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Christoph Zuschlag und Jun. Ulrike Sass. Thus, it is of importance to apply the best-fitting platform construct to suit the underlying context and research design s needs. The ecosystem perspective comprises two lenses. On the one hand, the case of a co-creation platform focuses on the organizational viewpoint, while the TSM or MSP help to explain organizational and market-based effects according to network externalities. In this case, the effect of technical aspects can influence either organizational roles on value co-creation activities or the whole market through economies of scale resulting from standardization, which leads to a more favorable pricing structure.
Hence, even if the level of analysis is correct, IS scholars still need to determine if the particular case fits the research s needs regarding the constructs specific effects. Their conceptualization differentiates between innovation as an actor-to-actor A2A network represented by a service ecosystem, a technical platform that incorporates resources for the facilitation of resources, as well as the value co-creation processes fostering resource integration. Our configuration of a technical platform maps to the concept of a service platform. Third, the value co-creation concept maps to the case of a co-creation platform.
The third configuration with the cases of an IS platform and a digital platform is a combination of the service platform and value co-creation or all three constructs . By combining our research with the results of Lusch and Nambisan , we contribute towards a better understanding in terms of the single- and multi-level characteristics of S-D logic in the light of different platform constructs.
Finally, all platform configurations share common features. Each of them aims to increase the value captured for the owner through, technology, organizational or market-based mechanisms ranging from modularization, value co-creation, and complementaries, to pricing and network effects. This contribution provides theoretical and practical implications. On the theoretical side, the literature review increases the awareness and transparency of the two most Here, the results indicate that the respective platform construct and configuration needs to suit the level of analysis.
Further, the platform constructs differ also within the configurations by applying different mechanisms like value co-creation and network externalities. Thus, a clear definition of the underlying term platform is of importance and should always go along with the research design. Those findings highlight the interrelatedness to the S-D logic, where central S-D concepts can be mapped to concrete platform cases. For the practitioners, this contribution reveals the systemic character of the term platform. The technical introduction of APIs and SDKs, for example, also influences the capability of value co-creation of external partners or complementors through an increased ease of use and economies of scale.
Also, the optimized value co-creation process might foster positive network effects that lead to positive reinforcement within an ecosystem. Those insights might help practitioners to take into account that technical changes on the infrastructure level might lead to consequences within the market or ecosystem level.
As a further result, the perspective on the different levels of analysis reveals fruitful avenues of future research. On the one hand, the results show the need of platform governance mechanisms in a new light. The different platform configurations could help to explain how different governance mechanisms [54, 55] mediate or moderate between the layers of analysis. Here, the introduction of boundary resources like APIs may influence the technical architecture, but also indirectly affect the organizational or market perspective.
Thus, especially platform governance literature could benefit from future research from the angle of a multi-level characteristic and the systemic implications of the different platform constructs. Lastly, the literature review faces limitations.
At first, this contribution can only be a first step towards demystifying the term platform, as the scope is limited to IoT platforms only. By expanding the view through a more established platform context, the study could benefit from an increased generalizability, which leads to a more accurate model of configurations. Also, the study focuses on the two most common platform constructs, ignoring similar constructs like electronic markets, which leads to a limitation regarding the completeness.
The literature review shows that IS scholars apply different platform constructs on different levels of analysis. On the example of IoT platforms, the results indicate that the technical and ecosystem layer are most frequently used. The usage varies depending on the underlying level of analysis. While the technical constructs focus on standardization and modularization, the ecosystem perspective differentiates between value co-creation and network externalities.
Consequently, the exclusive sole bearing of either technical or ecosystem considerations follows a single-level analysis. In addition to that, the literature search revealed that those layers can be combined in a multi-level analysis elaborating on both, the technical and ecosystem perspective. Concluding, this contribution stresses the CB Insights,, Accessed: 2. Peer, D. Nature nanotechnology 2, 3. Journal of the European Economic Association 1, 4. Baldwin, C. In: Gawer, A. Platforms, Markets and Innovation. Edward Elgar. Muffatto, M. International Journal of Production Economics 60, 6. Berkers, F.
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In: ECIS, pp. Business ecosystems are gaining more relevance both in research and in practice. The analysis of business ecosystems is thereby a data intense process. To better understand the current state-of-the-practice within enterprises addressing the analysis of business ecosystem we conducted an online survey asking participants about their division of labor, collection, documentation and processing of business ecosystem related data.
Keywords: Business Ecosystem, State-of-the-Practice, Competitor Analysis 1 Introduction Undoubtedly, companies increasingly recognize the relevance of their complex business environment, which is also referred to as business ecosystem and which is already reality for most companies nowadays . One aspect of the growing relevance of business ecosystems is the perceived shift of the competitive environment between no longer single companies and their supply chains but towards ecosystems competing against each other . Thereby, a business ecosystem enlarges the classic supply chain, consisting of suppliers and customer, by also including other entities within the business environment of the enterprise.
We define business ecosystems as the holistic environment of a company covering current and potential future business partners, customers, suppliers, competitors, regulatory institutions, and innovative start-ups. It exhibits a high dynamic as continuously entities enter and leave the ecosystem. For a comprehensive definition we refer to . Analogously to the metaphor of a biological ecosystem, which served as a basis for the initial definition of business ecosystems, the economic success of an enterprise can therefore depend on the health and ability to evolve their business ecosystem.
Due to the influence on the economic success of the enterprise and the dynamic characteristic, enterprises increasingly realize the need to analyze their business ecosystem continuously, in order to identify and address changes within their ecosystem, adapt own business activities accordingly and to learn what makes the environment tick . Through qualitative interviews with industry partners, we extracted a high-level understanding of the business ecosystem analysis process and the challenges perceived by enterprise stakeholders. The analysis is a data-intense process, consisting of the steps of a data collection; b data documentation; and c data processing and reporting using heterogeneous data sources within and beyond the enterprise.
To achieve a holistic picture of the business ecosystem, several stakeholders within an enterprise in different roles and responsibilities should contribute with their knowledge, but also by communicating their requirement towards the analysis outcomes. Succeeding in such a holistic ecosystem analysis would enable enterprises to play a more active role within its business ecosystem. With the here presented survey results, we aim at a contribution to capture the current state-of-the-practice how enterprises analyze their business ecosystem as part of their daily business.
Thus, the subsequent research questions are addressed in this work: RQ1. What is relevant business ecosystem related information and how do German companies organize the work associated with the analysis of their business ecosystem? How do German enterprises collect, document and process this business ecosystem related information?
Which challenges do German organizations perceive within the analysis of their business ecosystem? To answer these questions, we set up an online-based questionnaire inviting German experts to share their working experience analyzing their companies business ecosystem.
Particularly interesting for us were the sources the responsible stakeholders within the enterprise currently use and where they conceive the biggest challenges. The paper is organized as follows: in Section 2 related works in literature covering business ecosystems are presented. The work ends with an outlook in Section 5. Thereby, ecosystems are interconnected through a complex, global network of relationships . In a business ecosystem, firms take on roles such as suppliers, distributors, outsourcing firms, makers of related products or services, technology providers, and a host of other organizations , all affecting the As firms continuously enter and leave the ecosystem , they constantly evolve and exhibit a dynamic structure .
Research on business ecosystems has recently highlighted the role of novel challenges for ecosystem formation, including technology contexts, e. This has focused researchers attention on ecosystem modeling . Current approaches focus on frameworks to grasp the scope of ecosystem complexity ,  , or on visualization to understand emerging structures and patterns , . Thereby, to be informed about changes within the business environment at an early stage enterprises analyze their ecosystem in order to adjust its entrepreneurial actions to these changes.
Already existing decision support systems in research provide interactive visualizations that are presented to users and decision makers with models, methods, and problem-related data with the aim of decision support , . These systems are applied in various fields using different data. The visualization currently available in science for the support of decision-makers in relation to business ecosystems use data collected from trade journals, industry publications, registers, or paid data collections. However, the amount of data used is always huge and the data is diverse and often inaccurate .
To date, there are few scientific contributions to address this challenge or the inclusion of internal data with a focus on business ecosystem data , . To understand the business ecosystem both the rather static network of entities firms, technologies , and the dynamic network characteristics, i. Entities comprise small firms, large corporations, universities, research centers, public sector organizations, They are linked through a variety of different relationship types, such as past cooperations, negotiations or personnel changes.
Which entities and relationship types need to be analyzed depends on the requirements put forward by the business stakeholders. Their needs and demands that define which visual views are relevant, and which insights are vital, are fundamental for generating and adapting the model as a possible result of the analysis process.
This includes how the work is distributed within the enterprise, how data is collected, which sources are used, how data is documented and reported upon using various available tools and what main challenges of the business ecosystem analysis are considered. In addition, we try to identify where responsible stakeholders conceive challenges which can be addressed in future work. The questionnaire was adapted according to the received feedback.
The final version of the questionnaire was published as an online survey available between beginnings of July to end of August In total, we contacted 51 industry partners from various fields of business activity via and published an open call for participation using social media 1. All contacts were approached twice via whereby the survey was also posted once using LinkedIn 2.
Within the s, we briefly explained the concept of business ecosystems and the relevance for enterprises due to technological innovations. The completion time of the questionnaire was estimated with 15 to 20 minutes. The online questionnaire consisted of seven sections, starting with questions covering the participants and enterprises details and if the enterprise is active in analyzing their business ecosystem section one and two of the questionnaire , followed by the participants role and the division of labor section three.
The fourth section addressed the business ecosystem related data collection, followed by the process step of data documentation and processing section five , the perceived challenges within the analysis process in section six and finally the reasons why companies are not active in analyzing their business ecosystems section seven.
After the question if companies are active in analyzing their business ecosystem in the second part of the questionnaire, the questionnaire was split into two paths. One path following for participants and enterprises active in the business ecosystem analysis covering the section four to six with an overall of 21 questions and one for the others only comprising of section seven asking for the reasons of inactivity with an overall of 8 questions.
This allowed skipping questions not fitting to a certain group of participants. The only mandatory question for participants to answer within the questionnaire was if the company is active in analyzing its business ecosystem in section two, whereby the remaining other questions could be omitted by the participants. Whenever a question allowed multiple answers this was explicitly stated, for questions with exclusive options the used tool provided a suitable feature only allowing one answer to be given.
Wherever feasible the answer option other allowed the participants to enter additional information as free text. For each question addressing business ecosystems, the definition of business ecosystem was displayed in the header of each questionnaire page. The answers of the different questionnaire sections are described in detail in the according section below. Participants details. Descriptive statistics of the responses of the participants details are presented in Table 1.
Division of labor. Aiming to understand the participants roles within the analysis process, and how the work is divided within the company, we ask three questions. When asked for the responsibilities within the business ecosystem analysis, multiple answers were possible. The final single-answer question of this section addressed the potential existing teamwork within the enterprise and no participant answered that she is working alone, no other responsible colleague known.
The fourth section of the questionnaire entailed questions about the data collection process, the information gathered and the sources used. It comprised of seven questions, which could all be answered by the participants choosing multiple answers. The next two questions aimed at the data sources in use when collecting business ecosystem data, visualized in Figure 1 and Figure 2. When asked to name three sources according to their order of usage, internal sources were stated most as first source 9 participants and online search engine as second or third source 4 participants or rather 2 participants.
Often used are also multi client market research studies, e. Main Sources for the Business Ecosystem Analysis Using these data sources, the next questions addressed the information collected. Thus, besides the classic competitor analysis, enterprises also analyze their direct and indirect environment. As companies of the business ecosystem can be described using various attributes, we asked for company related information interesting for stakeholders analyzing the business ecosystem.
For a comprehensive list of answers, see also Figure 3. Data documentation and reporting. Section five of the questionnaire comprised five questions about the current state-of-the-practice in documentation and processing of business ecosystem related data. Multiple answers were allowed for all questions. Within this questions the participants were able to state the tool in used in a free text field for the different answer options. The answers of the questions which visualization types are already provided and in use and which are interesting for future usage is pictured in Figure 4.
Visualization types already in use blue versus visualizations interesting for future usage orange Perceived challenges. The final question of section six of the questionnaire addressed the perceived challenges within the business ecosystem analysis. No participant stated that no challenge is perceived within the business ecosystem analysis. All results are pictured in Figure 5. Figure 5. Reasons for inactivity.
The four participants completing the questionnaire not active in the analysis of their business ecosystem were asked three questions addressing their The final question addressed in which area support within the business ecosystem analysis would be interesting for the participants. That indicates a perceived need to address the enterprises business ecosystem analysis. Also, the survey - as it is presented here - targeted mainly German enterprises. Thus, further work could distribute the survey to a broader, international audience. As an additional limitation, the usage of the survey tool must be stated.
As participants completed the survey remotely, full transparency within the response process is not provided. Offering participants with predefined answer options might have led to a biased result as participants face specific choices instead of open questions, which we tried to counteract with the free text answer option other whenever feasible.
Finally, a shared understanding of analyzing business ecosystems amongst all participants might be missing. With regard to the five in depth semi-structured interviews we conducted prior, and during the execution of the survey, all interviewed participants stated that they are active in analyzing the company s business ecosystems or at least focus areas of interest within the business ecosystems. The reasons for conducting the business ecosystem analysis were very diverse among the interview participants.
Ranging from networking purposes to pure competitor analysis to gain a better understanding of all existing business relations with external partners. Of these five interviews, three representatives also completed the questionnaire. All five representatives stated that the greatest part of knowledge of the business ecosystem is bound to individuals within the company and not further documented.
All were dissatisfied with the current tool support in use, ranging from Customer-Relationship-Management tools, to Microsoft Office Products which were in use mainly , such as Microsoft Excel or Microsoft PowerPoint. These insights confirm the results we gained from our survey. This is reflected in the identified major challenge of timeconsuming processing of collected and documented information [RQ3. By analyzing their business ecosystem, enterprises can identify and address changes within their environment and adapt own business activities accordingly which could lead to a business benefit for these enterprises.
According to the survey results, the growing influence of business ecosystems on the economic success of an enterprise is increasingly perceived by enterprises. Nevertheless, enterprises are facing multiple challenges when analyzing their business ecosystem due to the data intense process it is based upon. For future work, besides the above-mentioned limitations of the presented survey, tasks and questions in the context of business ecosystem analysis could be identified in close collaboration with enterprises within future research.
These could be the basis to identify stakeholders to be integrated in a collaborative process to achieve a holistic model of the enterprise business ecosystems. In a next step, suitable data sources could be selected for the analysis. To address the identified challenges within the data collection process step, potential future research could address automated data analysis of heterogeneous data sources both enterprise internal but also external, such as in use CRM tools or news feeds.
For the challenges of data documentation and processing, current results in research already proved that visualizations help support business ecosystem stakeholders in their decision , . Future research could analyze suitable visualizations for the identified tasks and questions, the provided features of these visualizations, and how the visualizations could be provided within a collaborative process of analysis and modelling in an enterprise. Therefore, we are developing a prototype that facilitates data collection and implements findings from existing work on ecosystem visualization to support companies in this complex task.
Future work will evaluate and improve this prototype. Bavaria, an initiative of the Bavarian State Government. Peltoniemi and E. Vuori, Business ecosystem as the new approach to complex adaptive business environments, Proc. Forum, pp 2. Porter, How competitive forces shape strategy, Harv. March, pp , J.
Park and R. Basole, Bicentric diagrams: Design and applications of a graph-based relational set visualization technique, Decis. Support Syst. Uchihira, H. Ishimatsu, and K. Basole, M. Russell, J. Rubens, K. Still, and H. Iansiti and R. Levien, Strategy as Ecology, Harv. Iyer and R. Basole, Visualization to understand ecosystems, Commun. ACM, vol. Visnjic, A. Neely, C.
Cennamo, and N. Visnjic, Governing the City, Calif. Basole, J. Still, and M. Russell, Visual decision support for business ecosystem analysis, Expert Syst. Park, M. Bellamy, and R. Basole, Visual analytics for supply network management: System design and evaluation, Decis. Hao, J. Zhu, and R. Zhong, The rise of big data on urban studies and planning practices in China: Review and open research issues, J. Urban Manag. Thousand Oaks : Sage Publications This article addresses the shortcoming that existing service classifications cannot characterize services of platform businesses.
To resolve this shortcoming, a literature review has been conducted, identifying existing service classifications and their attributes. Our comparison shows also that there are similarities between existing classification schemes with respect to customer and managing processes. To identify limitations of existing classifications, a use case about a platform business was analyzed.
Our results show that traditional attributes are not sufficient to describe platform services. To settle this, we propose additional attributes that can also address services of platform businesses. Introduction Existing research has defined services in many different ways. Judd defines services as market transactions . According to Hill, services represent a change in the conditions of a person or a good belonging to some economic unit . Mills and Turk describe a service as a performance or an effort rendered by one party for another . Other scholars consider services as performance and processes [4, 5].
Furthermore, there is comprehensive definition from Vargo and Lusch. They define services as the application of specialized competencies knowledge and skills through deeds, processes, and performances for the benefits of another entity of the entity itself . In addition, Rathmell explains services by distinguishing goods and services , and Edvardsson views services as a part of the wider concept product .
As can be seen from this brief review, the definitions of services address different aspects in different research fields. Moreover, service classifications have been considered in marketing , service industry and business management , operation studies , and economics [2, 22, 23]. In general, marketing and operation studies discuss service classifications and characteristics, to develop and improve processes of delivery services. Economics studies rather focus on outputs, to understand and distinguish between goods and services in their classifications.
Although most of existing classifications have been discussed to provide strategic and managerial implications, there are difficulties to use them empirically . To overcome this problem, a few studies e. Recently, new services emerged through platform-based businesses. A platform is a centre for connections between providers and customers, and its development is based on an information and communication technology infrastructure, comprising software, ubiquitous networks, and computing power [26, 27].
One of the well-known platform businesses is TaskRabbit. The company connects providers, who are willing to provide labor, to customers, who need help for everyday tasks such as cleaning, moving, and assembling furniture 1. Through platform businesses, customers and providers join to conduct economic transactions freely and without any barrier. The technological availabilities of software, distributed computing, and the Internet make a platform possible . As platform businesses have the potential to change the shapes of industries and even affect whole economic structures, it is highly relevant to check whether existing service classifications allow covering these services properly.
Following this discussion on emerging platform businesses in services and their relevancy, our research objective is to provide a service classification scheme by identifying service types of existing service classifications and by proposing new attributes that allow characterizing services of platforms. Related to this research objective, the research questions are: What are the similarities of existing classification schemes?
What are the shortcomings of existing classification schemes with respect to platform services? What could be the definition of a comprehensive classification scheme for platform services? To answer the research questions, a literature review is carried out, capturing similarities of existing classifications. Cook et al. As the study of Cook et al. Additional papers between and that developed classification schemes have been determined.
From this literature review process, we obtain an understanding of which attributes exist and how they can be grouped into attribute types. In order to identify shortcomings of these attribute types, we explore a use case of a platform business that offers platform services. As a result of this analysis, we provide a comprehensive list of attribute types of existing service classifications and a list of additional attribute types for platform services.
These attribute types can be helpful to describe different service types and to are expected to explain transitions caused by technology developments. The remainder of the article is organized as follows: In chapter 2, we provide an overview about services and service classifications of previous studies on services. Chapter 3 describes our research methodology and findings from our literature review. A case study is introduced in chapter 4 and discussed in chapter 5.
Finally, chapter 6 concludes our study with a discussion on limitations and future research. Existing Research Directions on Services 2. Service Theories One notable study of services in marketing defined the service-dominant logic S-D logic. The S-D logic considers a service as a process of doing something for another party  and a customer as a coproducer of service .
Source code of the class german-dico part of termsuite-core version
By moving the goodscentered perspective of marketing into a service-dominant view, Vargo and Lusch determine value of services differently than in a goods-dominant logic G-D logic. In the traditional G-D logic, value has been determined by value-in-exchange, while S-D logic defines it as value-in-use . Therefore, service is viewed as a value co-creation by all involved actors in the logic . Another study in operation management defined the unified service theory UST. UST considers a customer as a significant input in the process of production .
Furthermore, UST also gives managerial implications for the production and extends customers role in the service supply chain. Overall, these theories S-D logic, UST are important, as they consider customers as co-creators and co-producers of services Service Characteristics With respect to characteristics, four service characteristics have been commonly known as natures of services: intangibility, heterogeneity, inseparability, and perishability.
These characteristics are called IHIP [31, 32]. Typically used in service research, despite the fact that IHIP comes with a few limitations [33, 34]: First, although intangibility relates to the fact that services are not physically visible as goods , it gets criticized as some services e. Second, as heterogeneity concerns services that vary in terms of service operations and customer experiences, it does not reflect the difficulties with respect to standardization through technology and equipment .
Third, inseparability is about simultaneous production and consumption. However, there are separable services that allow customers absences at production such as laundry service and maintenance service of equipment and facilities . Lastly, perishability means that services cannot be stored, as following service definitions that consider them to be processes and performances .
However, there is frequently mentioned example of ATMs, which store a standardized process of cash withdrawal  Service Classifications Customer contact has been considered as important in classifying services. Chase proposes the customer contact model that concerns customer s physical presence in service creation and classifies services into four groups: pure service, mixed service, quasi-manufacturing service, and manufacturing service . In other traditional service studies, the model has been adopted to explore service organization designs  and has been extended by Mersha, who considers that communication With respect to processes, Chase defines customer contact as customer s presence [36, 38], while Schmenner discusses that customers may have little interaction with service providers in processes, even if they are present physically.
Schmenner proposes a service process matrix with a degree of customer interaction and customization and a degree of labor intensity [14, 39]. In addition, Silvestro et al. Regardless of the perspective on physical presence of customers during services, it can be said that one significant classification type on services is performed based on customer contact.
Other studies take on a customer-centered perspective and consider service activities actions for classifying services. For example, Lovelock  and Kelley et al. Cunningham et al. Zysman et al. Furthermore, Maglio et al. Even though previous studies take various approaches to classify services, their service classifications are mostly suggested conceptually and would not be sufficient to embrace other than their own domains.
Methodology As services have been defined and classified differently depending on their foci, we believe that a literature review is a useful method, to find unrecognized aspects and to address current trends in a more inclusive service classification scheme. Webster and Watson provide a guideline for writing a literature review, stating that a thorough literature review can be a way of facilitating theory development, welding various research topics, and revealing unidentified research objects . They recommend three steps to determined review materials: first, finding major contributions from leading journals; second, finding articles by checking references of articles identified in step 1 going backward ; and lastly, finding articles by checking citations to the articles identified in step 1 using Web of Science going forward .
Following Webster and Watson, we explore well-known service studies with respect to service classification schemes Figure 1. Among them, Cook et al. Although the paper provides a fine start that can lead to major literature on service classifications, it includes studies only until s. To find more recent studies, we identified journal We use WoS for searching articles as Webster and Watson s recommendation. Research Framework As we wanted to observe direct connections between frequently used studies and more recent studies, we conducted forward citation searches.
A total of 1, journal articles were found since the year The abstracts, keywords, and research fields of those journals were examined. The analysis result of all 84 articles revealed that 19 studies developed and proposed their own classifications schemes. Overall, 35 articles were collected to be examined in this study. Of those 35 articles, 16 articles were proposed in the s and 19 since s Analysis of Attributes Used in Classification Schemes Based on the 35 articles about service classifications, we analyzed attributes that were used to classify and segment services.
Attributes have been reviewed with respect to relevancy and similarity, and have been categorized into attribute types. For example, attributes, such as contact, customer contact, client contact, degree of contact, and contact intensity, were covered with the general type degree of customer contact. Table 1 shows that 13 classifications have used an attribute to reflect customer contact.
They mostly followed an identical notion of Chase s that describes customer s physical presence in a service creation. We grouped these attributes under the attribute type degree of customer contact. In 11 classifications, attributes appeared that have addressed the concept of alteration of processes for meeting customers needs in services and that describe the level of modification of services.
The attribute type degree of customization aggregates these attributes. Most of the classifications have discussed service processes under the assumption of physical contact between customers and providers. They covered outcomes that customers get from services. We categorized them with the attribute type object of service. Service characteristics IHIP comprises attributes on the nature of services. These attributes, which were mentioned 9 times in classifications, are subsets of IHIP. Attributes, which concern the utilization of technology, knowledge, and equipment in services, were put into a group of attributes named degree of application and implementation.
Nine classifications used this type of attribute. The attribute type labor intensity concentrates on service providers activities and how services depend on individual workers. It has been used in 9 studies. Four of the studies, which also used labor intensity, consider also the depth of interactions between customers and providers. In total, seven studies applied this notion, which we named degree of interaction. An attribute regarding the level of routinization and standardization can be found more frequently in literature after In total, seven classifications considered this attribute type, which we named degree of standardization.
With the increasing attention on customer s role in services since the year , six classifications considered the attribute type 'customer participation'. It describes customers activities and roles for joining services. Four classifications focused on attributes about the variety of customer s demands. We named this attribute type diversity. Though expressed differently, three classifications followed a very similar concept of place. They expressed focus of a service to be at the front office, back office, or a virtual space.
The attribute type complexity, which has been addressed three times in classifications, describes the complexity of the provisioning of a service. The remaining six attribute types i. Application of Attribute Types to the Use Case TaskRabbit TaskRabbit is a company, which has been founded in It connects platform users: those Taskers , who are willing to provide labor, and customers, who need help with tasks e. TaskRabbit, which advertises itself as a same-day service platform, can be considered a platform service provider.
TaskRabbit is useful as a case study, as its service does not require any tangible products and does not involve complex transactions between its users. With the help of attribute types listed in Table 1, we can describe the service of TaskRabbit in detail. For establishing connections between customers and taskers, TaskRabbit's platform users do not need to be present physically, when the transactions on the platform happen.
Therefore, it is hard to measure customer contact between TaskRabbit and users. TaskRabbit provides information and connections using standardized processes to both types of its platform users. At the same time, the In addition, as TaskRabbit is a platform that connects both types of users, a relationship between a customer and a tasker can hardly be defined as unidirectional. Moreover, it is not only hard to measure the degree of contact but also the degree of interaction. Furthermore, as customers are provided labor for their needs by taskers, who are hired temporarily by TaskRabbit, TaskRabbit can be rated as high 'labor intensity'.
Based on the TaskRabbit's service description that followed our consolidated classification scheme Table 1 , we got a solid classification of the service of TaskRabbit. However, there are three properties of the TaskRabbit service, which could not be described accurately by the attribute types shown in Table 1. First, existing classification schemes assumed that service processes require physical contact between customers and providers [10, 14, 45, 48, 49]. However the TaskRabbit case showed that the service contract conclusion can be performed without any physical contact between customer and provider.
Moreover, there are platform services, which do not even require any physical contact for the entire service process, including contract conclusion, provisioning, and delivery. Examples of these platform services are online services such as music streaming, video on demand online, and cloud services. The lack to express this is a shortcoming. Second, in existing classification schemes, the degree of customization and the degree of standardization could not be rated as high at the same time. If one service has been rated as highly customized, the service could not be rated as standardized [14, 31, 32, 50].
However, businesses, which are based on platforms such TaskRabbit, were able to provide not only standardized services through the availability of public domain software, ubiquitous Internet, and computing power [26, 27] but also highly customized services e. This is a shortcoming of existing classifications. Third, although interactions between customers and providers have often been discussed , the environment virtual world or real world , in which the interactions have happened, did not get sufficient attention. Existing classifications have the shortcoming of only considering service transactions that can either occur in the real world or in the virtual world.
Combinations of occurrences in the real and virtual world as for the case of the platform TaskRabbit service cannot be expressed. New Attribute Types for Platform Services From the analysis of the TaskRabbit use case in chapter 4, it became clear that the attribute types of existing classifications are not sufficient to describe platform services.
To resolve the three shortcomings identified in chapter 4, additional attribute types are needed for comprehensively describing platform services. In detail, we propose to add three new attribute types to the classification shown in Table 1: The first new attribute type, which is related to the first shortcoming listed in chapter 4, describes the 'degree of involvement' of actors in the service process, The 'degree of involvement' is used to express the quality of interaction with the actors.
With respect to the second shortcoming listed in chapter 4, if some people are familiar with digital devices, they may join a platform with a higher likelihood as also in the case of TaskRabbit. Knowing how to use a technical device is a kind of competency skills and knowledge as described by Vargo and Lusch.
This new attribute type is called 'degree of competency'. A high 'degree of competency' allows the platform user to use a service more effectively and to obtain a more customized service through the interaction. Consequently, the higher the 'degree of competency'' is, the higher the 'degree of involvement' can be. We believe that it is important to consider 'service scene' as another new attribute type for classifying platform services, addressing the third shortcoming identified.
This definition allows for a combination of occurrences in the virtual world and the real world. This attribute type helps describing where services are agreed upon, provisioned, and delivered. Conclusion This research addressed shortcomings of existing service classifications. For this, we conducted a literature review, studying existing service classifications and their attributes.
There were similarities between existing classification schemes about considering customers and managing processes. To identify limitations, existing attributes were applied to the platform service TaskRabbit. We found that traditional attributes are not sufficient to explain the TaskRabbit use case. Therefore, we proposed three additional attributes, which can cover emerging platform services, and a consolidated service scheme. There are a few limitations to this research. First, there may be articles that are not included in the analysis, although we followed a comprehensive methodology to find all relevant studies.
The cause might be the focus on only one database, namely Web of Science. Second, as we analyzed the classification schemes and attributes based on our interpretations, a quantitative method using text and clustering analyses might be considered less subjective. In conclusion, although there are many service classifications, these classifications fail to explain current transitions from traditional services to pure online services e.
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