Using Transaction Cost Economics to explain outsourcing of accounting

Small Bus Econ (2010) 35:93–112 DOI 10.1007/s11187-008-9149-3

Using Transaction Cost Economics to explain outsourcing of accounting Patricia Everaert Æ Gerrit Sarens Æ Jan Rommel

Accepted: 28 July 2008 / Published online: 4 November 2008 Ó Springer Science+Business Media, LLC. 2008

Abstract This study explores whether SMEs involved in the outsourcing of accounting tasks differ, in terms of transactional and personal (CEO) characteristics, from others that perform the same tasks within the company. We rely on the transaction cost economics (TCE) model, while controlling for age, educational background, and trust of the SME executive in the external accountant. A survey was developed to investigate the outsourcing by Belgian SMEs both of routine (entry of invoices, interim reporting) and non-routine (period-end accounting,

P. Everaert (&) Department of Accounting, Faculty of Economics and Business Administration, Ghent University, Kuiperskaai 55/E, 9000, Ghent, Belgium e-mail: [email protected] G. Sarens Department of Finance, Universite´ Catholique de Louvain, Place des Doyens 1, 1348, Louvain-la-Neuve, Belgium e-mail: [email protected]

preparation of financial statements) accounting tasks. For the routine accounting tasks, frequency was significantly associated with outsourcing. Meanwhile, for non-routine accounting tasks, asset specificity and frequency were significantly associated. Highfrequency tasks were associated with lower levels of outsourcing. Similarly, higher asset specificity was associated with lower levels of outsourcing. Furthermore, the educational background of the CEO, as well as the CEO’s level of trust in the external accountant, was significantly associated with outsourcing, confirming the upper echelon theory. Having a more educated CEO was associated with lower levels of outsourcing, both for routine and nonroutine accounting tasks. Keywords Outsourcing Accounting SME Transaction cost economics

JEL Classifications C12 C24 C42 D23 M10 M41

G. Sarens Department of Finance, University of Antwerp, Prinsstraat 13, 2000, Antwerp, Belgium

1 Introduction

J. Rommel Public Management Institute, Katholieke Universiteit Leuven, Parkstraat 45 Box 3609, B-3000 Leuven, Belgium e-mail: [email protected]

Outsourcing can be defined as the act of subcontracting out all or parts of some function in a firm to an external party. The transaction cost theory of the firm (TCE), introduced by Coase (1937), has become

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a standard framework to explain why some firms choose to organize a given function internally, while other firms decide to outsource that function to an external party. An extensive part of the empirical research on outsourcing adopts this TCE framework (Boerner and Macher 2002). By considering the relative cost of transactions using its own employees on the one hand and external parties on the other, TCE tries to explain how companies are organized. Simply stated by Klein (2005), transactions differ in the degree to which relationship-specific assets are involved (asset specificity), the amount of uncertainty about the future (environmental uncertainty), the amount of uncertainty about other parties’ actions (behavior uncertainty), and the frequency with which a given transaction occurs. Many empirical studies have investigated the outsourcing of production tasks (so-called backward integration) and found that asset specificity is a significant driver in the outsourcing decision, both when studied within a single industry and in crosssectional studies (e.g., John and Weitz 1988; Masten et al. 1989; Monteverde and Teece 1982). Anderson and Gatignon (2005) discuss several studies wherein the TCE framework is used to explain outsourcing of marketing and distribution tasks (so-called forward integration). Both asset specificity (human capital) and behavioral uncertainty seemed significant in explaining entry mode (independent agent versus sales employees). With respect to the outsourcing of other service functions, such as human resources, IT, and accounting, only a few studies are available. For instance, Watjatrakul (2005) and Barthelemy and Geyer (2005) found asset specificity to be an important driver for the outsourcing of IT. Widener and Selto (1999) and Spekle´ et al. (2007) investigated the outsourcing of the internal audit function and found support for both asset specificity and frequency. As far as we know, research on the outsourcing of accounting using a TCE framework is missing. This limited number of studies on outsourcing of service functions is in contrast with the general trend we notice in practice, wherein companies tend to focus on core activities (Quinn and Hilmer 1994). Similarly, Vandaele et al. (2007) derive from their review of the TCE literature that, despite the service-dominant shift in practice, limited attention has been given to the

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specific characteristics of service functions and their impact upon the outsourcing decision. They conclude that, to govern the outsourcing decision of business services efficiently, more emphasis should be placed on behavioral uncertainty, (human) asset specificity, and trust. Also, Klein (2005) concludes in his review paper that strategic management papers have included alternative theories of the firm, based on capabilities, power, and trust, and that these variables could potentially become important rivals to the transaction cost view. For instance, the impact of trust in service firms has been investigated by Brouthers and Brouthers (2003), and trust turned out to be significant for entry mode choices in service firms (but not in manufacturing firms). Similarly, because of the people-intensive nature of the accounting tasks, we might expect that trust in the service provider will influence the outsourcing decision. Therefore, trust will be included in this paper. Furthermore, although small and medium-sized enterprises account for a significant portion of the economic activity, research on outsourcing within small and medium-sized enterprises (SME) is relatively scarce (Brouthers and Nakos 2004). Gilley et al. (2004b) have conducted research on the outsourcing of manufacturing tasks by small firms and have called for a more general theory of outsourcing, which would require the consideration of a number of personal and organizational drivers. This trend is in line with the upper echelon theory, which suggests that outsourcing decisions in SMEs may be influenced significantly by the personal characteristics of the SME executive (Hambrick and Mason 1984; Hitt and Tyler 1991; Wiersema and Bantel 1992). Therefore, for the purposes of this study, we will include personal characteristics, such as the age and educational background of the CEO. Overall, the purpose of this study is to identify why some SMEs are outsourcing their accounting tasks, whereas other SMEs perform the same accounting tasks within the company. We will investigate the impact of TCE variables, while controlling for trust and other personal characteristics of the SME executive. In doing so, we attempt to expand the body of knowledge about outsourcing in several ways. First, we focus on outsourcing of a service function, conforming to the trends identified by Gilley et al. (2004a), in that we focus, in depth, on

Using Transaction Cost Economics to explain outsourcing of accounting

one functional area, that being the accounting function. Second, we perform a cross-industry study within one country (Belgium). As suggested by Klein (2005), the progression from single-industry case studies to a cross-industry within-country analysis is a natural one as we strive to further develop our knowledge on outsourcing. Third, hypotheses are derived from the TCE model and include asset specificity, environmental uncertainty, behavioral uncertainty, and frequency as explanatory variables. Klein (2005) argues that empirical research on outsourcing often is hampered by confusion about the definitions of, and therefore the empirical proxies for key variables like asset specificity and uncertainty. In this study, we do not use general proxies, but develop measurement scales to capture the perception of the SME executive on asset specificity, and on behavioral and environmental uncertainty. A measure for frequency is developed, capturing both the volume (representing the resources invested) and repetitiveness of the transaction. Fourth, we include control variables referring to the personal characteristics of the CEO (education, age, trust in the external accountant). Park and Krishnan (2001) and Gilley et al. (2004b) suggest that personal variables may have an impact on outsourcing decisions, in particular for small firms. In addition, TCE has been criticized for being focused solely on opportunism as a basis for behavior, ignoring how relationships more often are based on cooperation and the personal relationships that exist between actors (Muthusamy and White 2005; Ring and Van de Ven 1992; Zaheer and Venkatraman 1995). Fifth, outsourcing often is modeled as a discrete variable. In this study, we consider the degree of outsourcing as the dependent variable since, particularly with accounting tasks, small firms often use a combination of outsourcing and internalizing. Finally, we collect data at the task level, thereby making it possible to distinguish between the outsourcing of routine and non-routine accounting tasks. In the following sections, we provide some background information and develop hypotheses by applying the TCE framework to accounting tasks. This is followed by a description of the survey methodology. Next, the findings are presented. Finally, the results are discussed and implications for future research are drawn.

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2 Background 2.1 Accounting tasks As we address the process of outsourcing, we refer to the ‘external accountant,’ including both the independent accountant and the accounting firm. Conversely, we refer to the ‘internal accountant’ as that company employee performing the accounting tasks within the SME, thereby addressing the process of internalizing. As indicated in Fig. 1, the annual accounting process in a company is composed of four main tasks: (1) entry of invoices and financial transactions; (2) preparation of an interim profit and loss account (e.g., monthly profit calculation); (3) period end accounting (e.g., depreciations, interest accruals); (4) preparation of financial statements (balance sheets, profit and loss account, notes). These four tasks are interconnected,1 and all are necessary to produce complete financial statements. Similar to the internal auditing function, we may distinguish between routine and non-routine tasks. The entry of invoices and preparation of interim reports are routine tasks, whereas period end accounting and the preparation of financial statements can be considered non-routine tasks. Routine tasks are those tasks for which the output is relatively straightforward and standardized, which require less judgment on the part of the accountant, whether internal or external (Abbott et al. 2007; Caplan and Emby 2004). In a similar vein, non-routine tasks require more judgment from the accountant, so that the decisions are less standardized and require valuable opinions. Since the accounting tasks are interconnected, the decision to outsource non-routine tasks depends, in a sense, on the outsourcing decision regarding routine tasks. For instance, firms that decide to fully outsource routine tasks also will need to rely on full outsourcing for non-routine tasks, since the firm will no longer possess the information that is required to perform the non-routine tasks internally. In other words, only firms that retain at least some part of the

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The interconnection of tasks is such that the accounting process cannot be disaggregated entirely into four separate tasks, as one could do with components in a production process (as in Masten et al. 1989). The information from the entry of invoices is needed to prepare the interim reports, to do periodend tasks, and to prepare financial statements.

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96 Fig. 1 The accounting process

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Entry of invoices and financial transactions

routine tasks internally can make a genuine make-orbuy decision concerning the subsequent non-routine tasks. Additional accounting tasks (e.g., business advice to managers) have been excluded from this study, since these activities are performed on an ad hoc basis and significantly differ in terms of content (Gooderham et al. 2004). 2.2 Outsourcing intensity Exploratory interviews2 with practitioners have taught us that considering the accounting process simply in terms of outsourcing or internalizing will not capture reality. Some companies use a hybrid of outsourcing and internalizing, even within a single task. For instance, an internal accountant often prepares the period-end accounting, while the final touches on this task are outsourced to an external accountant. Hence, in this study, we consider outsourcing intensity to be the dependent variable and define it as ‘the degree of outsourcing, expressed as the percentage of the workload performed by an external accountant.’ Outsourcing intensity will be considered for the whole accounting process, the socalled overall outsourcing intensity, as well as for routine and non-routine tasks separately.

3 Literature and hypotheses Transaction cost economics (TCE) has been the predominant framework employed to investigate the determinants of outsourcing in manufacturing (e.g., Masten 1984; Klein 2005; Walker and Weber 1984). In general, the decision to outsource or internalize in a given situation depends upon comparative transaction costs, that is, the costs of running the service, including 2

In an early stage of this study, we conducted two explorative interviews, one with an external accountant and one with an accounting software developer, to familiarize with the outsourcing of accounting tasks. The questions addressed were: (1) What kinds of tasks are outsourced to an accounting firm? (2) Why do SMEs outsource accounting tasks?

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Interim Reporting

Period end Accounting

Financial Statements

the ex-ante costs of negotiating a contract and the ex-post costs of monitoring performance and providing feedback (Williamson 1985). Outsourcing is favored in situations in which markets are competitive (i.e., where many potential suppliers are available). Market pressures minimize the need to monitor supplier behavior (Hennart 1989). When markets fail and the range of suppliers available to a firm is restricted, a supplier has a tendency to behave opportunistically. This opportunistic behavior only can be reduced through stringent negotiations and extensive supervision of contractual relationships, thereby increasing transaction costs (Dwyer and Oh 1988). In such circumstances, the firm can reduce its transaction costs significantly by replacing external suppliers with its own employees, whose behavior can be monitored and controlled more effectively (Hennart 1989). 3.1 Asset specificity The most important reason for market failure is the presence of specific assets (Williamson 1985; 1986; Klein et al. 1990). Two common types of specific assets include physical assets, referring to relationshipspecific equipment and machinery, and human assets, describing transaction-specific knowledge, skills, or human capital, achieved through specialized training or learning-by-doing (Klein 2005; Masten et al. 1989; Monteverde and Teece 1982). In general, specific assets are not re-deployable for alternative uses. When investments in these kinds of assets are made, a supplier and a buyer are ‘locked into’ a transaction, because the assets are specialized to that transaction and have limited or no value elsewhere (Williamson 1985). Specific assets make it costly to switch to a new relationship (John and Weitz 1988). The core proposition of TCE asserts that specialized assets have lower transaction costs within the firm, because the company has the ability to measure and reward behavior (Eisenhardt 1989). Hence, with asset specificity, firms would rather internalize than outsource. Human asset specificity is of particular importance in accounting. Similar to other services and contrary to manufacturing activities, accounting tasks tend to


be less capital-intensive and more people-intensive (Erramilli and Rao 1993; Brouthers and Brouthers 2003). The added value for the company tends to be derived from idiosyncratic assets, such as companyspecific accounting software and the employees’ (intangible) specialized knowledge of the specific characteristics of the company and its activities (Anderson and Gatignon 1986; Hennart 1988; Erramilli and Rao 1993; Murray and Kotabe 1999). In accordance with Erramilli and Rao (1993), we expect that, as accounting tasks become more customized to a company and more specialized, asset specificity increases and, consequently, transferring these tasks to an external accountant can be protracted, difficult, costly, and incomplete. This leads to the following hypothesis: H1 The higher the asset specificity of accounting tasks, the less intensely they are outsourced. 3.2 Environmental uncertainty In an internal audit context, environmental uncertainty refers to ‘expected variation in the demand for audit activities’ (Widener and Selto 1999; 48). Translated to the specific context of this study, environmental uncertainty concerns the stability and predictability of the workload related to accounting tasks, as a consequence of the volatility of business activities. If business activities are volatile (e.g., unstable number of purchase and sales invoices because of seasonal trends; unstable period-end tasks because of mergers, acquisitions or plant closures), the workload related to sequential accounting tasks also becomes unstable and unpredictable. If companies can predict and schedule the workload related to their accounting tasks accurately, the costs of contracting should be low, and firms may outsource their accounting tasks, while accruing low transactions costs. Conversely, it can be argued that low predictability of the workload related to accounting tasks creates high transaction costs, because contractual agreements with an external accountant may need to be renegotiated and changed (spot contracting) (Williamson 1991). This requires time that the SME executive may not have and reduces the flexibility needed to address these fluctuations in the workload in a timely fashion (Erramilli and Rao 1993). Moreover, a high level of uncertainty makes it very costly to write and enforce a contract, with an

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external accountant, which specifies all possible future conditions. Hence, when the workload of the accounting tasks is less stable and predictable, an internal accountant will be able to respond more quickly to these fluctuations (Williamson 1991; Hennart 1994), which compensates for any uncertainty (Klein et al. 1990). This leads to the following hypothesis: H2 The higher the environmental uncertainty in accounting tasks, the less intensely they are outsourced. 3.3 Behavioral uncertainty Behavioral uncertainty reflects difficulties in monitoring performance and controlling the human tendency toward opportunism, which can involve cheating, distortion of information, shirking of responsibilities, and other forms of dishonest behavior (Williamson 1985; Hill 1990). In the context of this study, behavioral uncertainty can be interpreted as the difficulty of evaluating whether the accountant did the job accurately and to the best of his or her ability. High behavioral uncertainty causes high transaction costs, due to writing, negotiating, monitoring, and enforcing contracts, all done to prevent opportunistic behavior. As a result, when contributions from an outside supplier cannot be assessed accurately, adequate contracts with external suppliers will be costly to draft. In this case, it is more efficient to internalize the service, which gives the firm a legal right to control the actions of its employees directly (Williamson 1985; Klein et al. 1990). Hence, if it is difficult to evaluate an external accountant, then TCE suggests that the accounting tasks will not be outsourced, as the SME prefers to control the performance of the accountant directly. This results in the following hypothesis: H3 The higher the behavioral uncertainty in accounting tasks, the less intensely they are outsourced. 3.4 Frequency Transaction frequency is the frequency with which transactions recur (Murray 2001). Frequent or recurrent activities can create benefits of economies of scale, which permit the recovery of setup costs (Widener and Selto 1999). Therefore, TCE predicts that frequent or recurrent services are more likely to

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be produced internally (Williamson 1985). The frequency of accounting tasks can be understood in two ways. First, frequency can be understood as the periodicity of the accounting task. In particular, each of the four accounting tasks can be performed every day, week, month, quarter, semester, or year. Although accounting laws require that financial statements are prepared once a year, many companies prepare financial statements (interim reports or period end accounting) more often. Similarly, some companies enter invoices daily, while others perform this task only once per month. Second, frequency also can be understood in terms of the size of the activity. More specifically, for the entry of invoices, the size of the activity is important, representing the number of resources invested (in this case, employee(s) needed to enter the invoices). A company that processes 10 invoices every week has a lower frequency of ‘invoice entry’ than a company that processes 1,000 invoices every week. The former company will be more attracted to outsourcing than the latter. In this study, we will consider frequency, including both a periodicity and size measure, as will be shown later in this paper. This leads to the fourth hypothesis: H4 The higher the frequency of accounting tasks, the less intensively they are outsourced.

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post mail. We received 135 surveys back. We based our analysis on 126 usable surveys, producing an effective response rate of 10.5%. This response rate is just above the normally low response rate of 5–10% expected from a postal survey (see Alreck and Settle 1985; Barnett 1991) and is comparable to other studies that have focused on SMEs (e.g., Thong 1999; Daniel and Grimshaw 2002). To detect possible non-response bias, we compared early and late respondents, as suggested by Armstrong and Overton (1977), but identified no significant differences between early and late respondents in terms of the number of employees or total assets (p = 0.49 for both measures). Furthermore, none of the dependent or independent variable was significantly different between early and late respondents. Including a dummy variable for late respondents into our Tobit models did not change any of the results. The dummy variable itself never was significant. 4.2 Measurement scales Table 1 reports the details of the measurement scales. For each TCE variable, three measures were developed. The first measure was the overall measure; the second (third) measure only captured the items that can be applied for routine tasks (non-routine tasks). The construction of the scales is described below.

4 Methodology

4.2.1 Dependent variable: Outsourcing intensity

4.1 Data collection

For each accounting task, participants were asked to indicate the percentage of the workload that was performed by an external accountant and the percentage of the workload performed by an internal accountant, summing up to 100%. The overall outsourcing intensity was calculated as the ‘average’ workload performed by the external accountant, ranging between 0% (no outsourcing at all) and 100% (complete outsourcing of all four accounting tasks). Similarly, the outsourcing intensity of routine tasks reflected the average workload for entry of invoices and the preparation of interim reporting performed by the external accountant. Outsourcing intensity of non-routine tasks captured the average workload of the period-end accounting and the preparation of financial statements performed by the external accountant.

The population of this study includes all SMEs located in the Flemish region of Belgium. We used the criteria of the European Commission (6 May 2003), defining SMEs as companies employing fewer than 250 employees, while excluding micro-enterprises (companies with fewer than 10 employees), because these companies hardly have any choice between outsourcing and internalizing. We used the Belfirst database of Bureau Van Dijk, excluding financial services and public companies, and included only companies with limited liability (to control for legal framework). This resulted in a population of 14,604 companies. A sample of 1,200 SMEs was generated randomly, using a systematic probability method. Surveys were sent to each SME executive by

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Table 1 Multi-item variable measurement Items

Chronbach’s alpha for: Overall

Asset specificity (1 totally disagree; 5 totally agree)

0.56

1. To acquire the routine accounting tasks the accountant needs to acquire company- X specific information 2. To perform the non-routine accounting tasks the accountant needs to acquire company-specific information

Routine tasks

Non-routine tasks

Source

0.46

0.48

1, 2, 4, 5: Adapted from Poppo and Zenger (1998)

X

X

X

3. The accounting software is custom-tailored to our company

X

X

4. The way we perform the accounting tasks is unique to our company

X

X

X 5. It would be costly in terms of time and resources to switch to an external accountant at the end of the financial year. Or in case the company uses an external accountant (for some tasks): It would be costly in terms of time and resources to switch to another external accountant at the end of the financial year Environmental uncertainty (1 totally disagree; 5 totally agree)

0.67

1. During the previous year, there was a lot of variation in the workload related to X routine accounting tasks

X X

0.53

0.44

2. During the previous year, there was a lot of variation in the workload related to X non-routine accounting tasks (e.g., period end-accounting)

X

3. During the previous year, there were relevant changes in the business organization X of the company (e.g., acquisitions, changes in corporate structure)

X

X

Behavioral uncertainty (1 totally disagree; 5 totally agree)

0.92

0.82

0.88

Is it possible to determine whether the accountant has correctly (accurately) performed the following activities?* 1. Entering up purchase invoices, sales invoices and financial transactions

X

X

2. Preparation of interim reports (e.g., interim profit and loss account)

X

X

3. Period end accounting (depreciations, stock changes, loans, accruals and deferred X income, etc.)

Adapted from Poppo and Zenger (1998)

X

4. Preparation of financial statements (balance sheet, profit and loss account)

X

Frequency

0.64

0.52

1. Entry of purchase invoices, sales invoices and financial transactions (1 daily; 6 annually)*

X

X

2. Preparation of interim profit and loss account (1 daily; 6 annually)*

X

X

3. Period end accounting (1 daily; 6 annually)*

X

4. Preparation of financial statements (1 daily; 6 annually)*

X

5. Total amount of invoices (sales and purchases) that the accountant has processed X during the previous year?

New measure

X

X 0.60

Based on Murray and Kotabe (1999)

X X X

Trust (1 totally dsagree; 5 totally agree)

0.89

0.89

0.89

1. The CEO has confidence that the external accountant will treat us fairly, this means to correctly charge for the performed duties

X

X

X

2. The CEO has confidence that the external accountant will inform us correctly

X

X

X

3. The CEO has confidence that the external accountant will accurately perform the X duties

X

X

4. The relationship between the CEO and the external accountant is based on trust X

X

X

Based on Zaheer et al. (1998) and Moorman et al. (1992)

* Reverse coded

4.2.2 Independent variables Accounting tasks are particularly people-intensive; therefore, our measure for asset specificity had to focus primarily on human asset specificity. This refers to

specialized knowledge, language, or skills regarding the specific characteristics of the company, related to routine and non-routine tasks. Therefore, we drew items 1, 2, 4, and 5 from Poppo and Zenger (1998), who focused on the outsourcing of human intensive

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services. For instance, in our case, human assets are specific when accountants need specialized knowledge of the specific characteristics of the company in order to perform a specific accounting task. Thus, we asked respondents whether the accountant needs to acquire company-specific information to adequately perform the accounting tasks. This item was split in two, so that we could distinguish between the human asset specificity of routine and non-routine tasks. In addition to the items adapted from Poppo and Zenger (1998), we included an item (item 3) to capture the extent to which physical assets (i.e., the accounting software used in the firm) were specific to the company. These five items allowed us to measure the degree to which human and physical assets used to produce the (routine and non-routine) accounting tasks were custom-tailored to the firm (Cronbach’s alpha = 0.55 for overall; 0.46 for routine and 0.48 for non-routine). The low Cronbach’s alpha can be explained by the fact that this construct had to be adapted to the accounting context in a major way.3 Previous studies adapting this construct to other specific contexts obtained a Cronbach’s alpha between 0.60 and 0.70 (Klein et al. 1990; Erramilli and Rao 1993; Brouthers and Brouthers 2003). Environmental uncertainty was defined in this study as the stability and predictability of the workload related to accounting tasks resulting from the volatility of business activities. Therefore, respondents were asked to what extent the routine and non-routine workload may vary (see items 1 and 2). Since both the routine and non-routine tasks change when the corporate structure changes, we added a third item to measure whether the business organization of the firm had changed in the previous year. Chronbach alpha’s were 0.67 overall, 0.53 for routine, and 0.44 for non-routine tasks.4 Similar to Poppo and Zenger (1998), behavioral uncertainty was defined as the difficulty of measuring the performance of the accountant. We adapted their measure, so that behavioral uncertainty could be 3

When we deleted items 1 and 2 for asset specificity, the Chronbach’s alpha improved to 0.63 for the overall measure and improved to 0.59 for routine tasks, but dropped to 0.47 for nonroutine. However, deleting these two items did not change our results. Asset specificity remained significant for non-routine tasks at alpha 5%, without influencing the significance of the other results. 4 Deleting item 3 did not change any of the results for routine or non-routine tasks. Environmental uncertainty never was significant, while the other results remained the same.

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measured for each accounting task. The Cronbach’s alpha for the overall measure was 0.88, for routine tasks 0.92, and for non-routine tasks 0.82. Frequency was measured by combining a periodicity measure with a volume measure, similar to Widener and Selto (1999). In accordance with Murray and Kotabe (1999), we asked respondents about the periodicity with which each of the four accounting tasks had been performed over the preceding year. Possible responses were: daily, weekly, monthly, quarterly, semi-annually, and yearly. The size of the transaction was measured as the total volume of invoices handled by the accountant over the whole year (six categories). For the frequency of routine tasks, we combined the periodicity measure of the two routine tasks (entry, interim) with the volume of invoices. For the frequency of non-routine tasks, we combined the periodicity measure for period-end and financial statement preparation.5 The Cronbach’s alpha of this newly developed construct was 0.64 (0.52 for routine and 0.60 for non-routine tasks), which is reasonable when compared with previous studies constructing new TCE measures (Murray and Kotabe 1999; Brouthers and Nakos 2004). 4.2.3 Control variables6 First, the ‘upper echelons’ perspective, espoused by Hambrick and Mason (1984), suggests that the 5

From our experience in practice, we believe that it would be incorrect to state that the volume of invoices also influences the size (resources invested) of the period-end or financial statement tasks. Non-routine tasks can be described as performing a step of judgment on the ‘totals’ of the accounting figures. These totals basically are provided by the accounting software and are independent of the underlying number of invoices. Indeed, if we included total invoices (size measure) in the frequency measure for the non-routine tasks, the Cronbach’s alpha decreased to 0.53. However, doing this did not influence the results. Frequency remained significant at alpha 5% for non-routine tasks and did not influence the significance levels of other variables. 6 We added some more control variables in the survey, for example, whether or not the company was outsourcing catering, payroll administration or IT. For catering, we had numerous missing values. About 96% of the companies were outsourcing payroll administration, and 50% of the companies were outsourcing IT. For IT we had 8% missing values, causing our effective sample to drop from 126 to 115. Therefore, we decided not to include this variable in our analysis. However, including the dummy for IT outsourcing (yes/no) did not alter results in the Tobit routine or non-routine model. The dummy for IT outsourcing was not significant for any of the models (p = 0.98 for routine; p = 0.60 for non-routine tasks).


personal characteristics of executives affect their decision making (Hitt and Tyler 1991; Wiersema and Bantel 1992). Park and Krishnan (2001) found that, when SMEs select suppliers, the age, and the level and field of education of the SME executive have a significant impact upon the way suppliers are selected. Therefore, we asked the executives to indicate their age, their highest degree of education, and the orientation of their education (economics or engineering). Second, many researchers have emphasized the importance of trust in supplier–buyer relationships (Moorman et al. 1992; Dyer and Chu 2000). A study by Bennet and Robson (1999) suggests that external accountants are in a position of high trust with their customers. We follow the lead of Zaheer et al. (1998) and define trust in the external accountant as the expectation of the executive that the accountant (1) can be relied upon to fulfil legal obligations, (2) will behave in a predictable manner, and (3) will act and negotiate fairly when the possibility for opportunism is present. This definition of trust includes expectations of reliability (‘the accountant is competent’), predictability (‘the accountant will behave in a consistent way’), and fairness (‘the accountant will charge fairly for services provided’). Trust is operationalized using the three items of Zaheer et al. (1998), but largely adapted to the accounting context. Considering the crucial role of the CEO in SMEs, we added a fourth item, asking directly about the trust relationship between the SME executive and the external accountant (Moorman et al. 1992). The Cronbach’s alpha for the trust measure was 0.89.7 Third, Gilley et al. (2004b) included firm maturity (firm age) as an antecedent of outsourcing, because less mature firms simply lack the resources to internalize all functional activities. We measured firm maturity using five ascending categories of firm age.8 Fourth, Bennett and Robson (1999) found that firm size is an important factor influencing the extent to 7

If we delete the general item 4 for trust, the Chronbach’s alpha is 0.88. Deleting item 4 did not influence the results. Trust was significant at alpha 5% in the routine Tobit model and significant at alpha 1% in the non-routine Tobit model. 8 Recoding firm age into a dichotomous variable (less or more than 10 years old) did not alter the results. Firm maturity was not significant in either the routine (p = 0.80) or non-routine (p = 0.70) Tobit model.

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which SMEs use external service providers. Firm size was measured by the natural logarithm of total assets, as found in the Belfirst database. Sales could not be used, since Belgian SMEs do not have to disclose their turnover. Moreover, the number of employees is not a good proxy when studying outsourcing, since the number of employees is directly dependent upon the outcome of the outsourcing decision (Leiblein and Miller 2003). Finally, Park and Krishnan (2001) found that the industry has a powerful influence on a small firm’s selection of suppliers. We measured industry by the first digit of the NACE code (codes 0 to 4 for manufacturing companies, codes 5 to 7 for service companies). 4.3 Model specification The following model will be tested using a two-limit Tobit analysis. Tobit analysis adjusts for a limited dependent variable, which may be viewed as being censored (Greene 1990; Breen 1996). Outsourcing Intensity ¼ a1 þ a2 Age ceo þ a3 University degree þ a4 Economic degree þ a5 Firm age þ a6 Firm size þ a7 Service firm þ a8 Trust þ a9 Asset spec þ a10 Environ unc þ a11 Behav unc þ a12 Frequency where * indicates that a separate measure is used for the routine and non-routine tasks. 5 Results 5.1 Descriptive statistics Descriptives are shown in Table 2, and correlations are shown in Table 3. The means for outsourcing intensity are 14.3% for routine and 46.6% for nonroutine accounting tasks. As companies go further in the accounting process (from entry of invoices towards more judgment tasks such as period-end and financial statements), the degree of outsourcing increases (the mean level of outsourcing for entry of invoices is 8.1%, for interim reporting 20.5%, for period-end 37.4%, and for financial statements 55.7%). With respect to the TCE variables, Table 2 indicates that substantial variability exists. Non-routine

123

102

P. Everaert et al.

Table 2 Descriptive statistics Panel A

N

Min.

Max.

Mean

SD

Outsourcing intensity 1. Overall

126

0.00

100.00

30.43

30.63

2. Routine accounting tasks Entry of invoices

126 126

0.00 0.00

100.00 100.00

14.31 8.11

27.88 25.50

126

0.00

100.00

20.52

35.71

126

0.00

100.00

46.55

40.17

Period-end

126

0.00

100.00

37.42

41.47

Financial statements

126

0.00

100.00

55.67

43.54

1. Overall

126

1.00

5.00

3.37

0.59

2. Routine accounting tasks

126

1.67

5.00

3.22

0.71

3. Non-routine accounting tasks

126

1.67

5.00

3.41

0.67

126

1.00

5.00

2.41

0.77

Interim reporting 3. Non-routine accounting tasks

Asset specificity

Environmental uncertainty 1. Overall 2. Routine accounting tasks

126

1.00

5.00

2.51

0.84


126

1.00

5.00

2.38

0.82

1. Overall

126

1.00

4.00

2.08

0.61

2. Routine accounting tasks 3. Non-routine accounting tasks

126 126

1.00 1.00

5.00 4.00

2.07 2.10

0.67 0.66

1. Overall

126

1.00

4.00

2.22

0.62

2. Routine accounting tasks

126

1.67

4.67

3.28

0.65


126

0.00

3.00

0.62

0.90

Behavioral uncertainty

Frequency

Trust

126

2.50

5.00

4.18

0.57

Age of SME executive

126

27.00

67.00

48.23

8.64

Firm size (total assets in 1,000 euros)

126

Panel B

400

578,910 N

11,884

53,913 Percentage

Highest degree of education of SME executive: University degree

56

44%

Lower than university degree

70

56%

Economic orientation of education

59

47%

No economic orientation of education

67

53%

Economic orientation of education of SME executive

Firm age \2 years

0

0

2–5 years

1

1%

6–10 years

8

6%

10–20 years [20 years

31 86

25% 68%

Industry Manufacturing company

80

63%

Service company

46

37%

123


103

Table 3 Correlation matrix 1

2

3

4

5

6

7

8

9

10

11

Panel A: Routine tasks 1. Age_ceo

1

2. University_degree -0.017 3. Economic_degree -0.141

1 0.153

1

4. Firm_age

0.099 -0.044

0.134

5. Firm_size

0.101

0.426**

0.156

0.158

6. Service_firm

0.010

0.085

0.114

0.007

-0.090

7. Trust

0.082

0.075

0.101

0.061

-0.073

8. Asset_spec

1 1 1 -0.104

1

-0.032

0.087

0.057

0.048

0.199*

-0.106

0.081

9. Environ_unc

0.012

0.121

0.015

0.259**

0.173

-0.139

-0.072

0.180*

10. Behav_unc

0.062 -0.210*

0.000

0.155

0.027

-0.089

-0.198*

0.046

11. Frequency

-0.015

0.284**

0.139 -0.061

0.532**

0.212* -0.042

1

0.118

1 0.197* -0.050

1 -0.173 1

Panel B: Non-routine tasks 1. Age_ceo

1

2. University_degree -0.017

1

3. Economic_degree -0.141

0.153

1

4. Firm_age

0.099 -0.044

0.134

5. Firm_size

0.101

0.426**

0.156

0.158

6. Service_firm 7. Trust

0.010 0.082

0.085 0.075

0.114 0.101

0.007 0.061

8. Asset_spec

0.082

0.037

-0.064

0.155

0.035

0.043 -0.128

-0.159

9. Environ_unc 10. Behav_unc 11. Frequency

-0.030

0.321**

1

-0.064 -0.031 0.283** 0.059

0.046 -0.088

tasks have a higher mean for asset specificity, whereas the mean value of environmental uncertainty, behavioral uncertainty, and frequency is higher for routine tasks than for non-routine tasks. With respect to the control variables, Table 2 indicates that the level of trust in the external accountant is high (mean of 4.2 on 5). Almost half of the responding executives have an university degree (44%), whereas 47% have an economicoriented degree. The majority of the responding executives (68%) indicated that their firm has already existed for more than 20 years. 5.2 Hypotheses testing Table 4 shows the results of the Tobit analysis, with a lower limit of 0% and upper limit of 100%. Panel A shows the results for the overall outsourcing intensity as dependent variable, while panels B and C show the

1 -0.090 -0.073 0.176*

1 -0.104

1

-0.123

0.155

0.286** -0.075

-0.095

-0.061 0.303**

-0.194* -0.165 0.063

0.073

1 0.272**

1

-0.043

0.059

0.152

0.055

1 -0.072 1

results for the outsourcing intensity of routine and non-routine tasks, respectively.

5.2.1 Outsourcing intensity of routine accounting tasks Panel B of Table 4 shows a significant negative coefficient for frequency, suggesting that the outsourcing intensity of routine accounting tasks is negatively associated with the frequency of these tasks, thereby confirming Hypothesis 4. In other words, the higher the frequency of routine tasks, the less intensely they are outsourced. Frequency also is the factor explaining most of the variance. Asset specificity is marginally associated with the outsourcing intensity of routine tasks. Moreover, trust in the accountant is significantly positively associated, whereas the educational background of the

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123 -0.414 (-1.22)

5.55%

55.97***

-476.416

7

92

27

-1.551 (-1.68)*

5.62%

28.59***

-240.048

7

35

84

126

62.626 (0.62)

26.575 (1.88)*

-21.008 (-2.37)** 8.801 (0.55)

13.193 (1.09)

-30.623 (-1.89)*

-31.656 (-1.82)*

The adjusted R2 increases from 5.62% to 6.30%, 6.31%, 6.34%, and 8.30% if we expand the reduced model with the TCE variables stepwise

The adjusted R2 increases from 2.98% to 3.73%, 3.78%, 3.78%, and 4.59% if we expand the reduced model with the TCE variables stepwise

2.98%

25.83***

-420.153

23

69

27

119

117.64 (1.71)*

12.357 (1.26)

-12.849 (-2.29)** -3.821 (-0.34)

6.557 (0.77)

-17.089 (-1.54)

-28.960 (-2.39)**

-0.617 (-1.00)

Model 3 reduced

b

8.30%a

42.23***

-233.227

7

35

84

126

144.300 (1.44)

-46.113 (-2.98)***

-1.640 (-0.14)

-1.341 (-0.14)

-18.132 (-1.77)*

28.482 (2.13)**

-1.989 (-0.21) 24.195 (1.46)

8.459 (0.72)

-27.834 (-1.84)*

-29.431 (-1.83)*

-1.786 (-2.04)**

Model 2 full

4.59%b

39.73***

-413.207

23

69

27

119

138.951 (2.02)**

16.648 (-2.64)***

-1.946 (-0.25)

4.158 (0.60)

-18.237 (-2.25)**

18.955 (1.98)**

-7.561 (-1.38) -3.037 (-0.28)

0.879 (0.11)

-19.673 (-1.89)*

-24.834 (-2.16)**

-0.607 (-1.05)

Model 3 full

Panel C: Non-routine tasks

a

Statistics shown: Coefficients (t-statistics in parenthesis)

*** Significant at 1% level; ** significant at 5% level; * significant at 10% level

3.52%

Log likelihood

35.55***

-486.626

Right-censored

Pseudo R2

7

Uncensored

LR chi2

27

92

Left-censored

126

95.824 (2.38)**

Constant

126

-19.912 (-3.35)***

Frequency

N

4.436 (1.07) -6.759 (-1.33)

Behav_unc

-13.315 (-2.65)***

16.104 (2.94)***

-3.027 (-0.87) 5.262 (0.82)

2.721 (0.56)

-15.007 (-2.48)**

-15.530 (-2.34)**

Environ_unc

53.065 (1.31)

12.833 (2.27)**

Asset_spec

Trust

6.382 (1.27)

-9.677 (-2.95)*** 1.877 (0.28)

Firm_size Service_firm

Firm_age

-13.997 (-2.13)**

Economic_degree

-0.381 (-1.04)

-15.990 (-2.25)**

University_degree

Age_ceo

Model 2 reduced

Model 1 reduced

Model 1 full

Panel B: Routine tasks

Panel A: Overall

Table 4 Tobit models of outsourcing intensity (0–100%)

104 P. Everaert et al.


executive was only marginally associated with the outsourcing intensity of routine accounting tasks. 5.2.2 Outsourcing intensity of non-routine accounting tasks Panel C of Table 4 shows the results of the Tobit analysis when the outsourcing intensity of nonroutine accounting tasks is used as the dependent variable. The seven SMEs that completely outsource their routine accounting tasks were excluded from this analysis, as these SMEs automatically must outsource their non-routine accounting tasks, given the interdependency that exists between the accounting tasks. Consistent with the two previous models, the outsourcing intensity of non-routine accounting tasks is significantly negatively associated with the frequency of these tasks, which provides further support for Hypothesis 4. Contrary to routine accounting tasks, asset specificity is now significantly negatively associated with the outsourcing intensity of non-routine tasks, providing support for Hypothesis 1. For the non-routine tasks, asset specificity accounts for more of the variance (20%) than frequency (18%). With respect to the control variables, panel C of Table 6 indicates that the outsourcing intensity of non-routine accounting tasks also is significantly positively associated with the trust in the external accountant and significantly negatively associated with the possession of a university degree by the executive. The economic orientation of his/her degree seems to be only marginally associated with the outsourcing intensity of non-routine accounting tasks (at alpha 10%). It also should be noted that, in all three models, environmental and behavioral uncertainty are not significantly associated with outsourcing intensity. Therefore, the data do not support Hypothesis 2 or Hypothesis 3.

105

variables associated with the outsourcing intensity. Consistent with Lacity and Willcocks (1998), SMEs with an outsourcing intensity below or equal to 20% were considered to be internalizing their accounting tasks, SMEs with an outsourcing intensity between 20 and 80% to be partially outsourcing their accounting tasks, and SMEs with an outsourcing intensity equal to or higher than 80% to be fully outsourcing their accounting tasks. Second, a logit analysis was performed to test the variables associated with the decision of whether or not to outsource accounting tasks (0/1). In each of these three logit analyses, SMEs were considered to outsource accounting tasks once their outsourcing intensity was higher than or equals 20%, similar to Monteverde and Teece (1982). Table 5 shows the results of the ordered logit analyses, and Table 6 shows the results of the Logit analyses. 5.3.1 Outsourcing intensity of routine accounting tasks Panel B of Tables 5 and 6 shows results that are consistent with those generated by the Tobit analyses in Table 4. In particular, frequency is significantly and negatively associated with the outsourcing of routine accounting tasks, both in the ordered logit and logit model. Also, asset specificity is only marginally significantly associated with the outsourcing of routine accounting tasks, both in the ordered logit and logit model. Contrary to the Tobit analysis presented before, trust in the external accountant is not significantly associated with the outsourcing of routine accounting tasks, either in the ordered logit or logit model. With respect to the educational background of the CEO, the ordered logit and logit analyses show consistent results for the possession of an economics-oriented degree by the CEO. 5.3.2 Outsourcing of non-routine accounting tasks

5.3 Additional analyses In order to test the consistency of the results presented above, the same three models were tested using two other types of analysis. First, an ordered logit analysis was performed, whereby the dependent variable was classified into three groups, which can be considered as an alternative way to identify the

Panel C of Tables 5 and 6 indicates that, consistent with the Tobit analysis outlined in Table 4, asset specificity and frequency are the two TCE variables that are significantly associated with the outsourcing of non-routine tasks, in the expected direction. Similarly, trust in the external accountant also is significantly and positively associated. With respect

123

123

13.00%

126

20.82%

49.93***

-94.974

-0.036 (0.028)

17.31%

29.45***

-70.330

126

0.070 (3.145)

-1.661 (3.140)

0.587 (0.437)

0.009 (0.518)

-0.788 (0.310)**

0.708 (0.405)*

-1.568 (0.555)***

-0.478 (0.556)

Statistics shown: Coefficients (standard errors in parenthesis)


31.19***

Pseudo R2

-104.346

LR chi2

Log likelihood

126

0.700 (2.390)

Cut2

N

-4.862 (2.827)

Cut1 -1.825 (2.788)

-1.271 (0.451)***

-1.995 (2.386)

Frequency

-0.248 (0.366)

Behav_unc

1.123 (0.396)*** -0.928 (0.354)*** 0.112 (0.283)

0.780 (0.341)**

Asset_spec Environ_unc

Trust

0.611 (0.435)

-0.134 (0.236)

0.320 (0.392)

-0.505 (0.201)**

-1.059 (0.419)** -0.042 (0.315)

Service_firm

Firm_size

-0.028 (0.023) -0.777 (0.449)*

0.145 (0.288)

-0.885 (0.395)**

Economic_degree

Firm_age

-0.022 (0.022)

-0.804 (0.419)*

Age_ceo

University_degree

Model 2 reduced

Model 1 reduced

Model 1 full


Panel A: Overall

25.95%

44.15***

-62.980

126

-2.874 (3.479)

-4.885 (3.508)

-1.552 (0.540)***

-0.202 (0.433)

-0.706 (0.363)* 0.229 (0.332)

0.600 (0.445)

0.558 (0.598)

-0.276 (0.346)

0.700 (0.477)

-1.637 (0.587)***

-0.600 (0.595)

-0.045 (0.030)

Model 2 full

9.88%

25.34***

-115.527

119

-0.130 (2.293)

-1.808 (2.301)

0.321 (0.338)

-0.357 (0.382)

-0.310 (0.186)*

0.214 (0.287)

-0.625 (0.377)*

-1.100 (0.409)***

-0.005 (0.021)

Model 3 reduced

17.10%

43.85***

-106.274

119

-0.623 (2.604)

-2.514 (2.621)

-0.554 (0.256)**

0.117 (0.287)

-1.029 (0.331)*** 0.220 (0.275)

0.745 (0.384)**

-0.313 (0.412)

-0.117 (0.204)

-0.045 (0.319)

-0.757 (0.398)*

-1.190 (0.441)***

0.002 (0.022)

Model 3 full


Table 5 Ordered logit models of outsourcing (1 = no outsourcing, 2 = combination outsourcing, and insourcing, 3 = no insourcing)

106 P. Everaert et al.

14.89%

25.960*** 28.45%

49.610***

-62.386 20.41%

27.750***

-54.098

126

1.601 (3.145)

0.457 (0.447)

-0.179 (0.524)

-0.697 (0.304)**

0.692 (0.416)*

-1.631 (0.565)***

-0.511 (0.573)

-0.034 (0.029)

Statistics shown: Coefficients (standard errors in parenthesis)


Pseudo R2

LR chi

-74.214

2

Log likelihood

126

7.150 (3.549)**

2.733 (2.686)

Constant

126

-0.349 (0.406) -1.763 (0.506)***

Behav_unc Frequency

N

0.089 (0.328)

Environ_unc

1.181 (0.492)** -1.148 (0.447)***

0.690 (0.375)*

Trust

0.544 (0.493)

0.082 (0.265)

-0.195 (0.387)

-1.100 (0.481)**

-1.004 (0.505)**

-0.054 (0.027)**

Asset_spec

0.173 (0.423)

-0.429 (0.214)**

Service_firm

Firm_size

-0.789 (0.419)*

Economic_degree

0.085 (0.319)

-0.942 (0.443)**

University_degree

Firm_age

-0.035 (0.024)

Age_ceo

Model 2 reduced

Model 1 reduced

Model 1 full


Panel A: Overall

Table 6 Logit models of outsourcing intensity (0 = no outsourcing, 1 = outsourcing)

30.50%

41.460***

-47.244

126

5.456 (3.735)

-0.183 (0.479) -1.605 (0.593)***

0.231 (0.354)

-0.751 (0.402)*

0.531 (0.482)

0.323 (0.624)

-0.182 (0.352)

0.620 (0.488)

-1.772 (0.626)***

-0.711 (0.630)

-0.050 (0.033)

Model 2 full

10.86%

17.590***

-72.166

119

0.429 (2.671)

0.492 (0.384)

-0.136 (0.427)

-0.237 (0.208)

0.143 (0.317)

-0.595 (0.421)

-1.094 (0.450)**

0.000 (0.024)

Model 3 reduced

21.72%

35.170***

-63.375

119

1.126 (3.367)

0.235 (0.369) -0.683 (0.275)***

0.305 (0.330)

-1.016 (0.392)***

0.973 (0.467)**

-0.003 (0.479)

-0.021 (0.237)

-0.222 (0.394)

-0.755 (0.467)

-1.107 (0.506)**

0.000 (0.026)

Model 3 full


Using Transaction Cost Economics to explain outsourcing of accounting 107

123

108

to the educational background of the CEO, the ordered logit and logit analyses both demonstrate a significant and negative relationship between outsourcing and the possession of a university degree by the CEO.

6 Conclusions and discussion Overall, the findings of this study demonstrate that the outsourcing of accounting in SMEs supports two major TCE predictions, i.e., for frequency and asset specificity. Frequency turns out to be significantly associated with the outsourcing intensity of both routine and non-routine accounting tasks. Consistent with the TCE model, the lower the frequency of the accounting tasks, the more intensely they are outsourced. SMEs seem to be able to create economies of scale for the routine accounting tasks that are recurrent and sizeable, so that they are inclined to maintain such accounting tasks internally. Furthermore, we found evidence for the important role of asset specificity, in particular for the non-routine tasks. Non-routine accounting tasks require more expertise and judgment from the accountant. When knowledge regarding the specific context of the company is important in making those judgments, it becomes costly to transfer them to an external accountant; hence, companies organize these accounting tasks internally. This result is consistent with previous research on the outsourcing of other service functions where human asset specificity is involved, such as for internal audit (Widener and Selto 1999; Spekle´ et al. 2007), distribution (Brouthers and Brouters 2003), IT (Watjatrakul 2005; Barthelemy and Geyer 2005), and R&D (Monteverde and Teece 1982). Conversely, performing routine accounting tasks requires less judgment, since these tasks are more standardized across firms. For such tasks, frequency turns out to be a more important factor than asset specificity when making a decision on outsourcing. We found no support for a role of environmental or behavioral uncertainty.9 Outsourcing intensity of 9

One of the reviewers suggested that a possible reason for the insignificant findings regarding uncertainty is that uncertainty, by itself, does not pose a contractual hazard in the absence of asset specificity. Uncertainty and asset specificity might have

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P. Everaert et al.

accounting tasks, whether routine or non-routine, is neither associated with the uncertainty of the workload of these accounting tasks nor with the extent to which it can be determined that the accountant has performed his/her job accurately. Similar to the outsourcing of internal audit activities (Widener and Selto 1999), environmental uncertainty does not explain the outsourcing of accounting. Whether or not there is considerable variation in the workload, if the accounting services are not specific to the firm, they may be readily available from accounting firms, with little difference between spot and negotiated prices. If they are specific to the firm, tasks need to be done internally, irrespective of the fact that their scheduling is uncertain or not. Even though Vandaele et al. (2007) expect that behavioral uncertainty is an important factor in the outsourcing decision of service functions, the hypothesis regarding behavioral uncertainty was not supported by the data. The data show a rather low mean (both for routine and non-routine tasks), suggesting that it is not difficult to determine whether the accountant accurately performed the accounting tasks. Many software tools are available to check the accuracy of routine accounting tasks (input of data). In addition, legal offices (e.g., the National Bank, collector of financial statements) will alert management if the accountant has made a calculation error. TCE has been criticized for being focused solely on opportunism as the basis for behavior, ignoring how relationships are more often based on cooperation and the personal relationships that exist between actors than on actual or perceived self-serving (Muthusamy and White 2005; Ring and Van de Ven 1992; Zaheer and Venkatraman 1995). Therefore, this study also has incorporated the issue of trust. Considering the crucial role of the CEO as described by the ‘upper echelon perspective,’ trust in the service provider may be a unique characteristic of SMEs compared to larger firms (Hambrick and

Footnote 9 continued an interactive effect, as in Williamson (1991). However, the analysis cannot support these interaction effects. For the routine tasks, the interaction term of asset specificity and environmental uncertainty is not significant (p = 0.35 in the Tobit regression). In a similar vein, the interaction term of asset specificity and behavioral uncertainty is not significant (p = 0.49 in the Tobit regression). The same results apply for the non-routine accounting tasks (p = 0.99 and p = 0.95 in the Tobit regressions).


Mason 1984). This study clearly shows that the SME’s decision to outsource accounting tasks is not only based on the characteristics of the transaction, but also is influenced by the interpersonal trust of the executive in the service provider (Bachmann 2001). In addition, the crucial role of the personal characteristics of the CEO was further confirmed by the significant association between the educational background of the CEO and outsourcing (Park and Krishnan 2001). It was found that SMEs in which the CEO does not have an economic-oriented background tend to outsource their routine accounting tasks more intensely. Similarly, SMEs in which the CEO does not have a university degree are likely to outsource their non-routine accounting tasks more intensely. Since performing accounting tasks requires a minimum of accounting expertise, such CEOs may lack these relevant skills and knowledge, so that they choose to outsource. One may wish to extend this study’s findings to other service functions, such as human resources, IT, and legal support, wherein there are also both routine (e.g., recruitment, help desk, warranties) and nonroutine tasks (e.g., remuneration policy, patent, court case). Frequently performed routine activities are candidates for internalizing. Similarly, non-routine tasks, requiring knowledge regarding firm-specific assets, particularly if performed on a frequent basis, will be retained within the firm. Furthermore, for accounting tasks, many firms use a combination of outsourcing and internalizing, even for individual tasks such as period end accounting. One might wonder whether this can be exclusively explained by transaction characteristics. The specific knowledge that is lacking in SMEs might be an alternative explanation (Conner and Prahalad 1996). We hesitate to make predictions, but it seems that service functions that require specific knowledge are more heavily outsourced when the CEO does not possess the relevant knowledge from previous education. This conclusion is similar to the results obtained by Arnett and Jones (1994), where the IT knowledge of the CEO was related to IT outsourcing. Furthermore, this study treats accounting just as one support function that SMEs might outsource, such as other staff functions, like human resources, IT, and legal support. Future research could investigate whether SMEs are outsourcing a bundle of services. From our data, we only know that half of the companies also

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outsource IT, but no relationship was found between the outsourcing of IT and accounting. This study has a few limitations. First, we utilized a rather traditional way of applying TCE. In other words, we did not examine whether the observed mechanisms were optimal responses designed to minimize transaction costs (Nickerson and Silverman 2003). We only investigated whether SMEs that outsource accounting tasks differ, in terms of transactional and personal (CEO) characteristics, from others that perform the same tasks within the company. However, it is possible that transactions are misaligned and that firms chose a different governance arrangement than what could be expected from TCE (Masten 1993; Nickerson and Silverman 2003). Such misalignment may have implications for firm performance and firm survival (Silverman et al. 1997). This study was not able to test the association between outsourcing decisions and performance. Most of the SMEs that participated in this study were mature firms that have survived for more than 20 years. Future research could focus on firm survival relative to outsourcing intensity. However, for the accounting function, we could wonder whether ‘misalignment’ is associated with firm survival or whether there is an association between firm survival and ‘the advice’ that external accountants give to SME executives. Recent studies (e.g., Berry et al. 2006) have found that business advice from service providers is important for survival of small companies; many small firms use their accountant as their first and most important business advisor (Kirby and King 1997; Bennett and Robson 1999; Gooderham et al. 2004). Both for an optimally and non-optimally organized small firm, the advice of the external accountant could be associated with performance and long-term survival. Second, we were not able to analyze data for each accounting task separately. Although our variables allowed us to compare routine and non-routine tasks, further research could disaggregate these further and collect data for TCE variables at the level of individual accounting tasks. Future research efforts also might focus on additional interesting issues. First, the constructs we used were adapted to fit the accounting context of the study. In particular, for frequency, we added a volume element to our operationalization. Preliminary interviews revealed that the size of the accounting

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task (length of the task) as well as its periodicity (the number of times an accounting task recurs during a year) both are important components of the frequency of recurrent services. We suggest that future research should investigate both interpretations of frequency in order to develop a better understanding of the frequency of service functions. Second, the role of trust should be examined more closely. Whereas previous empirical research on outsourcing largely has ignored trust, our results show that it plays an important role with respect to the outsourcing of accounting tasks. This may be especially important for SMEs, given the role of the CEO and the inter-personal nature of trust. Alternatively, the role of trust may be rather unique for the accounting function. Before they outsource accounting tasks, SMEs must be certain that the external accountant is trustworthy and capable given the confidentiality of accounting data. Future research on SMEs could investigate whether trust is an important factor when outsourcing other functional areas, where confidentiality is of importance. Finally, we support the call of Gilley and Rasheed (2000) to do more research on outsourcing intensity, because it better captures the nuances of intermediate forms of outsourcing, such as partial outsourcing, whereby companies both outsource and internalize for the same function (or even the same task). Again the specific knowledge of the service provider might be one explanation for such hybrid ways of organizing a service function. This also might explain why international comparisons, such as the study by Kakabadse and Kakabadse (2002), come up with lower degrees of outsourcing, since these studies limit outsourcing to the situation in which all tasks within a specific service function are 100% outsourced.

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