Data Analytics in Service Systems: Exploring Analyticsbased Value Co-creation

Data Analytics in Service Systems: Exploring Analyticsbased Value Co-creation Aya Rizk Luleå University of Technology, 971 87 Luleå, Sweden aya.rizk@ltu.se Abstract. With the increasing pervasiveness of ICT in our everyday practices, we are turning into moving data generators. Service systems, among many organizational configurations, are striving to create value from the vast data generated by utilizing data analytics, which in turn informs the design and delivery of their innovative services. While data analytics is perceived to generate high value to organizations, we know little about how this process unfolds, what values can be captured, and if they change on different loci of value within service systems. To address these gaps, this paper outlines a research study to explore analytics-driven service systems using a sociomaterial theoretical lens. The study will follow a qualitative research approach, drawing on empirical data from a smart city context. Keywords: Data analytics, service innovation, service systems, value, values, co-creation, sociomateriality 1 Introduction Ubiquitous information and communication technologies (ICT) are driving two interesting phenomena that are boosting one another: digitization and datafication [1]. From a sociotechnical viewpoint, digitization refers to the disruptive both positive and negative effects of digital and service innovations in today s economy and society, while datafication refers to the pervasiveness of data analytics and its constituent logic of value creation [1,2]. Accordingly, organizations are turning to digital services and data analytics to increase their competitiveness, growth and innovativeness [3,4]. As a result, these vast amounts of data and potential knowledge extracted from it are regarded as highly valuable assets in organizations as well as in open service systems [5,6]. Organizations that are able to harness the power of their data are believed be on the innovation frontier of their respective markets [2]. But what does harnessing the power of their data really mean? Indeed, digitization is making it easier, cheaper and faster to collect vast amounts of data. However, applying analytical techniques to extract insights, hidden patterns and previously unknown relationships in data, is what enables organizations to harness the power of their data, and consequently create value [7].

Existing research on value creation from data analytics is largely split between the technical and the economical motives of data analytics. Limited research has addressed the value co-creation from data analytics, particularly in shared service systems and from the viewpoint of different stakeholders. In this paper, a research proposal is outlined to study this emerging phenomenon, by utilizing sociomateriality as a theoretical lens. The paper is organized as follows: details on the research problem and research questions are first outlined, including a specification of key concepts. Section 2 lays the theoretical foundations of the research, followed by a conceptual framework in section 3. Then, section 4 provides a brief description of the research method before concluding in section 5. 1.1 Research Problem While organizations are continuously looking for new ways to create value from data, related contemporary research is split between an engineering view of big data analytics and an economic view of business analytics. The engineering view 1 focuses on topics such as big data architectures [8,9], data processing frameworks [10], and data mining and machine learning algorithms [11,12]. Offering new solutions characterized by performance, efficiency and accuracy, to mention a few, motivates this force. On the other hand, the economic view focuses on the return on investing in data analytics projects and programs vis-à-vis the associated costs of technology and skills [13,14], or how such projects take organizations one step further on the competitive landscape of their respective markets [16]. These deterministic metrics of analytical solutions, while in some practical aspects are very important, are not sufficient to represent their innovative value. Innovation capabilities and challenges are different from one environment to another, depending on various factors along its innovation value chain [15]. Accordingly, it is crucial to understand the factors and mechanisms underlying this process of value creation from data analytics. To address this demand, recent research proposes different guidelines, models and frameworks for maximizing value from data analytics initiatives (e.g. [7]). Different frameworks have different focus, though; some are application area-specific see Caya & Bourdon s [17] framework for analytics in competitive sports, while others are function-specific (e.g. Espinosa & Armour s [18] model for coordination and governance in big data analytics). Yet, such frameworks focus on analytics initiatives institutionally, where investments, skills and other resources are utilized within distinct organizational boundaries, leaving a knowledge gap when digital data is generated in open service systems. Accordingly, research in this area still requires further knowledge development with regard to the holistic perspective of co-creating value from analytics in service systems, one that overcomes the distinction between engineering view of technological innovation and economic view of market competition [19, 20]. This can be broken down by (a) capturing the values of different service system stakeholders based on data analytics [37], (b) understanding how data analytics impacts value co- 1 For a survey on academic and practitioner big data analytics literature, see [35]

creation in service systems [38], and (c) how analytics-based value co-creation changes on different loci of value [17]. 1.2 Key concepts Three main concepts are key to this research, namely data analytics, value co-creation and service systems. Here specific definitions of those concepts are used as my interpretations thereof. Data analytics refers to the application of statistical analysis, data mining or machine learning techniques on datasets to identify patterns in order to make economical, social, technical and legal claims [5]. Big data analytics and business analytics are related concepts. The former refers to analytics of big data characterized by its 3 Vs (volume, variety and velocity), while the latter refers to employing data analytics techniques for business and economic gains [14]. In this research study the wider concept of data analytics is employed in order to accommodate the diversity of stakeholders and datasets in service systems. Service system denotes configurations of resources (participants, information and technology) connected to other systems by value-propositions and shared information [28][40,41]. Last but not least, value co-creation refers to the joint creation of value by service provider(s) and customer(s), determined in-use [28][39]. Accordingly, value co-creation emerges in some form of interactive practices, through value propositions from the provider side and value drivers from the customer side [38]. 1.3 Research Questions The main objective of this research study is to explore how service systems co-create value from data analytics. Thus the overarching research question is: RQ. How do service systems co-create value from data analytics? This question can be deconstructed on three levels. First, on the stakeholders level, the aim is to capture the values [37] of different groups of stakeholders in relation to data analytics (indeed in a particular context), without taking into consideration or minimally so their interactions with other stakeholders in the service system. Thus, on this first layer, we set to answer the question: What values do service system stakeholders generate and realize from data analytics? Second, on the system level, the aim is to understand value co-creation at the interplay between different stakeholders actions and interactions as value-in-use. Tuunanen et al. [38] argue that value co-creation is central to successful design and delivery of services. Accordingly, the following question enables us explore: How do multiple stakeholders of a service system co-create value based on data analytics? Third, through knowledge accumulated from the first two questions, we embark on exploring the differences of analytics-based value co-creation on different loci of value [42], by navigating between the two above-mentioned levels and aiming to

answer the question: How does analytics-based value co-creation change on different locus of value? 2 Theoretical Foundation In order to avoid the discrete engineering versus economic views of value co-creation from data analytics, this study adopts sociomateriality as a theoretical lens. In IS research on service innovation, practice perspectives and sociomateriality are gaining more attention, for the very notion of service involving a wide set of interrelated practices [22]. A practice in this context refers to a routinized or recurrent behavior that incorporates activities, knowledge, and associated shared meanings and values, and utilizes objects and artifacts [22, 23]. Accordingly, service is also constituted in our everyday practices, allowing us to harmoniously combine different resources in configurations that generate value [23, 24]. Sociomateriality emerged in the IS research to tackle the limitations and increasing difficulty of the separation of the social and technical aspects of a technology, particularly in business organizations. It offers a view of constitutive entanglement where social practices are continuously enacted through the materiality of the technology [25]. Drawing on the philosophical grounds of actor-network theory and practice, sociomateriality inherits the notion of agency of non-human actants, within the sociomaterial assemblages [26, 27]. Sociomateriality can be a property of any phenomenon: a technology, an organization, a process or a service. However, what it means and implies to characterize a phenomenon as sociomaterial depends to a large extent on the epistemological and ontological foundations assumed in a given research [27]. In service innovation research, the service-dominant (SD) logic has contributed a great deal in the understanding of service, service exchange and how value is (co-) created [28, 29]. However, Orlikowski and Scott [23] extend the SD logic framing to the IS field through highlighting three key aspects of sociomateriality in service innovation. This first aspect is the constitutive role of practices in service (and goods), meaning that producing and consuming value occurs at the practice level. This level of abstraction, they argue, overcomes how both dominant logics overlook how similar they are. In this sense, the authors highlight the entanglement of goods and services, as much as that of sociomateriality. The second aspect is concerned with the differentiation between tangibility and materiality, arguing that both goods and services are material, even though the former is tangible and the latter is intangible. The third aspect is the performative consequences of materialization, referring to how the material configurations of activities, artifacts and objects affect the outcomes of their enactment. Incorporating such framing, this study will use sociomateriality to explore how data analytics both the process and the yielded knowledge, service and service system stakeholders interact towards value co-creation. However, such framing was also criticized by the over-socialized view of technology, which is indirectly empowered by the practice lens [27]. This is because its technological agency perspective still regards technology as a tool used by human actors, failing to take into perspective the autonomous and semi-autonomous features of today s analytics-based

services [4, 30]. In this research, it is argued that data analytics can be studied as sociomaterial practices, which would bring about the integrative view of sociomateriality and take data analytics at the heart of the analytics-driven service innovation. 3 Conceptual Framework Since the objective of this research is to explore how service systems co-create value from data analytics, we propose an initial conceptual framework based on literature screening, depicted in Fig. 1. This framework is process oriented, and aims to highlight the mechanisms in which sociomaterial practices unfold in a service system and the role of data analytics in this process. This framework, along with extensive literature and the theoretical foundations mentioned in the previous section, will inform an empirical study as described in the following section. Fig. 1. Conceptual framework for analytics-driven service innovation This framework describes the linear but iterative process of value co-creation from data analytics. The first and foremost material in this process is digital data, denoted by the component labeled Datasets and streams, which can either be collected through explicit action (e.g. rating a restaurant or searching for a near-by gym) or collected as a by-product of a particular service use (e.g. sensory data or web clicks) [30]. All types of data are considered in terms of volume, structure and timeliness. Analytical techniques and algorithms collectively referred to as Analytics in Fig.1 are then applied to data in order to identify interesting patterns and relationships

[16]. The generated knowledge accordingly enables decision making through various automation levels, represented by the knowledge representation/intermediation component. This knowledge is either presented to humans who take a decision regarding a specific product or service that ultimately affects a beneficiary, or embedded in yet another digital service (e.g. recommender systems) [7]. Moreover, through the interaction between service system stakeholders is where value co-creation takes place, through providers value propositions and beneficiaries value drivers [28][38]. This process of value co-creation entails value exchange, as well as data exchange digital traces of using the service [30]. This framework is an initial conceptualization of the process of value co-creation from data analytics, which will be further developed based on the following research methodology. 4 Research Method In order to address the aforementioned research questions, this research study will be conducted through qualitative research methods. More specifically, a case study will be carried out iteratively through planning, data collection, analysis, evaluation and reflection. Following is an outline for the research design and data collection and analysis, respectively 4.1 Research Design Case studies are found to be suitable for how research questions. In addition, they are suitable when the phenomenon of interest in this case, analytics-driven innovation is a contemporary one [34]. Indeed, digitization and datafication are contemporary, and we know too little about the complex phenomenon in relation to service systems (as opposed to business organizations). The unit of analysis will be embedded: on the service system and the process of value co-creation using data analytics. The case study will be conducted in the context of a smart city project. The project sets out to build Experimentation as a Service (EaaS) platform that enables various stakeholders experiment with urban data and software tools in three test cities. Having the project built upon the principles of co-creation, stakeholder groups such as experimenters, citizens, city municipalities and researchers all collaborate in the value co-creation process. 4.2 Data Collection and Analysis The data will be primarily collected through interviews, observations and documents in relation to other activities the researcher carries out for the project. Semistructured and open-ended interviews will be conducted with service system stakeholders collaborating in the co-creation of urban services. Interview guides will comprise questions on the overall process of design to use of analytics-driven services, the datasets and analytical techniques they use, decisions associated with design or use of the service, knowledge or skills acquired, focal services as well as

competing or alternative ones, situations or practices of use, value propositions, and value drivers. Data collected will be analyzed through concept-driven coding, starting with themes existing in the literature and the theoretical foundations [35]. 5 Conclusion The digitization and datafication of our everyday lives is creating a plethora of opportunities for innovation in service systems. Seizing these opportunities is thought to be minimal if such systems do not extract relevant and actionable knowledge from such reservoirs of digital traces, a process accomplished through data analytics. While data analytics is thought to provide organizations with competitiveness, value and growth, it is unclear how this value is co-created in service systems with various stakeholders. In this paper, a research proposal is outlined to explore how service systems co-create value from data analytics. Through the theoretical foundations of sociomateriality, a qualitative case study is proposed to identify how and where along the process service systems co-create value, from the perspective of different stakeholders. This research is expected to unearth crucial insights on the means and practices of analytics-driven service innovation, both on the theoretical level of sociomateriality and the practical level of designing and delivering services in collaborative service systems especially in smart cities. References 1. Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149-157. 2. Lycett, M. (2013). Datafication : making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381-386. 3. Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412-421. 4. Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D., & Moghaddam, Y. (2015). Innovations with Smart Service Systems: Analytics, Big Data, Cognitive Assistance, and the Internet of Everything. Communications of the Association for Information Systems, 37(1), 35. 5. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679. 6. McAfee, A., & Brynjolfsson, E. (2012). Big data. The management revolution. Harvard Bus Rev, 90(10), 61-67. 7. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. 8. Zulkernine, F., Martin, P., Zou, Y., Bauer, M., Gwadry-Sridhar, F., & Aboulnaga, A. (2013, June). Towards cloud-based analytics-as-a-service (claaas) for big data analytics in the cloud. In 2013 IEEE International Congress on Big Data, (pp. 62-69). IEEE.

9. Khan, Z., Anjum, A., Soomro, K., & Tahir, M. A. (2015). Towards cloud based big data analytics for smart future cities. Journal of Cloud Computing, 4(1), 1-11. 10. Landset, S., Khoshgoftaar, T. M., Richter, A. N., & Hasanin, T. (2015). A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, 2(1), 1-36. 11. Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM SIGKDD Explorations, 14(2), 1-5. 12. Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. Knowledge and Data Engineering, IEEE Transactions on, 26(1), 97-107. 13. Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The big-data revolution in US health care: Accelerating value and innovation. McKinsey & Company, 1-13. 14. Shim, J. P., French, A. M., Guo, C., & Jablonski, J. (2015). Big Data and Analytics: Issues, Solutions, and ROI. Communications of the Association for Information Systems, 37(1), 39. 15. Hansen, M. T., & Birkinshaw, J. (2007). The innovation value chain. Harvard business review, 85(6), 121. 16. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21. 17. Caya, O., & Bourdon, A. (2016). A Framework of Value Creation from Business Intelligence and Analytics in Competitive Sports. In 49 th Hawaii International Conference on System Sciences (HICSS), 2016, 1061-1071. IEEE. 18. Espinosa, J.A., & Armour, F. (2016). The Big Data Analytics Gold Rush: A Research Framework for Coordination and Governance. In 49th Hawaii International Conference on System Sciences (HICSS), 2016, 1112-1121. IEEE. 19. Grover, V., & Kohli, R. (2012). Cocreating IT Value: New Capabilities and Metrics for Multifirm Environments. MIS Quarterly, 36(1), 225-232. 20. Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239-1249. 21. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K.,... & Scholl, H. J. (2012, January). Understanding smart cities: An integrative framework. In System Science (HICSS), 2012 45th Hawaii International Conference on (pp. 2289-2297). IEEE. 22. Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age: key contributions and future directions. MIS Quarterly, 39(1), 135-154. 23. Orlikowski, W., & Scott, S. V. (2015). The Algorithm and the crowd: Considering the materiality of service innovation. MIS Quarterly, 39(1), 201-216 24. Akaka, M. A., & Vargo, S. L. (2014). Technology as an operant resource in service (eco) systems. Information Systems and e-business Management, 12(3), 367-384. 25. Orlikowski, W. J. (2007). Sociomaterial practices: Exploring technology at work. Organization studies, 28(9), 1435-1448. 26. Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: challenging the separation of technology, work and organization. The academy of management annals, 2(1), 433-474. 27. Leonardi, P. M. (2013). Theoretical foundations for the study of sociomateriality. Information and Organization, 23(2), 59-76. 28. Vargo, S. L., Maglio, P. P., & Akaka, M. A. (2008). On value and value co-creation: A service systems and service logic perspective. European Management Journal, 26(3), 145-152. 29. Vargo, S. L., & Lusch, R. F. (2011). It's all B2B and beyond: Toward a systems perspective of the market. Industrial Marketing Management, 40(2), 181-187. 30. Andersen, J. V., Lindberg, A., Lindgren, R., & Selander, L. (2016). Algorithmic Agency in Information Systems: Research Opportunities for Data Analytics of Digital Traces. In 49 th Hawaii International Conference on System Sciences (HICSS), 2016, 4597-4605. IEEE.

31. Daintith, J., & Wright, E. (2008). Retrieved online on March 30 th, 2016 from: http://www.oxfordreference.com/view/10.1093/acref/9780199234004.001.0001/acref- 9780199234004-e-119?rskey=uLxatv&result=145 32. Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of datification. The Journal of Strategic Information Systems, 24(1), 3-14. 33. Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149-157. 34. Yin, R. K. (2009). Case study research: Design and methods, 4th. Thousand Oaks. 35. Gibbs, G. R. (2008). Analysing qualitative data. Sage. 36. Phillips-Wren, G., Iyer, L. S., Kulkarni, U., & Ariyachandra, T. (2015). Business Analytics in the Context of Big Data: A Roadmap for Research. Communications of the Association for Information Systems, 37(1), 23. 37. Purao, S., Seng, T. C., & Wu, A. (2013). Modeling citizen-centric services in smart cities. In Conceptual Modeling (pp. 438-445). Springer Berlin Heidelberg. 38. Tuunanen, T., Myers, M., & Cassab, H. (2010). A conceptual framework for consumer information systems development. Pacific Asia Journal of the Association for Information Systems, 2(1), 5. 39. Prahalad, C. K., & Ramaswamy, V. (2004). Co creation experiences: The next practice in value creation. Journal of interactive marketing, 18(3), 5-14. 40. Maglio, P. P., & Spohrer, J. (2008). Fundamentals of service science. Journal of the Academy of Marketing Science, 36(1), 18-20. 41. Spohrer, J., Maglio, P. P., Bailey, J., & Gruhl, D. (2007). Steps toward a science of service systems. Computer, 40(1), 71-77. 42. Davern, M. J., & Kauffman, R. J. (2000). Discovering potential and realizing value from information technology investments. Journal of Management Information Systems, 16(4), 121-143.