Big Data Analytics for Service Innovation (Term Paper Sample)

Big Data Analytics for Service Innovation
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Institutional Affiliation
Date
Review and Analysis
Research Method
Lehrer et al., 2018 used explanatory and a multisite case study technique to formulate a method that is rooted to data analysis. Revelatory cases were used given that there is no sufficient empirical evidence based on the theoretical model which explains how BDA has enabled service innovation. Therefore, our technique was explanatory, they incorporated the element of materiality and affordability. With the methodology, Lehrer et al., 2018 triangulated across other researchers, methods and different sources to ensure credibility. Secondly, Lehrer et al., 2018 triangulated across the different sites using purposing sampling to establish similarities and differences to ensure the transferability of the data. Thirdly, to ensure dependability of the method, they ensured there was consistency by meeting with the respondents. Finally, two researchers were triangulated and were used to in conducting interviews to avoid preconception as well as subjectivity making sure that confirmability is attained (Roberts & Priest, 2006). The researchers used semi-structured interviews as a primary source of their data; this approach was considered appropriate for collecting empirical data for the episodic and irregular phenomenon.
The accepted measures of the findings were based on data pattern analysis in relation to the questions initially asked. The validity of the qualitative analysis findings solely depends on the methodology employed as well as room of error allowed by the researchers for the findings that veer off the hypothesis being tested. Lehrer et al., 2018 research supports the argument that BDA has the potential for service innovation and identifies the factors involved in creating new value propositions (Roberts & Priest, 2006). It identifies two key roles of BDA in the context of service innovation: the automation of user-friendly service delivery and the user-friendly, people-oriented service practices, which are key characteristics of BDA. Stresses its foundation. These two types of service innovation allow organizations to reshape their value proposition.
To satisfy the requirements, credibility, transferability, reliability and comfortability of the data was ensured (Roberts & Priest, 2006). Validity and reliability are important aspects of any research. By paying close attention to these two aspects, you can distinguish between good and bad research and ensure that your fellow scientists accept that your findings are credible. This is particularly important in qualitative research. Here, the subjectivity of researchers can easily cloud the interpretation of the data, and research results are often questioned or viewed by the scientific community.
To ensure the reliability of the results of the research, the methodology, data collection, sampling technique as well as the data analysis approach selected is standardized (Roberts & Priest, 2006). The reliability of the results is based on the strong research design created as well as selection of appropriate methodology to attain consistent results.
Case Organization Selection
Yes, With the increasing commoditization of products and increasing customer demand for personalized experiences and interactions, CEOs are shifting their focus from product innovation to service innovation. Service innovation provides customers with new and unique value propositions, enabling the company to differentiate itself from its competitors and create strategic value (Ularu et al., 2021). Therefore, companies are looking for opportunities to use the flexibility and adaptability of digital technology to innovate their services, and service innovation is now an important area in the wider field of digital innovation. Company A used BDA to Increase customer centricity and the firm’s position as a digital leader while company B used BDA to Provide efficient but high-quality personal advice. Company C and D used BDA to Enhance core business by offering excellent individual service at all contact points and Tailor customer interactions and extend the service value chain respectively.
Big data analytics can help organizations use the data to identify new opportunities. The result is smarter business operations, more efficient operations, higher profits, and happier customers (Ularu et al., 2021). When storing large amounts of data, big data technologies such as Hadoop and cloud-based analytics can provide significant cost advantages. Plus, you can determine a more efficient way to conduct your business. By combining the speed of Hadoop and in-memory analytics with the ability to analyze new