Literature
Book Kahn & Millner:
Chapter 1, 2, 5, 6, 7, 8, 10
Book Edwards & Edwards
Chapter 1, 2, 3, 4, 12
Book Cascio:
Chapter 1 & 2
Articles:
- Peeters, T., Paauwe, J., & Van De Voorde, K.
- Levenson, A., & Fink, A.
- Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M.
- Chamorro-Premuzic, T., Buchband, R., & Schettler, L.
- Rasmussen, T & Ulrich, D.
- Van den Heuvel, S. & Bondarouk, T.
- C.I.P.D.
- Henke, N., Levine, J., McInerney, P.
- Van der Laken, P.
1
,Core literature week 3 – Lecture 1:
1. Edwards & Edwards (2019): Chapter 1
2. Khan & Millner (2020): Chapter 1 & 2
Chapter 1. Understanding HR analytics (Edwards & Edwards)
Analytics present a tremendous opportunity to help organizations understand what
they don’t yet know… by identifying trends and patterns, HR professionals and
management teams can make better strategic decisions about the workforce
challenges that they may soon face (Huselid, 2014).
Predictive HR analytics → the systematic application of predictive modelling
using inferential statistics to existing HR people-related data in order to inform
judgements about possible causal factors driving key HR-related performance
indicators.
- This makes it possible to predict causes and to predict results and outcomes.
Understanding the need (and business case) for mastering and utilizing
predictive HR analytic techniques
Currently, many HR teams produce descriptive reports again and again. However,
these reports only present a picture or snapshot of what is occurring at that
particular time. Unfortunately, they say little about what is really going on, they
do not give us insights into why things are happening in the organization. Besides,
they lack to check whether the data is robust and/or valid. Also, this kind of reports
do not predict what will happen in the future.
Predictive HR analytics offers the opportunity to help model and analyse historical
data and interrogate patterns in order to help understand causal factors. This in
turn, will help to make better decisions.
Human capital data storage and ‘big (HR) data’ manipulation
The success of HR analytics is completely reliant on the availability of good people-
related information. However, HR managers will be probably facing with the
problem that there is too much data available to know what to do with it. The
biggest challenge is to get the data into the right format for analysis.
Predictors, prediction and predictive modelling
1. The term ‘prediction’ can be used in various ways. According to the authors
of this book, prediction entails the idea of identifying ‘predictors’ or potential
‘causal’ factors that help explain why a particular feature or measure shows
variation.
2. A second use of the term in the context of ‘predictive HR analytics’ is
‘predictive modelling’. Here, we take features and findings of our analysis,
then we apply our model to help demonstrate or ‘predict’ what would happen
to our key outcome variable if we could do something to change or adjust
2
, the key drivers that we have identifies (this use of prediction will be further
explained in Ch. 10).
3. A third use of the term ‘prediction’ that we can use in the context of
‘predictive HR analysis’ is that we can translate the findings from our
predictive models where we identified ‘predictors’ of variation in our
particular outcome variable, and we use the resulting model to ‘predict’ how
current or future employees may behave in the future.
Current state of HR analytics professional and academic training
At the time, the majority of HR functions do not have the core capabilities to carry
out predictive HR analytics activities. Besides, the majority of people who enter
the HR profession do not have the required skills to carry out predictive HR
analytics. However, whether the individual is an HR generalist, a specialist in one
particular area, or the head of HR for a large MNC, the need to identify and
understand trends and patterns, and to predict organizational challenges is
something that will set them apart in becoming a credible, high-performing HR
professional. Consequently, it can be stated that this gap needs to be filled.
Chapter 1. Redefining HR (Khan & Millner)
HR and the new world of work: The Three Ds
Through the fast pace of changes, business and employees are expected to
embrace innovations like never before. The following figure highlights the most
important themes that have to be considered to be ‘futureproof’.
The digital world of work
The key elements of the digital work are driven by the changing disruptors.
1. The overwhelmed workforce
Expectations of employees increase all the time. At the same time, managers have
to manage across more complex relationships. Through increasing automation and
ongoing changes, the key challenges revolve around:
- Execution: achieve more with less
- Employee expectations: there is an increasing need to learn new
processes, skills, and practices as automation increases.
- Manager priorities: challenge to find the right balance between
operational task and the desire for the ‘human touch’.
3
, - Leader ‘bandwidth’: beyond their responsibility to execute the demands
of stakeholders, issues are becoming increasingly complex (e.g. different
cultures)
- Wellbeing: operational challenges versus wellbeing
Data can be collected to understand how the workforce is really feeling.
2. Constant change is the new normal
Within change, challenges are:
- Resilience to change: resistance towards change always has been a
challenge. But the changes will come anyways, so resilience is needed.
- Change programmes: large-scale programmes through a series of smaller
pilots and products. As a result, a real connection with the workforce is built.
Employees need to be involved to make changes. Also, more and more
change succeeds with data and insights from the workforce.
3. Employee experience: making work personal
The ‘battle for the hearts and minds of employees’. It’s hard to manage the
workforce with three different generations. You never really know how someone
responds best until you’ve worked with them for a while. Therefore, employee
experience is really important. Besides, in an increasing automated world it’s hard
to remain work personal.
- Employee experience = the perceptions and feelings of the employees
towards their job experience at work and captures the following key
elements:
a. Belonging: feeling part of a team, group or organization
b. Purpose: understanding why one’s work matters
c. Achievement: a sense of accomplishment in the work
d. Happiness: the pleasant feeling arising in and around work
e. Vigour: the presence of energy, enthusiasm and excitement at
work.
Automation driving innovation occurs about the whole employee lifecycle. These
approaches are designed to make the experience at work more individualized and
future-focused.
4
Book Kahn & Millner:
Chapter 1, 2, 5, 6, 7, 8, 10
Book Edwards & Edwards
Chapter 1, 2, 3, 4, 12
Book Cascio:
Chapter 1 & 2
Articles:
- Peeters, T., Paauwe, J., & Van De Voorde, K.
- Levenson, A., & Fink, A.
- Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M.
- Chamorro-Premuzic, T., Buchband, R., & Schettler, L.
- Rasmussen, T & Ulrich, D.
- Van den Heuvel, S. & Bondarouk, T.
- C.I.P.D.
- Henke, N., Levine, J., McInerney, P.
- Van der Laken, P.
1
,Core literature week 3 – Lecture 1:
1. Edwards & Edwards (2019): Chapter 1
2. Khan & Millner (2020): Chapter 1 & 2
Chapter 1. Understanding HR analytics (Edwards & Edwards)
Analytics present a tremendous opportunity to help organizations understand what
they don’t yet know… by identifying trends and patterns, HR professionals and
management teams can make better strategic decisions about the workforce
challenges that they may soon face (Huselid, 2014).
Predictive HR analytics → the systematic application of predictive modelling
using inferential statistics to existing HR people-related data in order to inform
judgements about possible causal factors driving key HR-related performance
indicators.
- This makes it possible to predict causes and to predict results and outcomes.
Understanding the need (and business case) for mastering and utilizing
predictive HR analytic techniques
Currently, many HR teams produce descriptive reports again and again. However,
these reports only present a picture or snapshot of what is occurring at that
particular time. Unfortunately, they say little about what is really going on, they
do not give us insights into why things are happening in the organization. Besides,
they lack to check whether the data is robust and/or valid. Also, this kind of reports
do not predict what will happen in the future.
Predictive HR analytics offers the opportunity to help model and analyse historical
data and interrogate patterns in order to help understand causal factors. This in
turn, will help to make better decisions.
Human capital data storage and ‘big (HR) data’ manipulation
The success of HR analytics is completely reliant on the availability of good people-
related information. However, HR managers will be probably facing with the
problem that there is too much data available to know what to do with it. The
biggest challenge is to get the data into the right format for analysis.
Predictors, prediction and predictive modelling
1. The term ‘prediction’ can be used in various ways. According to the authors
of this book, prediction entails the idea of identifying ‘predictors’ or potential
‘causal’ factors that help explain why a particular feature or measure shows
variation.
2. A second use of the term in the context of ‘predictive HR analytics’ is
‘predictive modelling’. Here, we take features and findings of our analysis,
then we apply our model to help demonstrate or ‘predict’ what would happen
to our key outcome variable if we could do something to change or adjust
2
, the key drivers that we have identifies (this use of prediction will be further
explained in Ch. 10).
3. A third use of the term ‘prediction’ that we can use in the context of
‘predictive HR analysis’ is that we can translate the findings from our
predictive models where we identified ‘predictors’ of variation in our
particular outcome variable, and we use the resulting model to ‘predict’ how
current or future employees may behave in the future.
Current state of HR analytics professional and academic training
At the time, the majority of HR functions do not have the core capabilities to carry
out predictive HR analytics activities. Besides, the majority of people who enter
the HR profession do not have the required skills to carry out predictive HR
analytics. However, whether the individual is an HR generalist, a specialist in one
particular area, or the head of HR for a large MNC, the need to identify and
understand trends and patterns, and to predict organizational challenges is
something that will set them apart in becoming a credible, high-performing HR
professional. Consequently, it can be stated that this gap needs to be filled.
Chapter 1. Redefining HR (Khan & Millner)
HR and the new world of work: The Three Ds
Through the fast pace of changes, business and employees are expected to
embrace innovations like never before. The following figure highlights the most
important themes that have to be considered to be ‘futureproof’.
The digital world of work
The key elements of the digital work are driven by the changing disruptors.
1. The overwhelmed workforce
Expectations of employees increase all the time. At the same time, managers have
to manage across more complex relationships. Through increasing automation and
ongoing changes, the key challenges revolve around:
- Execution: achieve more with less
- Employee expectations: there is an increasing need to learn new
processes, skills, and practices as automation increases.
- Manager priorities: challenge to find the right balance between
operational task and the desire for the ‘human touch’.
3
, - Leader ‘bandwidth’: beyond their responsibility to execute the demands
of stakeholders, issues are becoming increasingly complex (e.g. different
cultures)
- Wellbeing: operational challenges versus wellbeing
Data can be collected to understand how the workforce is really feeling.
2. Constant change is the new normal
Within change, challenges are:
- Resilience to change: resistance towards change always has been a
challenge. But the changes will come anyways, so resilience is needed.
- Change programmes: large-scale programmes through a series of smaller
pilots and products. As a result, a real connection with the workforce is built.
Employees need to be involved to make changes. Also, more and more
change succeeds with data and insights from the workforce.
3. Employee experience: making work personal
The ‘battle for the hearts and minds of employees’. It’s hard to manage the
workforce with three different generations. You never really know how someone
responds best until you’ve worked with them for a while. Therefore, employee
experience is really important. Besides, in an increasing automated world it’s hard
to remain work personal.
- Employee experience = the perceptions and feelings of the employees
towards their job experience at work and captures the following key
elements:
a. Belonging: feeling part of a team, group or organization
b. Purpose: understanding why one’s work matters
c. Achievement: a sense of accomplishment in the work
d. Happiness: the pleasant feeling arising in and around work
e. Vigour: the presence of energy, enthusiasm and excitement at
work.
Automation driving innovation occurs about the whole employee lifecycle. These
approaches are designed to make the experience at work more individualized and
future-focused.
4
