th Hawaii International Conference on System Sciences

2016 49th Hawaii International Conference on System Sciences Business Analysis of Digital Discourse for New Service Development: A Theoretical Perspe...
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2016 49th Hawaii International Conference on System Sciences

Business Analysis of Digital Discourse for New Service Development: A Theoretical Perspective and a Method for Uncovering the Structure of Social Representations for Improved Service Development Friedrich Chasin University of Muenster, European Research Center for Information Systems (ERCIS), M¨unster, Germany Email: [email protected]

Abstract—Analysis of digital discourse in social networks can inform decisions about service design and align services with customers’ needs and expectations. Using the theory of social representations (SRT) as a theoretical lens, I propose a method for the systematic analysis of the digital discourse in order to identify the core representations on which a service depends. I demonstrate the method by analyzing the social representation of electric cars on Wikipedia to derive implications for the development of a new service (NSD) for the electric vehicle (EV) domain. The work has three primary contributions: First, a novel instrument for the business analysis within the NSD process is proposed. Second, the study spurs research on the SRT in IS and provides support for the theory’s applicability in the discipline. Third, a tool is presented, which practitioners can use to analyze social representations on Wikipedia in order to derive implications for NSD.

I. NSD MEETS SRT New services are prone to failure because of their inability to address customer needs and expectations sufficiently and to anticipate customers’ future engagement with the service [4]. Information Systems (IS) research traditionally addresses this problem within the interdisciplinary research filed of service science. New service development (NSD) — the formalization and standardization of procedures to guide firms in creating new service offerings — has been one of the most prevalent themes in this field [34] during the last two decades [21]. The recent service literature continues to appreciate the NSD procedure formulated by Johnson, Menor, Roth, and Chase [14] because “it represents the most synthesized model in NSD research” [6, p. 446]. Early NSD research [4] emphasized the need to include the customer’s perspective when developing new service offerings and suggested that managers both research their customers’ problems and needs and evaluate their likely acceptance of the service in advance of market launch. Later, theoretical debates on “customer value” [12, p. 138] and customers’ inevitable role as “co-creator of value” [41, p. 2] has called attention to customers’ important role in NSD. Therefore, researchers continually suggest modifications to existing NSD procedures and novel NSD procedures in order to strengthen the emphasis on the customer’s role (e.g., [1], [17], [19]). 1530-1605/16 $31.00 © 2016 IEEE DOI 10.1109/HICSS.2016.198

However, the prevalent theoretical underpinning of the discussion of the customer’s role in NSD has tended to ignore constructivist thinking [3]. New services are often seen as objective value propositions that can be determined and meet customers’ needs and expectations [38], [41][inline]Check the source. Although helpful in developing service systems, this perspective neglects the mediation processes that take place while customers learn about the new service and its underlying technologies. During these processes, customers’ knowledge about the service can become detached from the service’s independent characteristics and become dependent on the specific historical and cultural context of the social group to which the customer belongs [8]. One way to elicit the structure of the customers’ knowledge regarding the service constituents is by looking at the dynamic process of social knowledge construction through the lens of the social representations theory (SRT) [23], where social representations are “collaborative elaborations of a social object by the community for the purpose of behaving and communicating” [43, p. 95]. Despite the potential of social representations to inform the NSD process, their structures are challenging to assess using traditional methods like ethnographies, focus groups, questionnaires, and experiments [43]. Against this backdrop, the digital discourse can be a rich source of data on the emergence and evolution of knowledge [37] about social representations that social groups hold in regard to the service constituents. In this article, I argue that, before a service is developed, an analysis of the social representations potential customers hold regarding the technology that underlie a service should be performed as part of the business-analysis step of the NSD. Therefore, with the overall goal of improving the NSD process, I ask the question: how can social representations that circulate in digital discourse illuminate the NSD process and help to create services that are well-aligned with customers’ needs and expectations? To answer this question I first examine the role of social representations in NSD and then develop a method for the analysis of digital discourse on social media in order to un1567

cover relevant social representations that potential customers of the service share. I situate the method in the Analysis stage of the NSD process and demonstrate the application of the method. The demonstration consists of an analysis of the social representation of electric cars on Wikipedia. The decision to combine the NSD and SRT is due to the power of the individual theoretical lenses in regard to designing new services and studying sociopsycological phenomena, respectively. A combination of both theoretical underpinnings is therefore suitable to study sociopsycological implications in the context of NSD. The remainder of the paper is structured as follows. I begin in Section II by presenting the research background and related work in two research fields, service science and NSD, as well as on the phenomenon of social representations. After introducing the research approach in Section III, I propose the method for the analysis of digital discourse in Section IV. Section V demonstrates the method application in a concrete case of developing a novel service for the electric vehicle (EV) charging infrastructure. The discussion on this study’s results in Section VI is followed by a brief summary of the work in Section VII. II. S ERVICE RESEARCH AND SOCIAL REPRESENTATIONS A. NSD and service science Service can be defined as value created in relational interaction processes [38] that connect a company to “collaborators” [34, p. 492] like partners, employees, and suppliers. Together these entities form the service system, which Vargo and Lusch [41] defined as a dynamic configuration of four types of resources — people, technologies, organization, and information — to create value for the entities’ benefit. In this context, service development, which refers to a firm’s approach to develop new services, has been described as a cyclical process that includes various planning and implementation activities at the stages of design, analysis, development, and launch [33]. Among the normative procedures that the service research has suggested to guide practitioners in service development, the cyclic NSD process [14] represents “the most synthesized model” [6, p. 446]. Although the NSD processes are sufficiently generic so that their “stages” and “activities” apply to any service development project, firms must introduce project-specific adaptations to the generic NSD process [44] The NSD starts with the design stage, where business objectives are formulated and an overall strategy for new service offerings is adapted. Design is followed by idea generation and screening, where ideas are developed in accordance with the firm’s objectives and strategy [16]. Screening includes the first evaluation of the ideas in terms of their financial implications, taking into consideration the market, management input, and the firm’s strategic plans. Ideas that pass the screening become part of a service concept [6]. Within the “Analysis” stage, a decision regarding the project

authorization is made, which is based on a more thorough business analysis of the service concept [44]. The business analysis tests the service concept for its potential for growth and reward, profitability in terms of demand, competitive advantages [6], and operational feasibility [44]. (The focus of the study is on the analysis stage of NSD.) The final stages of “development” and “full launch” turn the service concept into a marketable service and develop strategies for market launch and commercialization, respectively [6]. While the aforementioned stages represent the process dimension of NSD, further dimensions exist including market research, design tools and techniques, metrics and performance measurement, and organization [45]. Market research refers to the reactive and proactive ways of identifying customers’ needs and requirements in the various activities of the process cycle [17], which feed mainly from “standard” qualitative and quantitative market research techniques. These techniques include focus groups, surveys, conjoint analysis, one-on-one interviews, choice modeling, as well as ethnographic methods and empathic approaches for experiential services [45]. The dimension of design tools and techniques summarizes ways to assist in creating new services at each of the stages — such as conceptual modeling, brainstorming, and simulation. Information Systems research has played an active role in advancing the instruments available to service managers, including prototyping and virtual reality labs to simulate service environments [20], [28]. In this context service science, management and engineering emerged as a subdiscipline of service science that works to build and evaluate IT artifacts that are useful in the service economy [35]. Metrics and performance measurement is concerned with evaluating a firm’s NSD performance on a single project or program level by using defining and monitoring indicators [36, p. 84]. These indicators can be financial (e.g., profit, sales, market share), customer-based (e.g., customer satisfaction, new customers), or internal (e.g., future potential, efficiency, strategic fit). The last dimension of NSD is the organization dimension. The focus of the the organization dimension is on addressing questions of how can crossfunctional teams and front-line employees [45] be involved in NSD. Service development procedures stand out from other product development procedures by acknowledging the customer’s role as co-creator of value [29]. Customers must understand a product if co-creation of the service delivery is to take place [29]. Therefore, for a successful service to be established and to ensure that the customer is closely involved with the process of co-creation, the design of a new service must include the analysis of customers’ representations regarding the core objects associated with the service under consideration.

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B. Social representations

sense of familiarity is possible, even if the unfamiliar is classified [27, p. 43]. However, anchoring is more than simply naming and classifying the unfamiliar. According to Moscovici, the main aim of the process is to allow interpretation of characteristics associated with the unfamiliar. By comparing an unknown to a prototype, the unknown acquires characteristics of the prototype’s category and can even be adapted to fit in the category. The second process in forming social representations is objectification, a complementary process which often occurs in parallel to anchoring [30, p. 20]. However, while every objectified phenomenon is necessarily anchored, not every anchored phenomenon is objectified. The reality for an individual can be described as the sum of the social representations the individual holds. In this reality, every object corresponds to a social representation, which has gone through the process of objectification. The result of the objectification is a concept that ceases to be a sign in the individual’s mind and becomes a replica of reality, something that the Scottish philosopher David Hume called “the mind’s property to spread itself on external objects” [26, p. 214].

The social representations theory (SRT) states that people’s knowledge of the world is inevitably mediated. Objects in the world, whether physical or not physical, acquire meaning only through the social representations [23] social groups create and change in a continuous process. Social representations are semiotic mediating devices [40] that members of a social group use to render their world meaningful [43]. During the last fifty years, the SRT took its place in the canon of social psychology, and it is the subject of thousands of scientific publications [13], including works in the IS field [5], [10], [39]. Social representation phenomenon is difficult to compress into a single definition. The author of the theory himself refuses to provide an exact definition, arguing that none of the definitions could do justice to its manifold nature [25, p. 213]. An alternative to a precise definition is a thorough elaboration of its characteristics [18]. In this article, I build upon a sketchy definition from Wagner (1999) and then emphasize key aspects of the theory. According to, “social representations are collaborative elaborations of a social object by the community for the purpose of behaving and communicating” [43, p. 95]. Social representations exist not only in people’s minds but also in the culture being collectively realized [31]. Once emerged, a social representation continues to exist on its own, on a trans-individual level [9], [26]. However, social representations are not necessarily completely shared by everyone in a social group but are distributed as pieces of knowledge shared by some people and possibly unknown to others [24, p. 168]. Different social representations of the same social object may also exist simultaneously across social groups [13]. Social representations can be either implicit or explicit. A social representation is explicit only if it becomes the subject of a discussion itself or when communication is interpreted in terms of the underlying representations [11, p. 477]. Apart from these cases, social representations are “buried under layers of words and images” [24, p. 168]. The focus of SRT is an explanation of how social representations are formed by two processes: anchoring and objectification [27]. Whenever a person experiences something unfamiliar, the process of overcoming unfamiliarity is triggered. In order to understand the phenomenon and allow communication, the unfamiliar must be named and classified [43] a process called anchoring. The classification reduces the unknown to familiar concepts [27, p.42] based on comparing the new phenomena to prototypes that generally represent the unfamiliar experience [27]. For example, in its early stage, the unfamiliar phenomenon of HIV/AIDS, before it acquired this name, was anchored in terms of a “gay plague” or “gay cancer” [9, p. 201]. Naming the unfamiliar is a distinct act without which neither effective communication nor a

III. R ESEARCH APPROACH The research approach in this study consists of two primary steps. First, I establish a link between the elements of structured digital discourse and the elements of SRT and propose a method for analyzing social representations in the context of NSD. This step is conceptual and is based on the extensive analysis of the SRT literature. The method of analysis uses quantitative techniques to extract data from the digital discourse and generate corresponding statistics, and qualitative coding techniques [7], [22] to make sense of the data. Second, I demonstrate the method by applying it to the digital discourse on electric cars on Wikipedia in order to derive implications for the design of an innovative service within the EV-charging infrastructure domain. The exemplary analysis is conducted using a tool that was specially implemented by the author of this work to study the formation of social representations on Wikipedia. A detailed description of the tool and its features is provided elsewhere [5]. The data used in the demonstration section, collected on 06-07-2015 reflects the collaborative editing of the Wikipedia article on electric cars by a German-speaking group of 936 users over the period from 07-21-2004 to 0601-2015, during which 3,193 article revisions emerged and became object of the analysis in this study. IV. D IGITAL DISCOURSE ANALYSIS FOR SERVICE DEVELOPMENT

The five-step-method presented in this work (cf. Figure 1) for analyzing social representations as part of the business

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analysis in NSD follows SRT naturally. To uncover the structure of a social representation in regard to the objects (e.g., underlying technologies) on which a service depends, both of the processes that form a representation — anchoring and objectification — must be analyzed (cf. Subsection II-B). This analysis is performed in steps 2 and 3 of the fivestep method for the anchoring process and in step 4 for the objectification process. The anchoring analysis is split into two steps because all anchors of the representation at hand must be categorized first (step 2) in order to identify the phases of evolution in the anchoring process (step 3). The remaining steps 1 and 5 wrap the analysis of the core processes. While step 1 ensures that the method can be applied to the representation at hand using the data available in the particular digital discourse, the purpose of step 5 is to derive implications from the analysis for the subsequent development phase of the NSD. The data source for the digital discourse must meet two requirements. First, it must be collaboratively generated by users of the platform. Since any social representation is “collaborative elaboration[...] of a social object by the community” [43, p.95], studying the formation of a social representation in the digital world is meaningful only when a number of users who represent a social group can freely elaborate on the object of analysis (e.g., a particular technology). Second, the digital platform must implement a mechanism that allows semantic connections to be established between representations. These connections or links can be inherent to the platform, such as on Twitter1 , where links are given through so-called hashtags, or on Wikipedia, where links to pieces of information take the form of hyperlinks. Alternatively, unstructured information in the digital discourse, such as plain text, can be preprocessed using ontologies to ensure semantic correspondence between pieces of information. Against this backdrop, the sources of digital discourse influence whether the proposed method can be used successfully. Wikipedia, as an online collaborative platform with an inherent implementation of semantic connections between pieces of information, is arguably best-suited for applying the method. First, Wikipedia is part of the social media concept [15]. Second, on Wikipedia, which is coming to be seen as a “proxy for knowledge in general” [37], most articles are collective elaborations of an object. To allow the data to be analyzed quantitatively, a tool was implemented by the author. The tool can gather statistics about who edited an article and when2 . Furthermore, the tool can also automatically identify all links to and from an article to another articles over time. In terms of SRT, an outgoing 1 Twitter is an online social networking service that enables users to send and receive short messages https://twitter.com. 2 The tool and the descriptions of all the statistics it can produce can be found at http://wikigen.org and in the study by Chasin, Gal, and Riemer [5].

link is an anchor, and the change of anchors over time is the anchoring process [5]. Incoming links and their change over time are the objectification process. Figure 1 shows the method when instantiated with Wikipedia as a data source for the digital discourse. Regardless of the sources of the digital discourse data, the nexus of the method — the identification and categorization of anchors, objectification analysis, and the derivation of implications for the service development — remains the same. What changes as the data source varies is the operationalization of the anchoring and objectification processes. For example, on Wikipedia a social representation is operationalized as a Wikipedia article, an anchor as a link to another article in the analyzed article, anchoring as an evolution of links, and objectification as the evolution of links from another article to the analyzed article. Except for differences in the operationalization, the method steps illustrated in Figure 1 can be formulated analogously by replacing Wikipedia with another platform that allows a digital discourse to take place, such as Twitter. The last step of the method requires elaboration. In this step, implications for the service development phase are derived based on insights from the preceding analysis, and every implication must be considered as a starting point for a potential change in the initial service design. This process begins with understanding the users’ perspective in regard to the service and its underlying technologies. Based on that understanding, any of three types of action can be undertaken: changing the service, facilitating a change in the representation itself, and anticipating a change in the social representation. These categories overlap, and their boundaries are blurry, but they are useful in bringing structure and increased clarity into the implication-derivation step of the method. For example, once an unfavorable and negatively charged social representation has been discovered in regard to a technology that underlies a service, the technology can be replaced or modified. Similarly, a discovery of a favorable representation strengthens the service’s technological underpinning. The structure of the social representation in the form of its anchors and their changes over time allows one to tackle the social representation itself by embracing or avoiding links with the associated phenomena discovered during the analysis. In practice, service providers often take this approach by omitting or embracing particular descriptions of a service, such as the “green” and “environment friendly” aspects of a service [42]. The benefit of the proposed method lies in its systematic approach and the rich information it provides when compared to best practice and intuitive approaches in the practice. Finally, understanding the dynamic structure of a social representation allows the service developer to anticipate changes in the development of a social representation by finding historical patterns in the digital discourse on the

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Step 1. Analysis Preparation

Input

Type

Task

Tool application

1.1 Identification of relevant articles

none

qualitative

Identify relevant Wikipedia articles for the analysis

none

1.2 Time frame and collaboration analysis

none

quantitative

Verify availability of data on Wikipedia

Edits over time Editors over time

Positive decision regarding applicability of the method from step 1

both

Outcome Decision regarding applicability of the subsequent steps Decision regarding applicability of the subsequent steps

2. Anchor Categorization Generate list of anchors with WikiGen Anchor snapshot (for the (quantitative). entire analysis time Delete irrelevant anchors and merge frame) duplicate anchors (qualitative) Examine text passages in which each 2.2 Identification of Anchor maps Cleansed list of anchor appears to understand the meaning qualitative anchors’ contexts valid anchors Revision map of the anchors in relation to the representation Either assign new anchors to existing List of valid anchors anchor categories or create new 2.3 Anchor coding qualitative none with context categories, depending on anchors’ contextual meaning 3. Identification of Anchor Evolution Phases (iterative procedure with a possibility to return to step 2, anchor categorization) Break down the overall analysis time 3.1 Determination of Anchor categories, Anchor stability statistics frame into sub-frames that potentially time frames for including the both Collaboration statistics correspond to the representation’s phases potential phases of anchors that Revision map of evolution. Verify quantitative evolution comprise them indications for distinct phases Calculate average statistic for each time Time frames for 3.2 Statistic calculation Anchor snapshots for distinct phases of quantitative frame, including average category Anchor stability statistics potential phases of strength evolution evolution Collaboration statistics Anchor snapshots Based on data, give a name to each Averaged statistics 3.3 Naming and Anchor stability statistics evolution phase and interpret changes of interpreting phases of qualitative for every potential Collaboration statistics the social representation under study evolution evolution phase (all on a monthly basis) within each phase 4. Objectification Analysis Generate objectification statistics Anchor evolution 4.1 Analysis of the (quantitative). Analyze objectification both Objectification statistics objectification process phases within distinct anchor phases of evolution (qualitative) 5. Derivation of Implications for the Development of the Service at Hand Using the insights from the anchoring Interpreted anchor evolution and objectification processes, evolution and 5.1 Derivation of derive implications for the service to be qualitative none objectification implications developed from the understanding of how processes the interpretation of the underlying (steps 3, 4) representations evolved over time 2.1 Anchor identification

Cleansed list of valid anchors

List of valid anchors with context Anchor categories, including the anchors that comprise them

Time frames for potential phases of evolution Averaged statistics for every potential phase of evolution Interpreted process of anchor evolution over identified phases Interpretation of the objectification process

List of implications for the new service to be developed

Figure 1: Method for studying social representations on Wikipedia as part of NSD business analysis

group of similar social representations.

community to derive implications for the service development. The choice to use this scenario for the demonstration of the method has two reasons. First, the author is involved into the development of the service and is therefore familiar with the domain. Second, the service represents a service that is developed upon technological innovations around electric vehicles while the representation of electric vehicles within social groups undergoes a change.

V. M ETHOD D EMONSTRATION The method is applied to the development process of a new service in the EV domain. The German Ministry for Education and Research (BMBF) sponsors the service development to enable individuals to share their private EV-charging stations with other individuals. Components of the service include a search for private charging stations, identification, actual charging, billing, and a digital platform that supports these processes. The service resembles popular peer-to-peer sharing and collaborative consumption services [2] like Airbnb and Uber, but it differs in the specifics and the market maturity of the EV domain. In the following, the proposed method (cf. Figure 1) is applied to the digital discourse on electric cars in German Wikipedia

A. Analysis preparation (Step 1) Wikipedia’s Elektroauto3 article is used for the analysis. The article contains the major discourse on electric cars. The quantitative analysis of the article using the tool revealed 3,193 article revisions created as a result of 936 users’ 3 http://de.wikipedia.org/wiki/Elektroauto

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digital discourse from 07-21-2004 to 06-01-2015. Figure 2 illustrates the distribution of 3,193 article revisions over time. The significant amount of available data facilitates the effort to understand the emergence of the EV social representation on Wikipedia and its evolution over time. Hence, the subsequent steps of the method are applicable.

By complementing the qualitative analysis with statistics provided by the tool, three distinct phases in the evolution of the social representation of the electric car were identified on Wikipedia. The first phase (July 2004 - December 2007) can be described as initialization-and-fall-of-interest phase. Three years after Wikipedia was launched, the first revision of the article appeared (on 21 July 2004). The first anchors of the social representation of electric cars were mostly from the benefits (emissions, oil crisis, power station)5 and disadvantages (energy, capacity) categories, as well as some from the underlying technologies (rechargeable battery and electric motor) and classifications (car, motorized quadrocycle) categories. The anchor category modern examples, which was represented in the beginning by anchors such as Renault,

Citroen, and Peugeot, point to French car manufacturers who announced at this time their strategic plans to re-engineer existing car models as electric cars. These announcements were arguably the trigger for starting a dedicated digital discourse on the phenomenon of electric cars. After the initial familiarization attempts, the absence of significant events in the EV market (since most of the electric car models were released later on) slowed the intensity of the discourse until the discourse on electric cars became part of the discourse on the more general phenomenon of EV. During the second phase of interest growth in (January 2008 - November 2014), the dedicated discourse on electric cars was renewed, likely triggered by a modern example (the Tesla roadster). However, with the absence of rapid developments in the EV domain, soon the discourse was dominated by anchors from the prototypes category (e.g. Apollo 17, Camille Jenatzy, Lunar Roving Vehicle, and NASA). The discourse’s shifted into the area of prototypical applications of electric cars. The shift represent an interesting and reoccurring pattern, which, according to SRT, leads to stereotypical characteristics of prototypes spread onto the representation of electric cars [27]. Simultaneously, the strongest anchor category6 is represented by anchors that seek suitable classifications for the phenomenon (e.g., fuel cell vehicle, diesel-electric transmission, and hybrid EV) Different underlying technologies obviously create a disagreement in terms of what should be considered an electric car and what should not during this phase, a disagreement that was likely to intensify with the absence of practical applications for electric cars on a global scale. A search for classifications and prototypes is also likely to be the reason for the slowing discourse on the benefits and intensifying discourse on disadvantages intensifies in this phase, as the phenomenon’s scope was not clearly defined and instantiations were missing. However, the classification attempts slow steadily while a consensus regarding what is to be considered an electric car is reached. The last phase of what can be called the deprototypization of electric cars occurred from August 2011 to June 2015. The most significant shift during this phase took place in terms of prototype anchors becoming obsolete, replaced by anchors from the category of modern examples (Tesla models, Nissan Leaf, Liebherr t282, BMW i3, etc.). Simultaneously, anchors from the classification and underlying technologies categories remained stable, signalizing less disagreement on the technical aspects of the representation. Because this phase is currently ongoing, it is not possible to say whether this tendency will continue. What is certain is that the appearance of new examples of electric cars

4 The complete list of anchors can be generated on line using the tool (http://wikigen.org) and specifying the analysis time frame. 5 The names of all anchors are translated from German to English using the semantic translation functionality provided by Wikipedia.

6 The strength of an anchor category is measured in terms of how long anchors from the category stayed in the article during a particular phase and how many revisions they survived. The analysis tool includes descriptions for these measures.

B. Anchor categorization (Step 2) The anchor identification (step 2.1) revealed a total of 122 anchors used in the definition section of the electric car article to point to other representations (i.e. articles4 ). After removing anchors that pointed at the same concept using different names, and irrelevant anchors like links to files, 94 relevant anchors remained for the subsequent analysis. In the next step (2.2), I looked at the context of each anchor over the entire period considered by the analysis. For example, one of the anchors for the electric car is Camille Jenatzy. The content analysis reveals that Camille Jenatzy is related to electric cars as he was the driver of an electric race car prototype. The tool proved itself essential in this step by facilitating navigation between revisions of the Wikipedia articles and automatically identifying anchors. In step (2.3), I coded the anchors based on the context of their occurrences, which revealed seven anchor categories: application examples, benefits, classifications, disadvantages, modern examples, prototypes and underlying technologies. Each category reflects the semantic connection between the anchors in the category and the social representation of electric cars. For example, the category classifications contains anchors like car, electric vehicles, and hybrid vehicles that classify the electric car and describing what is in its scope and what is not. Similarly, the category prototypes contains anchors that point to prototypical applications of electric cars. C. Identification of the phases of anchor evolution (Step 3)

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Figure 2: Number of revisions of the Wikipedia article Elektroauto over time

has shifted the digital discourse into the area of everyday applications for electric cars. In summary, the digital discourse on electric cars was first dominated by attempts to classify the phenomenon, as anchors in the classifications category made it the strongest category. Then anchoring the phenomenon in terms of historical prototypes gained attention over the years. Recent introductions of new electric cars to the digital discourse appear to have triggered changes in the social representation of electric cars, so the representation is currently moving away from the area of prototypes into the area of everyday applications.

Figure 3: Number of Wikipedia articles that refer to the social representation of electric cars over time. Bars represent the number of new references to the electric car’s social representation and the line represents the cumulative number of new references

D. Objectification analysis The process of objectification for electric cars reflects the degree to which other representations circulating in the digital discourse are anchored in terms of the social representation of electric cars. The analysis revealed 592 other representations anchored in terms of electric cars. The distribution of timestamps where electric cars were added as anchors to other representations reveals that the objectification process for electric cars is stagnating. Starting in 2008, a relatively constant but small number of new social representations on Wikipedia were anchored in terms of electric cars (Figure 3). According to SRT, a stagnating objectification process indicates that the process is not over and the representation is still in the process of becoming manifested. Thus, while the social representations’ anchors are changing, the understanding of the phenomenon is changing with them, indicating that the current representation of electric cars cannot be considered temporarily stable.

context of a new service is based on prototypes like the lunar roving vehicle, the characteristics of the prototype — unsafe, unpredictable, risky, experimental, and unreliable — will attach to the new service. Therefore, the service design for EV charging stations should account for this risk by avoiding anchoring in terms of prototypes and, instead, attempting to facilitate anchoring that would fit into customers’ routines, something very familiar. Service marketing efforts as well as improved visual design of the digital platform [32] are exemplary vehicles for the aforementioned shift to happen. The second implication for the development of a charging service for the EV domain, based on the observation that anchoring electric cars in terms of modern examples draws attention to the phenomenon and also helps to determine the scope of and clarify the underlying technologies, is that the service can be specialized for particular existing models of electric cars. A user of a charging service can be given an option to select a particular model from the list of all the electric cars that are currently available on the market and, depending on the selection, receive specialized information and additional service offerings.

E. Derivation of implications for the development of the service Several implications for the development of a charging service for the EV domain can be derived from the analysis. First, there appears to be a risk for any service in the EV domain that is associated with prototypes of electric cars. As the evolution of electric cars’ social representation on Wikipedia shows, once the anchoring of the EV in the

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Third, because of the many technologies associated with electric cars, the service should explicitly inform the customers about all of the types of electric cars that the service supports. Doing so will help to decrease users’ confusion regarding the kinds of cars that the service is designed for and how the various electric cars can use the service. Finally, because of the ongoing discourse on the benefits and disadvantages of electric cars, any service in the EV domain, including the charging service analyzed here, should build upon known benefits and disadvantages. Against this backdrop, an analysis like that provided here is essential, as it provides an overview of concrete disadvantages and benefits that are shared within a social group. For instance, the analysis shows that the majority of disadvantages are expressed in terms of a comparison to traditional combustionengine cars. Therefore, instead of building upon benefits and disadvantages that potential customers may find incoherent or confusing, the service design in the EV domain should focus on the direct comparison with traditional means of transportation.

in further discussions about the representation at hand. The method also focuses on identifying anchors in the definition sections of the articles. Including additional sections of an Wikipedia article would require the tool’s extension and additional effort during the anchor-identification phase because of the increased number of anchors to be interpreted and categorized. However, such an extension in the future would help designers to gain a deeper and more precise understanding of the digital discourse on the social representations that are relevant for the service development. Furthermore, a dedicated study is required to show the concrete service improvements that result from the application of the method within the service development process. An evaluation of this type would require the involvement of the service customers that will provide data on their attitudes towards the service before and after the application of the method. Instead of self-reports provided by the customers, experiments can be an alternative approach to examine the customers involvement with the a service before and after the implementation of the implications derived during the method application. An additional limitation of the study is its incomplete account for the features of the tool that was used in order to analyze Wikipedia articles. Although the more detailed description of the tool is provided elsewhere [5] and also embedded into the tool itself, a longer version of the paper that includes details on the used tool and its statistics can improve the value of the study. A combined article for an outlet without strict space limitation is already being developed by the author. Finally, operationalization of the analysis on platforms like Twitter could support our claim that the method can be applied to other platforms of digital discourse with limited adaptions. A promising approach is the combination of the digital discourse analysis presented in this study with traditional media analysis using the SRT as a theoretical lens. The media analysis has already been shown insightful in connection with SRT [43] and can therefore be valuable for the process of NSD that uses the SRT as one of the theoretical lenses.

VI. D ISCUSSION This study is subject to several limitations in terms of the method itself and the chosen example. Although I showed the method’s application in the EV domain using an example of a new service for an EV-charging infrastructure, the demonstration is fragmentary. Our aim was not to provide a detailed elaboration but to show the systematic approach while omitting details regarding the individual analysis steps, especially those based on the quantitative analyses performed with the tool. The focus of the present study is to point out the connection between the design of new services and social representations that circulate the digital discourse, as a systematic analysis of social representations in the digital discourse helps designers understand the customer who will eventually use the service. Since the tool is available on-line and enriched with descriptions of every statistic it offers, I hope that an interested researcher will evaluate the analysis techniques the tool provides. As to limitations in the method itself, the analysis presented here does not account for multiple social representations in connection to the same object, which would require additional techniques. However, using these additional techniques is difficult because the presence of multiple social representations implies the existence of multiple social groups that share the corresponding representations. The identification of these social groups and the representations they share using the data within the digital discourse is a subject for further research. Progress in this direction would also provide further support for the applicability of the SRT in the context of the digital discourse analysis. Limitations also emanate from the application of the method to Wikipedia, as the method does not involve the analysis of discussion pages, where Wikipedia users engage

VII. C ONCLUSION This work addressed the problem of aligning new services with customer needs and expectations. Using a combination of the well-established NSD approach and SRT as a theoretical lens, I introduced a method that uncovers the structure of social representations in digital discourse. These representations indicate the knowledge that social groups, including potential customers, hold about objects (e.g., the underlying technologies on which a new service depends). Insights from this business analysis step in NSD can be used to derive implications and to inform and improve the design of new services. I demonstrated the application of the method using a service that is being developed

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for the EV-charging infrastructure domain. In addition to showing the method as being of immediate utility to the exemplified service, the study contributes to the IS research field in general by promoting a novel instrument for business analysis in the NSD process. It also adds to research on SRT in the IS domain and provides support for the theory’s applicability within the discipline. Finally, this work contributes to practice by presenting a tool that can be used to analyze the social representations that circulate in the digital discourse in order to derive implications for NSD.

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