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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53

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)