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How Big Data Analytics Enables Service Innovation in NETFLIX




Service Innovation and Big Data Analytics (BDA)

Service innovation denotes increasing business revenues by adding customer value by serving them differently. Service innovation provides companies with a sustainable competitive advantage because an organization can use the strategy to maintain growth in saturated markets and circumvent problems caused by commoditization. Service-based strategies help businesses excel in delivery systems, technologies, cost structure, and service offerings. Big Data Analytics (BDA) involves processing large volumes of data into actionable information like customer preferences, market trends, correlations, and hidden patterns. BDA helps businesses make informed decisions. It provides the means to gather new information and analyze enormous data sets.

Major findings from Lehrer, Wieneke, Brocke, Jung, and Seidel (2018)

Lehrer et al. (2018) found that organizations from different industries benefit from flexible, re-programmable BDA technologies. BDA technologies enhance business features like event and behavior prediction and recognition, storage, sourcing, visualization, and rule-based actions that improve BDA-driven human practices and service automation. According to Lehrer et al. (2018), BDA technology is an indispensable element of service innovation because it helps organizations focus on service-dominant logic. As customer preferences shift from universal to individualized experiences coupled with increasing communization of services, businesses shift from product innovation strategies to service innovation strategies. Therefore, service innovation provides consumers with added value while businesses benefit through increased competitiveness.

The existing gap in the literature and practice on digitally enabled service innovation

Literature on digital service innovation has been predominant on the vital role of operative resources as enablers of the entire innovation process and service exchange. Service-dominant logic dictates that Information Technology (IT) enabled skills and knowledge to act on other resources to create value. However, recent research like Lusch and Nambisan (2015) report on the integral element of technology in service innovation as an operand and operant resource. These studies imply that technology manifests as both an actor and an enabler within the sociotechnical service ecosystem, making it difficult for researchers to separate the roles. Industry and academic experts call for further research on how BDA enables service innovation, the complex interconnection of enablers, and their functions.

The research question and primary research objectives of your paper

BDA and digital service innovation are heavily intertwined. However, research on the connection between the two platforms is limited and barely reflected in literature. This research will bridge the gap by undertaking a literature review that uncovers the potential of technology in digital service innovation. The study responds to how technology is developing the service innovation field and the opportunities drawn from using BDA. The aim is to identify the ways BDA adds value to service innovations.


A summary of the study

Service innovation continues to attract attention as service economies, technologies, and their respective industries expand. Massive developments in Information and Computing Technology (ICT) coupled with Digital service innovation continue to stir interest from industry and academia. Technological advancement and digitization are driving the call for extensive research in service innovation, especially on how it relates to BDA. The discussion provided above reviews digital service innovation literature. The study also explores how BDA contributes to digital service innovation and the different dimensions it takes. Previous literature lacked such studies. Therefore, the research team suggested investigating BDA-enabled digital service innovation, motivated by research gaps, research questions, and the opportunities present in the field. The research team expects the agenda propagated through this study will shape future research efforts to advance BDA-enabled digital service innovation.

Contribution to Digital Innovation literature

Literature revolving around service innovation, including the study by Lehrer et al. (2018), denotes three distinct trends. One, there is a momentous shift from market-driven innovation to a service-dominant approach. In this approach, researchers relate service to exchange as opposed to a product variant. Two, the advent and theorization of technologies and digital platforms using third-party benefactors to prompt digital services. Three, sociotechnical interest in service innovation, distinct actors and relationships, and the transactions that occur along with these processes and connections. The sociotechnical phenomenon aspect links digital service innovation to the notion of ecosystems. These trends provide anchors for grounding digital service innovation, which has been expanded through the investigation conducted in this paper.

Implications for BDA practice

Even though the three trends highlighted above are anchored to digital service innovation and are well documented, the same does not apply to BDA despite gaining traction in recent times. In the information systems field, it is clear from Lehrer et al. (2018) that special research issues have been included in articles and dedicated to big data. Data Science is one of the most lucrative jobs today, making the trend of incorporating BDA in studies materialize more in practice. We associate the highlight of data science and big data with advancements in data storage technologies, networking, sensors, and computing, which enable data collection. Besides, these technologies often collect data in real-time, making the field of BDA reach unprecedented heights. However, big data critics continue to emphasize the daunting problems in this area of study, pushing industry and academic experts to work on explicitly understanding the phenomenon.

Limitation of your project

The question of how BDA affects the flexibility of services remains unanswered. This project attempted to move along the innovation and technology frontiers from the scarcity of information on data and service to abundance. However, this study helped us uncover the dire need to create value and make sense of big data. The research team used complicated analytical methods that were distant from the conclusions they were supposed to support. The extracted information is hard to consume through operational actions, and decision-makers will find it challenging to comprehend the knowledge gained from this study. Our research created little value because of the scarcity of material on the subject. Even though research on visual analysis and interaction considers this problem, we still found it limited considering the existing exploration alternatives of BDA.

Recommendation for future research

Visual analytics is a potential area for research that could permit more intuitive studies into service decisions and innovation. Visual analytics would help simplify BDA service models and present them in formats that can be consumed, even with non-technical users. Future research into BDA-enabled service innovation should incorporate visualization techniques to enhance the information extracted from the studies. Undeniably, technological features and characteristics help in the understanding of service. However, there is a gap in the variations of innovative services future research needs to tackle. Studies into BDA-enabled service innovation identify the need for profound research into how BDA enables innovative service designs using knowledge-intensive environments and associated products.


Lehrer, C., Wieneke, A., Vom Brocke, J. A. N., Jung, R., & Seidel, S. (2018). How big data analytics enables service innovation: materiality, affordance, and the individualization of service. Journal of Management Information Systems35(2), 424-460.

Lusch, R. F., & Nambisan, S. (2015). Service innovation. MIS quarterly39(1), 155-176.

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