Abstract
The purpose of this paper is to investigate how can prototypes contribute to the requirements elicitation for smart services in the early development stages. Smart services are delivered to or via intelligent objects and are characterized by context awareness, connectivity, and data-driven value creation. Smart services and prototyping are emerging topics in requirements elicitation and pose challenges to existing approaches. This article creates a fundamental understanding for the requirements elicitation by characterizing smart services in a layer model that illustrates the structure, processes, and interaction of the networked components. Based on this, the strategies outline ways how prototypes for smart services can be composed in a result-oriented way and applied in requirements elicitation. The models are based on the results of a comprehensive literature review and demonstrate their relevance using case studies from the mobility sector.
You have full access to this open access chapter, Download conference paper PDF
Similar content being viewed by others
Keywords
1 Introduction
Technology has the potential to make users smart and overcome the limited capacities of the human mind [1]. To exploit the potential, it is important to understand how we can elicit user requirements and design solutions, to move from smart technologies to smart services that add value to users [2, 3]. In recent research on radical innovation, development of prototypes holds a considerable role [4, 5]. In tangible, fast learning cycles, the prototypes can be used to investigate newly emerging user behaviour and preferences and finally convert them into requirements.
Thus, the main objective of this article is to investigate how can prototypes contribute to the requirements elicitation for smart services in the early development stages. Based on the fundamental clarification of the concept of smart services, the theoretical section of the article examines the relevance of existing prototype approaches for applying to smart services. The lack of understanding of prototypes in the field of smart services is what motivates the attempt to define strategies for smart service prototypes. The strategy for prototypes defines the systematic design of the prototype components. The goal is to systematically influence the way participants perceive and interact with the prototype to achieve the desired prototyping aims. Due to the high complexity of smart services, a comprehensive understanding of the nature of smart services is indispensably for the requirements elicitation. By discussing existing models for smart services, the layer model for smart services is established. This model aims to create a common ground, necessary for the understanding and prototyping of smart services in the early development phases. The findings are based on an extensive literature review and a multi-case study. Using case studies from public transport, a structured investigation will be carried out to determine to what extent strategies for prototypes can be applied to the field of smart services.
2 Background – a Literature Review
2.1 How Smart Services Are Understood in Current Research
Smart services go beyond the traditional understanding of services. The term “smart” is thereby assigned a variety of capabilities or requirements, from connected [6] to context-aware [7], data-based [8], intelligent [3] and even ubiquitous [9]. A comparison of the origins of the definitions reveals that each discipline has its understanding of smart services and sets its focus (see Table 1).
According to the theories of service science, the composition of a system of people, processes, technologies, physical evidence, and other resources is a prerequisite for smart services and thus essential for the creation of value [15]. In addition to the system thinking, the effects of smartness on value creation are investigated to better understand the nature of smart services and their evolution [7]. In contrast, service engineering explores new technologies and methodologies to improve the scalability [16]. This requires an intensive analysis of the networking of products and services and their technical implementation using integrated platforms [17]. Emphasis is placed on how services are combined on the integrated platforms. The individual service components are no longer orchestrated in a supplier- but rather in a customer-oriented manner [18]. According to Spohrer [16], service management deals with the question, how to invest to improve service systems. The focus lies on the investigation of the capabilities of smart services to optimize the value to customers and the cost efficiency for the providers simultaneously.
The comprehensive view into the disciplines helps to understand the diversity of the different approaches. A common capability emphasized by most approaches is the use of data. Based on this observation, services can be described as data-based, where the use of data plays a central role in the creation of value. In the first definition of smart services given by Allmendinger and Lombreglia [6], they also describe the basic prerequisite for data-driven value creation: “To provide them, you must build intelligence - that is, awareness and connectivity - into the products themselves”.
However, the question is still open what exactly makes services smart? The comparison of the disciplines shows that there exists a lack of a common understanding. To approach a common understanding, a perspective from the user-centred design is used. Following Norman [19], designers who create modern, complex systems are studying the functionality, handling and interaction between humans and technology. Applying Norman’s approach to smart services, the following questions need to be clarified for a comprehensive understanding:
-
Structure of smart services – What elements and resources are required to provide the functionality?
-
Functionality of smart services – What visible and non-visible functionalities are provided by a smart service for the user?
-
Characteristics of smart services – What characteristics influence the handling and interaction between user and smart service?
2.2 Role of Prototypes in Requirements Elicitation
Creative thinking in the requirements engineering field is crucial to create new visions and discover requirements for future information systems [20, 21], such as smart services. A starting point for fostering creative thinking in requirements elicitation is the integration of creativity techniques [22]. Jensen et al. [23] consider that the use of prototyping techniques has significant potential to support the elicitation of requirements, especially when it comes to identifying uncertainties and unpredictability. In doing so, the prototype acts as a representative model or simulation of the final system [24]. A prototype can be interpreted as an approximation of the product or service along one or more dimensions of interest [25]. In addition to increasing creativity, prototypes pursue three main aims across disciplines. They allow early evaluation of design ideas, help designers to think through and solve design problems, and support communication within multidisciplinary design teams [26].
In contrast to general prototyping research, the investigation of the role of prototypes in requirements elicitation is still in its early stages. In prototyping research exist different streams that study prototyping and its effectiveness. Previous research has focused mainly on the following areas: Purpose of prototyping, prototyping process, anatomy of prototypes, involvement of users, and domain-specific application.
Above all, Houde and Hill [27] should be mentioned with their study on the purpose of the prototyping. They deal with the question which aspects of a product or service can be manifested by prototypes. They argue that by focusing on the purpose of prototyping, better decisions can be made for the structure of a prototype and its design. Houde and Hill [27] introduce three fundamental questions: “What role will the artefact play in a user’s life? How should it look and feel? How should it be implemented?”
Numerous works are dedicated to the prototyping process. In addition to the process itself, the dominant topic is the approaches of rapid prototyping. Holtzblatt and Beyer [28] mention, for example, that the primary requirement of the prototyping process is ease and speed of building. Further research work addresses the simultaneous use of several prototypes. According to Dow et al. [29], parallel prototyping leads to better design results, more divergence, and increased self-efficacy. Furthermore, the design of the prototypes is also being researched. For example, Lim et al. [30] create with the anatomy of prototypes a fundamental and systematic understanding of the structure and design of prototypes. On this basis, it is investigated which correlations exist between the prototype shape and the results of the prototyping. Several examples show how the levels of functionality influence the outcome of a prototypical interaction [cf. 5, 31, 32]. The aim is to show and measure the possibilities and limits of a design idea most simply and efficiently. Besides the prototypes themselves, other work focuses on the participatory design approach in prototyping. At the centre of this research is the use of prototypes with the active involvement of users to discover and create new solutions [cf. 33]. Finally, applied prototyping research is also worth mentioning. In this field, new techniques, such as hybrid prototyping, are investigated in particular. Hybrid approaches combine physical prototypes and digital models in virtual reality [34]. Complementing this, the transfer and application of prototyping approaches to other disciplines, such as service prototyping, is being studied [35, 36].
The present article focuses on the study of the prototype composition and more detailed on the design of prototypes for the requirements elicitation of smart services.
2.3 The Composition of Prototypes and the Existing Design Approaches
Discussions concerning the design of prototypes are mostly influenced by the approaches of horizontal and vertical prototyping as well as by the debate on fidelity. The motivation behind prototyping is to reduce the complexity of the implementation by eliminating parts of the entire system [37]. Horizontal prototypes reduce the level of functionality and therefore represent the user interface in its breadth. In contrast, vertical prototypes reduce the number of functions and implement the selected features in-depth. Nielsen’s concept is complemented by scenario prototypes [37]. To meet the requirements of rapid prototyping, the number of features and the depth of the functional implementation is reduced. As a result, a minimum of the system will be implemented in one scenario, resulting in cost and speed benefits.
Another ongoing controversy is how exactly a prototype should represent the final product in form and function. This debate relates to the fidelity of prototypes and discusses whether prototypes must be complete, realistic or reusable to be effective [38]. In designing the prototype, the question of costs is always part of the equation. For this reason, the use of low-fidelity prototyping techniques has been emphasized, especially in the early stages of development [38]. Although the fidelity approach is helpful for orientation in prototyping, several research results show that the simple distinction between low and high fidelity prototypes can be problematic [39, 40]. The concept leads to the fact that several aspects of the prototypes are considered in their entirety [40]. Mostly it is not obvious whether the low fidelity refers to the degree of functionality, interactivity or other aspects, for example. McCurdy el al. [40] demonstrate the effectiveness of a mixed fidelity approach by combining low and high fidelity on different dimensions of the prototype. Lim et al. [39] also show that besides fidelity, other factors such as the material of the prototypes and the test settings affect the results.
The debate on these approaches is focused on the discussion of methods instead of further analyzing the underlying composition of prototypes. According to Lim et al. [30], a lack exists in the fundamental understanding of the prototypes themselves. They describe research in prototyping as a constant attempt to find out what to do with prototypes without understanding what they actually are [30]. This discourse stresses the distinction between prototypes and prototyping. A prototype is a representative model or simulation of the final system [24]. Consequently, this is an approximation of the product along one or more dimensions of interest [25]. Prototyping, on the other hand, is the process of developing such an approximation [25] and describes the activity of making and utilizing prototypes [30].
McCurdy [40] confirms this view and calls it an oversimplification of the prototypes. The existing approaches to characterize prototypes are too crude to ensure that prototyping resources can be used efficiently and that the prototype provides the desired output. Instead, authors from the discipline of human-computer interaction advocate designing prototypes along various orthogonal dimensions [40,41,42].
From the different approaches, five core dimensions could be extracted for the characterization of the prototype composition: Breadth of functionality, depth of functionality, level of interactivity, level of visual refinement, and level of data model. Besides the discipline of human-computer interaction, authors try to develop dimensions that are valid beyond disciplinary boundaries. Lim et al. [30] with the “anatomy of prototypes” can be named as representative of these classification efforts. The proposed anatomy of prototypes includes filter dimensions and manifestation dimensions. In analogy to the approaches from human-computer interaction, the filter dimensions consist of appearance, data, functionality, interactivity, and spatial structure. With these dimensions, the designer can focus on certain areas within the design space and exclude other areas that should not be investigated. Also, the cross-disciplinary classification approaches broaden the focus and consider the manifested form of the prototypes [30, 43]. Lim et al. [30] recommend in detail the consideration of material, resolution, and scope as further dimensions (Table 2).
In the scientific discussion, however, the question remains open to what extent the discussed dimensions can be transferred to smart service prototypes. Moreover, previous research focuses on the anatomy of the prototypes and how the anatomy changes when the dimensions are consciously influenced. However, the anatomy itself does not instruct engineers and designers how to design prototypes [30]. This research gap will be investigated with the strategies for prototypes in the present article.
3 Research Design and Methods
The design of the study is structured in three steps to take into account the different facets of the research work. The first two steps are characterized by theoretical studies. In the beginning, a comprehensive research review describes and evaluates the status quo of research on smart services and prototyping. The authors combine a systematic literature search in relevant scientific databases with a search using the snowball method. In a second step, the different theories and approaches are combined in new theoretical models: The layer model for smart services, the two-part prototyping diamond, and the strategy for prototypes. Based on the findings of the theoretical work, the third step is the analysis of a multi-case study. Based on three cases from the public transport sector, a structured investigation is carried out to determine the extent to which the identified prototype strategies can be applied to the field of smart services. The evaluation of the case studies is based on secondary data collected by the authors in the context of three research projects.
4 Results
4.1 Understanding of Existing Smart Service Models and Implications for Prototyping of Smart Services
Service offerings are enabled by complex service systems [cf. 18]. Maglio et al. [15] define these systems as configurations of people, processes, technologies, physical evidence and other resources that enable value co-creation. For providing smart services, connectivity is a fundamental requirement [6]. Connectivity describes “the ability of a computer, program, device, or system to connect with one or more others” [44]. The existence of connectivity in smart services thus indicates that their underlying composition is characterized by systems structures. In addition to technological systems, e.g. for the communication of mobility data, socio-technical systems also arise out of the interdependence of stakeholders, such as in public transport between bus companies, car-sharing providers and passengers. Therefore Lim et al. [7] understand smart services as a system “in which value co-creation between customers, providers, and other stakeholders are automated or facilitated based on a connected network, data collection (sensing), context-aware computation, and wireless communications”. In an idealized way, a smart service system consists of a triangular relationship between customers, providers, and things [7]. As representatives of the discipline of service science, Lim et al. [7] describe the structure of smart services from the perspective of value creation.
Service engineering, on the other hand, understands smart services as integrated platforms that enable data acquisition, data storage, data analysis and the design of smart services [17]. The focus here is on the resources required for the provision of services. To structure the smart service platforms, Bullinger et al. [17] divides them into three levels (networked physical level, software-defined level and service level) and thus provides a basic framework for service production. Across the different levels, smart service offerings can be composed by services, digital services, and intelligent products [cf. 17, 45].
All models listed are abstractions and simplifications of reality and therefore show only partial aspects [46]. Each of the previous approaches illustrates the structure and complex interrelationships of smart services from an isolated perspective. Lim and Maglio [7] emphasize value co-creation with their model. Bullinger et al. [17] focus on service production with a focus on the required resources and their feasibility. It is therefore important that a model is adequately meaningful for the situation and problem in prototyping. For a model in prototyping, value, look and feel, and implementation are particularly valuable perspectives [27, 36]. These enable engineers and designers to determine and analyse the object of investigation for prototyping.
Layer Model for Smart Services.
The basic structure of smart services can be described as a layer model. In this representation, all components and functionalities required for the provision of a smart service are vertically orchestrated. The structure thus comprises five related layers: Smart space, smart product, smart data, smart service [cf. 8] and, smart interface. Horizontally, the sequential character of the service becomes apparent. Smart service is not a point interaction of components and functions, but rather generates its value in use [cf. 47], usually along the user journey. This phenomenon is indicated by the process, which is composed of individual activities of the user.
Perspective implementation—From a technological point of view, the realization of a smart service requires a complex structure of different resources. Using the layer model, the interplay between the components and functionalities of a smart service can be made transparent and provide valuable insights for the implementation. For example, in public transport smart service is used to inform passengers in real-time about changed departure times and available seats in the vehicles [cf. 48]. Buses and trains themselves act as smart products. Via sensors and microprocessors in the vehicles, the required information is transmitted to the control center system. The basic prerequisite for this communication is usually digital radio. It forms an intelligent environment (smart space) in which digitally connectable objects and devices, such as the on-board computers of the vehicles, can be networked. Coupled with smart space as the technical infrastructure, the networked information provider (smart products) form the prerequisite for the smart data layer. In this layer, the extracted data is collected, bundled and evaluated. Finally, at the service platform level, the data is combined and extended to smart services using context-specific algorithms and finally provided to users via the smart interface. For passengers, this means that they can use the passenger information system to request a forecast for departure times and seat occupancy directly from the control system and thus assess whether they will take their bus as planned or look for a more suitable alternative.
Perspective Value Creation—The value of smart services is created by the direct interaction between the user, the provider and the smart products. The central element here is the data, which is collected in the various layers, refined and provided to the user in the form of services. By analysing the different actors, components and functionalities along the data transformation process, the direct and indirect contribution to the value of a service can be identified. Using the value creation perspective in the model, it is thus possible to understand how the smart value chain is shaped by the data transformation. Continuing the example of public transport, it becomes apparent that the simple information about a free seat from the networked vehicle increases in value if this data is combined with the personal trip route of the passenger and is updated continuously.
Perspective Interaction—The difference between the layer model of smart service and other models is the interaction perspective. This perspective emphasizes the sequential nature of smart services and shows that user perception and feedback depend on the interaction with the provider and technology. Services are dynamic processes consisting of user and provider activities that extend over a certain period [49]. Shostack [50] distinguishes between activities visible to the user that directly influence the process and invisible activities in the background that indirectly affect the service (line of visibility). In the so-called backstage area, a distinction can be made between the physical infrastructure (line of infrastructure), which is the technological prerequisite for networking, and the software level. Both layers differ in their characteristics. The latter can, for example, use cloud computing to provide its activities largely independent of location.
4.2 Classification of the Strategy for Prototypes
The strategy for prototypes defines the systematic composition of a prototype. The goal is to systematically influence the way in which test persons perceive and interact with the prototype to achieve the desired prototyping aims.
To show the strategy and its significance, the strategy should be integrated into the concept of prototyping. For this purpose, the two-part prototyping diamond is introduced. The model structures the decision processes that engineers and designers have to face during the prototyping stage.
With the aim in mind, the fundamental question is raised: Why is a prototype created and what shall be achieved. It is common practice to use prototypes for the exploration, evaluation, and communication of future solutions [cf. 25, 27, 36, 51]. In the next step, the initial situation for prototyping is further specified by the object of investigation. For each prototyping iteration, it should be defined which aspect of the solution is being investigated. Houde and Hill [27] distinguish three main objects of investigation: “Role” investigates how the new solution creates value for users, “look and feel” explores the appearance and usability of the solution, and “implementation” looks at the feasibility of the solution. In addition, they introduce the “integration” as an additional object of investigation. The “integration” combines all three perspectives. Investigating how the various aspects work together in a prototype is also a relevant object of investigation for smart services. With the prototype and process, it now follows two elements in the model, which both influence the test persons and thus affect the output of the prototyping. In prototyping, designers are faced with the challenge of finding the simplest manifestation of the object of investigation without distorting the understanding of the whole [30].
The composition of prototype is always varied along different dimensions. The authors’ field studies have shown that the dimensions representation, scope and functionality, interactivity, appearance as well as data model are particularly valuable for the design of smart service prototypes. Overall, the quality of the prototyping is evaluated by the achieved output. The prototype serves quasi as a means to an end, to collect the desired findings with the test persons. This is where the prototype strategy comes in. The strategy designs the composition of prototypes according to a systematic procedure. The goal is to design the interplay of the individual dimensions in such a way that certain perceptions and responses are triggered in the test persons. Similar to the prototype, all efforts in the process are focused on the output to be achieved. The process includes all questions concerning the prototyping sequence and defines the methodological framework. For example, it is defined which method is used for data collection, whether several prototypes are tested in parallel and how many iteration loops are performed.
All decisions concerning the process must also take into account their effects on the probands. Because their feedback is the primary motivation for prototyping. Traditionally, this role is taken by the future target group. But for the prototyping purpose communication, for example, when the prototype is presented to the management and decision-makers, then these stakeholders take on the role of the proband. Even the designer himself can test the prototype. In this case, the designer himself is the test person for the prototype. In addition to the selection of test persons, the questions of how many test persons participate in the test and to what extent the approaches of participatory design should be applied in prototyping must also be clarified.
The endpoint of the model represents the output. The findings are extracted from the aggregated perceptions and responses of the probands. The overall quality of the prototyping is revealed by reflecting the findings with the aims defined at the beginning. This reflection thus closes the circle and can lead to research questions for the new prototyping iteration.
4.3 Strategies for Smart Service Prototypes
The quality of a prototype is defined by the information and insights gained with its help. In accordance with this maxim, the prototype strategy is faced with the challenge of designing the prototype in such a way that a maximum of new and correct insights can be collected. For the sector of smart services three relevant strategies for prototypes in other design disciplines could be identified: Functional prototypes, experience prototypes and contextual prototypes (Fig. 3).
Each strategy has its own focus and thus influences how the prototype is perceived by the probands. The focus of a prototype is derived from its composition. For smart services, the composition consists of six different dimensions, along which a prototype can be characterized (see Table 3). A prototype can be designed and implemented for each of these six dimensions with low or high fidelity, depending on the findings the engineers or designers intend to gather as output. In the strategy, the dimensions are consciously selected to provoke responses from the probands with regard to the object of investigation. Despite the emphasis on one dimension, the test persons do not perceive the individual dimensions in isolation, but the probands are influenced by the interplay of all involved dimensions [cf. 38].
Crucial for the selection of a strategy is the assessment of the test persons. In line with Nielsen’s findings in usability engineering [37], knowledge about the participants and their individual differences can also improve the outcome of a study in the requirements elicitation for smart services. To make a decision, engineers and designers have to ask themselves which strategy will produce the highest quality output from the test persons. In addition to the respondents, the prototyping diamond indicates two further dependencies for the selection of the strategy (see Fig. 2). The object of investigation should always be included in the strategy considerations, as well as the process.
Functional Prototypes
Traditionally, the functionality of a prototype increases during the design stages with the understanding of the product [25]. However, innovation research in product design that aims to create radical innovations emphasizes the value of different functional prototypes already in the early stages of development [cf. 5]. Jensen et al. [23] as representatives from product design have developed the prototrials approach for this purpose. Prototrials cover high-functional prototypes used in the concept development phases for requirements elicitation, but with low fidelity compared with the final product [23]. Exactly this approach is used in the strategy of functional prototypes. According to the name, the dimension functionality is very prominent. Considering the layer model of smart services (see Fig. 1), it becomes clear that the layers below the line of visibility are the subject of these prototypes. Besides the software-based functionality in the smart service layer, the dynamic interdependencies and data transformation are also relevant for a functional investigation. This leads to the fact that elements from the layers smart data and smart product are also represented in functional prototypes. Engineers and designers must, therefore, decide which level of fidelity is sufficient for the dimension data in the prototype. Furthermore, the question also arises to what extent a representation of the smart products or a visualization of the data flows is relevant for explaining the functional relationships.
In research work for public transport, the orientation and navigation within subway stations were investigated from the passengers’ point of view. One of the aims was to develop a tool that identifies the right wagon for the passenger according to individual needs when entering the subway [52]. In the first step, a prototype was developed to test its feasibility. The object of investigation was the data transmission and processing in real-time as well as whether the identification of a wagon is possible with the beacon technology. The researchers tested the prototype with 15 probands with the aim of eliciting new requirements for further development. The prototype focused on the data exchange between beacon and a rudimentary information display on the smartphone. In addition to the functionality, the data had a high level of fidelity. For the test, real data was exchanged in real-time between the subway wagons and the smartphone. By integrating the wagon and the smartphone into the test, the probands were able to understand the dynamic interrelationships of the smart service. Compared to the final application, the dimensions of interactivity and appearance were hardly pronounced.
The emphasis on the functional dimension leads the test persons to a structure-oriented view of the prototypes. In this way, the probands observe the functional elements of a system and their relationships, whereby the dynamic mechanisms and processes are of particular interest [cf. 46]. The focus is on how the functions are used to generate value for the user. Due to the concentration on the functional aspects, these prototypes have a significant demonstration character and only a low potential for immersion and involvement for the proband during the prototyping session.
Experience Prototypes.
By the term “experience erototype” Buchenau and Suri [53] mean to emphasize “the experiential aspect of whatever representations are needed to successfully (re)live or convey an experience with a product, space or system”. The objective of the experience prototypes is the discovery of the probands’ user experience. Based on the perceptions and responses of a test person resulting from the use of the system [cf. 54], new insights are generated for service development. The interaction of the test persons with the system is used as the central stimulus for the emergence of the user experience. Considering the experience prototypes in the layer model (see Fig. 1), it becomes clear that these prototypes concentrate on the smart interface layer. Experience prototypes can be described using the technique wizard of oz. While a test person interacts with a system that feels real, an engineer or designer simulates the system in the background [cf. 55]. The goal is to create a high degree of interactivity while maintaining low functionality in the prototype at the same time. The focus is on the dialogue between user and provider, which is made tangible through user inputs and system feedback. In service prototyping, there are special requirements due to the characteristics of the services. Stickdorn [49] therefore stresses that intangible services should be visualised in terms of physical artefacts. In general, the immaterial character of smart services is further reinforced. The reason for this is that interaction in smart service is characterized by automation and implicit interactions, which are fostered by adaptivity and context awareness. As a result, users perceive only a fraction of the activities of smart service. The majority of the activities are carried out in the backstage that is not visible to the user (see Fig. 1). For this reason, experience prototypes face the challenge to make the interaction tangible for the proband using the dimension representation. Another challenge for experience prototypes is the sequential nature of smart services. Stickdorn [49] recommends that the service should be visualised as a sequence of interrelated activities. For the prototype, this means that within the dimension scope all touchpoints along the user journey should be checked for relevance to the prototype. The focus on interaction promotes the behavioural analysis of the prototypes. The probands are primarily concerned with system behaviour and the generation of value creation during use, rather than focusing on internal, functional relationships. Furthermore, the interactive character of the experience prototypes promotes active participation [53] and the immersion of the probands in the test scenario.
Figure 4 shows a lego serious play prototype, which was carried out in combination with the technique service walkthrough. The aim of this experience prototype was to discover new requirements in the early phases of the research project within the first study with six test persons. The project explored the networking of local public transport and car-sharing with electric vehicles in a digital mobility platform [56]. Concretely, the process, as well as the interaction at the charging station and in the vehicles, was investigated. The basic structure for the session was defined using the service walkthrough method. Along the user journey, the test persons visited selected touchpoints, which became tangible artefacts for the test persons in the form of lego objects. At the individual touchpoints, interactions with the digital platform were simulated using paper prototypes to capture the mobility experience of the travel chain [cf. 57].
Contextual Prototypes.
“Context […] characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application” [58]. Contextual prototypes thus emphasize the situation in which the system is applied. At the same time, they take the context of use as a stimulus for the test persons in the prototyping session. Beaudouin-Lafon and Mackay [26] use the term “scenario prototypes” in this context and stress that prototypes are used in a more realistic scenario to simulate the system under real conditions. Hutchinson et al. [58] went one step further with their technology probes approach. Technology probes are simple, flexible, adaptable technologies that combine the social science goal of collecting information about the use and the users of the technology in a real-world setting [59]. Contextual prototypes use the realistic representation of the usage situation as a stimulus for the participants in the prototyping session. Consequently, when constructing the prototype, the usage context within the scope dimension must be considered in addition to the interaction at the touchpoint. Bittner [60] speaks in this context of servicescapes and mentions the possible factors influencing the user experience: Ambient conditions, spatial layout and functionality, signs, symbols, and artefacts as well as service typology, and environmental dimensions.
Due to the context awareness of smart services, contextual prototypes play a double role and contribute to the clarification of two questions. First, they provide information on how the context can hinder or improve the usage and experience of smart services. In this case, the focus of the prototypes lies on interactivity in combination with the representation to enable realistic interactions. Secondly, contextual prototypes offer the potential to understand how collected data from the usage context can contribute to the value generation of smart services. In doing so, no high fidelity in the dimensions data and functionality is required, but rather the logic of the system behaviour in the usage situation must be simulated. Both points lead to the fact that the participants analyse the prototypes from an environment-oriented perspective. The test persons perceive the external factors from the usage situation and their influence on the prototypes. Due to the real-world setting, a high level of immersion is generated among the probands during the prototyping session.
Figure 4 shows an example of a contextual prototype from a mobility research project. The project explored the agenda planning as a new mobility planning approach [cf. 48]. In this study with 25 test persons, it was examined how, for example, changed opening hours of shops and the current seat occupation of vehicles can be integrated into a digital planning application. The aim was to identify new requirements in the early development phases. The prototype included a rudimentary digital smartphone application that could be used in the local bus and train network. When composing the prototype, the focus was on interactivity and the usage context. It was important that all participants could perform their tasks independently in real-life usage situations.
5 Discussion—Contribution to Requirements Elicitation for Smart Services
The strategies for prototypes define the composition of prototypes and thus systematically influence how a prototype is perceived by the test persons. They constitute the missing link between the dimensions of a prototype and the targeted output. Based on the presentation of the strategies and the case studies carried out, the authors see two major contributions to requirements elicitation in the early development phases.
First, the strategies provide a critical thinking approach. The strategies can be used to better predict in advance of the requirements elicitation how a prototype will affect the test persons and which results can be achieved as output. The engineers and designers can thus better understand which features are important for prototyping in requirements elicitation. Furthermore, the strategy is the consequent continuation of the economic principle: “The best prototype is one that, in the simplest and the most efficient way, makes the possibilities and limitations of a design idea visible and measurable” [30]. By means of the output-oriented analysis, it is possible to assess in advance to what extent the effort for prototyping is cost-effective. Secondly, the strategies provide a guideline for the creation of prototypes in requirements elicitation. Following the example of a pattern language [cf. 61], the strategies provide engineers and designers a kind of pattern for the composition of the different prototype dimensions. On the other hand, in conjunction with the layer model of smart services, the strategies show which dimensions are relevant for the investigation of the different layers.
As a limitation, it must be noted that the strategies are a mindset for the composition of prototypes for smart services. The strategies do not contain concrete recommendations for the efficient use of prototype resources in economic terms. This requires controlled and detailed studies to prove the effect of single dimensions on the output of prototyping [cf. 32, 39, 62]. Furthermore, the three strategies for smart service prototypes are only a selection. Although the various strategies have proven themselves in practice, they have their origins in other design disciplines. The question remains unanswered whether new strategies are better suited for requirements elicitation in the early development phases. With novel approaches, the complex structure of smart services could be addressed more precisely. One possible approach would be to focus on data-driven value creation, for example.
6 Conclusion
In this article the question is investigated, how can prototypes contribute to the requirements elicitation for smart services in the early development stages. An elementary prerequisite for the use of prototypes in requirements elicitation is the comprehensive understanding of smart services. The present article reveals through extensive literature research that the understanding of smart services varies from discipline to discipline and that the existing models address only rudimentary prototyping issues. To overcome this shortcoming, the authors introduce the layer model of smart services and build the foundation for the investigation of smart service prototypes. The review of the existing approaches for the systematic design of prototypes shows that the relationship between the composition of a prototype and the output has been insufficiently investigated so far but is becoming increasingly relevant for smart services. The layer model demonstrates that five layers with different functionalities are required for the provision of smart services and that a conscious focus must, therefore, take place every time a prototype is created. The lack of discussion motivated the authors to define the strategies for the composition of prototypes and to work out the approach for the requirements elicitation of smart services. The strategies systematically guide the composition of prototypes in order to influence how a prototype is perceived by the test persons. They thus form the missing link between the dimensions of a prototype and the targeted output. For smart services, the authors identify three relevant strategies in related design disciplines: Functional prototypes, experience prototypes and contextual prototypes. The relevance of the defined strategies is reviewed and illustrated by case studies from the mobility sector.
References
Norman, D.: Things That Make Us Smart: Defending Human Attributes in the Age of The Machine. Diversion Books (2014)
Norman, D.: The Design of Future Things. Basic Books (2009)
Wünderlich, N.V., et al.: Futurizing smart service: implications for service researchers and managers. J. Serv. Mark. 29, 6 (2015)
Haines-Gadd, M., et al.: Cut the crap; design brief to pre-production in eight weeks: rapid development of an urban emergency low-tech toilet for Oxfam. Des. Stud. 40, 246–268 (2015)
Leifer, L.J., Steinert, M.: Dancing with ambiguity: causality behavior, design thinking, and triple-loop-learning. Inf. Knowl. Syst. Manag. 10(1–4), 151–173 (2011)
Allmendinger, G., Lombreglia, R.: Four strategies for the age of smart services. Harv. Bus. Rev. 83(10), 131 (2005)
Lim, C., Maglio, P.P.: Clarifying the concept of smart service system. In: Maglio, P.P., Kieliszewski, C.A., Spohrer, J.C., Lyons, K., Patrício, L., Sawatani, Y. (eds.) Handbook of Service Science, Volume II. SSRISE, pp. 349–376. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98512-1_16
Kagermann, H., et al.: Smart service welt: Umsetzungsempfehlungen für das Zukunftsprojekt Internetbasierte Dienste für die Wirtschaft. acatech, Berlin (2014)
Bruhn, M., Hadwich, K.: Dienstleistungen 4.0 – Erscheinungsformen, Transformationsprozesse und Managementimplikationen. Dienstleistungen 4.0, pp. 1–39. Springer, Wiesbaden (2017). https://doi.org/10.1007/978-3-658-17552-8_1
Carrubbo, L., Bruni, R., Cavacece, Y., Moretta Tartaglione, A.: Service system platforms to improve value co-creation: insights for translational medicine 2015, Service Dominant Logic, Network and Systems Theory and Service Science: Integrating three Perspectives for a New Service Agenda (2015)
IfM and IBM: Succeeding through service innovation: a discussion paper. In: Cambridge Service Science, Management and Engineering Symposium. University of Cambridge Institute for Manufacturing, Cambridge (2007)
Basole, R.C., Rouse, W.B.: Complexity of service value networks: conceptualization and empirical investigation. IBM Syst. J. 47(1), 53–70 (2008)
Demirkan, H., Kauffman, R.J., Vayghan, J.A., Fill, H.G., Karagiannis, D., Maglio, P.P.: Service-oriented technology and management: perspectives on research and practice for the coming decade. Electron. Commer. Res. Appl. 7(4), 356–376 (2008)
Spohrer, J.C., Demirkan, H.: Introduction to the smart service systems: analytics, cognition, and innovation minitrack. In: 2015 48th Hawaii International Conference on System Sciences, pp. 1442–1442. IEEE, January 2015
Maglio, P.P., Vargo, S.L., Caswell, N., Spohrer, J.: The service system is the basic abstraction of service science. Inf. Syst. e-bus. Manag. 7(4), 395–406 (2009)
Spohrer, J., Maglio, P.P., Bailey, J., Gruhl, D.: Steps toward a science of service systems. Computer 40(1), 71–77 (2007)
Bullinger, H.-J., Ganz, W., Neuhüttler, J.: Smart Services – Chancen und Herausforderungen digitalisierter Dienstleistungssysteme für Unternehmen. Dienstleistungen 4.0, pp. 97–120. Springer, Wiesbaden (2017). https://doi.org/10.1007/978-3-658-17550-4_4
Winter, A., et al.: Manifest-Kundeninduzierte Orchestrierung komplexer Dienstleistungen. Informatik-Spektrum 35(6), 399–408 (2012)
Norman, D.: The Design of Everyday Things: Revised and Expanded Edition. Basic Books (2013)
Robertson, J.: Requirements analysts must also be inventors. IEEE Softw. 22(1), 48 (2005)
Hoffmann, O., Cropley, D., Cropley, A., Nguyen, L., Swatman, P.: Creativity, requirements and perspectives. Australas. J. Inf. Syst. 13(1) (2005)
Nguyen, L., Shanks, G.: A framework for understanding creativity in requirements engineering. Inf. Softw. Technol. 51(3), 655–662 (2009)
Jensen, M.B., Elverum, C.W., Steinert, M.: Eliciting unknown unknowns with prototypes: introducing prototrials and prototrial-driven cultures. Des. Stud. 49, 1–31 (2017)
Warfel, T.Z.: Prototyping: a Practitioner’s Guide. Rosenfeld Media (2009)
Eppinger, S., Ulrich, K.: Product Design and Development. McGraw-Hill Higher Education (2015)
Beaudouin-Lafon, M., Mackay, W.E.: Prototyping tools and techniques. In: Human-Computer Interaction, pp. 137–160. CRC Press (2009)
Houde, S., Hill, C.: What do prototypes prototype?. In: Handbook of Human-Computer Interaction, pp. 367–381, North-Holland (1997)
Holtzblatt, K., Beyer, H.: Contextual design: evolved. Synth. Lect. Hum.-Cent. Inform. 7(4), 1–91 (2014)
Dow, S.P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D.L., Klemmer, S.R.: Parallel prototyping leads to better design results, more divergence, and increased self-efficacy. ACM Trans. Comput.-Hum. Interact. (TOCHI) 17(4), 1–24 (2010)
Lim, Y.K., Stolterman, E., Tenenberg, J.: The anatomy of prototypes: prototypes as filters, prototypes as manifestations of design ideas. ACM Trans. Comput.-Hum. Interact. (TOCHI) 15(2), 1–27 (2008)
Blackler, A.: Applications of high and low fidelity prototypes in researching intuitive interaction (2009)
Hare, J., Gill, S., Loudon, G., Lewis, A.: The effect of physicality on low fidelity interactive prototyping for design practice. In: Kotzé, P., Marsden, G., Lindgaard, G., Wesson, J., Winckler, M. (eds.) INTERACT 2013. LNCS, vol. 8117, pp. 495–510. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40483-2_36
Sanders, E.B.N.: From user-centered to participatory design approaches. In: Design and the Social Sciences, pp. 18–25. CRC Press (2002)
Exner, K., Sternitzke, A., Kind, S., Beckmann-Dobrev, B.: Hybrid prototyping. In: Gengnagel, C., Nagy, E., Stark, R. (eds.) Rethink! Prototyping, pp. 89–127. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24439-6_8
Blomkvist, J., Holmlid, S.: Existing prototyping perspectives: considerations for service design. NorDes, vol. 4 (2011)
Stickdorn, M., Hormess, M. E., Lawrence, A., Schneider, J.: This is Service Design Doing: Applying Service Design Thinking in the Real World. O’Reilly Media Inc. (2018)
Nielsen, J.: Usability Engineering. Morgan Kaufmann (1994)
Rudd, J., Stern, K., Isensee, S.: Low vs. high-fidelity prototyping debate. Interactions 3(1), 76–85 (1996)
Lim, Y.K., Pangam, A., Periyasami, S., Aneja, S.: Comparative analysis of high-and low-fidelity prototypes for more valid usability evaluations of mobile devices. In: Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing Roles, pp. 291–300, October 2006
McCurdy, M., Connors, C., Pyrzak, G., Kanefsky, B., Vera, A.: Breaking the fidelity barrier: an examination of our current characterization of prototypes and an example of a mixed-fidelity success. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1233–1242, April 2006
Arnowitz, J., Arent, M., Berger, N.: Effective prototyping for software makers. Elsevier (2010)
Virzi, R.A., Sokolov, J.L., Karis, D.: Usability problem identification using both low-and high-fidelity prototypes. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 236–243, April 1996
Exner, K., Lindow, K., Stark, R., Ängeslevä, J., Bähr, B., Nagy, E.: A transdisciplinary perspective on prototyping. In: 2015 IEEE International Conference on Engineering, Technology and Innovation/International Technology Management Conference (ICE/ITMC), pp. 1–8. IEEE, June 2015
Cambridge University Press. https://dictionary.cambridge.org/de/worterbuch/englisch/connectivity. Accessed 24 Feb 2020
Neuhuettler, J., Ganz, W., Liu, J.: An integrated approach for measuring and managing quality of smart senior care services. In: Ahram, T., Karwowski, W. (eds.) Advances in The Human Side of Service Engineering, vol. 494, pp. 309–318. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-41947-3_29
Haberfellner, R., de Weck, O., Fricke, E., Vössner, S.: Systems Engineering–Grundlagen und Anwendung. 12. Auflage, Orell Füssli, Zürich (2012). 978-3280040683
Grönroos, C., Voima, P.: Critical service logic: making sense of value creation and co-creation. J. Acad. Mark. Sci. 41(2), 133–150 (2013)
Wienken, T., Schoppe, C., Krömker, H.: Auf dem Weg zur Agendaplanung− Weiterentwicklung der Fahrplanauskunft zum Service-System für Mobilität. Nahverkehr, 35(9) (2017)
Stickdorn, M., Schneider, J., Andrews, K., Lawrence, A.: This is Service Design Thinking: Basics, Tools, Cases, vol. 1. Wiley, Hoboken, NJ (2011)
Shostack, G.L.: Designing services that deliver. Harv. Bus. Rev. 62, 133–139 (1984)
Voss, C., Zomerdijk, L.: Innovation in experiential services: an empirical view. AIM Research (2007)
Krömker, H., Schöne, C., Wienken, T., Steinert, T.: DiMo-FuH - Digitale Mobilität - Fahrzeug und Haltestelle - Schlussbericht. Technische Universität Ilmenau, Ilmenau (2018)
Buchenau, M., Suri, J.F.: Experience prototyping. In: Proceedings of the 3rd Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 424–433, August 2000
International Standards Organization: Ergonomics of Human-System Interaction—Part 210: Human Centred Design for Interactive Systems (2010). ISO 9241-210
Kelley, J.F.: An iterative design methodology for user-friendly natural language office information applications. ACM Trans. Inf. Syst. (TOIS) 2(1), 26–41 (1984)
Schmermbeck, S., et al.: Mobil im ländlichen Raum dank innovativer Dienstleistungen. Dienstleistungen als Erfolgsfaktor für Elektromobilität, pp. 128–139. Fraunhofer Verlag, Stuttgart (2017)
Wienken, T., Krömker, H.: Experience maps for mobility. In: Kurosu, M. (ed.) HCI 2018. LNCS, vol. 10902, pp. 615–627. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91244-8_47
Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48157-5_29
Hutchinson, H., et al.: Technology probes: inspiring design for and with families. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 17–24 (2003)
Bitner, M.J.: Servicescapes: the impact of physical surroundings on customers and employees. J. Mark. 56(2), 57–71 (1992)
Alexander, C.: A Pattern Language: Towns, Buildings. Construction. Oxford University Press, Oxford (1977)
Sefelin, R., Tscheligi, M., Giller, V.: Paper prototyping-what is it good for? A comparison of paper-and computer-based low-fidelity prototyping. In: CHI 2003 Extended Abstracts on Human Factors in Computing Systems, pp. 778–779, April 2003
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wienken, T., Krömker, H. (2020). Strategies for Smart Service Prototypes - Implications for the Requirements Elicitation in the Early Development Stages. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-60114-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60113-3
Online ISBN: 978-3-030-60114-0
eBook Packages: Computer ScienceComputer Science (R0)