launch of the Resource Connection website follows hard on the heels of a study
called Desperately Seeking Flexibility. This presents the strongest case for
flexible work in senior managerial posts yet (Flexible Working, 16 January).
Connection focused a major part of its commissioned study on job sharing – a
method that will play an increasingly important role in the UK workplace over
the next decade.
a result of the study, we came up with the “TAS model”, which measures the
levels of personality and motivation –
Thrusting, Agreeableness and Structured – in job-share partnerships.
conducted a series of personality and motivation tests and job-sharing
participants were given scores according to the model. Once we had established
the combined TAS types of job-share teams, we then set about identifying
whether managers judged that a particular combination outperformed others
across a number of dimensions.
discovered that there were four possible categories of matches in our job-share
partnerships, all of which had a different impact on the team. They are:
- Mirrored pairs: job-share
partners score the same across all three dimensions of the model, which
measures the levels of personality and motivation
- Opposites: job-share
partners have three different scores
- One degree of difference:
job-share partners scoring the same across two areas, and differently on
- Two degrees of difference:
job-share partners scoring the same on only one area, and differently on
the other two.
do pairs have to be alike to perform well? And as the differences increase
between partners, does their performance deteriorate accordingly?
the greater the differences the more negative the impact on teams’
effectiveness. Interestingly, few teams chose to partner a team that was
completely opposite to them.
“mirrored pairs”, the higher the total “thrusting” score, the lower the
the same level of agreeableness is important to the “one degree of difference”
“two degrees of difference”, the higher the difference the more negative the
model has enabled us to understand the components that make certain job-share
teams work more effectively. We are also able to use it as a predictive tool to
help job shares find their most suitable partner and maximise their