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Advances in Social Sciences Research Journal – Vol.7, No.8
Publication Date: August 25, 2020
DOI:10.14738/assrj.77.8797.
Schultz, C. M. (2020). The Nexus Between Analytical Skills, Employment Relations And Future Workspace. Advances in Social Sciences
Research Journal, 7(8) 425-440.
The Nexus Between Analytical Skills, Employment Relations And
Future Workspace
Cecile M. Schultz
People Management, Development
Tshwane University of Technology
Staatsartillerie Road, Pretoria, South Africa
ABSTRACT
Little research, if any, has been conducted on management’s
expectations from their human resource management related to the
nexus between analytical skills, employment relations and future
workspace. The purpose of this study is to determine if there is a
correlation between perceived analytical skills, employment relations
and future workspace. A survey research design and a quantitative
approach were utilised. The general managers of the human resource
managers that were affiliated with the South African Board of People
Management completed the questionnaire. Correlation analyses was
used to analyse the data. The results indicated that there was a
significant positive relationship between analytical skills, employment
relations and future workspace. The results contributed new insights
obtained from the views of management and their future expectations
from HR in relation to analytical skills, employment relations and future
workspace. The study is noteworthy in that its results may be used to
make substantial decisions to improve the HR’s analytical skills and
employment relations due to its relationship with future workspace.
Keywords: Future workspace, analytical skills, employment relations, future
expectations, human resources
INTRODUCTION
It is almost impossible to think about management without thinking about the future of work.
Managers should constantly plan and shape how their organisation can improve future workspace
so that it is beneficial for the business and the employees. Workspace is not an end in itself. It is an
input factor, supporting the organisation to meet its goals and fulfil its mission (Kämpf-Dern and
Konkol 2017). Karanika-Murray and Michaelides (2015, 238) state that “there is little, if any,
research on how inherently healthy and motivating workplaces can be developed. Organizational
intervention research could also focus on the potential to design workplaces supportive of
motivation.” Data-driven insights are needed to improve the workplace (Harris and Craig 2011).
Data gathering combined with rigorous analysis will support and inform business decision-making
(Van den Heuvel and Bondarouk 2017).
Analytics involves an insight into what has happened, explains it, and aims to predict what might
happen in the future (Van den Heuvel and Bondarouk 2017). The new workplace is digital and
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business oriented, as opposed to the mechanical manufacturing nature of the traditional workplace
(Chernyak-Hai and Rabenu 2018). This means that business knowledge and skills needed to
understand how analytics can be used to create business value are essential (Harris and Craig
2011). The newness of having and using data to inform and even predict HR outcomes is powerful
and has already created a compelling call to action for HR mangers (Fernandez 2019).
Another action that HR mangers must take is to improve employment relations. Employment
relations refer to the relationships between co-workers, teammates, supervisors (Sahoo and Sahoo
2018) and healthy employment relations are necessary for satisfactory organisational performance
and for the employes to feel engaged (Tansel and Gazîoğlu 2014). The organisation by ensuring
affirmative relations with the employees can deliver motivated, satisfied, and productive workforce
to challenge the dynamism of competition (Sahoo and Sahoo 2018). How workers engage in new
forms of employment relations is challenging only employers, but also governments and society at
large (Lansbury 2018) A link between analytical skills, employment relations and future workspace
was not found in literature.
Analytical skills
Analytical skills is the ability to visualise, articulate, conceptualise, or solve both complex and
uncomplicated problems by making decisions that are sensible given the available information
(Oliver, Vesty, and Brooks 2016). Lykins, Davis, Jamrog, Martin, DiRomualdo, Jamrog and Dixon
(2013) mention that analytical ability is a mindset and not the mastery of a specific software or
mathematical skill. Data analytics help to take fact-based decisions (Kiron, Shockley, Kruschwitz,
Finch and Haydock 2012) and help managers to focus on facts rather than intuition, which also
changes the power dynamics in the company (Falletta, 2014). Hecklaua, Galeitzkea, Flachsa and
Kohlb (2016) as well as Gray, Burel, Graser and Gallacci (2018) found analytical skills to be an
important future workplace competence. Data production, quantitative analysis and statistical
models assist HR managers to make better decisions and achieve better results (Harris et al. 2011).
HR managers should have enough business insight to be able to apply their quantitative knowledge
to business problems and processes (Harris and Craig 2011). Business intelligence (BI) tools such
as online analytical processing (OLAP) provides the dynamic analysis, synthesis and consolidation
of large quantity of multi-dimensional data (Mailvaganam 2007). OLAP applications can retrieve
summary statistics such as totals, averages, percentages, standard deviations, maximum, minimum
of data measurements from multiple dimensional views (Kapoor and Sherif 2012).
HR managers should also possess a wide range of quantitative skills for example trend analysis,
classification algorithms, predictive and statistical modeling, optimisation and simulation, along
with various data-, web- and text-mining techniques (Harris and Craig 2011). HR managers should
be re-educated in scientific thinking and statistical reasoning to advance the quality of people
decisions (Kahneman, 2011).
Statistical techniques can be used to sample data selected randomly and specifically to validate a
hypothesis and data mining is an extension of statistical techniques such as, classical and artificial
intelligence (Kapoor and Sherif 2012). Data mining includes techniques called discovery driven
techniques that do the exploration and analysis by automatic means of large quantities of data in
order to discover meaningful hidden patterns in order to find relationships, associations and trends
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Schultz, C. M. (2020). The Nexus Between Analytical Skills, Employment Relations And Future Workspace. Advances in Social Sciences Research Journal, 7(8)
425-440.
among the data measurements (Kim 2002; Shmueli, Nitin and Bruce 2010). Van der Togt and
Rasmussen (2017) caution that is important to ground projects in some form of sound theory or
else one may risk basing the decisions on statistical flukes caused by data mining.
The key to staying competitive is to conduct predictive analytics (quantitative methods to derive
actionable insights and outcomes from data) (Harris and Craig 2011). “Predictive analytics includes
statistical, mathematical and data mining analyst-guided techniques that do the exploration and
analysis of large quantities of data in order to make decisions by forecasting the outcomes” (Kapoor
and Sherif 2012: 232).
The key role of the HRM in enhancing firms’ competitive advantage has been frequently emphasised
in the literature (Buller and McEvoy 2012; Jiang, Lepak, Han, Hong, Kim and Winkler 2012).
Employee and workforce insights are the greatest competitive advantage for organisations dealing
with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in
this is the growing expectation of the human resource (HR) function to understand how workforce
analytics informs the business and fuels success (DiClaudio 2019). According to Van den Heuvel and
Bondarouk (2017), by 2025, HR analytics will be an accepted established practice within business
and the focus will be on predictive analytics, for example predicting peaks in staff turnover, rather
than simple data reporting (Van den Heuvel and Bondarouk 2017).
As the use of HR analytics provides integrated, consistent and trustworthy data (LaValle, Lesser,
Shockley, Hopkins and Kruschwitz 2011), it can significantly reduce biases related to human
cognition. Smart Human Resources 4.0 (SHR 4.0) is a new concept that is evolving as a part of the
overall 4th Industrial Revolution and characterised by innovations in digital technologies such as
Internet-of-Things, Big Data Analytics, and artificial intelligence (AI) and fast data networks such as
4G and 5G for the effective management of next-generation employees (Hecklaua et al. 2016).
An important issue of HR analytics in the future will be data privacy as there will be an increasing
amount of data available (“big data”) both from within the company and from external sources
including employee personal data (Van den Heuvel and Bondarouk 2017). “New concerns surfacing
just this year question whether it will even be possible to access HR data, stored and used for
analytics purposes” (Fernandez 2019, 24).
Employment relations
Employment relations refer to the relationships between co-workers, teammates, supervisors and
managers that exchange ideas, feelings and emotions (Sahoo and Sahoo 2018). In an organisational
context employment relations are part of the socialisation process, a source of information required
for successful performance and satisfaction, and a ground for social support and networking
(Chernyak-Hai and Rabenu 2018). While the regulation of the workplace and interaction between
employers and unions remain key concerns to the field of employment relations, other issues
related to people at work, employment and organisations are important in bringing a broader
perspective to the subject (Lansbury 2018).
For some time now an important strand of study has been emphasising the limitations of the
traditional paradigm of employment, according to which work and employment in the advanced
economies are supposed to be regulated by the interactions among three groups of IR actors labour
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Schultz, C. M. (2020). The Nexus Between Analytical Skills, Employment Relations And Future Workspace. Advances in Social Sciences Research Journal, 7(8)
425-440.
Totterdill and Exton (2014, 6) state that one of the signals for 2020 is that “trade unions and
employers’ organizations at national and local levels can play an important but largely unrecognized
role as knowledgeable participants in stimulating, guiding and resourcing workplace change.” Other
2020 signals, according to Totterdill and Exton (2014), are listening to the voice of employees to
generate greater workforce commitment to the organisation, as well as employee engagement,
involvement, and participation. Employment relations will be resilient in the future (Fenwick,
Kucera, Curtis, Lapeyre, Tchami, Stavrakis, Hunter and Marcadent 2016). Colbert, Yee and George
(2016) found that the digital workforce is an important factor in the future workplace. Favourable
workspace incorporated with fairness (Aryee, Budhwar and Chen 2002), autonomy in decision- making, and the harmonious management of conflicts incite them to be more prone towards
relationships (Sahoo and Sahoo 2018).
Future workspace
Hills and Levy’s (2014) mention that workspace entails workability, comfort, occupational density,
the need for privacy, control over the environment, adjacency to colleagues, and functionality, as
well as two further criteria, namely, location and customisability. Workspace consists of any human- made artifact such as tool, product, technical processes, service, software, built environment, task,
organisational design as well as other humans (Wilson, 2000). An ideal workspace evolves around
privacy, control, access, proximity, technology access, size, indication of status or equality, an
aesthetically pleasing environment, comfort, physical well-being, and flexibility (Wilhoit, Gettings,
Malik, Hearit, Buzzanell and Ludwig 2016). Previously, most research opined that workspace was
material, static, and functional (Kornberger and Clegg 2004). Other scholars are of the opinion that
workspace is a changing and multiple social production (Dale and Burrell 2007), while also
considering the lived experience of being in workspaces (Beyes and Steyaert 2011).
The past three decades have seen very significant changes to workspace and much of that change
has been the result of the increasingly profound impact of technology (Harris 2015). Managers
should consider the social aspects of workspace and possibilities that workers understand space
differently (Wilhoit et al. 2016). Crawford (2018) indicated that a creative future workspace is
essential to keep the business profitable. The nature of work has changed, becoming much more
flexible and virtual whereas employee characteristics have changed, with greater freedom in
employment such as free agents and freelancers (Chernyak-Hai and Rabenu 2018). The current
workspace is dynamic and complex and often referred to as VUCA (volatility, uncertainty,
complexity, and ambiguity; see Bennett & Lemoine, 2014). This could be a stressful environment
for employees, especially managers, because they have less influence on organisational outcomes
(Chernyak-Hai and Rabenu 2018).
According to Chen, Wang and Yang (2015) as well as Dul, Bruder, Buckle, Carayon, Falzon, Marras,
Wilson and van der Doelen (2012), ergonomics considers different aspects of the person (physical,
physiological, psychological) and different aspects of the environment (physical, social,
informational). Ergonomics can therefore not be ignored when dealing with workspace.
Harris (2015) found that the agility to support a constantly evolving workforce can be enabled by
the infrastructure to support agile working. Shakshuki and Matin (2010) mention that learning
agents are computer systems that are capable of learning user actions, which they can use to
determine what to do in the future within their environment. Such an agent should therefore be
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able to learn users’ actions and help to influence users’ future actions within the collaborative
virtual workspace (Shakshuki and Martin 2010). Abrishami, Goulding, Rahimian and Ganah (2015)
propose that optimal solutions should be found for conceptual design automation.
Crawford (2018) states the need to contextualise the office within its own operations. Ropo and
Höykinpuro (2017) also state that workspaces become filled with emotions and sensations,
influencing social action and performance. According to Haynes, Suckley and Nunnington (2017),
the term “downtime” is used to mean the office occupier is able to physically move away from their
desk and engage in either social interaction or just to have time to themselves away from their desk.
Positioning therefore requires careful consideration. It is proposed that “downtime” has a key role
to play both in terms of productivity and the health and well-being of office occupiers (Haynes et al.
2017).
“The success of workspace change projects is dependent on two components and their
interrelations: the organisation-specific effective (re)design of the workspace (the content) and the
management of change during its implementation phase (the processes)” (Kämpf-Dern and Konkol
2017, 228). Overall, a greater spread of square feet per worker should be expected over the next
several years, as some organisations reduce footprints significantly while others maintain current
practices with private, dedicated spaces (Miller 2014).
Steiner (2016, 22) asks the question: “Is the workplace flexible and future-proofed?” In the future
world of work employees may work from different places – in the office, at home, at third places or
in between (Kämpf-Dern and Konkol 2017). New workspace design thus has to consider a multitude
of interrelated dimensions to be effective. Miller (2014) found that there will be a new kind of space
being required, one that lets in more natural light with better natural ventilation, with better
temperature control and provides for more collaborative and more productive workspace.
To understand the future workspace it is essential to understand the activities and processes that
take place within it and this includes employee perceptions of how workspace design affects their
sense of well-being and productivity (Hills and Levy 2014). De Paoli and Ropo (2017) emphasise
the importance of involving end-users in planning and designing their workspace, not only to
comment on architect’s drawings, but to participate as an equal partner throughout the building or
renovating process. Kämpf-Dern and Konkol (2017) state that workplace design should consist of
an integration of change management parameters of workspace projects, the explicit performance
orientation and the inclusion of the multitude of actors such as users, facilities management, Human
Resources, ICT.
PROBLEM STATEMENT AND PRIMARY OBJECTIVE
Literature suggests that by applying advanced analytical techniques, HR managers will get
intelligent business insight, predict changes and make informed decisions at operational and
strategic levels (Kapoor and Sherif 2012); it remains important for organisations to provide an
attractive workspace that meets the needs of the future employees (De Bruyne and Gerritse 2018)
and to reinvent employment relations is inevitable (Van der Togt and Rasmussen 2017). In addition,
little is known about the nexus between analytical skills, employment relations and future
workspace. Against this background, the primary objective of this study was to determine if there
is a significant positive relationship between analytical skills, employment relations and future
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Table 1: Profile of the respondents
Demographic parameter Classification N n %
Gender Male
Female 240 98
142
40.7
58.9
Age
18–24
25–34
35–44
45–55
54–65
65 years and older
241
2
26
62
82
59
10
0.8
10.8
25.7
34.0
24.5
4.1
Managerial experience
Less than 5 years
6 to 10 years
11 to 15 years
16 to 20 years
21 to 25 years
More than 26 years
241
39
51
45
41
28
37
16.2
21.2
18.7
17.0
11.6
15.4
Managerial level
First-line manager
Middle manager
Top manager
235
113
77
45
46.9
32.0
18.7
An analysis of Table 1 illustrates that there are more female respondents (58.9%; n=142) than male
respondents (40.7%; n=98). With reference to age groups, a greater number of the employees fall
within the 45–55 years age group (34%; n=82), followed by individuals in the 35–44 years age
group (25.7%; n=62). With respect to managerial experience, the majority of the respondents have
six to 10 years of managerial experience (21.2%; n=51), followed by 11 to 15 years (18.7%; n=45).
Lastly, most of the respondents are at a first-line managerial level (46.9%; n=113), followed by
middle managers (32%; n=77), and top managers (18.7%; n=45).
Validity and reliability
Content validity was determined by using 10 Human Resource Management (HRM) academics who
reviewed the questionnaire. These academics possessed a doctorate degree in HRM and they had 5
years’ experience or more an HRM academic. They reviewed the language, structure, and design of
the questionnaire.
Face validity was ascertained by conducting a pilot test with 20 executive general managers. These
managers had 5 years’ experience or more as a manager.
Construct validity was ensured by conducting a principal factor analysis as shown in Table 2.
Convergent validity was checked by means of a Pearson correlation analysis. There are positive
correlations between the constructs, as seen in Table 4.