Resumo:
Supply chains are susceptible to uncertainties, such as large-scale natural disasters, manufacturing
fires, terrorist attacks, widespread electrical shutdowns, financial and political tension, and
wars. Therefore, rising unemployment rates have driven the workforce into short-term contracts
or the on-demand market known as the gig economy. However, selecting skilled professionals
is difficult and risky when organizations are immersed in fast-paced environments. In this
context, we investigated the analysis scenario of contracting professionals in global software
developments (GSD). This thesis aims to develop clusters of criteria for hiring self-employed
professionals in the “Global Software Development” or “Gig Economy” context. We systematically
reviewed 319 criteria in 65 papers and grouped them into two innovative ways. Thus,
we obtained 25 criteria clusters and a hierarchical structure with their relationships, indicating
that we had only 40% of the cause. We are proposing two innovative criteria grouping methods.
The first delivers a fast aggregation clustering, and the second with the relationships between
the criteria clusters. This tool can be handy for researchers in exploring new data via literature
review or even through surveys. Another point is that the practitioners could easily use the
spreadsheet with all the data, remove or join new criteria, and run the algorithm to create new
clusters on their own. The main results were, firstly, for the applicants, in software development,
the project requirements are gathered over the clients and stakeholders; this process involves rich
and looping communication. Secondly, the enterprises first check the criteria clusters. Then, the
list of criteria, and taking into account the job position or profile, they choose how to make the
hiring process, reflecting on the relationship of criteria clusters (cause/effects). Finally, these
results also imply the design of new subjects for computer science courses, mainly concerning
soft skills, as highlighted in the Communication criteria cluster, in which we have a list of criteria
highly cited in SLR.
