The aim of this qualitative, exploratory multiple-case study is to evaluate strategies that leaders use to improve software innovation development, quality and risk management. The research question for this study is as follow: What strategies do you use to encourage software innovation development?
Participants will be asked to respond to the following questions:
What software innovation factors do you use to improve quality in your company?
What strategies have you used to improve software quality in the IT field?
Which of these strategies have you implemented to improve software quality
in the firm?
Which of the strategies assist to enhance software quality?
Which of these strategies did not assist in the improvement of software quality?
Why did these strategies fail to improve software quality?
What else would you like to include that we did not cover?
Researchers may choose to employ data analysis to instill rigor in the qualitative research method and their process (Yin, 2018; Saunders et al., 2015). Saunders et al. (2015), Yin (2018) and Boblin, Ireland, Kirkpatrick and Robertson (2013) posit that case studies may be investigative, descriptive, or explanatory, or may combine more than one methods. Therefore, a qualitative case study involves examination of single or multiple events (Saunders et al., 2015; Yin, 2018; Boblin et al., 2013).
In performing this study, I will use multiple-qualitative design in exploring software innovation development, quality, and risk management, and then proceed with data collection. My chosen data analysis method for this case study was triangulation. Yin (2018) and Heale and Forbes (2013) posit that triangulation may be take place when multiple methods are employed to collect data which involve interviews, journal, observations, and other forms of records.
Burua and Anderson posit that triangulation may also incorporate data from interview questions, data collected from firm brochures, pamphlets, websites and other company publications such as prospectuses. Scholars and researchers, (Yin, 2018; Saunders, al., 2015; Gorgens-Ekermans & Herbert, 2013; Fusch & Ness, 2015; Anyan, 2013) contend that in analysis stage of the research, researchers take steps to refine the codes, reconfigure the codes or completely remove some codes when necessary.
Anyan (2013), Woods, Paulus, Atkins and MacKlin (2015); Paulus, Woods, Atkins & Macklin (2017) and Fusch & Ness (2015) encourage researchers to generate and refine data until they can no longer identify new codes for the research study. In this study I continued to assemble, and refine codes until new distinct code can no longer be obtained. Consequently, all discrepancies were identified and verified until I reached saturation point.
I continued to code transcript until I reached data saturation point. Consequently, I investigated any differences between in my codes until saturation point was reached. Researchers have used coding to uncover similarity or themes within interview transcripts and include data to reach data saturation point, ensure reliability, validity, credibility and high confidence level. I used an alpha-numeric identifier that is capable of identifying eight participants using letter and number. In this research data, the alpha-numeric identifiers identified “P” for participants and the number was 1 through 8 to identify the main themes that emerge from the interview process.
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