## 1 Introduction and Motivation  
Existing service research efforts have focused primarily on advancing concepts, principles, languages, and methodologies of service-oriented architecture (SOA) [1]. Whilst the significance of such technological innovations is undeniable, their successful use within the information society requires understanding the motivations, needs, and traits that characterize end-users of the SOA technologies. Using current SOA tools and methodologies requires strong technical and problem-solving skills [2], making them suitable for skillful programmers only. However, most of the users who engage in some sort of programming activity are not trained as programmers. For example, past projections revealed that nearly 13 million of the 90 million computer users in American workplaces were estimated to be involved in some sort of programming activities, whilst the number of trained programmers was only 3 million [3]. Other studies also suggest that at least 73% of professionals without programming experience are engaged in at least one activity, which amounts to programming [4]. This also applies to software services; for example, non-programmers can bring together information from different sources to search for a house within certain distance from a park, or to choose different modes of commuting to work depending on the rain forecast. At the same time, software services are more complex than other end-user development domains such as spreadsheets or home automation because of the diversity of interfaces, parameter types, and underlying implementation and deployment architectures. This implies there is an urgent need to simplify and disseminate the use of SOA technologies to a wider audience whose primary role is not software development. Whilst it is widely accepted that end-users have differing characteristics, skills, and practices from professional programmers [5], little is known regarding how they reason about service-composition activities. [PERSON] et al. [5] claim that professional programmers and end-user programmers encounter many similar software development challenges. Yet no systematic study has been performed to verify this claim in the domain of service composition. In our view, examining the decision-making strategies non-programmers adopt to compensate for their lack of technical knowledge and comparing them to a baseline expert sample would enable the understanding of end-users' strengths and weaknesses. This could lead to the creation of highly-dedicated methodologies and design guidelines, upon which service-development can be founded for a pleasant user experience. This study investigates the mental models of non-programmers in relation to software-service-development activities and compares it to a baseline sample of professional programmers. [PERSON] [6] defines a mental model as a person's mental representation of the real-world systems; thus, how a person perceives and uses these systems and makes decisions. In particular, our study investigated the conceptual challenges non-programmers face during service composition and how they approach two differing ways of creating service-based applications: (a) manual service composition (work-flow based) and (b) parametric design using templates with high degree of software support (assisted composition). The design tasks were chosen to differ in respect to the level of user support provided by the tools (Section 3). The contributions of this study towards software service research are twofold. Firstly, it provides a better understanding of non-programmers' mental models regarding software service composition, service design challenges, and problem-solving strategies (Sections 4 and 5). Currently, the literature contains a limited number of users' studies that employ empirical evaluations to understand the challenges and alternative decision-making strategies non-programmers adopt when indulged in service composition to compensate for their lack of technical knowledge and expertise, and report on their overall experiences and needs.