interaction of environmental uncertainty, organizational
TRANSCRIPT
Interaction of Environmental Uncertainty, Organizational Reputation
and Management Control in the Hiring Process in Professional Service
Firms
Name: Ruonan Xie
Student number: 11331097
Thesis supervisor: Ms H. Kloosterman
Date: June.25th
, 2017
Word count: 14,984
MSc Accountancy & Control, specialization Control
Faculty of Economics and Business, University of Amsterdam
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Statement of Originality
This document is written by student Ruonan Xie who declares to take full responsibility for the
contents of this document.
I declare that the text and the work presented in this document is original and that no sources other
than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the
work, not for the contents.
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Abstract
Service sector has been playing a dominant role in economy; however, the field of management
control system (MCS) design in professional service firm (PSF) is relatively less explored. The
attributes of PSFs cause problems due to the high human capitals and difficulty of measuring service
quality. Firm reputation can serve as a quality guarantee to lessen the ambiguity in measuring service
output while extensive hiring process can help to reduce the negativity brought by environmental
uncertainty. This study draws conclusion from survey conducted from the period 2015 to 2017.
Regression analyses were conducted using 414 questionnaires of professionals working in different
industries within a broader PSF setting. Consistent with prediction, empirical evidence shows
support that higher level of environmental uncertainty leads to more extensive use of personnel
control in the hiring process. On the other hand, however, empirical results provide no evidence of
interaction effect of firm reputation on the relationship between environmental uncertainty and
personnel control. The paper contributes to the literature of less explored MCS mechanisms by
investigating the interaction of environmental uncertainty, reputation and hiring process in a larger
PSF context.
Key words: management control system; professional service firm; reputation; personnel control;
environmental uncertainty; contingency theory
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Table of Contents
1. Introduction ...................................................................................................................................... 6
2. Literature Review ............................................................................................................................ 9
2.1 Professional Service Firms (PSFs)............................................................................................ 9
2.2 Firm Reputation in PSFs ......................................................................................................... 11
2.3 Management Control System (MCS) and Personnel Control ................................................. 12
2.4 Environmental Uncertainty in MCS and HRM ...................................................................... 14
2.5 Hypothesis Development ........................................................................................................ 16
2.6 Hypotheses Operationalization ............................................................................................... 18
3. Research Methodology .................................................................................................................. 19
3.1 Sample and Data Collection.................................................................................................... 19
3.2 Survey Demographics ............................................................................................................. 20
3.3 Variable Measurement ............................................................................................................ 21
3.3.1 Independent Variable..................................................................................................... 21
3.3.2 Moderating Variable ...................................................................................................... 23
3.3.3 Dependent Variable ....................................................................................................... 25
3.3.4 Control Variables .......................................................................................................... 27
3.4 Hypotheses Testing Models .................................................................................................... 29
4. Results ............................................................................................................................................. 31
4.1 Descriptive Statistics ............................................................................................................... 31
4.2 Main Findings ......................................................................................................................... 35
4.2.1 Hypothesis Testing H1 .................................................................................................. 35
4.2.2 Hypothesis Testing H2 .................................................................................................. 38
5. Conclusion ...................................................................................................................................... 43
5.1 Discussion ............................................................................................................................... 43
5.2 Limitations .............................................................................................................................. 45
5.3 Future Research Directions ..................................................................................................... 46
References ..................................................................................................................................... 48
Appendix: Survey questions ............................................................................................................. 53
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List of Tables and Figures
Table 1: Descriptive Statistics .............................................................................................................. 21
Table 2: Factor analysis – Environment uncertainty ............................................................................ 23
Table 3: Factor analysis – Reputation .................................................................................................. 24
Table 4: Factor analysis – Hiring in personnel control ........................................................................ 26
Table 5: Descriptive Statistics .............................................................................................................. 33
Table 6: Correlation Matrix ................................................................................................................. 34
Table 7: Regression results of model 1a .............................................................................................. 35
Table 8: Regression results of model 1b .............................................................................................. 37
Table 9: Result of Mann Whitney U test ............................................................................................. 38
Table 10: Regression results of model 2a ............................................................................................ 39
Table 11: Regression results of model 2b ............................................................................................ 40
Figure 1: Overview of the hypotheses ................................................................................................. 18
Figure 2: Plotting the moderating effect .............................................................................................. 42
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1. Introduction
According to IBISWorld’s report published in 2014, service sector alone added to $2.5 trillion dollars
of revenue in 2013. In a world of increasing growth of professional services in economy (Goodale et
al., 2008), however, a large body of research pay more attention to the management controls in
manufacture sector rather than service sector (Shields, 1997). The definition of the term ‘service’ in
professional service firm (PSF) encompasses a vast diverse service group, from accounting firms and
consulting firms to software development firms and health care institutions, just to name a few. The
broad range of professional service makes the service sector harder to investigate, thus the subject of
professional service firms and management control system (MCS) design in a broader context is
relatively unexplored (von Nordenflycht, 2010). Additionally, the four characteristics of services –
intangibility, variability, inseparability and perishability – contribute to the difficulty of measuring
the output of services in PSFs (Reichheld and Sasser, 1990), resulting in less study in professional
service research field. Together, the broad scope of industries and the characteristics of service lead
to a knowledge gap in studying of professional service firms. This paper aims to study MCS in a
boarder context of PSFs in respond to the call for further research in the field of MCS design and
PSFs (Chenhall, 2003).
In contingency literature, environmental uncertainty is a contingency variable and management
control systems can be applied to reduce environmental uncertainty (Chenhall, 2003). Rastogi (2003)
found that when there is high environmental uncertainty, firms are more inclined to set organizational
strategies and controls to reduce the impact caused by unfavorable conditions. When firm face the
environmental uncertain condition, Kren and Kerr (1993) found that this uncertainty calls for
additional investment in MCS, and investment in MCS can be taken in the form of an increase in
action controls. Herremans et al. (2011) did research on influence on result control in knowledge
intensive firms and found that in high environmental uncertain situation firm focuses more on result
controls. The papers mentioned above, however, have not yet investigated the relationship of
environmental uncertainty and MCS from an input perspective, for instance, the personnel control.
This lack of study is consistent with contemporary studies on MCS that researchers put more focus
on bureaucratic mechanisms such as action controls and result controls (Ouchi, 1979; Jaeger and
Baliga, 1985). However, professional service firms enjoy high human capitals and a professional
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workforce which differ from non-PSFs (von Nordenflycht, 2010). Perrow (1986) proposed that in
environmental uncertain condition, firms rely on a professional workforce will use professional
control, similar to personnel control, to offset the uncertainty. Lippman and Rumelt (1982) stated that
human capital is perceived as hard to reproduce because it is scarce and it owns specialized
knowledge, thus it can serve as a sustainable advantage for PSFs if qualified people are recruited.
When organizations find it difficult to align incentives by using output controls under environmental
uncertain condition, it might be an effective alternative to align preferences through hiring process
(Merchant, 1985; Prendergast, 2008). Yet no empirical result is provided to confirm that
environmental uncertainty will result in more extensive hiring. Hence, it is interesting to investigate
whether PSFs use more management control from an input perspective in uncertain environmental
conditions.
The notion of corporate reputation has been receiving more attention from the management as well
as stakeholders around the world (Fombrun, 2007). While the information asymmetry enlarges the
service output ambiguity, reputation can reduce the impact brought by environmental uncertainty
since reputation is regarded as a guarantee of service quality for customers (Greenwood et al., 2005).
Other than regarded as a proof, reputation can serve as success factors for PSFs since reputable firms
attract more qualified employees in hiring process (Cable and Turban, 2001). Firm reputation is
likely to interact with environmental uncertainty and hiring process; the possible interaction is worth
investigating in the PSF setting.
This paper contributes to study of MCS and PSF by using survey approach. Previous researchers
analyzed the relationship of MCS and PSF by applying case studies and not public available database,
for example a study on outsourcing relationship between two organizations (Langfield-Smith and
Smith, 2003), and white collar incentives in US tech-based firms using a private database (Baik,
2016). Using case study approach and private database, however, limit the research scope to a single
industry or single firm. For the service sector, it covers a wide range of industries and a case study
only sheds light on a small segment of the service sector, making it hard to generalize to other
settings. Thus, it is appealing to use survey approach to reach out to wider respondents and broader
industries.
In this study, survey sample of 414 professionals working in PSFs from wide range of industries will
be examined. The focus will be on environmental uncertainty and its effect on hiring process of MCS
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design in the PSF context. Additionally, firm reputation will be introduced as a moderator and
examined on the relationship between environmental uncertainty and personnel control. I believe this
study of the hiring process of professional service firms in an uncertain environment and reputation
as a moderator will bridge the knowledge gap in the study of PSF. This study seeks to answer the
following research questions:
1. How does environmental uncertainty influence the hiring process in PSFs?
2. How does firm’s reputation play a role in hiring process in PSFs when there is environmental
uncertainty?
The remainder of this paper is structured as followings. Literature review on PSFs, uncertainty,
reputation and personnel control will be discussed and then hypotheses proposed in the next section.
The third section addresses research methodology by providing with survey demographics and
variable measurement. The results of linear regression analysis to test the hypotheses will be
presented in the fourth section. Lastly, this paper will present conclusion and discussion and discuss
the possible future research directions.
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2. Literature Review
This section is structured as follows. First professional service firm will be illustrated. Then the
importance of reputation in PSFs will be highlighted. Next, the framework of management control
system will be summarized and personnel control of MCS will be zoomed in. Thereafter,
environmental uncertainty and contingency theory will be introduced to better understand the
conditions PSFs face. In the last part of this section the structure of theoretical frameworks will lead
to the hypotheses proposed.
2.1 Professional Service Firms (PSFs)
What is Professional service firm (PSF)? To answer this question, it is important to classify what a
professional service is. Professional service can be defined as actions that are intangible; actions one
party provides to another party will not result in the change of ownership (Kolter, 1994). Other
literature defined professional service in another way by asserting that service is not the same as
supplying goods but rather to provide solutions to problems (Gadrey et al., 1995) while more
recently von Nordenflychit (2010) argued that there is no universal definition of professional service
from the literature.
From a broad perspective, PSF is a particular type of service firms it shares the characteristics that
service firms have. Attributes of service firms give more insights into studying PSFs. Reichheld and
Sasser (1990) concluded four distinct characteristics of services: intangibility, perishability,
inseparability and variability. The outputs of service firms, as compared to non-service firms, are
intangible. Due to the intangible characteristic of service firms, customers find it hard to recognize
the difference of competence and quality of the services. The second characteristic service firms
share with PSFs is perishability, which means that services cannot be stored as inventory and those
not consumed are gone and cannot be recovered. For the inseparable characteristic, it implies that
customer is the input of service, thus service or product offered by service firms cannot be separated
from customer. The fourth characteristic variability refers to the outputs of service are usually not the
same since the activities carried out can be very different, thus it is hard to set stand criteria to
measure the results.
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The characteristics of service firms provide academic support to further define the characteristics of
the particular type of service firms - professional service firms. Von Nordenflychit (2010) defines
three attributes of PSFs as knowledge intensity, low capital intensity and professional workforce.
The first one is knowledge intensity. PSFs are knowledge intensive firms since outputs of
professionals working in PSFs encompass specific knowledge. PSF possesses high educated human
capitals and this type of organizations is often referred to as ‘intellect industry’. The outputs of this
type of firms rely heavily on knowledge input, which is embedded into services (Scott, 1998). Thus,
for PSF, the intellectual input is important because of high possession of human capitals. The second
one is low capital intensity, which refers to tangible and intangible assets which are non-human
assets in PSFs. Assets such as facilities and inventories are not largely applied in service production
in PSFs and this results in decreasing demand for investments and thus provides related opportunities
for professional service firms since the needs to protect investors are decreasing. The third attribute
of PSF is concluded as the professional workforce. Two features can be drawn from the professional
workforce characteristic; professionals own the knowledge; professionals create particular norms to
define their code of ethics and to define proper behaviors at work.
The attributes of PSFs mentioned above can lead to problems, however. Auzair and Langfield-Smith
(2005) argue that the attributes of professional service firms create challenge for firms such as the
needs to attract and retain both customers and employees, need for autonomy within the organization
and reliance on informal controls. Von Nordenflycht (2010) concludes two problems in his paper.
The first one is that talents are hard to retain in PSFs. On the one hand, professionals own the
knowledge and the skill sets they have is scarce in the market. Since knowledge is transferrable and
employees cannot be stored and are mobile, professionals thus enjoy stronger bargaining power with
PSFs. On the other hand, the intangibility and ambiguity attributes of service outputs made
customers rely on the employees from the professional workforce to deliver satisfying outcomes. The
bargaining power of professionals exposes PSF to losing talents at any moment. The second one is
that quality of services is difficult for customers to evaluate. The evaluation problem lasts even after
the service is delivered. This is not uncommon since customers do not know whether services
provided are the cause of certain subsequent consequences. The author concludes the problem as
‘opaque quality’ of professional services.
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2.2 Firm Reputation in PSFs
The potential problems of losing knowledgeable employees and the ambiguous service quality create
challenges for PSFs. However, previous literature suggested that the problems can be alleviated by
firm reputation. Von Nordenflycht (2010) stated that firms can design management control
mechanisms or pay attention to mechanisms such as firm reputation to handle the problems result
from ambiguous service quality. Greenwood et al. (2005) mention that because customers are
dependent on professional workforce of PSFs, firm reputation serves as proof of competence of
workforce and thus a social guarantee of service quality.
Building firm reputation as a way to alleviate problems result from PSF attributes leads to the
discussion of what reputation is. According to Fombrun (1996), reputation is ‘a perceptual
representation of a company’s past actions and future prospects that describes the firm’s overall
appeal to all of its key constituents when compared with other leading rivals.’ From a global
perspective, the concept of corporate reputation has been receiving more attention from the
management as well as stakeholders. In another Fombrun’s paper (2007) he stated that reputation
reflects firms’ historical performance, and reputation could be utilized to forecast performance and
actions conducted in the future.
Due to the high human capital and output ambiguity, firm reputation is considered especially crucial
for PSF (Greenwood et al., 2005). Additionally, firm reputation can bring professional service firms
benefits. Firstly, for customers, reputation plays a signaling effect of the services PSFs provide and
helps to decrease customers’ purchase risk. Because of the intangibility and ambiguity of service
outputs, it is difficult for customers to judge a PSF’s service quality or compared to other PSFs based
on the service quality. Fombrun (2000) mentions in his paper that reputation can generate favorable
public opinion and create a business-friendly environment for PSFs. Podolny (1993) is convinced
that reputation is an important indicator to attract customers for PSF because reputable firm signals a
guarantee of service. Paper from Rao et al. (2001) further linked firm reputation to uncertain
environment by stating that in environmental uncertain condition, firm reputation can serve as social
guarantee to signal the quality PSF provides to customers.
Secondly, reputation helps employers to attract talents and future employees to seek for firms they
want to stay with. For firms, reputation can serve as a sustainable competitive advantage for
reputable PSFs. Cable and Turban (2001) suggest that a firm's reputation has an impact on hiring
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qualified employees and can be a critical success factor for organizations. Human capital is the most
valuable intangible resource in professional service firms and reputation enables firms the ability to
attract high quality candidates and thus bring benefits to PSFs (Greenwood et al., 2005). For
employees, firm reputation helps them find jobs and companies they prefer, which in turn benefits
professional service firms by obtaining qualified professionals. Firm reputation shows its value
through the signaling effect when observing and measuring outputs are typically difficult (Hirshleifer,
Hsu and Li, 2013). Job seekers turn to firm reputation as a signaling indicator in the hiring market
(Kreps and Wilson, 1982) since output of PSF is characterized as intangible and opaque.
Thirdly, from a long-run perspective, reputation creates more profits for firms. Reputable PSF can
charge more service fees because their brand recognition is high (Beatty 1989), thereby reputation
helps PSFs achieve better financial performance (Fombrun et al., 2000). Wilson (1985) proposed that
a desired reputation can bring excess returns for firms because the competitive advantages
reputations bring can inhibit the mobility of competitors. In addition, because of the quality of
service in PSFs is opaque, customers tend to stay with the current service providers they have
experience with and are reluctant to change to other PSFs since they are uncertain about the service
quality of other service providers (Greenwood et al., 2005). Therefore, reputation helps firms achieve
more profits compared with firms with the same order of talents but are less reputable.
Summarizing, firm reputation is an important influencing factor to investigate in PSF research field
because 1) customers see reputation as a proof of good service quality; 2) reputation helps firms
attract more talented employees and help employees seek for companies to stay with; 3) profits firms
in the long-term.
2.3 Management Control System (MCS) and Personnel Control
Management control refers to the processes by which management ensures that employees carry out
the firm’s objectives and strategies. Management control systems are applied to help in achieving
desired behaviors and outcomes in organizations (Simons, 1994; Chenhall, 2003). Several
widely-used management control system frameworks can be found in management accounting
literature such as Ouchi (1979), Simon’s four levers of controls (1994), Ferreira and Otley (2009),
and the most recent one from Merchant and van der Stede (2012). These MCS frameworks are not
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completely independent of each other; together they provide a clear and broad outline in better
understanding of how management control systems work.
Merchant (2012) developed a framework which describes three different types of MCS mechanisms:
social controls, action controls and result controls. In his framework social controls can be further
divided into cultural control and personnel control. The personnel control is fundamentally the same
as input control or clan control as in Ouchi’s paper (1979) but Merchant elaborates social control in a
more detailed way by mentioning selection, placement, and training of employees in personnel
control. As for cultural control, it refers to code of conduct, shared values and beliefs for employees;
the use of social controls can serve action controls and result controls better in the MCS mechanisms.
Action controls can be associated with bureaucratic mechanisms in Ouchi’s framework (1979),
focusing on prescribing and controlling behavior through monitoring activities. Employees are given
clear protocols to follow in order to achieve targets. Action controls are applied to ensure employees’
behaviors are aligned with organizational objectives when they conduct tasks. The last MCS
mechanism is result control, similar to Ouchi’s market mechanism (1979), which mainly looks at the
performance measures and incentive systems. Result controls set targets for employees and measure
employees’ output through performance indicators; incentive systems are used to reward employees’
performance when targets are reached.
Personnel control is worthy of more attention in professional service firms. First of all, other than
non professional service firms, PSFs have characteristics that other firms do not have, which are
highly knowledge intensive, low non-human assets employed and possession of professional
workforce. For PSF, the most critical resource is their employees (Hitt et al., 2001) and labor
intensity is usually higher than capital intensity in PSFs. However, this brings high mobility to PSFs
due to the higher bargaining power employees enjoy (von Nordenflychit, 2010). To deliver
professional services, complex knowledge and personal judgment are required (Larsson and Bowen,
1989). The uniqueness of professional service has made PSFs dependent on its professional
workforce. Since PSFs need more talented employees, the input of PSFs, personnel controls are
needed to secure a high quality labor. Therefore, selection in personnel control becomes more
important for PSFs as compared to non-PSFs. Secondly, it appears that actions controls and result
controls can be difficult to apply and therefore personnel controls become more favorable in PSF.
The characteristics of service are that service is intangible and variable (Reichheld and Sasser, 1990),
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therefore making it harder to set standards to evaluate the quality service output. Studies from
Merchant (2012) and Brivot (2011) argue that when output is hard to measure, control mechanisms
that are more formal, such as action controls and result controls, tend to be less effective and even
counter-productive. Hence, more input controls, such as personnel controls are favorable when action
controls and result controls are less effective. Last but not least, the hiring process, as one component
of personnel control, interacts with firm’s reputation in PSF setting. Jones (1996) stated that hiring
would be influenced by firm reputation. Other academic papers (Becker and Gerhart, 1996; Dess and
Shaw, 2001) proposed that reputable companies put more focus on obtaining better quality of
professionals.
Based on the highly professional workforce of PSF and ambiguous performance measures and
outputs, control mechanisms such as action controls and results controls are less effective. Firm’s
reputation as discussed in previous sections, interacts with the hiring process of personnel control in
professional service firms. Therefore, the personnel control deserves more focus in PSF than
non-PSF. This paper chooses hiring process of personnel control from the framework of Merchant
and van der Stede (2012) to look into the relationship of MCS and PSF.
2.4 Environmental Uncertainty in MCS and HRM
In management control, contingency theory holds that no universally best management control
system can be found to apply to every situation and organization (Burns and Stalker, 1961).
Contingency theory claims that control systems must be aligned with organizational characteristics
(Fisher, 1995). For PSFs, if the designed management control system aligns with organizational
objectives, it can lead to better performance (King and Clarkson, 2015). Environment has been
explored frequently as influencing factor in organizational practices (Child, 1972). In more recent
research, Chenhall (2003) confirmed that environment is one of the most frequently discussed
variables in contingency-based research and linked environment to uncertainty as a contingency
variable. Uncertainty encompasses two aspects - environment and technology in management control
research.
Various definitions can be found on ‘uncertainty’ in management accounting literature. Argote (1982)
describes uncertainty as ‘the absence of complete information about an organizational phenomenon,
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which in turn leads to an inability to predict its outcome’. Chenhall (2003) states that uncertainty
raises from a lack of information and uncertainty can lead to difficulty in making contingency plans.
More recent research defines uncertainty as organizations having difficulty in predicting the future
because of the dynamic conditions and incomplete information (Germain et al., 2008). Since this
study concentrates on discussion of the environmental aspect of uncertainty, I define environmental
uncertainty as the dynamic change of industrial intensity, increasing the difficulty of translating
actions into desire output, therefore intensifies the difficulty of output prediction and measurement.
In MCS research field, previous research has shown that environmental uncertainty has an impact on
MCS design and organizational outcomes. The link between environmental uncertainty and
contingency theory can be seen from Burns and Stalker’s paper (1961). They investigated the effects
of environmental uncertainty on organization structure in a study of Scottish defense electronics
industry and found that regardless of organizational structures, organizations respond to both low
environmental uncertainty condition and high environmental uncertainty condition effectively. Their
paper is perhaps one of the earliest literatures that describe the link between environmental
uncertainty and contingency in management control. More recent research showed that when firms
are operating in an uncertain environment, the use of traditional MCS is not effective and this could
lead to undesired decision making and consequently undesired outcomes (Eldridge et al., 2013).
Contingency theory encourages designing appropriate MCS based on contingency factors (Chenall,
2003). Given the fact that environmental uncertainty is a contingent variable, MCS design in
professional service firms should take environmental uncertainty into consideration. Perrow (1986)
used the concept of professional control, similar to Merchant’s personnel control mechanism, to
stress that professionals rely more on self-control and social controls. Perrow stated that professional
control can be used to cope with uncertain conditions for firms which need expertise to complete
tasks. When information is absent or when desirable performance is not clear, action controls and
result controls appear to be less effective in these conditions (Brivot, 2011). Chenhall (2003) stated
that environmental uncertainty makes it harder for employees to understand how to conduct tasks
and turn actions into favorable outputs. Employees are the most critical resource and it adds value to
firms (Barney et al., 2011), therefore it is important for PSFs to recruit qualified employees who have
the knowledge and are flexible in tasks. Hence, from MCS research findings, personnel control is
likely to be an alternative for organizations in uncertain environment conditions.
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Support of using more personnel control to combat environmental uncertain conditions can also be
found in human resource management (HRM) papers. Discussion of the importance of human capital
to overcome the impact brought by environmental uncertainty can be found in HRM literature. When
firms face a high environmental uncertain condition, firms will try to find a buffer, for example
through ensuring the input resources, to offset the negative influence (Ghosh et al., 2009). Barney
(2011) is convinced of the importance of people factor as company resource by stating that human
capital can add value to firms and consequently generate a firm's core competence. Ghosh et al.
(2009) stated that employees with specified knowledge are more flexible at work and less influenced
by environmental uncertainty because these employees can find solutions to problems effectively
when compared with other employees who do not require specified knowledge at work. Baron and
Kreps (1999) are convinced that firms can transfer the pressure resulting from environmental
uncertainty to employees as they are the ones who conduct the task and that the skill sets and
expertise employees have help firms to stay efficient. Consequently the educated professionals need
fewer action controls such as guidance and monitoring activities from employees when they face
environmental uncertainty. Therefore, inputs of professional services, which are the service
professionals, are valuable for PSFs when affected by uncertain environment. The professional
workforce can deal with environmental uncertainty with more flexibility and therefore help firms to
offset unfavorable situations. This results in firms' demand to hire more talents as buffer to lower the
undesired impact of environmental uncertainty.
In light of the above mentioned literature from MCS and HRM literature, contingency theory and
professional human capitals can thus be served as ground theories in explaining that need to use
more extensive hiring processes accordingly in environmental uncertain conditions.
2.5 Hypothesis Development
Employees and customers are important inputs for PSFs (Auzair and Langfield-Smith, 2005). For
professional service firms, the knowledge intensive attribute and the professional workforce attribute
cause the possibility of losing talents and the difficulty in telling service quality (von Nordenflychit,
2010). The nature of PSF exposes PSF to uncertain environment. As a contingency variable,
uncertainty encompasses environmental and technological dimensions (Chenall, 2003). Drawing
17
from literature, environmental uncertainty is an important influencing factor of MCS design.
Literature shows that environmental uncertainty influences management outcomes and suggest
personnel control can be used to reduce the environmental uncertainty. Ouchi (1979) argues that
personnel control might be the most appropriate strategy under conditions of incomplete information
about the task and ambiguous standards of desirable performance. Brivot (2011) found that when
information is absent or when outputs are hard to measure, action controls and result controls appear
to be less effective. Ghosh et al. (2009) suggested that environmental uncertainty triggers firms to
apply more personnel control as a buffer. Despite the importance of hiring process in management
control systems (Campbell, 2012), past studies focused more on bureaucratic mechanisms such as
action controls and result controls in organizational controls thereby personnel control has not yet
been addressed. Based on the literature review, a possible relationship between environmental
uncertainty and hiring process from MCS can be expected, leading to the first hypothesis:
H1: Firms facing higher environmental uncertain conditions use more extensive personnel
controls than firms that are confronted with less environmental uncertain conditions.
The high human capital nature of professional service firms makes employees the most critical
resource for firms (Hitt et al., 2001). Besides the employee input component, service output
ambiguity and intangibility is another important attribute of PSF. These two characteristics of
professional service firms, however, can lead to problems (von Nordenflychit, 2010). Firm reputation
helps to deal with the PSF problems for the signaling effect reputation plays. Empirical results of a
survey of 100 firms found a positive effect reputation has on performance of PSFs (Greenwood et al.,
2005). Reputation provides benefits to the stakeholders in PSFs. Customers, another important input
of PSF, see reputation as a proof of good service quality; employers use this signaling effect to
attract more future employees while future employees turn to firm reputation as signal of ‘goodness’
to seek for companies to stay with (Wilson, 1985; Rao et al., 2001; Greenwood et al., 2005). As
mention in previous sections, the nature of PSF exposes PSF to a more uncertain environment. The
quality of outputs is even harder to measure in uncertain environment, and this leads more
importance of developing and maintaining firm reputation (Greenword et al., 2005). Reo et al. (2001)
linked the reputation to the environmental uncertainty by stating that in environmental uncertain
18
condition, firm reputation can serve as social guarantee to signal the quality PSF provides to
customers. Reputation is not only important for PSF because of the uncertain environment PSF is in
but also important because it impacts hiring in PSF. Jones (1996) stated that hiring would be
influenced by reputation. Other academic papers (Becker and Gerhart, 1996; Dess and Shaw, 2001)
proposed that reputable companies put more focus on obtaining better quality of professionals.
Therefore, reputation is valued in PSF since the service output is hard to measure and reputation
impacts hiring process. Although reputation is especially important for PSFs, no study of reputation
can be found in the PSF literature. The discussion of previous academic papers leads to a logical
proposal that reputable PSFs invest more in obtaining qualified professionals in uncertain
environment. Therefore, this leads to the second hypothesis:
H2: The environmental uncertainty conditions firms are facing together with firm’s reputation
will lead to more extensive use of personnel controls.
2.6 Hypotheses Operationalization
In the previous section, hypotheses are proposed. Environmental uncertainty is the independent
variable; personnel control is the dependent variable, and firm reputation as the moderator. The
dependent variable personnel control is measured by hiring process. This paper uses hiring process
as a lens to look into the MCS design. Below is the figure represents the two hypotheses:
Figure 1: Overview of the hypotheses
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3. Research Methodology
3.1 Sample and Data Collection
This study applies an empirical approach to test the hypotheses of the interaction of environmental
uncertainty, reputation and the hiring process. Data is collected by sending out questionnaires online
through Qualtrics as part of a larger PSF survey project by Faculty of Economics and Business at
University of Amsterdam. Acquiring the data needed for this research topic is difficult due to the low
availability in collecting field data. Professional service firms cover a wide range of industries and
this has made collecting surveys through personal efforts unrealistic. Thus, joining a survey project
can help to solve the problems by using a joint dataset. Since this questionnaire is designed for a
target group of people, it cannot be spread out randomly. So participants in this survey project help to
contribute to reliable survey information through their connections.
The PSF project mainly looks into factors that influence professional service firms and their
management control systems on an individual level. The questionnaire was designed to cover broad
range of topics, for example performance, tight and loose control and four types of controls. The
survey was conducted online from 2015 to 2017 with 5 survey collection deadlines throughout the
time period.
The design of this questionnaire is for individuals who work in PSFs, thus a few criteria must be met
to be eligible to fill in the survey. First, the respondent should be working in a professional service
firm, thus non-profit firms, such as government organizations are not taken into consideration.
Secondly, individuals should be working in medium to large size professional service firms (more
than 50 employees), regardless of their nationalities and working locations. Thirdly, respondents
should have at least three years of working experience but a maximum of ten years. This criterion is
designed to ensure that respondents have acquire the necessary experience to perform their jobs since
employees in the learning phase of their jobs are often subject to various control systems and they
might respond differently to controls. As for setting a ceiling for working experience this is because
the survey is aimed to analyze how individuals experience MCS but rather than individuals who
design MCS. When individuals have been working longer it is probable they get to a higher level that
will be part of MCS design in larger organizations. Lastly, the questionnaire is written in English,
20
thus surveyed individuals are expected to have a good command of business English in order to fill
out the surveys.
3.2 Survey Demographics
Questionnaires were sent out online to individuals working in medium and large PSFs worldwide.
Two pre-tests were carried out to ensure the reliability of the questionnaire. The first pre-test was
conducted after the survey design and 20 people were asked to sort the items provided with two
sheets of paper. The first sheet of paper included definitions for the eight control constructs, which
were implicit and explicit results control, behavior control and personnel control. Then subjects were
provided with the second paper with 52 statements and were asked to match definition to each
statement. The items that were sorted wrong most were removed from the questionnaire. The second
pre-test was conducted with another group of 20 individuals to do the entire survey online to assess
the quality. The questionnaire was then improved with the feedback provided.
The PSF survey project questionnaire collection was closed in February 2017. After the closure of
the survey collection, the total recorded responses amounted to 612. Through a data cleaning process,
surveys that are not fully completed, respondents lacking working experience, and some other
outliers such as did not read definitions and remarks were excluded from the analysis. Of the total
612 entries, 198 entries were deducted from the dataset and this gives total entries of 414, amounting
to a usable rate of 67.6%. Among the valid questionnaires, the occupation of respondents spread in
different fields. Most respondents (16%) work as accountants, 33 respondents (8%) work in
physician practices; following by consulting management (29 respondents, 7%), consulting IT (25
respondents, 6%) and engineering (25 respondents, 6%). Eighteen percent of survey respondents
give ‘other’ as they did not find relevant choices of occupation matches with their jobs. For the
gender of survey respondents, number of female respondent counts for 147 (35.6%), while number
of male respondent counts for 266 (64.4%). As for highest education level, three scales are designed
to classify respondents’ level of education with 1 as the lowest and 3 as the highest. For the
education level, 40.8% (169 individuals) of respondents have obtained Bachelor’s degree, 44.4%
(184 individuals) of respondents with Master degree, and the rest 14.7% (61 individuals) achieve
PhD or other equivalent degree. From our respondents’ education level, approximately 60%
21
individuals have obtained at least a master degree, which are the subjects we are looking for as PSF
is known for high human intensity. In the years of experience in field and organization, descriptive
statistics have shown that on average respondents have 7.47 years in a specific field and
approximately 6.21 years in an organization; with a median of 7 years and 6 years respectively. For
key demographic information, a statistic summary of respondents can be seen in Table 1.
Table 1: Descriptive Statistics
Items # Sample Min Max Mean Median Std.
deviation
Age 414 22 63 35.72 34.00 8.586
Education* 414 1 3 1.74 2.00 .699
Experience in field**
414 1 11 7.47 7.00 2.908
Experience in organization**
413 1 11 6.21 6.00 3.153
*. Bachelor degree or lower=1; Master degree=2; PhD or other professional doctorate degree=3
**. Less than 1 year=1, 1 year=2, … 10 or more=11
3.3 Variable Measurement
From the previous section, after survey screening, 414 observations will be processed and analyzed.
According to Hair et al. (1995), a survey with more than a hundred samples should be sufficient for
analysis. For this study, although the questionnaire has passed two pre-tests, whether items are
grouped as one factor or a defined number of factors are unknown. Thus, in this section, independent,
mediating and dependent variables will be introduced, following by exploratory factor analysis (EFA)
and reliability analysis for each variable before going to the next step analysis.
3.3.1 Independent Variable
The independent variable in this paper is the environmental uncertainty. For this variable originally 6
items were designed and questions on uncertainty were asked on three aspects: intensity, innovation
and predictability of industry. The constructs are designed to ask respondents about predictability of
22
the business environment the professional service firm faces on a five-point Likert Scale. Questions
involved intensity of price competition and competition for talents, for instance. For the intensity
items designed, 1 is defined as of negligible intensity whereas 5 as extremely intense. A higher score
refers to a more dynamic and uncertain external environment; on the contrary, a lower score
represents for an external environment with less uncertainty.
The descriptions were adapted from several academic papers. Gordon and Narayanan (1984)
analyzed the correlation between perceived environmental uncertainty and the degree of organic
organization structures. In Child's paper (1972), he defined uncertainty as one of the two
characteristics of environment, which can be expressed as "environmental variability", or in simpler
terms, the degree of change. He proposed that to measure this construct, market characteristics such
as competition in the market should be taken into consideration. In the survey, we use a subjective
measure from our respondents by using perceived uncertainty. Applying perceived uncertainty
instead of objective measures is supported by previous papers. Leifer and Huber (1977) are
convinced that to study environment factors, what people think provides more insights into
organizational behavior. They claim that perceptual information is more relevant than archival data.
The Keyser-Meyer Olkin of the environmental uncertainty variable is .657, and the Bartlett’s test of
Sphericity shows P value lower than .001, therefore the data is suitable for factor analysis. From the
factor analysis a screen plot is drawn and two components can be found from six items. Two
components explained 56.13% for the variance. Items have to load well when compared with the
benchmark of .32 recommended by Tabachnick and Fidell (2001). For the innovativeness item,
stated as “How many new products and/or services have been marketed during the past 5 years by
your industry” the loading factor (.302) is lower than the benchmark. Therefore, this item is removed
from the analysis. In addition, for the environmental uncertainty variable, the questionnaire was
originally constructed to combine three components. However, from the first factor analysis, results
show that innovativeness item and two other items referring to predictability of the industry are
grouped together as one component. A reliability test is carried out to see if the remaining five items
are valid, and this gives value of Cronbach’s alpha of .594. While if the three items (included the
deleted one) grouped together are removed, factor analysis shows a result of one component with
loading factors all above .60 (.855, .624, .815), which indicates items remained load well. When
another reliability test is conducted, a much higher Cronbach’s alpha can be seen ( =.657 . The
23
variance explained is 59.49%. Given the above analysis, two items stated as “How could you
describe the tastes and preferences of your clients” and “How could you classify the market activities
of other firms in the industry” will no longer be considered in the variable analysis. According to
Nunnally (1978), Cronbach’s alpha value of around .50 to .60 is considered to be acceptable for
exploratory research; the Cronbach’s alpha result for the second loading analysis is above the lowest
acceptable level for this study. The values of the items will be summated into a composite score and
also a mean score for further analysis.
Table 2: Factor analysis – Environment uncertainty
First Loadings Analysis Second Loadings Analysis
Items Component Component
1 1 2
Price competition .847 .855
Competition for manpower .561 .624
Bidding for new contracts/clients .836 .815
New products/services numbers .302
Predictability of tastes and preferences .689
Predictability of market activities .503
Variance explained (%)
Cronbach’s alpha
56.13%
.59
59.49%
.66
3.3.2 Moderating Variable
The moderating variable in this paper is reputation of a professional service firm. Four items were
constructed for this variable on a five-point Likert Scale and questionnaire was designed to compose
them in one general factor. Questions for this variable can be seen in Appendix. Individuals are
24
required to answer questions about perception of their companies. In this construct, a 5 indicates
‘strongly agree’ whereas 1 indicates ‘strongly disagree’. Thus, a higher score means respondents
perceive their firms more reputable and vice versa.
The measurement for reputation is mainly adapted from Combs and Ketchen (1999). Jones (1996)
thinks that hiring would be influenced by reputation, which means the perception of employees of
whether the company is a suitable place to work for or not affects the selection procedure. Thus,
reputation is applied as a moderating variable to test if it positively correlates with environmental
uncertainty and thus contributes to more extensive use of personnel control.
The Keyser-Meyer Olkin and the Bertlett’s test of Sphericity for this variable are .750 and p < .000
respectively, which indicate reputation construct is suitable for factor analysis. In the next step of
factor analysis, a screen plot is created and of the four items only one component is found and the
total variance explained is 62.972%. This matches with the survey design, which aims to collect a
general reputation perception of a firm.
To test the internal validity for this variable, reliability analysis is done in SPSS and this results in a
Cronbach’s alpha of .799. This is above the upper limit of acceptability for exploratory research,
which is usually approximately .70 (Nunnally, 1978). The data set passed the internal validity test
and the scores for the four questions are computed as sum and as mean for next step analysis.
Table 3: Factor analysis – Reputation
Items Loadings
Perceived to provide good value for the price .872
Well respected in its field .701
Strong brand name recognition .862
Strong reputation for consistent quality and service .723
Variance explained (%)
Cronbach’s alpha
62.97
.80
25
3.3.3 Dependent Variable
The dependent variable in the research is personnel control and this paper investigates the hiring
process in the personnel control. The construct to measure personnel control a new construct
developed from the definition of personnel control by Merchant and van der Stede (2012) and Ouchi
(1979). For this construct, survey respondents need to answer 8 questions designed in a five-point
Likert Scale on personnel control in their organizations. Questions asked all relate to the hiring
process in respondent’s organizations. These questions are constructed with most questions designed
to correspond with the rest, which means a higher score indicates more use of personnel control
while a lower score indicates less extensive use of personnel control. There are two items, however,
were designed to ask in an opposite way which means a lower score indicates more extensive use of
personnel control and vice versa. The answers from these two items are reverse coded. For 5 which
indicates ‘strongly agree’ and 1 indicates ‘strongly disagree’; they are coded as new variable in SPSS
setting new value of ‘1’ for a ‘5’ while ‘5’ for a ‘1’. By recoding into new variables, the two
reverse-coded items are aligned with the rest of the items in terms of degree of personnel control
measured. The reason behind this is to see if respondents can do the survey and indicate answers in
the same positive or negative degree without actually finding out the differences in constructs
(Bryman, 2012). The two items are:
There seems to be little consistency in the type of professional that gets hired for my job. (Q3_7)
The competence of employees within my job title varies greatly. (Q3_11)
The results for Keyser Meyer Olkin (.681) and the Bartlett’s test of Sphericity (p <.001) proves that
data base is suitable for factor analysis. Three components are found from the screen plot and the
variance explained is 62.25%. A follow up analysis was done to see if items load well with the three
components. However, from the component matrix it can be seen only first five items group together
as one component which represents for explicit personnel control items. While two reverse coded
items share the same component being a new factor, these two items were originally designed to be
grouped with two other implicit personnel control items. A reliability analysis is also conducted and
result shows a Cronbach’s alpha of .666.
Another factor loading analysis is carried out to see the results if taken out the four not aligned items
how to rest items load. Keyser Meyer Olkin (.733) and the Bartlett’s test of Sphericity (p <.001)
26
show it is suitable for factor analysis. This time the screen plot shows one component among the rest
items and variable explained for 57.63%. Cronbach’s alpha verified the validity of .745, which
improves the reliability of the personnel control variable and it is above the limit of acceptability for
exploratory research which considered being around .50 to .60 (Nunnally, 1978). Factor analysis
helps to make items and database more structured for next step analysis. Erroneous items which do
not load with the same factor could be removed to improve reliability (Bryman, 2012). Based on the
findings from factor analysis and reliability test, this study will proceed with four items from the
personnel control construct from the second factor analysis. The other four items which are originally
designed to load with one component are removed because they do not measure the same component
as desired and cannot capture the implicit personnel control. After taking out those four items, the
remaining items conducted an individual sampling adequacy test, also called MSA test. Results show
the remaining constructs all have figures higher than .50 and can be processed to further research
(Hair et al., 1995). Statistical results can be seen in Table 4. The scores of the four items on this
dependent variable from the second loadings analysis are calculated and sum up into composite
score.
Table 4: Factor analysis – Hiring in personnel control
First Loadings Analysis Second
Loadings
Analysis
Items Component Component
1 2 3 1
Extensive hiring process .744 .826
Go through many steps to be hired .767 .854
Interview with several people .630 .730
Evaluation at hiring process .607 .602
Same kind of job experience .611
27
Consistency in the type of professional .592
Same kind of education and training .662
Competence within job title .564
Variance explained (%)
Cronbach’s alpha
66.25
.67
57.63
.75
3.3.4 Control Variables
Regression analysis needs to be conducted in order to test the hypotheses. Thus, it is important to
consider variables that are not the subjects of this paper but might have impact on results of the
analysis and control for them. In this section, several control variables will be set and taken into
consideration in regression model. Two control variables are used in the following regression
analysis.
3.3.4.1 Size
Organizational size is commonly used as control variable in management control system literature.
King and Clarkson (2015) list size as control variable since size could possibly have a relationship
with performance in MCS design. The paper from Chenhall (2003) concludes 6 contextual factors
that can be studied by applying contingency theory, among one of the factors proposed is the
company size. Therefore, size is considered as control variable given the possibility that it could
have impact on personnel control discussed in this paper. This control variable is designed in the
following two statements to analyze organizational size from business unit to company as a whole:
How many people are employed by your entire company? (Q15)
How many people work in your organizational unit? (Q16)
The first item includes four choices which vary in numbers of employees within the organization as a
whole (less than 100, 100 to 500, more than 500 but less than 5000, above 5000) and the categories
are recorded from 1 to 4 scales with the increase in total employee numbers. The other item also
28
contains four categories vary from small amount to a large amount in organizational units. The
answers are recorded from 1 to 4 in scales from less than 10 people in one unit to more than 100
people in unit. For sizes variables, the organizational size is referred to ‘ORG_SIZE’ and business
unit size is referred to as ‘UNIT_SIZE’.
3.3.4.2 Firm structure and ownership
Results of survey sample of 120 managers in PSFs confirm the authors' prediction that organizational
ownership type and MCS design are correlated and performance outcome is positively affected by
the interplay between ownership and management control system design. Findings also conclude that
organizational structure exerts impact on MCS design in PSFs (King and Clarkson, 2015). Based on
their findings, a firm's structure and ownership type influence the hiring process since personnel
control is part of MCS design. Thus, from the questionnaires, two items related to organizational
structure and ownership are proposed, which can be seen in the following:
Which of the following best describes your job? (Q18)
Which of the following best describes the ownership type of your organization? (Q19)
For organizational structure question, respondents have to choose if the services they provide
represent the primary service provided by their firms, and answers are recoded as 0 or 1 as dummy
variable ‘STRUCT’. For another question related to ownership type, respondents have to choose
from three types of ownership: partnership, owned by shareholders and investors, or non-profit
organizations. Answers to this question are coded as two dummy variables to classify different types;
they are expressed as ‘IN_ORG’ and ‘OUT_ORG’ to represent ownership types as partnership and
owned by people from outside the organization own the firm, for instance, shareholders and
investors.
3.3.4.3 Education level
Abernethy et al. (2004) found that the education level of individuals has a positive relationship with
the level of trust. They used education level to examine if the degree of trust has impact on the
management control system and found education level is linked the degree of trust and a higher level
of trust between employees and organization, the more effective MCS is. Another reason for using
this construct as control variable is based on the characteristics of PSFs. PSFs enjoy high human
29
capital intensity and professional workforce, which are linked to employees’ degree of education.
Education level represents the knowledge one has and it is the building stone of professions. The
education received by employees and potential new hires can possibly effect hiring processes and
practices. The knowledge professionals own provide them better employment, better skills and
higher level of job security. For professionals who are highly educated, mostly they do not want to be
subject to formal control mechanisms (Goodale et al., 2008). Thus, PSFs might apply more informal
controls such as personnel control and cultural control compared with other types of firms. This
indicates that education level is an important factor for PSFs and should be added into the analysis as
a control variable. One item is taken from the questionnaire asking respondents the highest education
level they have achieved. The item is measured based on scales, where 1 represents ‘Bachelor
degree’, 2 for ‘Master degree’, and 3 for ‘PhD and other professional doctorate degree’.
3.4 Hypotheses Testing Models
In literature review part, two hypotheses are proposed and the figure for hypotheses testing is shown.
In this section, models for testing hypotheses will be described. To test them, multiple linear
regression approach is applied. Model 1a consist of independent variable uncertainty, dependent
variable hiring process while model 1b add various control variables; together two models are used
to test the first hypothesis. Model 2a and 2b add the moderator reputation mentioned in last section,
that is, independent variable, dependent variable, moderating variable in model 2a and control
variables are added in model 2b to test the second hypothesis.
Hypothesis 1 states that firms face higher environmental uncertainty conditions use more extensive
personnel controls. Environmental uncertainty is the independent variable and the composite scores
are measured on ordinal scale. Personnel control here refers to as hiring process and the composite
scores are also treated as an ordinal scale. Model 1a first does a regression analysis without all
control variables to see the possible relationship between the environmental uncertainty and the
hiring process. Model 1b adds all the control variables - size, firm structure, firm ownership and
education level - into the formula to test the relationship between environmental uncertainty and
hiring process in a more complex situation. The models are proposed as followings:
Model 1a:
30
Model 1b:
Hypothesis 2 proposes that when firm reputation is added as a moderator, this will positively impact
the link between environmental uncertainty and personnel control. That is, together with a high firm
reputation, environmental uncertainty will lead to a higher degree of personnel control. The model is
similar to model 1, while this time reputation is a moderator should also be considered in the formula.
To further investigate moderating effect of firm reputation, the reputation variable is split into two
groups: high reputation (REP) and less reputable firms. The aggregate mean split (4.21) is used as a
cut-off since the items on reputation are in continuous scale. A higher figure than the mean is
classified as more reputable firms and coded as ‘1’ in REP, ‘0’ if the standard is not met. Model 2a is
designed to first test the moderating effect of firm reputation on environmental uncertainty and hiring
process without all control variables, and model 2b refines model 2a by introducing all control
variables size, firm structure, firm ownership and education level into regression model to test if the
moderating effect still exists. This gives the regression models in the followings:
Model 2a:
Model 2b:
For the two models above, in the regression model stands for regression coefficients and stands
for residual deviation when results are interpreted in the next section.
31
4. Results
4.1 Descriptive Statistics
Descriptive statistics of the survey can be seen in Table 5. A total sample size of 414 is observed and
table reports the range of the corresponding variables, minimum and maximum scales, the mean and
median and standard deviation. For different items there are different scores. For example, for
organization size item, there are four choices thus the scores are set from 1 to 4, and the range is
from 1 to 4. On average, respondents consider the environment they are in quite uncertain (3.53).
The relatively high median score is consistent with the literature review that PSF is subject to
uncertain environment due to the nature of PSF. As for company reputation, most of our respondents
think highly of the organizations they are working for, thus we can see a higher mean and higher
median compared with other variables (4.21 and 4.25 respectively). Additionally, the standard
deviation for this reputation variable is low too; in general, respondents rate their companies high
and they perceive the firms in good reputation. From the mean (2.94) and median (3.00) it can be
perceived that a large percentage of respondents we surveyed work for medium to large companies.
As for highest education received, the statistics indicate that a large amount of our respondents have
obtained Bachelor degree and on average a Master degree (mean=1.74, median=2.00).
Table 6 presents the correlation matrix of independent variable, dependent variable and control
variables. From the results of the correlation matrix, it can be seen that there is no correlation larger
than .70 for any two variables. Therefore, the possibility of multicollinearity is believed to be low
(Tabachnick and Fidell, 2001). The variable construct HIRING measured four items on regards of
hiring procedures, UNCERT stands for environmental uncertainty, REP stands for moderator
reputation, and moderating effect is measured based on the interaction variable UNCERT*REP.
From the correlation we can see that environmental uncertainty is significantly positively correlated
with dependent variable hiring procedures, in other words, personnel control (r =.104, p .05). The
moderator reputation is significantly positively correlated with both dependent variable hiring
process (r =.152, p .01) and independent variable environmental uncertainty (r =.130, p .01). This
is consistent with the hypothesis that reputation has an influence on the relationship between hiring
process and environmental uncertainty. The moderating effect of uncertainty and high reputation has
32
a significant positive correlation with hiring procedures and environmental uncertainty
Control variables are listed under moderating effect
UNCERT*REP. Both the organization size (ORG_SIZE) and unit size (UNIT_SIZE) are positively
correlated with hiring procedures at a significant level r =.207, p .01 r =.102, p .01 . Both
variables are also significantly positively correlated with moderator reputation and moderating
variable. However,
organization size and unit size do not show significant correlation with the independent variable
environmental uncertainty. For organizational ownership type it can be seen that firms owned by
individuals inside the organization (IN_ORG) is significantly positively correlated with the
independent variable environmental uncertainty . The control variable level of
education (EDU), on the other hand, shows a positive relationship with the hiring process and the
result is significant . This is consistent with the findings that education level has a
positive effect on MCS design by Abernethy (2004).
33
Table 5: Descriptive Statistics
N= 414 Min Max Mean Median S.D. Range
Dependent variable
Hiring procedures
Independent variable
1
5
3.27
3.25
.816
1 – 5
Environmental
Uncertainty
Moderator
1 5 3.53 3.67 .841 1 – 5
Reputation
Control variables
1 5 4.21 4.25 .667 1 – 5
Organization size 1 4 2.94 3.00 1.087 1 – 4
Unit size 1 4 2.58 2.00 1.065 1 – 4
Structure 1 2 1.45 1.00 .498 1 – 2
Ownership 1 3 1.60 1.00 .702 1 – 3
Education level 1 3 1.74 2.00 .699 1 – 3
34
Table 6: Correlation Matrix
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
(1) HIRING
(2)UNCERT .104*
(3)REP .152**
.130**
(4)UNCERT*REP .155**
.314**
.657**
(5)ORG_SIZE .207**
-.028 .181**
.175**
(6)UNIT_SIZE .102* .070 .132
** .157
** .561
**
(7)STRUCT -.014 .042 -.046 -.029 -.095 .099*
(8)IN_ORG -.029 .211**
.052 .080 -.249**
-.115* .233
**
(9)OUT_ORG .043 -.020 .042 .027 .196**
.103* -.308
** -.671
**
(10)EDU .114* -.121
* -.048 -.053 .131
** .107
* .214
** -.051 -.154
**
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
35
4.2 Main Findings
4.2.1 Hypothesis Testing H1
To test hypothesis 1, regression analysis is conducted to first test model 1a and then model 1b.
Independent variable environmental uncertainty and dependent variable hiring process are analyzed
in the regression model 1a:
. Table 7 shows the regression results.
The F value is 4.474 at a significant level (p < .05), meaning that this model is suitable for analysis.
The value of R2 is .107 and adjusted R
2 is .083, meaning that model 1a explains for 8.3% of the
variation with the rest explained by other factors. From the regression model 1a environmental
uncertainty and hiring process are significantly positively correlated ( =.134, t = 2.115, p .05).
The result from model 1a supports the previous stated prediction of environmental uncertainty and
hiring process. The results of this model indicate that without any control variable, there is a positive
correlation between independent variable environmental uncertainty and hiring process.
Table 7: Regression results of model 1a
Model
Unstandardized Coefficients Standardized
Coefficients
t Sig.
Beta Std. Error Beta
1a (Constant) 11.614 .691 16.808 .000
UNCERT .134 .063 .104 2.115 .035
R2 .107
Adjusted R2 .083
F-statistic 4.474*
*. Correlation is significant at the 0.05 level (2-tailed).
Another regression analysis is conducted using model 1b to further test the relationship :
HIRING= 0
1UNCERT
2ORG_SIZE
3UNIT_SIZE
4STRUCT
5IN_ORG
6OUT_ORG
36
7EDU . All control variables are added to see if the relationship between environmental
uncertainty and hiring process stands. The regression model results in Table 8 show that model 1b is
suitable to test hypothesis 1. From the result, the F value and the significance level (F=5.347, p < .01)
show that the model provides explanation better than an intercept-only model. The value of R2
is .068 and the adjusted R2 gives .052, meaning that this model explains for 5.2% of the variation,
whereas the rest can be explained by other factors. However, this is not considered to be the reason
of failing the hypothesis testing, since the adjusted R square is expected to be relatively low when
using the survey method. In Auzair and Langfield-Smith’s study (2005), they used 149 samples to
investigate the design of management control system design and the adjusted R2 for model
explanation is also relatively low (12.7%).
The result of model 1b is shown in the following table. Hypothesis 1 predicts that environmental
uncertainty is positively correlated with personnel control in MCS, which is measured by hiring
process. Consistent with prediction of hypothesis 1, the table indicates that environmental
uncertainty (UNCERT) is significantly positively correlated ( =.153, t = 2.324, p .05) with
personnel control (HIRING). However, the relationship between independent variable environmental
uncertainty and dependent variable personnel control is not strong because the value of beta is not
high. Two control variables, organizational size and education level, have positive correlation with
personnel control ( =.652, =.557 respectively) and the correlations are
significant (p .01, p .05 respectively). The results for these two control variables indicate that
organizational size and education level have positive impact on personnel control in MCS. For the
unit size and organizational structure control variables, both of the variables show negative
correlations with hiring procedures; however, neither of the correlations shows a significant
result ( = -.123, - p =.499 =-.0 5, - p =. 05 respectively) . For two dummy
variables (IN_ORG and OUT_ORG) which are designed to measure ownership types, both of the
dummy variables show positive correlation with dependent variable HIRING ( =.414, =.557 ) but
neither of the correlations is significant (t =.620, p =.536; t =.740, p =.459 respectively). To conclude,
the result shows the first step analysis for hypothesis 1 is supported as environmental uncertainty has
a positively significant correlation with dependent variable hiring procedures.
37
Table 8: Regression results of model 1b
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
Beta Std. Error Beta
1b (Constant) 8.574 1.003 8.550 .000
UNCERT .153 .066 .118 2.324 .021
ORG_SIZE .652 .181 .217 3.594 .000
UNIT_SIZE -.123 .182 -.040 -.676 .499
STRUCT -.085 .343 -.013 -.247 .805
IN_ORG .333 .536 .051 .620 .536
OUT_ORG .414 .559 .060 .740 .459
EDU .557 .242 .119 2.302 .022
R2 .068
Adjusted R2 .052
F-statistic 4.234**
**. Correlation is significant at the 0.01 level (2-tailed).
Hypothesis 1 proposes higher environmental uncertainty results in more extensive personnel control
than in lower environmental uncertainty condition, thus it is important to categorize environmental
uncertainty into high and low environmental uncertainty groups. To test this, a second step analysis
is conducted using Mann Whitney U test. This is used to test whether a randomly chosen value from
one sample will be higher or lower than value from another chosen sample. This test is used to test
whether a randomly selected high environmental uncertainty sample is more likely to result in more
extensive use of personnel control than randomly chosen low environmental uncertainty sample. The
independent variable environmental uncertainty (UNCERT) is divided into two groups using median
of all items (4.00) as a cut. A score equal or higher than median will be grouped as high
environmental uncertainty group while a score lower than median will be classified as a low
environmental uncertainty group.
The result shows in Table 9 that when independent variable uncertainty is divided into low and high
38
groups, the scores of mean rank for low uncertainty group (195.26) is lower than the mean rank of
high uncertainty group (215.37), which means that comparing with high environmental uncertainty
condition, fewer hiring procedures will be applied when there is lower environmental uncertainty
level at a significant level (Z = -1.677, p .05). The result is consistent with the expectation of
hypothesis 1 which states that higher environmental uncertainty will result in more personnel
controls than in lower environmental uncertainty level. Thus, combining the results of two regression
analyses and Mann Whitney U test, hypothesis 1 is supported.
Table 9: Result of Mann Whitney U test
Ranks
UNCERT N Mean Rank Sum of Ranks
HIRING
Z value -1.677*
Low uncertainty 162 195.26 31632.50
High uncertainty 252 215.37 54272.50
Total 414
p .05 (two tailed)
4.2.2 Hypothesis Testing H2
From previous section hypothesis testing H1, the results indicate a positive correlation between the
hiring process in personnel control and the environmental uncertainty. Hypothesis 2 proposes that the
environmental uncertainty conditions firms face together with firm’s reputation will lead to more
extensive use of hiring process. For model 2a, we can test when the moderator reputation is
considered, if this relation still stands and if the moderating effect is working. In variable
management section, the moderating variable reputation is split into two groups, one higher
reputation group and one less reputable group. The moderating variable reputation is expressed as
REP and the moderating effect between environmental uncertainty and reputation is expressed as
UNCE*REP in the regression model 2a.
Based on the formula for model 2a:
,
the regression analysis is conducted to test the second hypothesis without any control variable. It is
39
predicted in this paper that firm’s reputation has a moderating effect on the positive relationship
between environmental uncertainty and hiring process. The result of summary is presented in the
following Table 10. The value of R2 is .041, and the adjusted R
2 is .034. The model summary
indicates that it is suitable for hypothesis testing since F-statistic is at a significant level (F=4.955, p
<.01). The regression result of model 2a is, however, not consistent with the prediction that
moderating effect exists in environmental uncertain condition. The result of moderating effect
UNCERT*REP indicates that there is a slight negative interaction between moderating effect and
hiring process, however this relation is not significant ( = -.00 , t =-.1 1, p =. 57). The moderating
effect is proved not significant and there is no moderating effect of reputation on the relationship
between the environmental uncertainty and the hiring process.
Table 10: Regression results of model 2a
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
2a (Constant) 8.048 1.676 4.802 .000
UNCERT .112 .067 .087 1.670 .096
REP .228 .094 .186 2.431 .015
UNCERT*REP -.008 .046 -.014 -.181 .857
R2 .041
Adjusted R2 .034
F-statistic 5.879**
**. Correlation is significant at the 0.01 level (2-tailed).
To further test the hypothesis 2, another regression analysis is made by adding all control variables
by using model 2b:
HIRING= 0
1UNCERT
2UNCERT REP
3REP
4ORG_SIZE
5UNIT_SIZE
6STRUCT
7IN_ORG
OUT_ORG
9EDU
The regression results of model 2b are given in Table 11. Consistent with regression results in
40
hypothesis 1, there is a significantly positive correlation between independent variable environmental
uncertainty and dependent variable hiring process ( =.146, t =2.092, p .05) . The reputation
moderating variable itself is significantly positively correlated with hiring
process ( =.1 6, t =1.976, p =.049). The interplay between environmental uncertainty and hiring
process exists when reputation variable is introduced into the model. The result for the moderating
effect between environmental uncertainty and reputation (UNCERT*REP) shows a weak negative
correlation, however, the result is not significant ( =-.010, t =-.220, p =. 26). This result indicates
that on the contrary of what hypothesis 2 predicts, no significant relationship can be found for the
moderating effect between environmental uncertainty and reputation. As for control variables,
organizational size is significantly positively correlated to dependent variable hiring process
=.560, t =3.054, p .01). Education level is strongly correlated with dependent variable hiring
process, as there is a significant and positive correlation shown in the results
=.56 , t =2.363, p .05). For other control variables, no significance is found. The unit size and
organizational structure have negative correlations with personnel control ( =-.153, =-.097)
whereas organizational ownership variables (IN_ORG and OUT_ORG) show a positive correlation
with personnel control =.123, =.241).
Table 11: Regression results of model 2b
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
Beta Std. Error Beta
2b (Constant) 6.082 1.744 3.488 .001
UNCERT .146 .070 .112 2.092 .037
REP .186 .094 .152 1.976 .049
UNCERT*REP -.010 .045 -.017 -.220 .826
ORG_SIZE .560 .183 .186 3.054 .002
UNIT_SIZE -.153 .181 -.050 -.847 .398
STRUCT -.097 .341 -.015 -.285 .775
IN_ORG .123 .538 .019 .228 .819
41
OUT_ORG .241 .559 .035 .431 .666
EDU .568 .240 .122 2.363 .019
R2 .086
Adjusted R2 .065
F-statistic 4.204**
**. Correlation is significant at the 0.01 level (2-tailed).
To understand the interaction effect of hypothesis 2 clearly, the result summary is visualized in
Figure 2. The figure shows the direct relationship of environmental uncertainty and hiring process as
proposed in hypothesis 1. When there is higher environmental uncertainty, more extensive personnel
control is used. When degree of the environmental uncertainty reduces, less extensive personnel
control is applied compared with situation in high environmental uncertainty. This is consistent with
the regression results of model 1, where environmental uncertainty and hiring process is significantly
correlated while the effect is not proved to be strong. However, when reputation moderator is added,
we can see that the slopes of the two lines are similar. The line of high reputation group and the line
of low reputation are close to parallel, which means there is no moderating effect exists. This
indicates that regardless of low or high environmental uncertainty conditions, the use of personnel
control is not affected by firms’ reputation. Surprisingly, this is not consistent with the prediction in
hypothesis 2 however, which proposes that when reputation is added in, the interaction between
environmental uncertainty and personnel control will be strengthened.
To sum up, based on the regression results and the interaction figure, the results of the moderating
effect between environmental uncertainty and reputation do not show empirical evidence as what
hypothesis predicts, thus hypothesis 2 is not supported. Section 5.1 will further discuss the
limitations and implications of the model and results.
42
Figure 2: Plotting the moderating effect
Lastly, in order to make sure there is no multicollinearity issue of the regression analysis made to test
hypothesis 1 and hypothesis 2, a variance inflation factor (VIF) test is conducted. For VIF value, the
threshold is usually 3.00 and a value larger than 10.00 is considered to be a problem (Hair et al,
1995). For the two control variables IN_ORG and OUT_ORG which refer to ownership type, the
VIF values are below 2.00. The VIF values for other variables are all below 3.00, ranging from one
to three. From the variance inflation factor test it can be concluded that all variables pass the test,
thus multicollinearity issue cannot be found.
Low Environmental
Uncertainty
High Environmental
Uncertainty
Dep
end
ent
vari
ab
le
Low Reputation
High Reputation
43
5. Conclusion
This research seeks to fill in the knowledge gap of personnel control of MCS in professional service
firms. Survey questionnaires from 414 individuals working in wide range of industries provide
valuable insight into investigating the hiring process and firm reputation in professional service firms.
Consistent with prediction of hypothesis 1, regression results indicate a significantly positive
correlation between the environmental uncertainty and hiring process of personnel control. This
provides evidence that professional service firm uses more extensive hiring in uncertain environment.
Regression results of firm reputation, on the other hand, do not provide empirical support for the
moderating effect. Nevertheless, this study provides interesting empirical evidence and implications
through studying newly developed constructs – environmental uncertainty and firm reputation, in
professional service firms.
5.1 Discussion
This paper contributes to the existing MCS literature by studying the less explored personnel control
dimension in MCS. In the introduction part, two research questions are proposed:
1. How does environmental uncertainty influence the hiring process in PSFs?
2. How does firm’s reputation play a role in hiring process in PSFs when there is environmental
uncertainty?
Campbell (2012) provides evidence that hiring in personnel control leads to better MCS results.
Inspired by the importance of selection process in control systems, this paper seek to further
investigate influencing factor that affects hiring process in PSF setting. Chenhall (2003) listed
environmental uncertainty as important contingency variable. Perrow (1986) suggested using
professional control, which is a similar notion to the personnel control, to deal with the
environmental uncertainty. Brivot (2011) stated that action controls and result controls are less
effective in uncertain environment while Ghosh et al. (2009) further aruged that firms will use hiring
as a buffer to ensure input resource of PSFs to cope with environmental uncertainty. Results from
414 questionnaires confirm the predications from both MCS and HRM literature that environmental
uncertainty is linked to use of hiring process. Hypothesis 1 states that environmental uncertainty is
44
positive associate with the use of personnel control. This direct relation is proved to be significant
given the empirical results from two regression analyses. The Mann-Whitney U test classifies
environmental uncertainty into high environmental uncertainty group and low environmental
uncertainty group; results from this test further indicate that firms use more extensive personnel
controls in higher level of environmental uncertainty than firms that are confronted with less
environmental uncertainty. This concludes the answer for the first research question: for professional
service firms, there is a positive relationship between environmental uncertainty and hiring process
and the higher level of environmental uncertainty will result in more extensive hiring process.
To answer the second research question, hypothesis 2 is proposed to test the moderating effect of
firm reputation has on PSFs. No previous literature can be found investigating firm reputation in
uncertain environment condition and the empirical findings can contribute to the existing
contingency-based research. Past research (Jones, 1996; Becker and Gerhart, 1996; Dess and Shaw,
2001) proposed that reputable companies put more focus on obtaining better quality of professionals.
Papers on firm reputation state that reputation shows its value when observing and measuring output
becomes difficult (Hirshleifer et al., 2013). Therefore, reputation is especially crucial for PSF since
the service output is hard to measure. Reo et al. (2001) linked the reputation to the environmental
uncertainty by stating that in environmental uncertainty condition, firm reputation can serve as social
guarantee to signal the quality PSF provides to customers. Based on the important influencing role
reputation has on PSF and in uncertain environment, this paper predicts a positive correlation of the
moderating effect reputation has on the relationship between environmental uncertainty and hiring
process. Surprisingly, although reputation is described as key success factor for firms in literature
(Cable and Turban, 2001), no significant empirical results can be found and therefore there is no
underlying moderating relationship between the environmental uncertainty and the hiring process.
Therefore, the moderating role of reputation on professional service firms remains unproved since
reputation effect seems to exist regardless of the level of environmental uncertainty.
One possible explanation for this insignificant result is the questions designed for firm reputation.
Both the sum and the mean of the reputation variable and the frequency of different rating scales
seeing from Histogram indicate that a majority of the observations perceive the firms they work for
reputable since there is a higher frequency for respondents to rate reputation around the mean (4.21)
and a high frequency to rate the companies they are working with as ‘very reputable’. On the other
45
hand, only a few observations in the dataset rate reputation of their firms low. It seems difficult to
define what a reputable firm is since individual perceptions can be different as people hold different
criteria. One approach to improve the reputation measure is to set a baseline question for reputation
so that researchers have clear picture of what respondents perceive to be reputable firms. For
example, items can be designed to first ask what type of companies respondents consider as
reputable, and then rate their firms’ reputation based on the criteria they perceived as reputable.
5.2 Limitations
This research has several internal and external validity limitations. Survey method is subject to
misinterpretation and therefore measurement errors. The items designed to measure personnel
control construct might be misinterpreted by respondents in this study, since the factor analysis
shows items asked load on different components instead of one component presenting hiring process.
The survey questionnaire was designed to cover multi-dimensions of PSF but not especially for the
interaction of environmental uncertainty, reputation and hiring process. According to respondents’
feedback, it is lengthy to complete the questionnaire since it covers wide range of constructs in PSF.
This might lead to less accurate answers due to the decrease level of concentration. In addition, some
items in the questionnaire were designed to be reverse-coded; less attention-focused respondents
might misinterpret questions and therefore results in biased answers. In the future, more pre-tests and
a more condensed questionnaire design can reduce the possibility of collecting biased data.
Secondly, this questionnaire is designed for individual assessment and perception of individual varies
greatly. Although items are design in five-point Likert Scale, this rather quantitative measure cannot
ensure the same standard and criteria for each respondent. As for representation of survey
observations, the sample size is not large enough to collect questionnaires from board range of
regions and industries. Some industries are over-represented in the overall observations, for example,
16% of the observations are working in accounting firms and 7% of respondents are working in
management consulting firms. This over-representation makes it hard to generalize findings to a
broader range of PSF. The sample availability of wider industries and regions needs to be improved.
Last but not least, the regression models have limited explanation power although models are proved
to be significant. Large variances in the models remain unexplained and these imply other factors
46
which are not discussed in this study effect the model variance. The relatively low variances in
models lead to a relatively limited prediction power of models as a result.
5.3 Future Research Directions
The conclusions drawn from this paper provide several suggestions for future research.
Firstly, in MCS research field, a vast body of literature has been focusing on more bureaucratic
control mechanisms such as action controls and result controls. However, literature suggests that
when contingency factor such as environmental uncertainty is introduced, bureaucratic control
mechanisms tend to be less effective and control mechanisms such as personnel control and social
control cannot be ignored in MCS design. This study, as a starting point, is the first study to use
hiring process as a lens to investigate the relationship of personnel control and professional service
firms. The results provide interesting insight into future research. Future research can conduct a more
in-depth study to look into different aspects of personnel controls such as trainings and placement in
the PSF setting.
Secondly, this study examines the use of personnel control by using a summated score of four items
from the questionnaire. The analysis is based on the four validated items (extensity, steps in hiring,
interview rounds and evaluation at hiring process) of personnel control in professional service firms.
The employee selection of PSFs in this paper looks at different measure metrics, which means a
boarder context of selection. Future research can further segment the hiring variable to investigate
which type of employee selection has a more apparent and dominant relationship with the
environmental uncertainty. Professional service firms can benefit from this possible in-depth study as
they can pay more attention to the type in hiring process that effects organizations most.
Thirdly, this research is based on a survey project from University of Amsterdam and the data set is
still being updated. In the future, researchers can investigate different scenarios using the updated
data base, focus on the firm reputation’s relationship with MCS and PSF, or classified samples into
different groups based on organizational size and level of education as this paper indicates that both
organizational size and level of education are significantly correlated with hiring process in
professional service firms.
47
Last but not least, this paper applies survey approach only; as a result this approach leads to
limitations, for instance the internal validity of the data set. The moderating effect of the reputation
factor this paper sought to find is rather difficult to capture. Future studies can combine different
research methodologies, for example interviews or case study, to further study the reputation factor
in PSFs, and thereby more in-depth knowledge can be obtained. Combining quantitative results from
the survey and qualitative results from the interviews or case study renders more insights and less
bias.
48
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Appendix: Survey questions
Thesis Survey Project 2016-2017: Helena Kloosterman
Personnel Control Implicit/Explicit
This section addresses the hiring process in your organization. (Q3)
Environmental Uncertainty
How intense is each of the following in your industry? (Q37)
54
How many new products and/or services have been marketed during the past 5 years by your
industry? (Q38)
The next questions address the predictability of your industry. (Q39)
Reputation
How is your organization viewed in general? (Q14)
55
Size
How many people are employed by your entire company? (Q15)
How many people work in your organizational unit? (Q16)
Organizational Structure
Which of the following best describes our job? (Q18)
56
Ownership Type
Which of the following best describes the ownership type of your organization? (Q19)
My organization is primarily owned by …
Education
What is the highest level of education you have completed? (Q29)