A System Architecture for Integrating User Activities in Social Networks ...

A System Architecture for Integrating User Activities in Social Networks with Customer Relationship Management Marcel Rosenberger1, Christiane Lehrer1 und Reinhard Jung1 1

University of St. Gallen, Institute of Information Management, [email protected]

Abstract Companies have long recognised that their participation in social networks is expected by existing and new potential customers, who request support, provide feedback to products they own, post complaints and exchange views about the company, for example. Information systems are required for identifying relevant user activities that must be dealt with in a given time. Therefore, existing social media tools provide analytical functions. The integration of these tools and/or social networks directly with Customer Relationship Management (CRM) systems, however, is still sparse. We propose a system architecture for integrating user activities in social networks with CRM, which describes eight subsystems and serves as a construction plan to implement an integration software. The research follows the process of a design science research and includes proposed components of related system models from existing literature. Parts of the architecture have been evaluated in the context of a software-prototype that creates opportunity-tickets based on new posts, and thereby triggers the lead management process of a consulting company.

1

Introduction

Social networks have rapidly gained popularity in the past years and are frequently used by billions of users. They belong to the group of social media, which is a concept that encourages connecting, participation, and collaboration of users and sharing of content over the Internet (Musser and O’Reilly 2007). Companies have long recognised that their participation is expected by existing and new potential customers (Acker et al. 2011). Most companies start with creating and maintaining profiles in the prominent social networks, such as Facebook, Twitter, and LinkedIn (Baird and Parasnis 2011). Users that add these profiles to their list of interest receive updates, are encouraged to comment on the company's and other customers' posts, provide feedback to products they own, and also produce new ideas (Helms et al. 2012; Jarvenpaa and Tuunainen 2013). At a first glance, social networks are just another communication-channel that expands the possibilities to reach customers. This view, however, is short-sighted. A closer study reveals the massive impact on the relationship to customers, because of the rapidity, new dynamism, and shifted ownership of the communication (Kietzmann et al. 2011; Lehmkuhl et al. 2013). Social customer relationship management (CRM) is a philosophy and business strategy that professionalises the relationship to customers and includes social media (Greenberg 2010a). The vision is to respect a customer with his needs and wishes as a single person instead of handling target groups only. Especially in organisations with many customers this is an enormous challenge, because thousands of customers that request attention in social networks cannot be listened and treated individually in the given time (Kaplan and Haenlein 2010; Chau and Xu 2012). Short response times are not only a competitive

2

Marcel Rosenberger, Christiane Lehrer and Reinhard Jung

advantage and a chance to influence emerging debates, which may turn good or bad for the company's reputation, but are also expected by social network users (Singh et al. 2012). Therefore, information systems (IS) are necessary that assist to observe user activities in the various social networks, where tweets, posts, photos, comments, profiles and locations are created, updated, and shared, and allow distinguishing relevant and irrelevant user activities automatically (Jayachandran et al. 2005; Acker et al. 2011; Williams 2014). Depending on the time, location, user, and kind of activity the right business processes need to be triggered by the system (Reinhold and Alt 2011; Woodcock et al. 2011). Many social media monitoring tools are available. Küpper et al. (2014) show results from a market study of 40 vendor solutions for social media tools. The findings indicate that most tools provide features to capture and analyse aggregated social media data. The capturing and analysis of individual data (i.e. single posts, user profiles, etc.) as well as the integration into CRM systems, however, is still sparse. Similarly, other authors state that the integration into enterprise systems is insufficient (Sarner et al. 2012; Reinhold and Alt 2013). In particular, Trainor et al. (2013) identify a lack of interaction between CRM systems and social media technology. For example, customer data and user data in social media are not interrelated and business-processes are not triggered automatically from incidents in social networks (Reinhold and Alt 2012). The aim of the presented paper is to support the development of a software solution for integrating user activities of various social networks with various CRM systems. The integration solution helps to automate tasks across multiple social networks without implementing the filtering, monitoring, and processing of user activities for each social network separately. Examples of the supported tasks are identification of a support request by matching keywords in a post of a specific group, adding a new prospective customer (i.e. lead) to a target list of opportunities based on profile and/or location updates, and enriching the information about a customer by extracting his interests based on group memberships and profile updates. The research questions are: What are necessary system components to support the mutual integration of user activities in multiple social networks with CRM systems? How are these components interrelated? For answering these research questions a system architecture is an adequate result type, because it describes components and their relationships, and it provides guidance for constructing, i.e. developing software (Aier et al. 2011). According to ISO/IEC/IEEE 42010:2011(E) architecture is defined as the “fundamental concepts or properties of a system in its environment embodied in its elements, relationships, and in the principles of its design and evolution”. It structures the integration task into logical units and allows focussing further research activities on parts of the problem by regarding specific system components in their context. In terms of design science research an architecture is a model-artefact that is built on constructs and shows their relationships and dependencies (March and Smith 1995). The paper is structured in five chapters. Chapter 2 connects to related literature and compares the proposed subsystems with propositions of other system models. The system architecture is depicted in chapter 3. Chapter 4 comprises a description of the prototypical software-implementation, which evaluates parts of the architecture. The conclusion summarises the findings and gives an outlook to further research (chapter 5).

MKWI 2016 – Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy)

2

3

Related Work

We conducted a literature review for finding the existing architectures for integrating social networks with CRM systems. Vom Brocke et al. (2009) propose guidelines of a rigour process of literature reviews. They state that not only results should be presented, but, to allow replicability, also the approach. Table 1 characterises the conducted literature review following the taxonomy proposed by Cooper (1988). The focus (1) is on existing models and instantiations that support the design and/or implementation of an integration between social networks and CRM systems. The goal (2) is to connect to existing knowledge to solve the research problem on a conceptual level (3). The perspective (4) can be characterised as neutral representation, because the position is unbiased. Practitioners and researchers of IS focussing on integration and social networks are the target audience (5). The results are representative (6) for the IS community, because prominent data sources have been queried.

Characteristic (1) focus

Categories research outcomes

(2) goal (3) organisation (4) perspective

research methods

theories

applications

integration

criticism

central issues

historical

conceptual

methodological

neutral representation

espousal of position

(5) audience

specialised scholars

general scholars

practitioners

general public

(6) coverage

exhaustive

exhaustive and selective

representative

central/pivotal

Table 1:

Taxonomy of the conducted literature review (cf. Cooper (1988))

We applied a keyword search in the databases of AISeL, EBSCO, Emerald, IEEE, JSTOR, ProQuest, and Web of Science in title (TI), topic (TO), abstract (AB), keyword (KW) and full text (TX) fields. The search-string was built to find existing applicable architectures containing the terms “social CRM system” or “system model” or “architecture” in combination with “CRM”, “social network”, “social media”, and/or “web 2.0”. Only reviewed publications have been considered to ensure the level of quality. Duplicate publications have been removed. The relevance of the distinct papers has been determined by reading the full texts. For example, publications that defined the term “CRM” as “component reference model” or “core reaction model” have been treated as not relevant. Only original publications written in English or German have been incorporated. Table 2 relates the eight subsystems of our proposed architecture with components included in the identified publications. The subsystems represent the core functionality of the integration software, which we synthesised from the functions of the components described in literature. On that point, we created a list of the features, which are mentioned in all found system models. In case different terms for similar features are used, we chose a common name and proposed a subsystem. The approach for grouping components is called family resemblance (Rosch and Mervis 1975). The idea is that the most prototypical components, i.e. candidates for a subsystem, are those, which have most functions in common with that component, and have least commonalities with other components. For example, the subsystem Detect subsumes the functionality intended in the found

4

Marcel Rosenberger, Christiane Lehrer and Reinhard Jung

components, which are termed “data ingestion” (Ajmera et al. 2013), “collect information” (Chau and Xu 2012), “fetch engine” (Hussain and Vatrapu 2014), “monitoring system” (Reinhold and Alt 2011) and “observe” etc. These components are functionally similar. The authors of all publications describe an analytical system in the first place, and mention a component to store data from social networks (or social media respectively). The authors coincide with the need for analysing the captured data. Hussain and Vatrapu (2014), however, do not define a dedicated analytics component like the other authors. But, they state that the purpose of their tool is to “prepare data for analysis”. Hence, a component that implements analytical features can be imputed. They propose a Fetch Engine, which is run in batch and connects to the social media’s APIs for fetching the data asynchronously. Ajmera et al. (2013) describe a subsystem for extraction (SystemT), which filters relevant comments based on tags, question patterns and sentiments. In all other system models, the components for capturing data are not explicitly defined. Alt and Wittwer (2014) propose that CRM functions/processes are triggered based on analytical results, and Botzenhardt et al. (2011) route identified innovative ideas posted in social media to the product development process. However, it is unclear, which components are necessary to invoke these processes and how they function. Hussain and Vatrapu (2014) realise the need for administrative components, which they term Data Export Console and Fetch Console respectively. The purpose is to manage and configure other components (i.e. Data Export Engine and Fetch Engine). Alt and Wittwer (2014) include multiple social media types, which are social networks, blogs/micro blogs, communities, and collaborative projects, and propose a Social Media Interface. This interface is functionally comparable to the proposed subsystem Adapt, which transforms proprietary data and message structures into a common format. Only Reinhold and Alt (2011) imply features to search in social media, which they subsume in a subsystem termed Interaction System. This subsystem may not be confused with the Interactions-component proposed by Chau and Xu (2012), which follows the users’ conversations in blogs, and does not provide features to publish content.

Invoke

Analyse

Detect

Store

Administer

Adapt

Publish

Search

proposed subsystems

Ajmera et al. (2013)

◩

◩

■

■

□

◩

□

◩

Alt and Wittwer (2014)

◩

■

◩

■

□

■

□

□

Botzenhardt et al. (2011)

◩

■

◩

■

□

□

□

□

Chau and Xu (2012)

□

■

◩

■

□

□

□

□

Hussain and Vatrapu (2014)

□

◩

■

■

◩

■

□

□

Reinhold and Alt (2011)

□

■

◩

■

□

◩

◩

■

related publications

Legend:

□ subsystem is not regarded, ◩ subsystem is intended, but not made explicit, ■ subsystem or comparable components are mentioned Table 2:

Results of the literature review

MKWI 2016 – Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy)

5

None of the authors include comparable subsystems or components in their system models that provide all features of all proposed subsystems in a single architecture model. Reasons are the complexity of a social CRM system (Kumar 2012; Ajmera et al. 2013) and that the authors determined analytical operations as the key task rather than the integration of user activities. In this sense, our architecture expands the existing propositions.

3

System Architecture

The research project follows a design science research paradigm, which aims at solving real-world problems by designing general solutions (Winter 2008). It is a fundamental IS discipline, which develops artefacts that improve the capabilities of organisations (Hevner et al. 2004). Generality means that an artefact solves a class of problems instead of an individual problem of a single organisation. March and Smith (1995) identify four artefact-types, which are constructs, models, methods, and instantiations. Constructs are the basic language of concepts needed to describe phenomena. Models build on constructs and relate them with each other. Methods describe activities to meet specified targets. These forgoing artefacts can be instantiated in specific implementations representing the fourth artefact-type. The two main evaluation criteria are that artefacts are innovative and valuable (Peffers et al. 2007). A model-artefact is the ideal result type for answering the research questions, because it makes components and its relationships explicit. The proposed architecture is a system model that suggests a software solution, which is composed of eight subsystems, which are distributable on separate physical or virtual machines (Figure 1). Subsystem Detect contains the Rule Engine and the Event Generator. The purpose of the subsystem is to observe user activities in various social networks, and to create technical events if specific rules are met. In this way, the flood of possible user activities is reduced. Only relevant user activities are stored in the User Activity Repository, which is a component of the subsystem Store. The component provides an interface to read (historical) data, and is used by Data Analyser and Query Executor. Data Analyser, Stream Analyser, and Report Creator & Notifier are parts of subsystem Analyse. The basic features are analytical operations in real-time (Stream Analyser), such as sending alerts (Report Creator & Notifier) when a defined maximum of posts have been created on the companies’ Facebook-page in a short period of time. Analytical operations on historical user events, such as calculating statistical numbers, are provided by the Data Analyser. Subsystem Invoke is responsible for triggering the target functions of the existing CRM systems. The implemented prototype allows connecting to external interfaces using Hypertext Transfer Protocol (HTTP) or Simple Mail Transfer Protocol (SMTP). Thus, web services can be invoked and e-mails can be sent. Subsystem Adapt subsumes the Social Network API Wrapper and the Data Structure Transformer. The main task of this subsystem is to overcome structural differences between the connected social networks. Proprietary data formats are transformed into a common format. The data model proposed by Rosenberger et al. (2016) served as guidance to implement parts of the Data Structure Transformer. The component Content Creator provides a generic interface to publish posts and comments, update the company’s profile, and send private messages to individual users across multiple social networks (provided that the requested functionality is offered by the social network).

6

Marcel Rosenberger, Christiane Lehrer and Reinhard Jung

Figure 1:

Architecture for integrating user activities in social networks with CRM systems

MKWI 2016 – Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy)

7

This interface is used by the CRM systems to interact with customers in the social networks. The provided interface of the Query Executor allows searching for historical user activities in the User Activity Repository and offers an online search in defined containers of the connected social networks (e.g. groups and users’ walls). Subsystem Administer is an administration tool containing Connection Manager, Rule Editor, Data Mapper, and Privacy Manager. Its components are used to configure the Data Structure Transfomer, Social Network API Wrapper, and the Rule Engine. The Privacy Manager forces the Rule Editor to configure rules for discarding specific user activities. For example, a customer may not have given the company his permission to connect to his social network profile and thus, all activities of this customer should be discarded. The rules forced by the Privacy Manager have priority over all rules created using the Rule Editor. The proper sequence of the evaluation of rules is ensured by the Rule Engine.

4

Prototype Implementation and Evaluation

Parts of the system architecture have been implemented as a software prototype at a consulting company in the context of customer acquisition. The use case is to identify relevant consulting needs and call for projects in the social networks LinkedIn, Facebook, and Twitter, and to trigger the creation of opportunities in the lead management module of SugarCRM (2015). The solution is based on Spring XD, which is a “unified, distributed, and extensible system for data ingestion, real time analytics, batch processing, and data export” (Pivotal Software 2015). The Social Network API Wrapper is composed of three source-modules written in the programming language Java, which connect the social networks with the subsystem Adapt. The Data Structure Transformer is implemented as a transform-processor for Spring XD. Its data output is structurally equal for all three social networks. The common data structure is defined in the form of a XML Schema Definition (W3C 2012a; W3C 2012b). Consequently, the Event Generator applies the same set of rules, which are represented by XPath-expressions (W3C 2014). The software prototype observes all posts in defined groups and pages, and selects those that contain the keywords “Consultant” and “Project” in combination with “Enterprise Architecture” or “IT-Service Management” in the content-fields or in the title of the posts. As soon as a new post is created that matches the rule, the data of the user activity serves as input for submitting an opportunity-ticket. The connection to SugarCRM is realised using the web service interface, which provides an appropriate operation that is invoked by the Service/Job Invoker. In terms of Spring XD, the component implements a sinkmodule for SugarCRM. In the period of four weeks 69 posts have been observed in three groups/pages. Two of them led to an opportunity-ticket in SugarCRM. The scope of the prototype has been deliberately kept small so that a potential error could be easier tracked down to its cause. The XPath-expression for filtering relevant user activities, for example, has been iteratively refined in the early versions of the prototype. Later, the Rule Editor has been implemented, so that modifications could be made without the need to restart the Spring XD system. Some parts of the architecture have not been implemented, yet. The subsystems Publish, Search, and Analyse were not required for the use case of the prototype. Basic features of the User Activity Repository are provided by the platform Spring XD, so that the subsystem Store did not need to be developed from scratch. The Privacy Manager of the subsystem Administer has been implemented,

8

Marcel Rosenberger, Christiane Lehrer and Reinhard Jung

but has not been used. Rule Editor, Connection Manager, and Data Mapper consist of a simple properties file. In future versions, these components will be accessible from an administration user interface, which allows easier configuration based on web forms. The consulting company is currently evaluating if the desired analytical features can be provided by available tools from market suppliers, so that the subsystem Analyse can be substituted. Two basic phases of a design science study are build and evaluate (Peffers et al. 2007). Demonstration, evaluation, and communication are important steps of the evaluate phase. The prototype demonstrates that the architecture can be used as a reference meta model for a software implementation. Sonnenberg and vom Brocke (2012) propose that also partial results of the artefact are communicated in order to reach consensus regarding relevance, novelty, and usefulness within the target group. The authors make clear that evaluations must be conducted throughout the whole design science study. In this regard, the authors classify four types of evaluation activities. Eval 1 and eval 2 are ex ante evaluations, which are conducted before building the artefact. Eval 3 and eval 4 are ex post evaluations and are conducted after the artefact has been constructed. Initially, we prepared a research proposal, which has been approved by social media experts from three different insurance companies, to make sure that the envisaged solution addresses an existing practical problem. The literature review revealed that a system architecture had not been rigorously documented or developed, yet (eval 1). We used the UML notation, which is an established standard in the field, to make the architecture readable and understandable for practitioners and scholars of IS. The eight subsystems are connected through interfaces showing the internal relationship among the used constructs. The purpose of eval 2 was to ensure understandability, clarity, internal consistency, and simplicity. The prototype is an instance of the architecture, which serves as a means to show feasibility, operationality, and fidelity with real world phenomenon (eval 3). Eval 4 requires the use of an instance of the architecture in a naturalistic setting for validating generality and efficiency. This task is still outstanding.

5

Conclusion

The integration of social networks with existing CRM systems is a contemporary task of companies that have many existing or new prospective customers that are active in these media (Greenberg 2010b; Shankararaman 2013; Arman 2014). An integration software helps to automate tasks, such as identification of relevant user activities, and thereby improves efficiency. With a high number of active users that should be followed, a technical integration is “enabler” in the first place (Lehmkuhl 2014), because otherwise the recognition of relevant user activities in the given period of time is not possible. User activities can be implemented as technical events, which are triggers for invoking functions in CRM systems. An example is the submission of an opportunity-ticket that is routed to the appropriate business department (e.g. Lead Management). Additionally, social networks provide features for publishing and searching users’ contents, and thus are a communicationchannel and a potential source of information about customers. Social media tools can be obtained from software vendors in the market. Most tools provide features for monitoring and analysing data from social media (Reinhold and Alt 2011; Küpper et al. 2014). However, the integration with existing CRM systems is still sparse (Reinhold and Alt 2013; Trainor et al. 2014). The proposed architecture identifies required system components, shows their dependencies, and guides the implementation of integration software. The modular structure allows replacing selected subsystems with already available tools, and the combination with self-developed

MKWI 2016 – Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy)

9

software parts. In this sense, the architecture serves as a construction plan that also allows a partial implementation, i.e. only selected subsystems. The research problem, i.e. integration of social networks with CRM systems, is approached on a conceptual level. The architecture incorporates identified components from existing literature, and expands the body of knowledge by proposing new components for publishing, searching, invoking, and administration. The architecture leads to a more flexible and interactive system rather than a reactive, analytical system. The artefact serves as a reference meta model enabling specific integration projects to identify (and interlink) relevant aspects of integration. The eight subsystems structure the planned further research by grouping logical concerns of the integration task. For example, the detection of relevant user activities based on algorithms can be separated from the invocation of CRM functions. This allows both areas to be researched by different scholars, even though there are strong correlations between the subsystem Detect and Invoke. In the given example, the required data for invoking the targeted CRM functions must first be captured (i.e. detected). A limitation of this research is that only parts of the architecture have been evaluated hitherto in the implemented prototype. However, we found out that other parts, e.g. analytical components, can be substituted by existing social media tools. The use of an instance of the architecture in a naturalistic setting is still outstanding. The architecture allows that comments could also be published automatically in response to identified, relevant customers’ posts. Standardised responses, however, are contradictory to the idea behind social CRM, which is characterised by individual and personal treatment of customers (Faase et al. 2011). The integration of social networks with CRM is not only a technical issue, but also needs a strategy, organisational change, and customer-oriented business transformations (Ang 2011; Askool and Nakata 2012; Jarvenpaa and Tuunainen 2013; Killian and McManus 2015).

6

Literature

Acker O, Gröne F, Akkad F, et al (2011) Social CRM: How companies can link into the social web of consumers. J Direct, Data Digit Mark Pract 13:3–10. Aier S, Gleichauf B, Winter R (2011) Understanding Enterprise Architecture Management Design – An Empirical Analysis. 645–654. Ajmera J, Ahn H Il, Nagarajan M, et al (2013) A CRM system for Social Media. WWW 2013 Proc 22nd Int Conf World Wide Web 49–58. doi: 978-1-4503-2035-1/13/05 Alt R, Wittwer M (2014) Towards an Ontology-Based Approach for Social Media Analysis. In: Proceedings 22nd European Conference on Information Systems. Tel Aviv, pp 1–10 Ang L (2011) Is SCRM really a good social media strategy? J Database Mark Cust Strateg Manag 18:149–153. Arman S (2014) Integrated model of Social Media and Customer Relationship Management: A Literature Review. Int J Information, Bus Manag 6:118–131. Askool S, Nakata K (2012) Investigation into the adoption intention of social CRM in Saudi banks. Int Conf Inf Soc (i-Society 2012) 402.

10

Marcel Rosenberger, Christiane Lehrer and Reinhard Jung

Baird CH, Parasnis G (2011) From social media to social customer relationship management. Strateg Leadersh 39:30–37. Botzenhardt A, Witt A, Maedche A (2011) A Text Mining Application for Exploring the Voice of the Customer. Proc. Seventeenth Am. Conf. Inf. Syst. 1–11. Chau M, Xu J (2012) Business intelligence in blogs: Understanding consumer interactions and communities. MIS Q Manag Inf Syst 36:1189–1216. Cooper HM (1988) Organizing knowledge syntheses: A taxonomy of literature reviews. Knowl Soc 1:104–126. doi: 10.1007/BF03177550 Faase R, Helms R, Spruit M (2011) Web 2.0 in the CRM domain: defining social CRM. Int J Electron Cust Relatsh Manag 5:1. Greenberg P (2010a) The impact of CRM 2.0 on customer insight. J Bus Ind Mark 25:410–419. doi: 10.1108/08858621011066008 Greenberg P (2010b) CRM at the speed of light: social CRM 2.0 Strategies, tools, and techniques for engaging your customers, 4th edn. McGraw-Hill Osborne Media Helms RW, Booij E, Spruit M (2012) Reaching Out: Involving users in Innovation Tasks through Social Media. In: ECIS 2012 Proceedings. p Paper 193 Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28:75–105. Hussain A, Vatrapu R (2014) Social Data Analytics Tool (SODATO). In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp 368–372 ISO/IEC/IEEE (2011) Systems and software engineering - Architecture description, Reference number ISO/IEC/IEEE 42010:2011(E). IEEE Comput Soc. doi: 10.1109/IEEESTD.2011.6129467 Jarvenpaa SL, Tuunainen VK (2013) How Finnair Socialized Customers for Service Co-Creation with Social Media. MIS Q Exec 12:125–136. Jayachandran S, Sharma S, Kaufman P, Raman P (2005) The role of relational information processes and technology use in customer relationship management. J Mark 69:177–192. Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz 53:59–68. doi: 10.1016/j.bushor.2009.09.003 Kietzmann JH, Hermkens K, McCarthy IP, Silvestre BS (2011) Social media? Get serious! Understanding the functional building blocks of social media. Bus Horiz 54:241–251. doi: 10.1016/j.bushor.2011.01.005 Killian G, McManus K (2015) A marketing communications approach for the digital era: Managerial guidelines for social media integration. Bus Horiz 58:539–549. doi: 10.1016/j.bushor.2015.05.006 Kumar DM (2012) Social Customer Relationship Management Proposal for SCRM for an IT Services Company.

MKWI 2016 – Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy)

11

Küpper T, Lehmkuhl T, Jung R, Wieneke A (2014) Features for Social CRM Technology – An Organizational Perspective. AMCIS 2014 Proc 1–10. Lehmkuhl T (2014) Towards Social CRM - A Model for Deploying Web 2.0 in Customer Relationship Management. University of St.Gallen Lehmkuhl T, Baumöl U, Jung R (2013) Towards a maturity model for the adoption of Social Media as a means of organizational innovation. In: Proceedings of the Annual Hawaii International Conference on System Sciences. pp 3067–3076 March ST, Smith GF (1995) Design and natural science research on information technology. Decis Support Syst 15:251–266. doi: 10.1016/0167-9236(94)00041-2 Musser J, O’Reilly T (2007) Web 2.0 - Principles and Best Practices. O’Reilly Media, Inc., Sebastopol, CA, USA Peffers K, Tuunanen T, Rothenberger M a., Chatterjee S (2007) A Design Science Research Methodology for Information Systems Research. J Manag Inf Syst 24:45–77. doi: 10.2753/MIS0742-1222240302 Pivotal Software (2015) Spring XD. http://projects.spring.io/spring-xd/. Accessed 1 Feb 2015 Reinhold O, Alt R (2011) Analytical Social CRM: Concept and Tool Support. In: Proceedings 24th Bled eConference. pp 226–241 Reinhold O, Alt R (2013) How Companies are Implementing Social Customer Relationship Management : Insights From Two Case Studies. In: Proceedings of the 26th Bled eConference. pp 206–221 Reinhold O, Alt R (2012) Social Customer Relationship Management: State of the Art and Learnings from Current Projects. In: BLED 2012 Proceedings. pp 155–169 Rosch E, Mervis CB (1975) Family resemblances: Studies in the internal structure of categories. Cogn Psychol 7:573–605. doi: 10.1016/0010-0285(75)90024-9 Rosenberger M, Lehrer C, Jung R (2016) Ein Datenmodell zur Unterstützung der Datenintegration von Nutzeraktivitäten aus verschiedenen sozialen Netzwerken. In: Multikonferenz Wirtschaftsinformatik. Ilmenau, Sarner A, Thompson E, Sussin J, et al (2012) Magic Quadrant for Social CRM. 1–20. Shankararaman V (2013) Integration of Social Media Technologies with ERP : A Prototype Implementation Integration of Social Media Technologies with ERP. Singh N, Lehnert K, Bostick K (2012) Global Social Media Usage : Insights Into Reaching Consumers Worldwide. Thunderbird Int Bus Rev 54:683–700. doi: 10.1002/tie Sonnenberg C, vom Brocke J (2012) Evaluations in the Science of the Artificial – Reconsidering the Build-Evaluate Pattern in Design Science Research. In: Peffers K, Rothenberger M, Kuechler B (eds) Design Science Research in Information Systems. Advances in Theory and Practice. Proceedings of the 7th DESRIST Conference. Springer Berlin / Heidelberg, Las Vegas, NV, USA, pp 381–397 SugarCRM (2015) CRM Software & Online http://www.sugarcrm.com/. Accessed 1 Feb 2015

Customer

Relationship

Management.

12

Marcel Rosenberger, Christiane Lehrer and Reinhard Jung

Trainor KJ, Andzulis J (Mick), Rapp A, Agnihotri R (2014) Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. J Bus Res 67:1201–1208. doi: 10.1016/j.jbusres.2013.05.002 vom Brocke J, Simons A, Niehaves B, et al (2009) Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process. In: 17th European Conference on Information Systems. W3C (2012a) XML Schema Definition Language (XSD) http://www.w3.org/TR/xmlschema11-1/. Accessed 6 Sep 2014

1.1

Part

1:

Structures.

W3C (2012b) XML Schema Definition Language (XSD) http://www.w3.org/TR/xmlschema11-2/. Accessed 6 Sep 2014

1.1

Part

2:

Datatypes.

W3C (2014) XML Path Language (XPath) 3.0. http://www.w3.org/TR/xpath-30/. Accessed 6 Sep 2014 Williams DS (2014) Connected CRM : implementing a data-driven, customer-centric business strategy. Hoboken, New Jersey Winter R (2008) Design science research in Europe. Eur J Inf Syst 17:470–475. doi: 10.1057/ejis.2008.44 Woodcock N, Broomfield N, Downer G, McKee S (2011) The evolving data architecture of social customer relationship management. J Direct, Data Digit Mark Pract 12:249–266. doi: 10.1057/dddmp.2010.45