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Management Control Systems in SMEs: the influence of family involvement in a firm

Parichart Maneemai

University of Antwerp Faculty of Applied Economics

Department of Accounting and Finance

Ann Jorissen

University of Antwerp Faculty of Applied Economics

Department of Accounting and Finance

Corresponding author: Ann Jorissen The authors thank Matthias Nordqvist, Wim Voordekkers, Eddy Laveren and the participants of the EIASM workshop on family firms management research (Witten, May 2011) for their useful comments and suggestions on earlier versions of this paper.

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MANAGEMENT CONTROL SYSTEMS IN SMEs: THE INFLUENCE OF FAMILY

INVOLVEMENT IN A FIRM

ABSTRACT Using survey data from private small and mediums sized enterprises (SMEs), we investigate how

family influence on a firm affects the controls adopted and used by that firm. For this purpose,

we examine through an agency lens how different levels of family involvement in ownership,

different levels of family involvement in management, and different levels of generational

involvement are associated with differences in the controls adopted and used by family firms.

Through analysis of non-overlapping subsamples with distinctly different types of family

involvement, we find that composition of the management team, presence of a minority

shareholder, and type of generational involvement are all significant determinants of the controls

adopted by SMEs in addition to traditional explanatory contingent variables such as perceived

environmental uncertainty (PEU), strategy, size, and firm age. Consistent with traditional

agency, we find that when agency problems occur (principal/agent problem as well as

principal/principal problem) controls are adopted by family firms. Moreover, we provide

evidence that even in family-owned / family-managed firms, considered by traditional agency

theorists to be a zero-agency-cost organization, family firms adopt controls to mitigate the

negative influences of family involvement in the firm.

Key words: control choices, results controls, action controls, performance measurement, agency

theory, family influence

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1. INTRODUCTION

Although research on family business emerged more than 20 years ago, Schulze and Gedajlovic (2010)

state that research on family business is still in its infancy, as the diversity of theories and perspectives

represented in the developing literature portray a cluttered and conflicted landscape. There is an ongoing

debate on the advantages and disadvantages associated with family involvement in business. Within this

debate, considerable attention has been paid to family influence on firm performance, entrepreneurial

orientation and successful generational turnover. Less emphasis has been placed on accounting choices

made within family firms. Recently Gnan et al. (2011) stated that accounting is an under-researched area

in family business studies. Prencipe et al. (2011) stated that accounting issues in family businesses are

virtually unexplored. Although some studies have explored financial accounting and auditing issues

(e.g., Cascino et al., 2010; Prencipe et al., 2008; Trotman and Trotman, 2010), management accounting

issues in family businesses have been largely overlooked in the literature (Giovannoni et al., 2011).

Salvato and Moores (2010) claim that despite the potential relevance of management accounting to a theory

of strategic management in family firms, very few studies were developed with a focus on this issue. The

scarcity of accounting studies in family firms has led to several calls for research in this domain (Salvato

and Moores, 2010; Gnan et al., 2011; Prencipe et al., 2011).

Family influence on the controls adopted by a firm has attracted little research attention in the

management control systems (MCS) literature as well. The majority of MCS studies are rooted in agency

theory (Baiman, 1982) and/or in contingency theory (Chapman, 1997; Chenhall, 2003). Agency theory

suggests investing in information systems (e.g., control mechanisms) to mitigate agency costs (Jensen and

Meckling, 1976; Eisenhardt, 1989), whereas under a contingency perspective, MCS is designed to assist

managers in making progress toward firm goals and in achieving desired outcomes (Chapman, 1997;

Chenhall, 2003; Davila, 2005). Within the traditional agency view, family firms are considered zero-

agency-cost organizations in which no investment in controls is needed (see Jensen and Meckling, 1976;

Fama and Jensen, 1983). As a result, the MCS literature focuses mainly on those firm types thought to be

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subject to agency problems. However, more recently, family business researchers (Gomez-Mejia et al.,

2001; Schulze et al., 2001, 2003; Chua et al., 2003; Blanco-Mazagatos et al., 2007) argue that agency

issues can also arise in family-owned / family-managed firms due to family entrenchment, shirking,

perquisite consumption, and adverse selection resulting from the appointment of less qualified family

members. This conflicting perspective on the presence of agency costs in family firms is another impetus

behind researching the adoption of controls by family firms. In response to calls for academic research on

accounting in family firms and with the purpose of shedding more light on the opposing views on agency

conflicts in family firms, the central research question of this paper is whether differences in the level of

family involvement in a firm are significantly associated with differences in the controls adopted and used

by such firms.

Since family influence on a business passes through ownership and management (Chua et al., 1999), we

examine this influence by investigating how different degrees of family involvement in ownership and in

the management team influence company controls. Like Fiegener (2010), we consider family ownership

as antecedent to involvement of family members in the management of the company. It is through family

involvement in operations and management decision making that family aspirations and values become

manifest and shape the distinctiveness of family firm behavior and outcomes (Zahra, 2003; Chrisman et

al., 2005 and Klein et al., 2005). With the use of survey data on Thai privately held industrial companies

and through an agency lens, which is an appropriate theoretical framework for studying MCS (Baiman,

1982; Chapman, 1997; Chenhall, 2003) and family firm behavior (Chua et al., 2003; Chrisman et al.,

2005; Blanco-Mazagatos et al., 2007), we investigate how different levels of family involvement, defined

as different degrees of family ownership, different degrees of family involvement in top management

teams, and different levels of generational involvement, are associated with different designs and uses of

MCS. For this research purpose, the population of respondents is parsed into non-overlapping

subsamples with distinctly different types of agency problems, captured by different degrees of family

involvement in ownership, in management, and in different types of generational influences.

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We investigate a broad spectrum of different controls to build a comprehensive view on family influence

on controls adopted in family firms. Considering the findings of empirical MCS research grounded in the

contingency paradigm, our study controls for the impact of significant contextual and organizational

drivers on MCS design and use (size, age, PEU, strategy, industry (see Chapman, 1997; Chenhall, 2003;

Moores and Yuen, 2003; Davila 2005)). In contrast with the few studies investigating management

control systems in family firms in the MCS literature, our findings are based on survey results. The

findings of Amat et al. (1994) and Giovannoni et al. (2011) are based on case study evidence, whereas

Craig and Moores (2005) took a normative approach based on action research. In contrast with the

studies examining MCS in family firms in the management literature (Moores and Mula (2000), Upton et

al. (2001), Becker et al. (2011), Duller et al. (2011)), this study controls for the influence of contextual

and organizational variables such as PEU, strategy, and size on MCS, whereas other studies do not

control for these variables, focus only on a few control elements, and most often analyze differences

between family firms via a univariate approach.

Our results show that a general statement with regard to how family firms adopt and use MCS cannot be

made. The data indicate that there is much heterogeneity within the group of family firms with regard to

how these firms adopt and use controls. Consistent with traditional agency theory, we find that when

agency problems occur (principal/agent and principal/principal), action controls as well as results controls

are adopted by family firms. When a minority shareholder is present, differences in adoption and use of

action and results controls in family firms in comparison to non-family firms disappear. Only the

performance evaluation system is marginally less formal and there is significantly less focus on customer-

oriented performance information. If we focus on the composition of the management team, we observe

that when the majority of members are non-family members; no differences are found with regard to

adoption and use of controls between family firms and non-family firms. When only a minority of non-

family members is present on the management team of a family firm, these non-family managers (both

non-family CEO and other non-family managers) do not succeed in altering the informal planning and

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performance evaluation practices of the family firm toward more formalization. However, these non-

family managers, despite their minority position, do succeed in broadening the scope of the performance

information taken into account (either in a formal or an informal way). Only when non-family managers

are present in the management teams of family firms is customer-oriented performance information and

performance information on the innovative capacity of the company included in the scope of performance

measurement. Further, non-family managers introduce the diagnostic use of performance information in

the company. When zooming in on 100% family-owned / 100% family-managed firms, firms that

according to traditional agency theory represent zero-agency-cost firms, we observe that these family

firms also adopt controls. We find that when these family firms move from the first generation to the

second generation, they introduce more action controls and they increase the level of formality of their

budgeting processes and performance evaluation processes.

These findings allow us to conclude that degree of family involvement in ownership, degree of family

involvement in the management team, and the type of generational involvement are significant

determinants in explaining the action and results controls adopted and the use of performance information

by SMEs, in addition to the significant explanatory power of contextual variables such as PEU, strategy,

firm size, and firm age. Moreover, we observe that the economic significance of these variables that

represent family involvement in a business are at least equal in significance to the contextual variables

explaining MCS choices. These results indicate that family characteristics might not be omitted in

research designs explaining the controls adopted in companies, particularly in the context of SMEs.

This paper proceeds as follows. In Section 2, we discuss the relevant literature and develop our

hypotheses. In Section 3, we present the research population, measurement of the variables, and method

of analysis used. The results are presented in Section 4 and the paper ends with a discussion and

conclusion.

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2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

Several definitions of family firms are used in the literature. Many family business researchers focus only

on ownership involvement when distinguishing between family firms and non-family firms (Berry, 1975;

Lansberg et al., 1988). Other authors focus on management involvement (Barnes and Hershon, 1976) or

an organizational transfer from the first generation to the second and later generations (Ward, 1987) as

characteristics of family business. According to a number of authors (Chrisman et al., 2003; Klein et al.,

2005; Zahra, 2003) family ability to influence decision making is an essential form of family involvement

that shapes the distinctiveness of family firm behavior and outcomes. Whereas family involvement in

ownership empowers family members via property rights to act on behalf of family interests, it is the

family’s involvement in decision making that infuses family aspirations and values into the business and

that affects firm behavior and outcomes. So for the purpose of this study, we call a family firm a firm in

which a family owns more than 50% of shares. In order to examine a given family’s influence, we follow

Chua et al. (1999) and take into account, in addition to the level of family involvement in ownership, the

level of family involvement in the top management team.

These different levels of family involvement in the firm lead to different types and degrees of agency

problems. According to agency theory, principals have two options to avoid agency problems: first to

invest in information systems that reveal behavior, and second to contract on outcome variables (Jensen

and Meckling, 1976; Eisenhardt, 1989). Eisenhardt (1985) argues that, in general, three forms of control

systems are employed to mitigate agency problems: behavior-based or action controls, outcome-based or

results controls, and social controls. Action controls influence organizational actors by prescribing the

actions they should take (Merchant and Van der Stede, 2011). Results controls are applied when behavior

is difficult to observe, and make up the performance measurement system of the firm. Linked to results

controls is the use of compensation and incentive contracts to force the agent to bear certain

responsibilities and risks (Milgrom & Roberts, 1988). Jensen and Meckling (1991) also include incentive

systems as a control choice, Milgrom and Roberts (1992), however, make no distinction between

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incentive systems and performance measurement systems, as they consider the performance measurement

system the heart of the incentive system. Consistent with Milgrom and Roberts (1992) and other authors

(e.g., Abernathy et al., 2004) we follow a similar approach. Our main focus is on the design of

performance measurement systems, rather than on incentive systems. We focus on the adoption and use

of the formal control mechanisms (whether action controls or results controls) enumerated by Eisenhardt

(1985, 1989), which can be used to mitigate agency costs.

2.1. Agency costs in family firms under the traditional agency perspective

As the core of agency theory is the potential conflict between firm owners (the principals) and the

managers (the agents) under contract to run the firm on the owner’s behalf, whereby under conditions of

information asymmetry and in the absence of complete contracts, two main kinds of agency problems

arise, namely adverse selection (contracting with an agent that is not well qualified) and moral hazard

(form of opportunism that includes shirking, free riding, and consumption of perks). Traditional agency

theory assumes that the threat of moral hazard and the costs of safeguarding against it are lowest in

closely held firms, because personal involvement by owners/managers supposedly eliminates the agency

problem resulting from separation of ownership and control (Jensen and Meckling, 1976). So traditional

agency theorists argue that agency costs in family firms are negligible or absent, because the interests of

family members are likely to be closely aligned (Fama and Jensen, 1983, 1985). Jensen and Meckling

(1976) even suggest that formal governance mechanisms (of which management control systems are a

part) are at best unnecessary in family firms. This assumption is strongly held by finance researchers in

that the owner-managed firm is used as the zero agency cost base in the finance literature (Ang et al.,

2000). When principals and agents are family members, principals are more likely to form positive

attributions about agent motives and behavior and less likely to monitor agents attentively (Fiegener

2010: 300).

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Thus, in a situation in which a firm is fully owned (100%) by a family and in which family members are

the only managers, agency costs will be zero or close to zero. Under the traditional agency view, agency

costs will increase when non-family members join the management team of the family-owned company,

and as a result a divergence between the interests of the owners and the interests of the managers might

arise. This type of agency problem, called the type I agency problem, is the principal-agent problem and

is present when ownership and management are separated and which occurs when in 100% family owned

firms, non family members join the management team of the company. In line with this traditional

principal/agent view, we hypothesize that:

H1: There is a negative relation between the degree of family involvement in the management team of the

company and the number of controls adopted in the company.

Subsequent work has generalized agency analysis to a wider set of relationships. Rather than the

traditional principal-agent conflicts discussed by Jensen and Meckling (1976), conflicts between controlling

shareholders and minority shareholders have been identified as another major type of agency problem that

may be present, especially in private firms. When individuals or family members are the majority

shareholders, they may enjoy substantial control as a result of their concentrated equity holding in the

firm (Ali et al., 2007: 242). This control gives the family the power to seek private benefits at the

expense of minority shareholder(s) by freezing them out (Gilson and Gordon, 2003; Villalonga and Amit,

2006), by engaging in related-party transactions (Anderson and Reeb, 2003), and through managerial

entrenchment (Shleifer and Vishny, 1997). These agency conflicts between majority and minority

shareholders are referred to in the literature as type II agency problems. Family firms are susceptible to

this principal/principal type of agency problem, whereby the controlling family owners exploit their

superior knowledge at the expense of non-controlling shareholders (Wasserman, 2006).

Thus, agency costs can also arise in family firms when the ownership of the firm is shared by a

controlling family and a non-controlling minority shareholder (or shareholders). In such cases, minority

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shareholders will support the adoption of controls by the firm to help protect them from this potential

threat of expropriation. In line with this principal/principal agency problem, we hypothesize that:

H2: There is a negative relation between the degree of family involvement in the ownership of the

company and the number of control mechanisms adopted by the company.

Under traditional agency, we observe a series of agency problems lying on a continuum between the

dispersed non-family-controlled firm and the 100% family-owned/family-managed firm.

2.2. Agency costs in family firms

With regard to the occurrence of agency costs in family firms, a different perspective emerged in the

family business literature. In contrast to traditional agency theorists, who argue that family firms are

more efficient due to reduced agency costs because family relationships align goals and incentives of

family owners and managers, there is the alternative perspective that family firms are breeding grounds

for relationships fraught with conflict (Ward, 1987; Lansberg, 1999; Schulze et al., 2001; Gomez-Mejia et

al., 2001; Schulze et al., 2003; Blanco-Mazagatos et al., 2007). The underlying assumption of the

traditional agency scholars is that individuals, households, and firms are rational actors seeking to

maximize economic utility. However, a key assumption in the family business literature is that in

addition to economic goals, families also hold non-economic goals, such as providing employment to

family members and building family cohesion. In explaining these characteristics of family firms,

altruism features prominently. In religious and in philosophy studies, altruism refers to a moral value that

leads individuals to act in the interests of others without the expectation of reward or positive enforcement

in return (Karra et al., 2006). In economics, altruism is considered a utility function that connects the

welfare of one individual to that of others (Schulze et al., 2003).

Altruism might lead to agency problems that cannot easily be controlled using economic incentives (like

incentive compensation) and, as a consequence, they may be more difficult to resolve than agency

problems faced by non-family firms (Schulze et al. 2003a, 2003b, Chrisman et al. 2003). In a family

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contracting situation, a relational contract between a firm owned by a family and an agent (a family

member) involves a common bond and a set of mutual expectations that are more likely to be based on

emotions and sentiments than in a non-family contracting situation. Agency threats under family

contracting come in a variety of forms: (1) potential incongruity between the goals of the executive and

the family, or the executive may pursue objectives that are not in the best interests of the firm, but in the

best interests of the family or some family members; (2) family CEOs may use their equity positions to

consume perks that reduce the residual claim of the owner; (3) family executives may be able to impose

self-serving desires onto key decisions of the firm, as family executives hold a disproportionate amount of

power, emanating not only from the skills they might have, but more importantly from their family status;

(4) adverse selection, in which managers are chosen out of a pool of family candidates; (5) managerial

entrenchment, which refers to executives holding their jobs past the point where stewardship is beneficial

to owners (Walsh and Seward, 1990; Gomez-Mejia and Wiseman, 1997; Gomez-Mejia et al., 2001;

Schulze et al., 2001; Schulze et al., 2003a, 2003b).

The nature of altruism and the types of agency problems engendered by it are contingent on the

ownership stage of the family firms (Lubatkin et al. 2005: 323). In the first generation, close family ties

act as a regulator and instigate cooperative attitudes adopted by family members. Ownership dispersion,

which occurs in later-generation firms, the diverse role of family members, and lessened intensity of

family ties will increase conflicts of interest and information asymmetries between family owners and

family managers (Schulze et al., 2003a; Schulze et al., 2003b). With succession, family firms suffer from

adverse selection costs, since managers are selected altruistically (Schulze et al. 2003a; Villalonga and

Amit, 2006). Empirical evidence shows that there are limits to altruism as family firms develop and

grow. Blanco-Mazagatos et al. (2007) and Karra et al. (2006) suggest that the intensity and nature of

agency problems in family firms change across time. Based on these insights from the family business

literature, we expect agency costs to be lower in the first-generation family firms than in second- and

later-generation family firms. Therefore we hypothesize that:

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H3: Under an identical degree of family involvement in the firm (i.e., degree of family involvement in

ownership and family involvement in management), first-generation family firms will use fewer controls

than family firms of second and later generations.

3. METHODOLOGY

In this section, we provide information about the context of the research population and the criteria used

to determine the survey population. Next we pay attention to the measurement of the variables and we

describe the method of analysis applied.

3.1. Research population

The data for this study were collected from Thai private firms. In the relevant literature, evidence is

available to support that the agency paradigm also applies in the East Asian context. The empirical

literature provides evidence of expropriation of minority shareholders in East Asia (Claessens et al., 1999,

Young et al. 2008) and describes the usefulness of agency theory to explain effects of managerial

ownership in this region (Mustapha and Ahmad, 2011). Further, Wiseman et al. (2012) argue that

applying agency theory to diverse contexts requires a broadening of the conceptions of the key elements

within agency theory, but leaves the underlying premises of the theory intact.

The target population for this research consists of Thai private limited liability industrial companies. The

Thai commercial code provides for the limited liability company in the one share/one vote system

(section 1182 of the Thai Commercial Code). This implies that a family controls a company through

ownership if they hold 51% of the shares or more. We limited the survey population to private companies

that belonged to three major industries in Thailand, namely the textile, plastics, and metal products

industries, to control for the impact of industry on the design and use of MCS. Based on the databases of

the Ministry of Industry and the Ministry of Commerce, we collected information on the names of the

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companies in these industries, along with their addresses and numbers of employees. In total, 6,661

companies belong to these three industries (26.3% of Thai industrial companies). Within this group,

11.9% of companies belong to the textile industry, 9% to the plastics industry, and the remainder to the

metal products industry. We then applied two dimension criteria to this initial population of 6,661

companies. First, we excluded firms with less than 10 full-time employees, since these firms are too

small to have formal control systems (Davila, 2005). Second, we omitted companies with more than 200

employees from the survey population, because evidence is available showing that large companies use

elaborate formal MCS because of their complexity (Davila, 2005). Therefore, the SME context is more

appropriate to detecting family influence on the controls adopted in a company than is the context of

larger companies. Applying these industry and dimension criteria left us with a target sample of 2,000

companies for the survey.

The data on family involvement in a company and the controls adopted and used by the company were

collected through a structured questionnaire sent by mail to the CEOs of the companies. All

measurements of control variables are taken from the literature. The Thai version of the English

questionnaire was developed and validated (Behling and Law, 2000). Then, the Thai questionnaire was

translated back into English to check whether the measurements still had the same initial information

content (Brislin, 1970). The questionnaire was pretested by 11 CEOs of private firms from the three

chosen industries. In early October 2010, the questionnaires were mailed to the CEOs of all companies

included in the target sample. The package included a cover letter, the questionnaire, and a business-

reply envelope. Thereafter, two follow-up mailings were sent in the same year. A total of 227 companies

returned usable questionnaires. During the survey period, we received information that a number of firms

in the target sample had ceased to exist (bankruptcy and liquidation) and that a number of firms

apparently could not be contacted at the address mentioned in the databases used. Deducting all these

companies from the target sample of 2,000 companies, we arrive at the response rate of 15%. This

response rate is similar to other surveys undertaken in Asia (Harzing, 2000).

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In order to verify the representativeness of the population of respondents, two tests are performed.

First we use t-tests and ANOVA to compare the size, location, and industry of the respondents with

the proportions of industry, dimensions, and location of the total population. According to this test,

our population of respondents was representative of the initial population, with a minor bias toward

representation of larger companies. Second, we test for the possible presence or absence of a non-

response bias. Since the literature suggests that late respondents are similar to non-respondents

(Kanuk and Berenson, 1975; Oppenheim, 1996), the late respondents were treated as representative

of non-respondents and were compared with the early respondents. We tested whether differences

between early respondents and late respondents are present by using t-tests and a Chi-square test; we

found no statistically significant differences between these two groups of respondents (see

Appendix B).

A large majority of the surveys were filled out by the CEOs of the companies; the other surveys were

filled out either by the Vice President or the secretary general. Since we collect our data via a

questionnaire filled out by one person, common method bias may be a concern (Podsakoff et al. 2003).

In order to avoid or reduce common method bias, several methods are used. In the questionnaire

design stage, we ask for “harder” and factual information rather than respondents’ perceptions (Ittner

and Larcker, 2001). Moreover, our respondents are assured of anonymity and confidentiality, and that

there is no right or wrong answer (Chang et al., 2010; Podsakoff et al., 2003). After receiving the

responses, we conduct the Harman (1967) single-factor test. In this test, all variables are entered into

an exploratory factor analysis simultaneously. The explanatory factor analysis using principal axis

factoring shows that no single factor can be extracted, which indicates that our sample is unlikely to be

subject to common method bias.

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3.2. Measurement of variables

The various controls adopted and used by firms are the dependent variables. The variables capturing the

degree of family involvement and generational involvement are the main independent research variables.

The variables discovered by empirical contingency studies as significant explanatory variables of MCS

design and use, are included as control variables in the analyses.

3.2.1. Dependent variables

To gain a comprehensive view of the controls adopted by family firms, we choose consistent with

Chow et al. (1999) a broad set of control mechanisms that provide an overview of the following

control functions: organizing, planning, evaluating and rewarding. According to Chow et al. (1999),

these control functions provide coverage of the MCS in place in firm. With regard to organizing

function and planning function of control, we include the action controls “structuring of activities” and

“planning and budgeting.” With regard to the presence of planning and budgeting practices, we also

investigate the level of formality with which these plans are prepared and we include questions on the

use of participative budgeting. With regard to the performance measurement system which is the core

of the results controls adopted by a firm, we examine on the one hand the scope of the performance

information system available in the company, and on the other hand we ask questions with regard to the

degree of formality with which this performance information is collected. In addition, questions were

added with regard to an organizational design choice, namely the level of decentralization and with

regard to the incentive systems applied in the company. These last two elements are usually jointly

determined together with the results and action controls applied in the company. The questions used to

capture the presence of these controls are included in Appendix A. Below, we describe in brief the

contents of the dependent variables.

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. MCS design

Level of decentralization. The degree of decentralization applied in a firm is measured by the instrument

developed by Chow et al. (1999). These authors define eight areas of responsibility in which decision

rights can be delegated to lower levels.

Structuring of activities. We include questions with regard to whether written policies, rules,

standardized procedures, and manuals specifying how to and, sometimes how not to, perform activities,

exist in the firm. This control element, called “structuring of activities,” is measured by the instrument

used in the study of Chow et al. (1999) and is an element of action control.

Planning Formality. In order to know the number of budgets used in the company and the degree of

formality with which the budgets are prepared, we use the measurement instrument developed by Bracker

and Pearson (1986). This measurement instrument was subsequently used in a number of other SME

studies investigating planning practices (Matthews and Scott 1995; Rue and Ibrahim 1998; Gibson and

Cassar, 2002; Yusuf and Saffu 2005). The degree of planning formality was captured by asking

respondents to select out of four descriptions the one that most resembles the firm’s planning practices.

Participative Budgeting. The extent to which lower-level managers participate in the budget process is

measured by four questions from Chow et al. (1999), who adapted them from Brownell (1982, 1985) and

Shields and Young (1993). Based on the agency view, it is assumed that a significant reason for the

existence of participative budgeting is the transfer of information from the subordinate to the superior,

and that there is a potential gain for both parties (Shields and Young, 1993).

Control Formality. The degree of formality with which information about different performance

indicators was collected, is measured with the use of another instrument of Bracker and Pearson (1986).

This measurement was also subsequently used in other studies (e.g., Yusuf and Saffu, 2005). Control or

performance measurement formality was captured by asking the respondents to indicate out of four

different levels of formality, the level that describes best their firm’s control practices.

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Scope of performance measurement. In order to measure the diversity or scope of the performance

measurement system applied in the firm, we used the measurement developed by Henri (2006) and Hoque

and James (2000). This instrument asks for the frequency of use of 20 performance measures. These

items are based on the four dimensions of the balanced scorecard: financial, customer, internal business

processes, and innovation and learning (Kaplan and Norton, 1996; Henri, 2006).

Performance-related rewards. In order to measure the use of performance-related rewards in a company,

we asked respondents to indicate the extent to which the CEO of the firm compensates the lower-level

managers based on their performance relative to targets. This instrument is based on the study of Chow et

al. (1999).

. MCS use

These questions on the use of performance measurement information are based on Simon’s framework

(Simon, 1987, 1995, and 2000). If performance information is used in a diagnostic way, managers focus

on targets and manage results on an exception basis; in a diagnostic system the focus is really on control

(Simon, 2000). Performance information can also be used in an interactive way. Performance

information is then used in a forward looking manner and this performance information stimulates active

and frequent dialogue among managers. Using the measurements developed by Henri (2006) and

Widener (2007), which are adaptations of the instrument of Vandenbosch (1999), we asked whether MCS

information was used in the company in a diagnostic way and/or in an interactive way, or used in neither

way.

3.2.2. Independent research variables

The independent research variables represent level of family involvement and type of generational

involvement in the firm. We measure level of family involvement in several steps (see also Chua et al.,

1999; Chrisman et al. 2003). First, we consider the level of family involvement in the ownership

structure of the company; with this measurement we focus on the principal/principal problem. Second,

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we add to the level of family involvement in ownership, the level of family involvement in management

of the firm. This element introduces the principal/agent problem to the analysis. Third we consider the

type of generational involvement in the firm. These variables represent different types and degrees of

family involvement, and all represent different agency situations.

Family involvement in ownership of the company (FIO). We distinguish three different categories of FIO.

The first category consists of firms in which no family owns more than 50% of the shares of the firm.

These firms are not controlled by a family and we refer to them as non-family firms in this research.

Second, we distinguish firms in which one family owns more than 50% but less than 100% of the shares

of the firm. This group consists of family-controlled firms in which minority shareholders are present.

Third, we distinguish firms in which a single family owns 100% of the shares of a firm. In this last

category, 100% family-owned companies, no principal/principal problem is present. We include FIO in

the analysis with the use of a categorical variable whose value is 1 when no family owns more than 50%

of the shares, 2 when a family owns more than 50% of the shares, and 3 when a single family owns 100%

of the shares. We perform additional analyses, taking into account percentage of ownership as a measure

of family involvement in ownership.

As differences among family firms and non-family firms are often explored by considering family

involvement in top positions as well (Anderson and Reeb, 2003; Villalonga and Amit, 2006), we use two

variables to measure the level of family involvement in the top management team of the firm (MGMT).

First we measure the number of family members present in the top management team of the company,

and second we consider family CEOs and non-family CEOs separately (see also Minichilli et al. 2010).

Family involvement in the management team (FIM). The level of FIM was measured by the percentage of

the number of family members involved in the management team (see also Klein et al., 2005; Chrisman et

al., 2004). We include the level of FIM by creating a categorical variable consisting of three categories of

family involvement in the management team. The first category consists of firms with management

19

teams in which family members hold only a minority position. This implies that family members occupy

less than 50% of the management positions in the top management team. The second category consists of

firms in which the management team is controlled by family members. So, family managers occupy a

majority of the seats in the top management team. The third category consists of firms with management

teams made up only of family members.

Family CEO - non-family CEO (CEO). Next we take into account whether the leading position in the

firm is occupied by a member of the controlling family. This variable is treated as a dummy variable,

taking the value 1 when the CEO is a family member, and zero when the CEO is not a family member.

To determine the level of family involvement in the firm, we combine the level of FIO with the level of

FIM of the firm.

Family involvement in the firm: a combination of FIO and FIM. The combination of these two types of

family involvement in the firm results in seven non-overlapping groups according to the different degrees

of family involvement in ownership and the different degrees of family involvement in management.

These seven groups all represent different types of agency conflicts. The seven groups are: (1) non-

family firms, (2) family-controlled firms with minority shareholder(s) and with a non-family-controlled

MGMT, (3) family-controlled firms with minority shareholder(s) and with a family-controlled MGMT,

(4) family-controlled firms with minority shareholder(s) and with only family members in MGMT, (5)

100% family-owned firms with a non-family-controlled MGMT, (6) 100% family-owned firms with a

family-controlled MGMT, and (7) 100% family-controlled firms with 100% family members involved in

MGMT. In categories (5) and (6), different degrees of the traditional principal/agent problems are

present. In category (4) we face only a principal/principal agency cost situation. In categories (1), (2),

and (3), we have a combination of principal/agent and principal/principal problems. Only the last

category (7) represents firms with a zero-cost agency situation according to the traditional agency view.

To examine the presence of agency issues in these “zero-cost” agency firms, we introduce a variable

capturing the type of generational involvement in the firm.

20

Generational involvement (GEN). To capture the type of generational involvement in the firm, we

distinguish between generational involvement in ownership and generational involvement in the

management team. To single out possible agency differences between first- and later-generation firms

and to examine the impact of these agency issues on the controls adopted in firms, we isolate from the

population of respondents those family firms where ownership and management are only and fully in the

hands of the first generation and family firms where ownership and management are only and fully in the

hands of the second generation. As a result, firms in which ownership and management is shared

between first- and second-generation family members are left out of the analysis of the generational

impact on the controls adopted in family firms. In this way, we also exclude family firms in which

principal/agent and principal/principal agency issues are present and focus only on the so called “zero-

agency-cost” firms in this analysis.

3.2.3. Control variables

Empirical contingency MCS studies provide evidence of a number of contextual variables that

significantly influence the design and use of MCS (Chenhall, 2003, 2007). We control for the most

significant contingent variables and include, consistent with prior studies, PEU, strategy, firm size, and

firm age as control variables in the analyses. Through the design of the survey population, we control for

the influence of industry on controls adopted and used by the firm.

Perceived environmental uncertainty (PEU) was measured through questions that related to the uncertainty

of eight external factors: suppliers, customers, competitors, deregulation and globalization, government

regulations/policies, economic environment, industrial relations, and production and information

technology. This instrument was developed by Gordon and Narayanan (1984) and includes turbulence,

hostility, diversity, and complexity (Chenhall, 2003). We measured strategy by using the self-typing

selection method (Kober, Ng, and Paul, 2007; Aragón-Sanchez and Sánchez-Marín, 2005), which follows

the Miles and Snow (1978) strategy taxonomies: defender, prospector, analyzer, and reactor. The natural

logarithm of the total number of full time employees is used to control for firm size, (e.g., Eddleston et al.,

21

2008; Davila, 2005). Firm age is also included as a control variable, since empirical evidence is available

that age can be a driver to explain the emergence of MCS (Moores and Yuen, 2001; Davila, 2005).

3.3. Statistical analyses

To investigate the influence of the different levels of agency costs, created by different levels of family

involvement and generational involvement in the firm, on the control mechanisms adopted by SMEs, we

apply the technique of hierarchical regression. First, we run regressions explaining the different elements

of MCS including only control variables as explanatory variables. In the second step, we introduce the

main research variables into the analysis. At first, we introduce the ownership characteristics into the

analysis. Then we add the top management team characteristics and later we introduce the type of

generational involvement. Hierarchical regressions allow us to investigate the incremental increase in the

explained variance of the dependent variables via successive addition of sets of independent variables.

4. RESULTS

First we present characteristics of the respondent population together with the descriptive statistics.

Thereafter, we discuss the results of the hierarchical regressions.

4.1. Characteristics of respondent population and descriptive statistics

Table 1 and Table 2 describe the organizational and family characteristics of the companies that

responded to the survey.

[Please insert Table 1 here]

We observe that the majority of respondents consist of 100% family-owned firms. The respondents are

almost evenly distributed over the different categories of firm dimension and firm age. We see that the

top position of the firm (CEO) usually remains in the hands of a family member even when minority

shareholder(s) are present. Further, it is necessary to mention that within the group of second- and later-

22

generation firms, only six firms belong to the third or later generation. This can be explained by our

decision to limit the size of the target companies for the survey to companies with less than or equal to

200 employees. Probably third- or later-generation family firms are of a larger size. We also note that

the presence of a minority shareholder decreases in second- and later-generation family firms.


Table 3 reports the means and standard deviations for the variables representing the design and use of

MCS, classified by the three categories of ownership structure of firms in the population.


Due to missing variables for some of the controls, we run the regressions with 174 observations. We use

102 observations when we analyze the association between the type of generational impact on the firm

and the presence of control mechanisms in firms. Two key procedures in MCS studies are used to assess

construct validity and reliability, namely factor analyses and the computation of Cronbach’s Alpha

(Nunnally, 1978). Factor analysis was performed for all multi-items variables; the results reveal that all

measures are uni-dimensional. Cronbach’s Alpha ranges between 0.76 and 0.92 (see Appendix A).

Table 4 presents correlations between variables. We do not observe correlation problems between the

dependent variables, the research variables, and the control variables. Average VIF of each regression

ranges from 1.41 to 1.57; tolerance level (1/VIF) ranges from 0.41 to 0.98. Only between the main

research variables are high correlations found. To examine the impact of the different levels of family

involvement in ownership and management, we split the population into non-overlapping subsamples

characterized by different degrees of family involvement in ownership and in management.


23

4.2. Family influence on MCS

The first regressions executed in this procedure of hierarchical regression analysis are the regressions with

only control variables as explanatory variables. We learn from Table 5 that the significance of the

contingent variables in the different models is consistent with the findings of previous MCS contingency

studies. PEU is a significant contextual variable influencing the formality of MCS and diagnostic and

interactive use of MCS. Firms facing high environmental uncertainty use less formal MCS and adopt

more organic or informal controls (Khandwalla, 1977; Chenhall, 2003). Further, the adopted strategy of

the firm has an influence on the control practices in the firm. Prospectors are more associated with formal

planning than are defenders. Prospectors, reactors, and analyzers are more associated with performance

measurement based on innovation and learning than are defenders. With regard to firm size and firm age,

we find the expected results.

We examine the impact of family involvement in the firm on the control mechanisms adopted by the firm,

along the following steps. First we concentrate on the impact of FIO on the control mechanisms adopted

by the firm. This analysis investigates the impact of the principal/principal problem on the controls

adopted. Second, we add to these differing levels of family involvement in ownership the varying levels

of FIM. We then examine the impact of the combination of FIO and FIM on the controls adopted and

used by companies. We then isolate the impact of a non-family CEO on the controls adopted. Thereafter,

we focus on the presence of generational differences in the adoption of controls in the so-called zero-

agency-cost family firms.


4.2.1. Family involvement in ownership and MCS

The results of the association between FIO and the MCS in a company are presented in Table 6. In all

these analyses, the non-family firms are the reference category.


24

If we compare the adoption of controls in family firms versus non-family firms, we observe that with

regard to the adoption of action controls, these controls are only significantly less adopted when a family

owns 100% of the shares. These 100% family-owned firms adopt significantly less structuring of

activities and plan in a less formal way. The results reveal that when a minority shareholder is present,

family firms adopt action controls to the same degree as non-family firms. So when minority shareholders

are present, controls that guide the behavior of people are installed just as in non-family firms.

Focusing on the adoption of results controls, we find that only 100% family-owned firms collect

significantly less performance information in a formal way, in comparison with non-family firms and

majority owned family firms. Majority owned family firms collect performance information only

marginally less formally than non-family firms. With respect to the scope of performance information,

we observe that family firms, whether 100% family-owned or majority owned by a family, collect

significantly less information on customer performance. If the firm is 100% family-owned, it also

collects less performance information on innovation and learning processes in the firm in comparison to

majority owned family firms and non-family firms. Performance information is collected in an informal

way and with a narrow scope, so it is no surprise that the 100% family-owned firms also use performance

measurement information in a significantly less diagnostic way as well as in an interactive way than non-

family firms and majority owned family firms. When minority shareholders are present, the use of

performance information hardly differs from the use of performance information observed in non-family

firms. These results, presented in Table 6, indicate that the principal-principal problem leads indeed to an

investment in controls in family firms. Introducing ownership characteristics into the analysis increases

the explanatory power of the models significantly when examining the action controls adopted by the

firm, the formality of the performance evaluation system, the scope of performance information, and the

use of this performance information in the firm. The results in Table 7 reveal further that organizational

design choices in terms of the level of decentralization and the type of reward system present in the

company are not influenced by firm ownership characteristics.

25

4.2.2. Combined impact of family involvement in ownership and management on MCS

The data in Table 7 reveal the results of family influence on the control choices of a company when both

family involvement in ownership of the firm as well as family involvement in the management team are

taken into account. The results shown in Table 7 constitute evidence that when both agency problems

(principal/principal as well as principal/agent) are present, family firms, majority owned family firms

with a family minority in the management team, and majority owned family firms with a family majority

in the management team adopt and use controls to the same extent as non-family firms. When only a

principal/principal problem is present (majority owned family firm with only family members in the

management team), we find that indeed these majority owned family firms collect less information on

customer performance and that the formality of the collection of performance information is significantly

less. This result, in combination with the results shown in Table 6, provide evidence that the composition

of the management team in a family firm with minority shareholder(s) influences the formality of the

results controls. When only family members occupy the management team, less formal performance

information is available in those firms. This might weaken the control mechanisms available to the

minority shareholder(s).


In Table 7, which shows the results of the 100% family-owned firms and the various top management

team compositions, we examine situations in which there is only a principal/agent problem (100% family-

owned firms with a family minority in the management team and 100% family-owned firms with a family

majority in the management team) or a zero agency cost situation according to traditional agency theory

(100% family-owned firm with only family members in the management team). We observe that the

divergence between ownership and management induces the 100% family-owned firms to install controls.

When only a minority of the seats on the management team is in the hands of family members, 100%

family-owned firms adopt and use controls like non-family firms. In the zero-agency-cost situation

(100% family-owned and managed firm), we observe that action controls are adopted significantly less.

26

Further, we see that the performance information is present in a very informal way and there is hardly any

focus on customer-oriented performance information or on performance information with regard to

innovation and learning. Since performance information is not formally available, it is also significantly

less used in a diagnostic or in an interactive way by these zero agency cost firms. Moreover, we see that

the presence of a minority of non-family members on the management team of these 100% family-owned

firms only increases to a minor degree the adoption of action controls in comparison to the zero-agency-

cost family firm. With regard to the level of formality of collected performance information, we observe

no difference with these zero agency cost family firms. However, although collected in an informal way,

the scope of performance information is broader when a non-family member is present in the

management team of the company. Thus, the presence of a minority of non-family members in the

management team of a 100% family-owned firm does not instigate these 100% family-owned firms to

adopt and use significantly more controls. With regard to the increase in the explanatory power of the

model, we learn from Table 7 that adding variables representing the level of family involvement in

ownership and in management increases the explanatory power with regard to the following dependent

variables: the adoption of action controls, the formality of the performance information collection

process, the presence of customer performance information, and the use of this performance information.

To analyze further the impact of the composition of the management team on the controls adopted, we

focus on the presence of a family CEO.


Particularly in SMEs, the CEO is the principal decision maker. Therefore, we examine the association

between the presence of a family CEO and the control mechanisms of the firm. The impact of CEO type

can best be examined in those cases in which there is only a principal/agent problem (100% family-owned

firms – non-family CEO and 100% family-owned firms – family CEO). The results in Table 8 allow us to

conclude that the presence of a non-family CEO is not significantly associated with the presence of

formal planning and results control systems in family firms. Viewed from the traditional principal/agent

27

angle, this finding comes as a surprise since this situation is the typical principal/agent situation. These

results, however, are consistent with observations found in the family business literature on family choice

with regard to a non-family CEO. When families select external managers, these managers have to

support and share the family’s values and goals (Arregle et al., 2007). Successful non-family CEOs are

those that are not only excellent in their role as manager, but who also have the capabilities to manage

relationships with the family effectively (Blumentritt et al., 2007; Poza, 2007). This high job complexity

and the developed trust between family and non-family managers may trigger the development in non-

family managers of feelings of psychological ownership toward the organization (Pierce et al., 2001).

The fact that the owning family chooses a non-family CEO whose interests are in alignment with the

family explains that controls are viewed by the owners as less necessary, since agency issues seem to be

less prominent. Thus, planning and performance evaluation remain informal. However, due to the small

number of non-family CEOs present in the population, we must exercise care with this interpretation.

The results of the regressions, presented in Table 6 and Table 7, allow us to confirm H1 and H2, which

state that there is a negative relation between the degree of family involvement in the firm, measured in

terms of FIO (H2) and measured in terms of FIM (H1) and the number of controls adopted and used in a

company.

4.2.3. The impact of type of generational involvement on MCS

Based on the insights of family business scholars, we hypothesize that in second- and later-generation

firms, agency costs and information asymmetry increase in comparison to first-generation firms, and that

therefore more controls will be used in second-generation firms in comparison to first-generation firms.


To examine the generational impact, we run regressions in which we include the data from non-family

firms with the data from first-generation 100% family-owned and family-managed firms and second-

generation 100% family-owned and family-managed firms. The data from firms in which two

28

generations are simultaneously active either as owner or as manager are left out of the analysis. The

results shown in Table 9 allow us to confirm H3, since we observe that family firms of the first generation

adopt significantly less controls than family firms of the second generation. Second-generation family

firms do adopt action controls more in line with non-family firms. The formality of their planning and

control practices is higher than the level of formality with regard to planning and performance

measurement found in first-generation family firms. No difference is found with regard to the focus on

customer-oriented performance measures. Although second-generation firms possess performance

evaluation information in a more formal way than do first-generation firms, they do not use it more

diagnostically, nor more interactively. If we add data on generational involvement to the analyses, the

explanatory power of the model increases with regard to planning, performance evaluation, and the use of

this performance information.

In Table 10, we summarize the findings with regard to the hypotheses tested. The results confirm the

insights of traditional agency theory, implying that when a principal/agent or a principal/principal

problem occurs, controls are adopted. Moreover, the results also allow us to delve deeper into the so-

called zero-agency-cost situation and to confirm the findings of family business research scholars that in

100% family-owned and family-managed firms, agency issues occur, since in second- and later-

generation firms, more controls are adopted than in first-generation firms. These latter results challenge

the traditional view of agency theory with respect to the family firm.

4.3. Additional analyses

To test the robustness of our results, we run the regressions examining the impact of the level of FIO and

the level of FIM on the controls adopted with the percent of family ownership and the percent of family

managers in the management team as independent variables, instead of using dummy variables. The

results are consistent with the results of the main analyses, in which the levels of involvement are

represented by dummy variables.

29

A second type of additional analysis consists of introducing the main research variables and the control

variables into a structural equation analysis in which the dependent variables are the various control

elements. Since MCS consists of multiple control elements, we take into account that the introduction of

one control element could influence the introduction of another element and that control systems are

usually balanced (Collier, 2005; Sandino, 2007; Revellino and Mouritsen, 2009; Mundy, 2009). MCS is

considered to be comprised of multiple controls that work together as a control package (Otley, 1980;

Widener 2007). The results obtained from the structural equations model (SEM) are consistent with the

significance of the results found in the main analyses executed with hierarchical regressions.

5. DISCUSSION AND CONCLUSION

Since researchers believe that family involvement in a firm makes family businesses distinct from non-

family businesses (Chua et al., 2003), numerous studies have undertaken to investigate the impact of

family involvement on the business behavior of the family firm and on the performance of such family

firms. In this stream of research, the area of management accounting and management control systems

remains understudied (Salvato and Moores, 2010; Gnan et al., 2010). Thus, calls for academic research

on the role of management accounting and management control in family firms have been launched.

MCS in family firms received little research attention even in the MCS literature. In the traditional

agency view, family firms need not invest in controls, since traditional agency theory considers these

family-owned / family-managed firms as zero agency cost firms. More recently, however, family

business scholars have argued that agency conflicts occur in family firms as well, especially when family

ownership becomes dispersed in later-generation firms. In response to the calls for more research on

MCS in family firms and to shed more light on these conflicting views on agency costs in family firms,

we analyze through an agency lens how family involvement in a firm affects the controls adopted by such

family firm. Given the empirical evidence that larger companies adopt management controls as a result

30

of their complexity, we consider the SME context a more appropriate context in which to discern the

impact of family involvement and of generational involvement on the control mechanisms adopted.

By splitting up the population of firms into non-overlapping samples characterized by different levels of

family involvement in ownership and family involvement in management, we are able to observe a

continuum of different traditional agency problems, from the non-family-controlled firm to the fully

owned and fully managed family firm. If we do not control for the type of generational involvement, we

indeed find that when principal/agent and principal/principal problems are absent, less action controls and

less results controls are used. We observe that planning and performance evaluation is done in an

informal way. Further, the scope of the performance information is less focused on the customer

perspective and less on innovation and learning, and that performance information is used less in a

diagnostic and an interactive way. However, the results of this study also indicate that if we move away

from the end of the spectrum (100% family-owned/family-managed firms), family firms are quickly

found to begin to adopt controls. The entrance of a non-family member in the management team causes

family firms to use the same broad spectrum of performance indicators as do non-family firms, except for

customer-oriented performance information, but still in an informal way. Only when a majority of non-

family members are present in the management team do controls become more formalized. When

minority shareholders are present, action controls are used in the same way as in non-family firms and

results controls are adopted with almost the same degree of formality. Only the scope of performance

indicators remains narrower, with no focus on customer-oriented performance information.

Focusing on those firms that, by traditional agency theory, are supposed to face zero-agency costs, we

find that this assumption of agency theory only applies to first-generation 100% family-owned / 100%

family-managed firms. Family firms in second and later generations do adopt controls even if they are

100% family-owned and 100% family-managed. Ownership dispersion around family members and the

lessened intensity of family ties induces agency conflicts and instigates these family firms to adopt

controls.

31

We observe further that all companies, irrespective of the level of family involvement, adopt financial

performance indicators and performance indicators with regard to the efficiency of internal business

processes, although the level of formality decreases with increasing family involvement. The focus on

financial aspects is probably due to fulfillment of external requirements. All Thai private companies must

publish their annual accounts (art. 1196 of the Thai commercial code), and for lending purposes, financial

accounting data must be provided. This finding is in line with two of the reasons Davila et al. (2008)

found to adopt MCS, namely, legitimizing the company and contracting with external parties. MCS plays

various roles in organizations: they make goals more explicit; they have a planning and coordination role;

and they can help in decision making (Merchant and Otley, 2007). Further, Henri (2006b) and Widener

(2007) state that when controlling (diagnostic use) is combined with an enabling use (or interactive use)

of control information, this combination can lead to unique organizational capabilities and competitive

advantages (see also De Geuser et al., 2009). Family firms make significantly less use of this possibility

given that the diagnostic and interactive use of performance information is negatively related to the level

of family involvement in the firm. With regard to forward looking and future income increasing

information, we observe a significant difference in the presence of customer-related information and

information with regard to innovation and learning. When family involvement increases, less focus is

placed on these indicators. These findings suggest that strategic decisions in fully owned and majority

owned family firms, managed by a majority or 100% management team (in the case of family majority

owned company) cannot be taken on the basis of formal information about performance indicators that

provide information about the future income generating potential of a company. It would be

advantageous for these family firms to substitute this kind of formal information through knowledge

acquired through their social networks. Given these findings we also contribute to the call for research of

Salvato and Moores (2010) on the use of management accounting information for strategic management

in family firms.

32

These results also support and broaden the findings of a number of MCS studies, grounded in the life

cycle theory of the firm, which investigate the changes in MCS over the life cycle of the firm. Whereas

Moores and Yuen (2001), Davila (2005), Granlund and Taipaleenmäki (2005) and Davila and Foster

(2009) find that the introduction of venture capitalists is a significant driver to adopting more formal

control systems, our study shows that when any minority shareholder is present in a company (we provide

evidence for a family-controlled company), control processes become more formal, more action controls

are enacted, and a broader scope of performance measurement is employed. The results of this study,

being in line with the findings of the case studies of Amat et al. (1994) and Giovannoni et al. (2011),

reveal that formalization of controls takes place when a minority shareholder is present, when the

majority of the management team consists of non-family members, and when the firm moves from the

first generation to the second generation. This study indicates that in research on management control

systems in medium sized and smaller firms, data on ownership and management composition are

variables not to be omitted from the research design. We further observe that the economic significance

of family variables is often equal to the economic significance of the traditional contextual variables

explaining the adoption and use of controls. The results show that a so-called unified family firm

approach to controls does not exist. We observe much heterogeneity in the adoption of controls within

family firms. This heterogeneity is driven by the level of family involvement in ownership, by the level

of family involvement in management, and by the type of generational involvement.

Despite its contribution to the literature, this study also suffers from shortcomings, which provide avenues

toward future research. Due to the composition of the sample of respondents (very few third- and later-

generation firms), the study of the generational impact on control choices is mainly limited to the

differences in impact on control choices between first- and second-generation family firms. Some authors

claim that agency costs (Schulze et al., 2003a; 2003b) have a U-shape and begin to decline in the third

generation. Future research is needed to determine whether third- and later-generation firms indeed

33

return to less monitoring. Due to the very small number of non-family CEOs appointed in the group of

responding family firms, the results with respect to the impact of a professional CEO on firm controls

should be interpreted with the necessary caution. Although the data reveal that one professional manager

entering the management team of the family firm can already make a difference with regard to controls in

the firm, future research is needed to determine the impact of the professional/non-family CEO on the

control choices of family firms.

34

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41

Table 1. Characteristics of respondents

Percentage Percentage

Industry Ownership structure

Textile 22.84 A single family owns 100% of firm 58.85

Plastics 25.31 A single family owns >50% of firm 16.81

Metal Products 51.85 A single family owns <50% of firm 24.34

Firm size Firm age

10–50 employees 26.87 5–15 years 36.12

51–100 employees 35.68 16–25 years 42.29

101–200 employees 37.44 More than 25 year 21.59

Table 2. Characteristics of family firms

Total firms

n = 133

Family ownership proportion

Family owns 100% of the shares of the firm n = 102 (76.69%)

Family firm with minority shareholder(s) n = 31 (23.31%)

Owner generation (%)

First generation 45.86 42.16 61.29

Second and later generation 54.14 57.84 38.71

Management involvement (%)

Family minority in MGMT 18.05 12.75 35.48

Family majority in MGMT 20.30 20.59 19.35

Only family members in MGMT 61.65 66.66 45.16

Type of CEO (%)

Non-family CEO 5.26 3.92 9.68

Family CEO 94.74 96.08 90.32

42

Table 3. Descriptive statistics of dependent variables

Name of variable

Theoretical range A single family owns 100% (N = 102)

A single family owns >50% (N = 31)

A single family owns <50% (N = 41)

Min

Max

Actual range Actual range Actual range Min Max Mean S.D. Min Max Mean S.D. Min Max Mean S.D.

MCS design Decentralization DC 1 7 1 7 4.56 1.52 1.63 6.5 4.35 1.25 1 6.63 4.52 1.37 Action control

Structuring of activities SA 1 7 1 7 4.17 1.45 2.63 7 4.64 1.19 1.88 6.75 4.83 1.28 Planning formality PF 1 4 1 4 2.19 0.85 1 4 2.51 0.92 1 4 2.80 1.00

Participative budgetinga PB 1 7 1 7 4.04 1.52 1.5 6.5 4.15 1.31 1.75 6.25 4.34 1.24

Result control Control formality CF 1 4 1 4 2.94 0.97 2 4 3.25 0.72 1 4 3.43 0.83 Performance evaluation PE 1 7 1.75 7 5.39 1.15 3.75 7 5.45 0.94 2.75 7 5.65 0.93 (1) Financial PE-FC 1 7 1 7 5.86 1.42 4 7 6.00 1.03 3 7 5.73 1.24 (2) Customer PE-CM 1 7 2 7 5.53 1.31 2 7 5.45 1.17 3 7 6.04 1.02 (3) Internal business processes PE-IB 1 7 1 7 5.50 1.34 3 7 5.35 1.27 3 7 5.68 1.05 (4) Innovation and learning PE-IL 1 7 1 7 4.66 1.64 2 7 5.00 1.34 2 7 5.17 1.43

Performance-related rewardsa PR 1 7 1 7 3.95 1.43 1 7 3.94 1.11 1 7 4.14 1.56

MCS use Diagnostic use DIA_USE 1 7 1 7 5.15 1.20 3.25 7 5.54 0.98 3 7 5.47 0.87 Interactive use INT_USE 1 7 1 7 4.92 1.28 3.14 7 5.10 0.92 2.86 7 5.45 1.02

Note: a There are only 142 observations for the “participative budgeting” variable; a single family owns 100% = 82; a single family owns >50% = 26; a single family owns <50% = 34, and 165 observations for “performance-related rewards” variable; a single family owns 100% = 97; a single family owns >50% = 31; and a single family owns <50% = 37.

43

Table 4. Correlation matrix of variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)

Control variables

(1) PEU 1.00

(2) Strategy 0.03 1.00

(3) Ln(size) -0.01 0.07 1.00

(4) Ln(age) -0.06 -0.01 0.22 1.00

Independent variables

(5) FIO 0.09 -0.02 0.09 0.21 1.00

(6) FIM 0.06 -0.01 0.05 0.21 0.96 1.00

(7) GEN -0.07 0.15 0.13 0.15 -0.05 0.01 1.00

(8) CEO 0.08 -0.01 0.09 0.23 0.99 0.96 -0.03 1.00

Dependent variables

(9) DC 0.14 -0.04 0.14 -0.08 -0.08 -0.13 -0.03 -0.08 1.00

(10) SA 0.16 -0.00 0.08 -0.20 -0.20 -0.26 -0.06 -0.20 0.70 1.00

(11) PF -0.00 -0.04 0.09 -0.14 -0.27 -0.28 0.17 -0.27 0.23 0.25 1.00

(12) PB 0.18 -0.12 -0.03 -0.07 -0.09 -0.11 0.02 -0.09 0.49 0.41 0.35 1.00

(13) CF 0.14 0.02 0.19 -0.11 -0.23 -0.28 0.13 -0.22 0.18 0.17 0.42 0.29 1.00

(14) PE 0.30 -0.02 0.02 -0.02 -0.10 -0.12 -0.07 -0.10 0.18 0.28 0.21 0.31 0.32 1.00

(15) PE-FC 0.20 0.08 0.04 0.01 0.03 0.01 -0.10 0.03 0.05 0.08 0.07 0.08 0.12 0.67 1.00

(16) PE-CM 0.26 -0.05 0.08 0.00 -0.15 -0.19 -0.16 -0.15 0.17 0.28 0.13 0.26 0.34 0.82 0.39 1.00

(17) PE-IB 0.21 0.00 0.02 0.03 -0.04 -0.02 -0.01 -0.03 0.16 0.19 0.22 0.24 0.26 0.85 0.49 0.57 1.00

(18) PE-IL 0.29 -0.08 -0.07 -0.09 -0.14 -0.16 -0.09 -0.14 0.19 0.31 0.24 0.34 0.29 0.83 0.35 0.61 0.62 1.00

(19) PR 0.04 0.12 0.03 -0.02 -0.05 -0.08 0.09 -0.06 -0.01 -0.03 0.03 0.07 0.12 0.13 0.12 0.08 0.11 0.13 1.00

(20) DI_USE 0.34 0.09 0.01 -0.08 -0.14 -0.18 -0.15 -0.14 0.26 0.26 0.20 0.42 0.41 0.55 0.43 0.39 0.44 0.49 0.43 1.00

(21) INT_USE 0.25 0.04 0.08 -0.08 -0.19 -0.21 -0.03 -0.17 0.30 0.29 0.23 0.52 0.39 0.40 0.18 0.35 0.29 0.43 0.40 0.68 1.00

Notes: Correlation is significant at * 10% confidence level, ** 5% confidence level, *** 1% confidence level. N = 174.

***

***

*** *** ***

* *

** *** *** *** *** ***

* ***

***

*** *** *** ***

** *** *** ***

* ** *** ***

*

** ** *** ***

*** *** ** *** *** ***

*** ***

*** ** ** ** *** * *** *** *** ***

*** ** ** *** *** *** *** *** ***

*** *** *** *** *** *** *** *** ** * ** * ***

**

*** *** ** *** ** *** *** *** *** *** *** *** *** *** **

*** *** *** *** *** *** *** *** *** *** * ** * *** ***

***

*

*** ***

44

Table 5. Effects of traditional contingent variables on MCS

MCS design MCS use

DC

Action controls Results controls PR DIA_USE INT_USE

SA PFa PB CFa PE PE-FC PE-CM PE-IB PE-IL

Control variable

PEU 0.152* 0.153* -0.096 0.208** 0.240 0.296*** 0.289*** 0.332*** 0.253** 0.403*** 0.029 0.360*** 0.266***

Strategy

Prospector -0.168 0.075 1.359*** -0.348 0.589 0.368* -0.034 0.158 0.517* 0.936*** 0.186 -0.060 -0.057

Analyzer -0.092 0.037 0.773* -0.190 0.453 0.364* 0.186 0.224 0.527** 0.612** 0.322 0.302 0.133

Reactor -0.343 -0.087 -0.251 -0.887** -0.089 -0.420 0.207 -0.649* -0.422 -0.861** 0.434 -0.044 -0.133

Ln(Size) 0.200** 0.148* 0.101 0.029 0.355** -0.007 0.060 0.095 -0.027 -0.174 0.034 0.028 0.121

Ln(Age) -0.208 -0.403*** -0.423 -0.082 -0.494* 0.073 0.031 0.060 0.202 -0.011 -0.045 -0.101 -0.164

N 174 174 174 142 174 174 174 174 174 174 165 174 174 R2 (%) 6.01 8.09 4.19 7.97 3.49 15.35 4.85 11.52 10.76 20.25 1.91 14.70 8.83 Adjust R2 (%) 2.63 4.79 - 3.88 - 12.31 1.43 8.34 7.56 17.39 -1.82 11.63 5.55 F 1.78 2.45** 18.65*** 1.95* 14.85** 5.05*** 1.42 3.62*** 3.36*** 7.07*** 0.51 4.79*** 2.70**

Notes: R2, adjusted R2 reported for OLS regression and a pseudo-R2 based on logistic regressions. *10% confidence level, **5% confidence level, ***1% confidence level.

45

Table 6. Effects on MCS of family involvement in ownership

MCS design MCS use

DC



Control variable

PEU 0.169** 0.180** -0.052 0.217** 0.310** 0.319*** 0.281** 0.380*** 0.275*** 0.432*** 0.0.04 0.374*** 0.300***

Strategy

Prospector -0.181 0.040 1.238*** -0.358 0.545 0.350 -0.044 0.135 0.515* 0.891*** 0.181 -0.105 -0.089

Analyzer -0.105 0.016 0.704* -0.187 0.439 0.345* 0.194 0.185 0.508* 0.589* 0.312 0.292 0.105

Reactor -0.341 -0.109 -0.421 -0.889**

-0.190 -0.418 0.181 -0.623* -0.396 -0.894** 0.353 -0.097 -0.138

Ln(Size) 0.206** 0.161* 0.147 0.032 0.413** 0.002 0.061 0.110 -0.022 -0.158 0.036 0.041 0.135

Ln(Age) -0.191 -0.340**

-0.260 -0.058 -0.332 0.098 0.058 0.079 0.192 0.070 -0.043 -0.009 -0.116

Family involvement in ownership (FIO)

>50% FIO -0.218 -0.289 -0.692 -0.181 -0.787* -0.301 0.174 -0.685** -0.355 -0.301 -0.158 -0.041 -0.431*

100% FIO -0.242 -0.475**

-1.243***

-0.237 -1.281***

-0.340* 0.0342 -0.628***

-0.250 -0.556** -0.125 -0.390** -0.535***

N 174 174 174 142 174 174 174 174 174 174 165 174 174

R2 (%) 6.99 11.74 6.80 8.91 6.26 17.28 5.03 16.20 11.67 22.34 2.24 17.78 13.51

Adjust R2 (%) 2.49 7.46 - 3.43 - 13.27 0.43 12.14 7.38 18.58 -2.77 13.80 9.32

F 1.55 2.74*** 30.30*** 1.63 26.65*** 4.31*** 1.09 3.99*** 2.72*** 5.93*** 0.45 4.46*** 3.22***

Incremental explanatory power

0.88 3.41** 11.44** 0.68 11.12*** 1.92 0.16 4.61** 0.85 2.22 0.27 3.10** 4.47**


46

Table 7. Effects on MCS of family involvement in ownership and management

MCS design MCS use

DC

Action controls Results controls PR DIA_USE

INT_USE SA PFa PB CFa PE PE-FC PE-CM PE-IB PE-IL

Control variable PEU 0.155* 0.160** -0.059 0.209** 0.297* 0.312*** 0.278** 0.367*** 0.272*** 0.420*** 0.0.031 0.361*** 0.287***

Strategy

Prospector -0.231 -0.038 1.203** -0.416 0.415 0.327 -0.033 0.132 0.462 0.841** 0.119 -0.147 -0.154

Analyzer -0.130 -0.017 0.684 -0.219 0.329 0.338 0.198 0.190 0.481* 0.577 0.270 0.274 0.071

Reactor -0.324 -0.120 -0.435 -0.907** -0.189 -0.422 0.202 -0.580 -0.458 -0.904** 0.353 -0.094 -0.156

Ln(Size) 0.183** 0.141 0.135 0.027 0.375** -0.005 0.047 0.073 0.002 -0.165 0.020 0.022 0.125

Ln(Age) -0.241 -0.407*** -0.340 -0.110 -0.452 0.077 0.072 0.084 0.147 0.010 -0.078 -0.039 -0.165

Family involvement in ownership and management

>50% FIO – family minority in MGMT

0.039 -0.229 -0.802 -0.121 0.042 -0.326 0.252 -0.578 -0.535 -0.400 0.146 0.043 -0.324

>50% FIO – family majority in MGMT

-0.451 -0.419 -1.016 -0.202 -1.161 -0.338 0.342 -0.449 -0.483 -0.770 -0.229 0.005 -0.408

>50% FIO – only family members in MGMT

-0.336 -0.288 -0.503 -0.230 -1.138** -0.265 0.045 -0.862** -0.161 -0.033 -0.402 -0.130 -0.531*

100% FIO – family minority in MGMT

0.380 0.373 -0.546 0.176 0.090 -0.031 0.157 -0.023 -0.246 0.040 -0.232 0.148 -0.019

100% FIO – family minority in MGMT

-0.183 -0.493* -1.196** -0.431 -1.438*** -0.321 0.119 -0.403 -0.528 -0.478 -0.247 -0.377 -0.660**

100% FIO – only family members in MGMT

-0.357* -0.602*** -1.384*** -0.255 -1.422*** -0.395** -0.015 -0.802*** -0.158 -0.672** -0.151 -0.481** -0.576***

N 174 174 174 142 174 174 174 174 174 174 165 174 174

R2 (%) 10.98 17.55 7.36 10.76 8.18 18.10 5.35 19.40 12.75 24.21 4.40 20.29 15.76

Adjust R2 (%) 4.35 11.40 - 2.45 - 11.99 -1.71 13.39 6.24 18.56 -3.15 14.35 09.48

F 1.65* 2.86*** 32.77*** 1.30 34.80*** 2.96*** 0.76 3.23*** 1.96** 4.29*** 0.58 3.42*** 2.51***

Incremental explanatory power 1.50 3.08** 13.71** 0.67 18.54*** 0.90 0.14 2.62** 0.61 1.40 1.50 1.88* 2.21**


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Table 8. Effects on MCS of family involvement in ownership and type of CEO

MCS design MCS use

DC



Control variable

PEU 0.179** 0.186** -0.026 0.235** 0.343** 0.318*** 0.281** 0.373*** 0.289*** 0.418*** 0.028 0.369*** 0.311***

Strategy

Prospector -0.231 0.012 1.184** -0.419 0.389 0.362 -0.042 0.176 0.456 0.969*** 0.227 -0.082 -0.126

Analyzer -0.144 -0.002 0.589 -0.259 0.314 0.365* 0.198 0.230 0.476* 0.663** 0.333 0.314 0.105

Reactor -0.375 -0.131 -0.459 -0.925**

-0.309 -0.420 0.180 -0.607* -0.446 -0.854** 0.396 -0.085 -0.185

Ln(Size) 0.208** 0.165* 0.113 0.018 0.425** 0.009 0.062 0.118 -0.009 -0.151 0.023 0.042 0.156*

Ln(Age) -0.226 -0.362**

-0.327 -0.085 -0.437 0.101 0.058 0.101 0.145 0.118 0.000 0.005 -0.154

FIOand types of CEO

>50% FIO – non-family CEO

-0.005 -0.331 1.506 0.444 -0.387 -0.780 0.045 -1.421** -0.622 -1.212 0.183 -0.302 -1.363**

>50% FIO – family CEO

-0.253 -0.291 -0.974** -0.285 -0.868* -0.245 0.189 -0.594** -0.339 -0.183 -0.188

-0.007 -0.337

100% FIO – non-family CEO

-0.919* -0.845 -2.846***

-1.563**

-2.934***

-0.167 0.063 -0.058 -1.019 0.509 0.492 -0.069 -0.976*

100% FIO – family CEO

-0.213 -0.459**

-1.204***

-0.200 -1.208***

-0.347** 0.033 -0.654***

-0.216 -0.603** -0.155

-0.405** -0.516***

N 174 174 174 142 174 174 174 174 174 174 165 174 174 R2 (%)

8.14 12.05 8.43 12.36 7.18 17.79 5.05 17.37 12.58 24.10 3.36 18.16 15.54 Adjust R2 (%)

2.50 6.66 - 5.67 - 12.74 -0.77 12.30 7.21 19.44 -2.91 13.13 10.36

F 1.44 2.23** 37.54*** 1.85* 30.53*** 3.53*** 0.87 3.43*** 2.34** 5.18*** 0.54 3.62*** 3.00***

Incremental explanatory power

0.95 1.83 17.70*** 1.64 15.03*** 1.21 0.09 2.88** 0.85 2.07* 0.58 1.72 3.24**


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Table 9. Effects on MCS of type of generational involvement

MCS design MCS use

DC



Control variable PEU 0.204* 0.254 0.012 0.167 0.179 0.168 0.207 0.105 0.138 0.297* -0.084 0.237** 0.160 Strategy

Prospector -0.182 -0.129 0.785 -0.223 -0.314 0.366 0.329 0.150 0.687* 0.373 0.512 -0.317 -0.321 Analyzer -0.160 -0.150 0.278 -0.078 -0.230 0.521* 0.307 0.400 0.836** 0.633 0.319 0.233 0.100 Reactor 0.054 -0.218 -1.221 -0.468 -1.150 -0.140 0.531 -0.203 0.032 -0.967 0.320 -0.265 -0.046

Ln(Size) 0.149 0.168 0.226 0.101 0.731*** 0.087 0.064 0.298* 0.054 -0.083 0.023 0.035 0.278 Ln(Age) -0.129 -0.423* -0.216 -0.180 -0.793* 0.129 0.157 0.183 0.242 -0.069 -0.139 0.043 -0.098

Generational involvement 1st GEN of 100% FIO – family minority in MGMT

-0.154 0.163 -0.306 0.167 0.150 0.041 0.300 0.102 -0.394 0.282 -0.612 0.277 0.276

2nd GEN of 100% FIO – family minority in MGMT

0.557 -0.057 -0.952 0.435 -0.929 -0.319 -1.222 -0.131 0.093 -0.309 -0.620 -0.277 -0.342

1st GEN of 100% FIO – family majority in MGMT

-0.029 -0.470 -0.805 -0.323 -0.970 -0.593* -0.413 -0.675* -0.926** -0.419 -0.275 -0.346 -0.645*

2nd GEN of 100% FIO – family majority in MGMT

-0.706 -0.587 -0.883 -0.166 -0.695 -0.205 0.166 -0.217 -0.216 -0.597 -0.557 -0.526 -1.118*

1st GEN of 100% FIO – only family members in MGMT

-0.570* -0.825*** -1.799*** -0.573* -1.675*** -0.310 0.216 -0.832** 0.049 -0.717 0.198 -0.347 -0.571*

2nd GEN of 100% FIO – only family members in MGMT

-0.402 -0.395 -1.323** -0.169 -1.147* -0.470 -0.280 -0.752** -0.416 -0.514 0.005 -0.684** -0.759**

N 102 102 102 81 102 102 102 102 102 102 96 102 102 R2 (%) 13.65 19.10 8.12 10.61 9.29 15.20 9.30 19.75 18.62 14.77 12.19 17.45 19.83 Adjust R2 (%) 2.01 8.19 - -5.16 - 3.77 2.93 8.93 7.65 3.28 -0.21 6.32 9.02 F 1.17 1.75* 21.83** 0.67 22.39** 1.33 0.76 1.83* 1.70* 1.29 0.98 1.57 1.83* Incremental explanatory power 1.33 1.79 12.33* 0.79 10.65* 0.93 0.83 1.93* 1.19 0.70 1.39 1.37 2.12*


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Table 10. Summary of hypothesis testing

Traditional agency view Type I agency problem (Principal–agent)

Type II agency problem (Principal–principal)

Zero agency costs (Family-owned and family-managed firm)

in comparison to the adoption of controls in non-family firms

H1 H2 Ownership proportion

H3 Type of generational involvement in the firm (1) Family managers in MGMT (2) Type of CEO

Family managers are a minority

Family managers are the majority

Only family members

Non-family CEO

Family CEO

FF with minority shareholder(s)

100% FF

1st generation 2nd and later generation

MCS design Decentralization - - - - - - - - - Action control

Structuring of activities - * *** - ** - *** *** - Planning formality - ** *** *** *** - *** *** ** Participative budgeting - - - ** - - -

Result control Control formality - *** *** *** *** * *** *** * Performance evaluation - ** - ** * - -

Financial - - - - - - - - - Customer - - *** - *** ** *** ** ** Internal business processes - - - - - - - - - Innovation and learning - - ** - ** - ** - -

Performance-related rewards - - - - - - - - -

MCS use Diagnostic use - - ** - ** - ** - - Interactive use - ** *** * *** * *** * **

Note: Significantly less used than in non-family firms: ***, **, * negatively significant at 1%, 5%, and 10% confidence levels, respectively.

50

Appendix A. Questionnaire items and statistics of measurement analysis

Variable

Items in questionnaire

Confirmatory factor analysis Cronbach’s alpha Loading on

first factor Variance explained

Engenvalue

MCS design

Decentralization To what extent does CEO delegate decision making authority to lower-level managers following activities? (scale anchors: 1 = extremely low to 7 = extremely high)

(a) Development of new outputs (b) Purchase of capital equipment (c) Hiring and firing of personnel (d) Sourcing of inputs (e) Operating procedures and schedules (f) Pricing of outputs (g) Distribution of outputs (h) Making tradeoffs within unit’s budget

0.77 0.75 0.57 0.78 0.77 0.83 0.79 0.82

0.58 4.675 0.90

Structure of activities

To what extent does CEO develop standardized procedures for lower-level manager’s following activities? (scale anchors: 1 = extremely low to 7 = extremely high)

(a) Development of new outputs (b) Purchase of capital equipment (c) Hiring and firing of personnel (d) Sourcing of inputs (e) Operating procedures and schedules (f) Pricing of outputs (g) Distribution of outputs (h) Making tradeoffs within unit’s budget

0.71 0.75 0.67 0.81 0.82 0.80 0.82 0.82

0.60 4.834 0.90

Planning formality

Which description best describes the firm’s planning practices (1 answer possible)

(a) We have written budgets/plans with regard to many (4 or more) different operational areas

(b) We have written budgets/plans with regard to a restricted number operational areas

(c) We do not have written budgets/plans. Budgeting/planning is intuitive on the basis of manager’s experience

(d) We have no written nor intuitive budgets/plans

n/a n/a n/a n/a

n/a n/a n/a

Participative budgeting

Please rate the degree of importance: ` (scale anchors: 1 = extremely low to 7 = extremely high)

(a) CEO seeks input of lower-level managers in budgeting process

(b) CEO includes budget change suggested by lower-level managers

(c) CEO finalizes budget until lower-level managers are satisfied with budget

(d) Overall influence lower-level managers have in setting budget target

0.88 0.86 0.82 0.84

0.72 2.881 0.87

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Variable




Engenvalue

Control formality Which description best describes firm’s control practices (1 answer possible)

(a) We have written control reports with regard to many (4 or more) different operational areas

(b) We have written control reports with regard to a restricted number operational areas

(c) We do not have written control reports. Control method is intuitive on the basis of manager’s experience

(d) We have no written nor intuitive control methods

n/a n/a n/a n/a

n/a n/a n/a

Performance evaluation

How much does the CEO take into account the following factors when setting objectives and evaluating company’s performance? (scale anchors: 1 = extremely low to 7 = extremely high)

(a) Financial (e.g., operating income, sales growth, net cash flows, costs per unit produced)

(b) Customer (e.g., market share, customer response time, number of customer complaints, number of warranty claims, survey of customer satisfaction)

(c) Internal business processes (e.g., materials efficiency variance, manufacturing lead time, rate of material scrap loss, labor efficiency variance)

(d) Innovative and learning (e.g., number of new patents, number of new product launches, time-to-market for new products, employee satisfaction)

0.67 0.82 0.85 0.83

0.63 2.532 0.80

Performance-related rewards

Please rate the extent to which (scale anchors: 1 = extremely low to 7 = extremely high)

(a) The lower-level manager compensation contract clearly specifies how their compensation is related to the unit performance relative to the targets put forward.

(b) The lower-level manager’s financial rewards increase as their unit increasingly outperforms its targets.

(c) The managers whose units’ performances relative to their targets are among the top 25% are given larger financial rewards than those given to managers among the bottom 25%.

(d) The percentage of a manager’s total annual compensation typically coming from a pre-set salary as opposed to a bonus based on the unit’s performance relative to the targets.

0.84 0.87 0.78 0.56

0.59 2.379 0.76

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Variable




Engenvalue

MCS use

Diagnostic use Your company uses performance measures to (scale anchors: 1 = extremely low to 7 = extremely high)

(a) Track progress towards goals (b) Monitor results (c) Compare outcomes to expectations (d) Review key measures of firms

0.91 0.86 0.93 0.89

0.81 3.228 0.92

Interactive use Your company uses performance measures to (scale anchors: 1 = extremely low to 7 = extremely high)

(a) Enable discussion in meetings of superiors, subordinates and peers

(b) Enable continual challenge and debate underlying data, assumptions and action plans

(c) Provide a common view of the company (d) Tie the organization together (e) Enable the organization to focus on

common issues (f) Enable the organization to focus on critical

success factors (g) Develop a common vocabulary in the

company

0.65 0.71 0.89 0.91 0.93 0.85 0.86

0.69 4.853 0.92

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Appendix B. Investigation of non-response bias

Early respondents (n = 69)

Late respondents (n = 41) t-test/Chi-square

Panel A: t-test Mean S.D. Mean S.D. Diff.

Firm size 84.88 43.99 119.51 90.26 -34.63*** Firm age 16.75 7.42 16.76 9.28 -0.002 PEU 4.51 0.88 4.49 0.89 0.02 Ownership proportion 67.92 42.67 67.68 6.75 0.24 Management proportion 77.05 31.48 69.95 6.32 7.09 MCS design

Decentralization 4.41 1.49 4.39 1.34 0.14 Structure of activities 4.54 1.29 4.51 1.34 0.02 Participative budgeting 4.14 1.47 4.25 1.34 -0.11 Performance evaluation 5.43 1.02 5.60 1.01 -0.18

Financial 5.88 1.33 5.95 1.24 -0.07 Customer 5.65 1.11 5.59 1.28 0.07 Internal business processes 5.49 1.17 5.73 1.07 -0.24 Innovation and learning 4.68 1.58 5.15 1.48 -0.47

Performance-related rewards 4.22 1.39 3.88 1.42 0.34

MCS use Diagnostic use 5.24 1.15 5.46 0.98 -0.22 Interactive use 5.01 1.30 5.17 1.10 -0.16

Strategic planning 5.25 1.12 5.46 1.04 -0.21

Panel B: Chi-square test Frequency Percent Frequency Percent Pearson Chi-square

Strategy 3.61 Defender 13 18.84 9 21.95 Prospector 18 26.09 13 31.71 Analyzer 29 42.03 18 43.90 Reactor 9 13.04 1 2.44

Generation of family firm 1.68 Non-family firm 18 26.09 11 26.83 1st-generation family firm 28 40.58 12 29.27 2nd- and later-generation family firm

23 33.33 18 43.90

MCS design Planning formality 5.02

Level 1 10 14.49 3 7.32 Level 2 34 49.28 17 41.46 Level 3 16 23.19 9 21.95 Level 4 9 13.04 12 29.27

Control formality 5.11 Level 1 6 8.70 0 0.00 Level 2 15 21.74 6 14.63 Level 3 20 28.99 15 36.59 Level 4 28 40.58 20 48.78

Notes: * 10% confidence level, ** 5% confidence level, *** 1% confidence level.

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