UNIVERSITY OF VAASA SCHOOL OF MANAGEMENT Anni Kaivola CURRENT AND FUTURE ...

78 Pages·2017·1 MB·English

UNIVERSITY OF VAASA
SCHOOL OF MANAGEMENT

Anni Kaivola

CURRENT AND FUTURE TRENDS IN DATA DRIVEN TALENT
IDENTIFICATION IN MNCS

Master’s Thesis
in International Business

VAASA 2018
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TABLE OF CONTENTS page
LIST OF FIGURES 5
LIST OF TABLES 5
ABSTRACT 7
1. INTRODUCTION 9
1.1. Research gap 11
1.2. Purpose of the study and research questions 11
1.2.1. Research question 12
1.2.2. Research objectives 12
1.3. Key concepts 12
1.4. Structure of the thesis 13
2. TALENT IDENTIFICATION IN MNCS 14
2.1. Definition 14
2.2. The ability component 15
2.3. The affective component 17
2.4. Talent identification process in MNCs 20
3. BIG DATA AND HR ANALYTICS 26
3.1. What is big data 26
3.2. Making sense of big data 28
3.3. Data analytics and HR 30
3.3.1. HR data 31
3.3.2. HR metrics 32
3.4. Data mining tools for personnel selection 33
3.4.1. Decision trees 33
3.4.2. Artificial neural networks 35
3.4.3. Support vector machines 36
3.5. Implementing HR analytics in MNCs 38
4. METHODOLOGY 40
4.1. Research approach 40
4.2. Research design 40
4.3. Data collection and sample 41
4.4. Data quality 44
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5. FINDINGS 46
5.1. Defining and identifying talent in MNCs 46
5.2. HR analytics and data in MNCs 48
5.2.1. Challenges related to data 49
5.2.2. Perceived benefits 50
5.2.3. Perceived challenges 51
5.2.4. HR ability to take on the data challenge 52
5.2.5. Visions for the future 53
6. DISCUSSION 55
6.1. Defining and identifying talent in MNCs 55
6.2. Data, analytics and HR 59
6.2.1. The challenges 60
6.2.2. The benefits and future applications 62
7. CONCLUSIONS 65
7.1. Managerial implications 66
7.2. Limitations and suggestions for future research 67
LIST OF REFERENCES 68
APPENDIX 1. 77
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LIST OF FIGURES

Figure 1. Performance-potential –matrix 19
Figure 2. The potential pyramid 19
Figure 3. Talent multiplication model 23
Figure 4. LAMP framework 25
Figure 5. The machine learning cycle 29
Figure 6. Hyperplane in a non-linearly classifiable set 37
Figure 7. The HCM:21 model 38

LIST OF TABLES

Table 1. Skills, competencies and personality traits for talent identification 21
Table 2a. MNCs participating in the study 42
Table 2b. Consulting firms participating in the study 42
Table 3. Overview of interviews 42
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UNIVERSITY OF VAASA
School of Management
Author: Anni Kaivola
Topic of the thesis: Current and future trends in data driven talent
identification in MNCs
Name of the Supervisor: Jennie Sumelius
Degree: Master of Science in Economics and
Business Administration
Department: Department of Management
Master’s Programme: International Business
Year of Entering the University: 2016
Year of Completing the Thesis: 2018 Pages: 77

ABSTRACT

In today’s data-driven business world information is omnipresent and companies are
required to make smarter and faster decisions derived from data. At the same time, having
the right talent, at the right time, in the right place is a challenge that companies continue
to struggle with. Combining the data-driven decision-making is only starting to spread to
the HR functions of global companies. Technological advancements are shaking HR
functions and reshaping the way companies approach people management.

This master’s thesis focuses on the intersection of talent identification and big data
analytics tools where HR analytics and evidence-based talent identification are found.
The purpose is to examine how big data tools can be used to facilitate talent identification
in MNCs. Data and tools applicable for identifying talent are examined as well as the
concept of big data and the tools to process it, such as artificial intelligence and its
applications. Data mining techniques show great potential for more efficient and accurate
personnel selection.

The empirical evidence in this qualitative, exploratory study was collected from eight
MNCs and four consulting firms, through interviews with fourteen participants from
Europe and North America. The findings of the study are discussed in connection to
existing literature.

The findings indicate that HR analytics has not yet reached its full potential in the case
organisations, and that further implementation is still underway. The HR function
struggles to cope with the big data challenge. Predictive analytics are seen as a future
trend that hold an immense amount of potential for increasing organisational performance
from the people management perspective.

KEYWORDS: talent identification, HR analytics
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1. INTRODUCTION

Today, more than ever before, there is an abundance of data and information available
for organisations and managers. There are a great number of means to collect data as well
as to store it, but what remains challenging is using that data in an efficient way. The
abundance of available data and methods for processing it can facilitate managers’
decision-making process in multinational companies (MNC) but at the same time it
creates its challenges. (van Knippenberg, Dahlander, Haas & George 2015.) Proper
analytical methods to make sense of the data can serve as a tool to facilitate decision-
making processes and improve performance (McAfee & Brynjolfsson 2012; Sivarajah,
Kamal, Irani & Weerakkody 2017), and organisations are engaging with human resource
(HR) data and analytics. However, the challenge remains on how big data analytics can
be used to create, capture, leverage and protect value from HR data, and how the existing
HR analytics programs can be further developed to better measure and model the strategic
impact of human capital inputs. (Angrave, Charlwood, Kirkpatrick, Lawrence & Stuart
2016.)

Talent identification can on its own be a challenging process for an organisation (Nijs,
Gallardo-Gallardo, Dries & Sels 2014) and finding the right talent, at the right time, in
the right place, is a real challenge in today’s business environment where organisations
face a diversified workforce (Beechler & Woodward 2009). Research shows that HR
professionals often lack the skills, knowledge and insight to be able to ask the right
questions when it comes to HR data they have at their disposal. (Angrave et al. 2016).
This means that organisations are not using the available HR data to its full potential. HR
has trouble structuring the data, as well as interpreting it correctly, and often managers
make decisions based on data even though they do not fully understand it (Shah, Horne
& Capellá 2012). The right questions enable accurate measurement of the valid metrics
for HR analytics purposes, something that could and should be used in talent
identification.

The role of HR has been evolving over the past decades from an administrative and
maintenance-oriented function towards becoming more of a core business function and a
strategic partner. Traditionally, HR has had an inside/outside perspective where its
objectives have been to support the business, contribute to business results and add value.
However, for HR to be able to add value to business and strategy also in the future, a shift
towards outside/inside approach is needed. This means that the value is no longer created
by only serving employees or redesigning HR practices for increased efficiency, but