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A web service is a software system designed to support interoperable machine‐to‐machine or application‐to‐application interactions over networks. Descriptions enable web services to be discovered, used by other web services, and composed into new web services. Web service composition is a mechanism for creating new web services by reusing existing ones. In order to compose a web service, the right primitive services have to be discovered. A matchmaking technique enables discovering these services. Web services have functional, non‐functional, behavioral, and semantic characteristics. These four aspects of web services provide different key information about the service; therefore they have to be considered for description, matching, and composition. The purpose of this paper is to propose a formal description framework and a formal matchmaking technique that allows describing and discovering web services by considering their four characteristics.

In this paper, the description framework combines two existing languages for functional, semantic, and behavioral description, along with a simple and new language for non‐functional description.

A case study is used to illustrate the description framework and the matchmaking technique. The implementation and performance evaluation of the matchmaking technique is presented. The framework formalizes and integrates the languages in a common semantic domain in order to match and manipulate the different aspects together and formally. Isabelle is used by the matchmaking technique for discovering the partially and fully matched services.

The contribution of this paper lies in the new description framework and the new matchmaking technique.

The purpose of this paper is to understand the differences between proactive and reactive market research techniques during the development of new market offerings. The study focused on the financial and innovative performance of traditional market research techniques, such as focus groups and in‐depth interviews, in comparison to more co‐creation‐oriented techniques that are designed to capture customers' value‐in‐use.

The study was a two‐stage process. Study I, an empirical investigation of 195 development projects in European companies, examined how these companies use different market research techniques and how this relates to the profit margins of new products and services. Study II designed an experiment with 50 users of a consumer good and evaluated the contribution of different market research techniques, based on the degree of originality and customer value.

Significant differences were found, in terms of both content and originality, between the technique based on customer co‐creation and the two traditional market research techniques (Study II). These findings can help to explain why the relationship between the use of market research techniques and profit margin (Study I) is stronger for co‐creation techniques than it is for traditional market research techniques.

Despite empirical evidence that the application of market research techniques based on co‐creation can lead to original ideas, there is a lack of valid studies regarding how co‐creation techniques perform in relation to more traditional methods of collaboration with customers.

This survey aims to study and analyze current techniques and methods for context‐aware web service systems, to discuss future trends and propose further steps on making web services systems context‐aware.

The paper analyzes and compares existing context‐aware web service‐based systems based on techniques they support, such as context information modeling, context sensing, distribution, security and privacy, and adaptation techniques. Existing systems are also examined in terms of application domains, system type, mobility support, multi‐organization support and level of web services implementation.

Supporting context‐aware web service‐based systems is increasing. It is hard to find a truly context‐aware web service‐based system that is interoperable and secure, and operates on multi‐organizational environments. Various issues, such as distributed context management, context‐aware service modeling and engineering, context reasoning and quality of context, security and privacy issues have not been well addressed.

The number of systems analyzed is limited. Furthermore, the survey is based on published papers. Therefore, up‐to‐date information and development might not be taken into account.

Existing surveys do not focus on context‐awareness techniques for web services. This paper helps to understand the state of the art in context‐aware techniques for web services that can be employed in the future of services which is built around, amongst others, mobile devices, web services, and pervasive environments.

Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue.

In this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures.

The experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively.

One possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques.

In the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

The purpose of this paper is to present a study of how quality improvement tools and techniques are framed within healthcare settings.

The paper employs an interpretive approach to understand how quality improvement tools and techniques are mobilised and legitimated. It does so using a case study of the NHS Modernisation Agency Improvement Leaders' Guides in England.

Improvement Leaders' Guides were framed within a service improvement approach encouraging the use of quality improvement tools and techniques within healthcare settings. Their use formed part of enacting tools and techniques across different contexts. Whilst this enactment was believed to support the mobilisation of tools and techniques, the experience also illustrated the challenges in distributing such approaches.

The paper provides an important contribution in furthering our understanding of framing the “social act” of quality improvement. Given the ongoing emphasis on quality improvement in health systems and the persistent challenges involved, it also provides important information for healthcare leaders globally in seeking to develop, implement or modify similar tools and distribute leadership within health and social care settings.

It is imperative that management make the most effective use of their resources as the environment in which organisations operate becomes increasingly competitive and complex. There are many tools to help the manager striving for organisational effectiveness, but difficulties can arise in defining these techniques.

The purpose of this paper is to develop and apply the sequential incident laddering technique as a novel approach for measuring customer service experiences. The proposed approach aims to correspond with the concept's theoretical foundation in the extant literature.

The paper applies the sequential incident laddering technique to measure customer service experiences. The technique integrates two well‐established methods in service marketing: sequential incident and laddering techniques. The data collected from 41 customers in a hotel and restaurant experience context illustrate that the method corresponds with the key themes of the proposed experience concept and experience formation process.

Applying the proposed technique reveals first, the customer's cognitive and emotional responses to company stimuli. Second, the salient customer cognitions and emotions across several episodes of the service interaction process are identified. Third, the personal values which drive the customer's service experience are disclosed.

The empirical study is a first illustration of the proposed measurement approach in only one company based on a limited sample size. The methodological contributions and development opportunities for further applications are set out for different contexts and in combination with other methods.

The proposed method integrates customer and company‐related constructs. Therefore, the data collected can provide managers with guidelines for customer service experience design based on detailed customer feedback.

The paper proposes an innovative measurement approach to customer service experiences which can support knowledge development in an important marketing area.

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).

Maintains that, although service industries would benefit from research concerning the implementation of just‐in‐time (JIT) techniques, most research has focused on JIT only in manufacturing firms. A review of applied journals and articles, however, revealed JIT concepts migrating to non‐manufacturing environments. These articles describe JIT techniques successfully moving from the factory floor to other environments and suggest a potentially rich research opportunity. Summarizes these articles using Benson′s guidelines for applying JIT in service. Illustrates various JIT applications within each of the JIT categories.

Considers the need to select customer contact personnel who already exhibit the desirable trait of adaptability, thus reducing the need for training. Discusses the issue of adaptability in service employees and how to select for adaptability. Examines several methods which can be used in the selection process, such as abstract questioning, situational vignette interviewing, role playing. Concludes that whiletraining is vital for all employees, creative interviewing techniques can help to secure service‐oriented employees who represent the most potential for service businesses.