty of design intent, assembly intent, design experience knowledge and functional structures. To acquire quickly CAD assembly models associated with specific functions by using product function requirement information in the product conceptual design phase for model reuse, this paper aims to find an approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model before functional semantic-based assembly retrieval.

An approach for structure-function correlations analysis and functional semantic annotation of CAD assembly model is proposed. First, the product knowledge model is constructed based on ontology including design knowledge and function knowledge. Then, CAD assembly model is represented by part attributed adjacency graph and partitioned into multiple functional regions. Assembly region and flow-activity region are defined for structure-function correlations analysis of CAD assembly model. Meanwhile, the extraction process of assembly region and flow-activity region is given in detail. Furthermore, structure-function correlations analysis and functional semantic annotation are achieved by considering comprehensively assembly structure and assembled part shape structure in CAD assembly model. After that, a structure-function relation model is established based on polychromatic sets for expressing explicitly and formally relationships between functional structures, assembled parts and functional semantics.

The correlation between structure and function is analyzed effectively, and functional semantics corresponding to structures in CAD assembly model are labeled. Additionally, the relationships between functional structures, assembled parts and functional semantics can be described explicitly and formally.

The approach can be used to help designers accomplish functional semantic annotation of CAD assembly models in model repository, which provides support for functional semantic-based CAD assembly retrieval in the product conceptual design phase. These assembly models can be reused for product structure design and assembly process design.

The paper proposes a novel approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model. Functional structures in assembly model are extracted and analyzed from the point of view of assembly structure and function part structure. Furthermore, the correlation relation between structures, assembled parts and functional semantics is expressed explicitly and formally based on polychromatic sets.

The purpose of this paper is to present a novel modeling approach that combines a balanced systems engineering design model with a geospatial model to explore the complex interactions between natural hazards and engineered systems.

The approach taken in this work was to assemble a combined systems engineering design/geospatial model and interface it with a physics‐based hazard model to assess how to visualize the coupling of potential hazard effects from the physical domain into the functional/requirements domain.

It was demonstrated that it is possible to combine the two models and apply them to realistic hazard cases. A number of potential benefits are described and made possible by this approach including the generation of systems‐level damage assessments, the potential reduction of geo‐information data collection requirements, the incorporation of socio‐technical elements, the generation of functional templates, and the creation of a superior mitigation framework.

This approach offers a way to better understand natural hazard impacts on built systems, systemic effects of hazards, functional interdependencies between infrastructural elements, and a practical means to reduce geo‐information collection requirements.

The work is original in that it is the first time a balanced systems engineering design model has been made spatially aware and used to explore the impact of natural disasters on human systems. This work is valuable in that it directly addresses the shortcomings of spatial‐only approaches and could be used in data‐poor regions of the world.

To extend understanding of service quality by empirically examining the conceptualisation of service quality (both technical and functional).

Because the popular service‐quality instrument, SERVQUAL, concentrates on functional quality, a model incorporating both technical quality and functional quality is employed here. Structural equation modeling (SEM) is utilised to examine empirically a two‐components model of service quality.

A two‐component model yields better fit than a model concentrating on functional quality alone (such as SERVQUAL).

Because the present study tests the model using a single service industry, an exhaustive description of technical quality could not be provided. This could be overcome in future studies by employing multiple service industries.

A useful foundation whereby practitioners can appreciate the importance of technical service quality (in addition to functional quality).

This paper fulfils an identified information and resources need, and offers practical assistance to academics and practitioners in the field.

Service quality researchers to date have paid scant attention to the issue of the dimensions of service quality. Much of the earlier work accepted the content measured by the SERVQUAL instrument. Following the argument that SERVQUAL only reflects the service delivery process, the study empirically examines the European perspective (i.e. Grönroos' model) suggesting that service quality consists of three dimensions, technical, functional and image, and that image functions as a filter in service quality perception. The results from a cell phone service sample revealed that Grönroos' model is a more appropriate representation of service quality than the American perspective with its limited concentration on the dimension of functional quality.

The purpose of this paper is to propose a novel quality of service (QoS)‐aware service composition approach, called SEQOIA, capable of defining at run‐time a service composition plan meeting both functional and non‐functional constraints and optimizing the overall quality of service.

SEQOIA is a semantic‐driven QoS‐aware dynamic composition approach leveraging on an integer linear programming technique (ILP). It exploits the expressiveness of an ontology‐based service profile model handling structural and semantic properties of service descriptions. It represents the service composition problem as a set of functional and non‐functional constraints and an objective function.

The authors developed a proof of concept implementing SEQOIA, as well as an alternative composition solution based on state‐of‐the‐art AI planning and ILP techniques. Results of testing activities show that SEQOIA performs better than the alternative solution over a limited set of candidate services. This behaviour was expected, as SEQOIA guarantees to find the service composition providing the optimal QoS value, while the alternative approach does not provide this guarantee, as it handles separately the specification of the functional service composition flow and the QoS‐based service selection step.

SEQOIA leverages on semantic annotations in order to make service composition feasible by coping with syntactic and structural differences typically existing across different, even similar, service implementations. To ease the adoption of SEQOIA in real enterprise scenarios, the authors chose to leverage on an XML‐based message model of services interfaces (including but not strictly requiring the use of WSDL).

The purpose of this article is to generate theory on how functional integration and conflict interrelate by studying the interface between production and purchasing.

An interpretive single case research methodology is adopted. The authors rely on in-depth interviewing of managers in the production and purchasing functions of a construction company, as well as by its suppliers.

Given low functional integration, antagonistic reasoning within each function and resultant conflicting behaviors are allowed to develop in a negative cycle, escalating the conflict between purchasing and production. This process leads to the creation of two opposing functional sourcing models that serve as blueprints for behavior.

The single case methodology was chosen to maximize depth and detail and form an ideal foundation for theory building. Future qualitative and quantitative studies should inquire further into the studied phenomenon to increase analytical and statistical generalizability of the proposed model.

The findings can help managers understand how poor integration between functions can develop into cross-functional conflict. Facing a conflicting functional relationship, managers must resort to conflict resolution methods, instead of attempting to integrate, as several integrative devices are not appropriate in conflicting interfaces.

The proposed model contributes by connecting the constructs of integration, group reasoning, and conflict, thereby generating knowledge on conflict development processes in cross-functional interfaces. Furthermore, the article contributes by uncovering the difficulties associated with implementing spend consolidation, a prevailing sourcing strategy.

The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA).

The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors.

Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting.

The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue.

To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.