Research Report: Workforce Transformation at Keller William (HND)

This research report aims to examine the strategies driving workforce transformation in the digital age, focusing on a case study of Keller William. The objectives include identifying the concept and prominence of workforce transformation, analyzing various strategies used by Keller William, and examining the challenges faced during implementation. The research methodology employs quantitative research with a deductive approach and positivism philosophy. Data collection involves both secondary and primary data, with SPSS used for analysis. Findings are presented through diagrams and tables, highlighting themes such as employee awareness and satisfaction with workforce transformation strategies, the impact of infrastructural changes, and recommendations for leveraging modern business tools. The report also suggests qualitative research methods as an alternative approach for deeper insights. The research concludes with recommendations for Keller William, particularly regarding addressing decreased predictability associated with workforce transformation.

RESEARCH REPORT SELFREFLECTION

Table of Content
 Aim and objectives
 Research methodology
 Data analysis
 Recommendations
 References

Aim and objectives
 Aim:
 “To examine the strategies that drive workforce transformation in
digital age”. A case study on the Keller William.
 Objectives:
 To identify the concept and prominence of workforce
transformation in the management.
 To analyse various workforce transformation strategies used in
Keller William.
 To examine several challenges confronted by Keller William while
carry out implementation of workforce transformation strategies in
the organization.

Research methodology
Methodologies Description
Research type The quantitative research has been used to determine the statistical significance
between organizational success and the strategies meant for workplace
transformation.
Approach and Philosophies Deductive approach and positivism philosophy has been chosen for collecting &
evaluating quantitative data.
Data collection Data collection can be defined as a set of process, where data is being collected
in order to fulfil needs of a research study. In this research both secondary and
primary data will be used.
Data analysis In this research SPSS will be used for analysing data. The collected information
was presented in the form of diagrams; tables so can make it affective. To carry
out the process some calculative tools are used.
Sampling Through probabilistic random sampling, 30 employees of Keller William has
been chosen as a sample of this research.

Data analysis
 Theme: 1 – Employees are aware of
the WTS followed at Keller Williams
Theme: 2 – Employees are agreed with the
claim that there is a great contribution of WT
in organizational success

Continued….
 Theme: 3 – Infrastructural changes at workplace
led to very high motivation among employees
 Theme: 4 – There is a very high satisfaction of
employees as results of following WTS by Keller
Williams

Continued….
Theme: 5 – Job Satisfaction is the positive impact
of infrastructural transformation at workplace
 Theme: 6 – Leveraging modern business tools &
processes is the highly suggested strategy to Keller
William for WT

 Theme: 7 – Decreased predictability is the major issue faced by Keller
William with regards to WT
Continued….

Recommendations on alternative method
 The another method that could be used alternatively is
qualitative research methods for analyzing non – numeric data
by adopting inductive approach and interpritivism research
philosophy during data collection as well as analysis (Fu and
et.al., 2021). Under this methods, the gathered data are
analyzed thematically by supporting the findings of the
research through already existing theories. The benefit of this
method is that deeper insights of the issue can be obtained
through understanding of respondents feelings & attitudes.

References
 Fu, J., and et.al., 2021. Pharmacometabonomics: data processing and statistical
analysis. Briefings in Bioinformatics, 22(5), p.bbab138.
 Xia, Y., Sun, J. and Chen, D. G., 2018. Statistical analysis of microbiome data
with R (Vol. 847). Singapore: Springer.
 Sun, S., Zhu, J. and Zhou, X., 2020. Statistical analysis of spatial expression
patterns for spatially resolved transcriptomic studies. Nature methods, 17(2),
pp.193-200.