Surveys are a critical channel of information from various stake holders in most service processes. Design of surveys and analysis of data collected from them is a well-established discipline within social sciences, and the associated analysis methods can be broadly categorized into quantitative and qualitative techniques. We propose information theoretic metrics to measure the value of combining qualitative and quantitative data from surveys. Specifically, we use the concepts of information entropy to estimate uncertainty, the concepts of information gain, mutual information, and conditional entropy to measure triangulation, complementation or paradox. Finally, we apply the proposed metrics in a case study to illustrate the practical application of our work in analysis of a consumer survey from a service industry. The proposed information theoretic approach offers a scientific basis for automating survey data analysis in service processes, especially when such surveys contain quantitative and qualitative responses.

In this paper, three different scenarios of multi-criteria mathematical programming models are explored under a framework of network based analysis in web service composition. This work takes care of the issues pertaining to inputs and outputs matching of web services and Quality-of-Service (QoS) at the same time. Six multi-criteria programming models are explored to select the desirable service composition in a variety of categories in accordance with customers\' preferences in three different scenarios: (1)Optimal, (2)Compromised optimal, and (3)Acceptable. This set of multi-criteria models have both advantages and disadvantages comparing with each other, and can be used as different solvers in the network based service composition framework. The proposed regular multi-criteria programming (MCP) models is used in Scenario (1): Optimal. The proposed multi-criteria goal programming for optimal composition (MCGPO) and multi-criteria goal programming for non-optimal solution (MCGPN) models are designed for Scenarios (2):Compromised optimal and (3)Acceptable respectively. And they can find a compromised composition based on the trade-off of customer\'s preference on the QoS goals in case that the optimal composition satisfying both functional and QoS constraints does not exist in the network.

What drives new service business designs? More precisely, what could be a generic strategy to grow value cocreation among persons? The emerging network science and service science may offer an answer: comprehensively connecting, or hyper-networking, the customers, providers, and resources via digital means. This connectionist perspective has led to the Digital Connections Scaling (DCS) theory, which suggests that new service business designs arise from thrusting the connections up to span the population, deepening down to facilitate individual persons’ life cycle needs, and cutting across business designs and domains to transform them. This paper develops the DCS theory into a generic strategy for designing new service businesses and justifies the strategy with empirical evidence taken from the field of e-commerce and social networking. The study then applies the proposed strategy to analyze what new models and paradigms may come next. An information systems design framework for implementing the strategy is also proposed.

Many social studies analyze attitude responses using the linear regression model. This model typically treats questionnaire data as continuous scales, although the data is merely ordinal. One type of regression model that is more appropriate to analyze rank-order responses is the Ordinal Logistic Regression (OLR) model. In addition to the use of regression, the Artificial Neural Network (ANN) model has recently been applied in various studies. This paper delivered comparative descriptions of both the ANN and OLR models. The theoretical features and properties, which include parameters, variable selection, and model evaluation, followed by comparisons of the disadvantages and advantages of both models were analytically reviewed.

Clear-cut classification of exchanges into services and non-services is vital for governments to estimate contributions of service and other sectors to national incomes, for businesses to know their tax and tortious liabilities and for researchers to develop new theories and concepts of services. Unfortunately, the definitions and schema they use to classify exchanges have serious shortcomings. This paper proposes a legal approach to classify exchanges. Whereas existing definitions and schema are concerned with trading of acts or goods, the proposed approach is concerned with trading of legal rights and obligations. The time taken by contracting parties to fulfill their contractual obligations has been used to define service and non-service exchanges. The proposed definitions are unambiguous and they overcome the shortcomings of two most-cited definitions of services.

This paper presents an original description and a semi-formal definition of the concept of a value proposition, which has been so far used in service science rather intuitively. Our approach is based on util- ity functions and conceptual modelling techniques. The proposed semi-formalization can be exploited to describe services from the point of view of their (potential) utility for their clients. This description can be used especially to organize a service portfolio in an enterprise in a better way, aid in computer-assisted service composition/decomposition, and provide additional criteria for indexing services in a service brokering task. In order to be able to describe a value proposition more accurately, we present a semi-formal definition of the concept of a service system. We perceive a value proposition as the main input taken into account by a future service client when evaluating whether or not to become the client of the service proposed by a service provider. A value proposition itself is modelled as a collection of values which indicate the extent of “how much” a given service behaves according to a given set of service characteristics. The presented approach is illustrated on the example of a concrete service.

The rise of Software as a Service (SaaS) composition platforms and so called Compute Clouds demonstrates the growing demand for the agile composition of Web Services. In order to facilitate the composition of services and value-creation, service providers need to collaborate. This collaboration is regulated by means of Service Level Agreements (SLAs) where the parties, the executed service as well as guarantees on the service execution are specified. This work presents the concept of Service Value Networks and Agreement Networks as the underlying legal structure. Furthermore, an approach is introduced that allows a service provider to select the risk-minimal SLA portfolio. In a further step, the approach is extended in order to allow for a tradeoff between risk and expected profit from the service execution. Finally, the computational complexity of the optimization model is discussed and solutions are proposed.

This article addresses a relevant question arising in knowledge intensive industries: defining productivity models; an important issue of increasing importance in developed economies (Drucker 1999),(Ramirez 2004),(Neely 2002). According to the findings presented in this paper service science is a useful framework upon which to understand and further operationalize service productivity, moreover it has been found that service science promotes systems thinking in complicated servicings such as the one considered in this research. The research approach adopted combines relevant theory in productivity analysis with a real experience as practised by a worldwide leading healthcare agency to ensure the validity of the concepts proposed to other industries.

One of the ideas underlying the field of Service Science is that we can recognize, understand, and, hence, improve the various systems in which humans operate. Many of the systems studied are, understandably, those intentionally designed and built by humans. Yet just as important are those systems whose structures emerge unintentionally from various actions and that subsequently shape our behavior. One such system is the macro-economy, which is perpetually changing and shaping individual activity in an ever-evolving recursive process. At the heart of economic activity is entrepreneurship, the creation of new businesses, which is seen as the quintessential act of economic agency. The historiography of entrepreneurship is dominated by portraits of men and women bending the world to their will. While not denying a role for agency in business creation, an increasing amount of data as well as new ways of studying startups raises important questions about the structural elements that shape entrepreneurship.

The emergence of widespread offshoring of information-based services is arguably one of the more transformative business phenomena of the last ten years. A growing body of research has examined the firm-level drivers and location factors (i.e., the “whys” and “wheres”) of services offshoring. However, little empirical research has examined the temporal dynamics (or “whens”) of services offshoring. Adopting industry life cycle theory as a framework and a Bayesian methodological approach, we explore two key research questions: (i) when do different categories of offshoring services provision change from being emergent sectors to more mature ones relative to one another? and (ii) how do different types of offshoring activity differentially progress through this sequence? Employing a database of 1,420 offshore services FDI projects, we find that the relative skill level and the information sensitivity of the specific service category are associated with the temporal sequence of industry life cycle progression such that activities with decreased information sensitivity are offshored earlier than those with greater information sensitivity. We draw implications for our findings in terms of future waves of service offshoring.