The Relationship between Internal Controls and - Semantic Scholar

Do Internal Controls Improve Operating Efficiency of Universities?

Rong-Ruey Duh (Contact author) Department of Accounting National Taiwan University No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan Tel: +886-2-3366-3888 Fax: +886-2-2363-7440 E-mail: [email protected] Kuo-Tay Chen Department of Accounting National Taiwan University No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan Tel: +886-2-3366-1125 E-mail: [email protected] Ruey-Ching Lin Department of Accounting Ming Chuan University No.250, Sec. 5, Jhongshan N. Road., Taipei 111, Taiwan Tel: +886-2- 2882-4564 Ext.2157 E-mail: [email protected]

Li-Chun Kuo Department of Accounting National Taiwan University No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan Tel: +886-2-3366-1136 E-mail: [email protected]

Do Internal Controls Improve Operating Efficiency of Universities?

ABSTRACT Improving operating efficiency is one objective of internal controls (IC). This paper investigates the relationship between IC implementation and operating efficiency of universities. Using data from questionnaire survey and from the field, this study measures IC implementation and applies data envelopment analysis to estimate operating efficiency of 99 universities in Taiwan. The results indicate that IC implementation has a positive but insignificant association with overall efficiency as well as teaching-related efficiency, but has a negative and significant association with research-related efficiency. Dividing the sample into public and private universities, the analysis indicates that for public universities, IC implementation has no significant association with any of the three measures of efficiency. But, for private universities, there is a positive and significant association between IC implementation and teaching-related efficiency. The association between IC implementation and research-related efficiency is negative and significant.

Keywords: internal control, operating efficiency, university.

1

1. INTRODUCTION This study examines the relationship between internal controls and operating efficiency of universities. Unlike business organizations where shareholders are the major class of stakeholders, and profitability is the primary objective, universities serve the society through delivering teaching and conducting research, and have a diffuse group of stakeholders. Thus, efficiency rather than profitability becomes an important measure of performance. In addition, relying on a focused class of stakeholders (e.g., shareholders) to monitor performance becomes less feasible because universities do not have alienable residual interest (Sunder 1997). As a consequence, internal controls are no less important for universities than for business organizations. The Committee of Sponsoring Organizations (COSO) of the Treadway Commission defines internal controls as “a process, effected by an entity’s board of directors, management and other personnel, designed to provide reasonable assurance regarding the achievement of objectives in the following categories: effectiveness and efficiency of operations, reliability of financial information, and compliance with the applicable laws and regulations” (COSO 1992). Though it is important for both business and not-for-profit organizations to establish internal controls to improve operating efficiency, issues pertaining to internal controls had not been extensively studied until after the passage of the Sarbanes-Oxley Act. Per Section 302 and Section 404 of the Sarbanes-Oxley Act, Securities and Exchange Commission (SEC) in the U.S. mandated the enterprises to disclose information on internal controls (SEC 2002, 2004), whereby researchers used publicly available data to empirically examine the determinants of internal control weakness (Krishnan 2005; Ashbaugh-Skaife et al. 2007; Doyle et al. 2007a), and the association between internal controls and the quality of financial reporting (Doyle et al. 2007b). While these studies have provided useful insights into possible determinants and consequences of internal control strength, they 2

focus on business organizations; and research on internal controls of universities has received less attention. In particular, the relationship between internal controls and operating efficiency of universities has not been empirically explored before. Among the universities, private universities may have stronger incentives to emphasize internal control implementation than public universities. First, public universities primarily rely on public funding whereas private universities do not. Second, composition of board of trustees (BOT) differs between public and private universities. The former consists of members representing a diffuse group while the latter represents a more focused group. Such differences give rise to differential incentives to use internal controls to monitor operating efficiency. As such, the association between internal controls and operating efficiency may be more pronounced for private universities than for public ones. Whether this differential association between internal control implementation and operating efficiency exists has not been explored either. A university performs public services primarily through delivering teaching and conducting research. While teaching and research can be supplemental to each other (Kinney 1989; Kaplan 1989), teaching is more subject to routine and is less subject to uncertainty or exceptions than research (Abernethy and Brownell 1997; Sanchez and Perez 2002). Thus, relative to research activities, teaching activities are more amenable to rules and procedures required by internal controls. As such, the association between internal controls and university efficiency may vary with types of activities. This paper attempts to answer the following questions: what is the relationship between internal control implementation and operating efficiency of universities? Does the relationship differ between teaching and research? Does the relationship differ between public and private universities? An examination of these issues not only fills the void in the literature, but also will have implications for the establishment and 3

implementation of internal controls as well as the operation of universities. In pursuit of these issues, we chose universities in Taiwan as the sample for the following reasons. Since the education reform in 1994, universities in Taiwan proliferated in an accelerated rate. The number of universities has increased by 18 percent in the past ten years; 1 however the ratio of high education expenditure over GDP has decreased from 2.16 percent to 2.09 percent. 2 Hence, to efficiently fulfill the mission of a university with ever decreasing public funding is critical. Further, there has been warning in recent years that some private universities might not be able to sustain. While the sustainability of a university can be attributed to several factors (e.g., history, and size) we believe that operating efficiency is an important factor as it is mostly used as a performance measure for not-for-profit organizations like universities. Second, we were able to obtain the support from the Ministry of Education in Taiwan (to be explained later). Such support would enhance the availability and quality of data. Using data from the questionnaire survey and from the field, we perform data envelopment analysis (DEA) and Tobit regression analysis with 35 public and 64 private universities as the sample. Our results indicate that internal control implementation has positive but insignificant associations with technical efficiency of universities (hereafter “overall efficiency”), and with teaching-related technical efficiency (hereafter “teaching-related efficiency”), but has a negative and significant association with research-related technical efficiency (hereafter “research-related efficiency”). Dividing the sample into public and private universities and performing the same analysis for each, the results indicate that for the private universities, there is a positive and significant association between internal controls and teaching-related efficiency. But, the association between

1 2

The number of universities in Taiwan in 1998 was 137, and it became 162 in 2008 (Ministry of Education in Taiwan). Data source: Ministry of Education in Taiwan. 4

internal controls and research-related efficiency becomes negative and significant. The above findings do not hold for the public universities. This paper is organized as follows. Section 2 reviews the extant literature and develops research hypotheses. Section 3 explains the methodology, including data sources, variable measurement, and the regression model. Section 4 presents and discusses the empirical results, including descriptive statistics and regression results. The final section offers conclusions and suggestions.

II. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT Prior Literature Empirical research on internal controls for business organizations emerged after the passage of the Sarbanes-Oxley Act. Krishnan (2005) finds that audit committee independence and audit committee with financial expertise are both significantly and negatively related to internal control problems. Ashbaugh-Skaife et al. (2007) indicate that firms with recent organizational changes, greater accounting risk, more complex operations, more auditor resignations and have fewer resources available for internal control tend to have more disclosed internal control deficiencies. Doyle et al. (2007a) find that firms with more weaknesses of internal control over financial reporting are more likely to be younger, smaller, financially weaker, and more complex, growing rapidly, or undergoing restructuring. Doyle et al. (2007b) further examine the relation between internal control and accrual quality, and find a significant and positive association between them. In the area of nonprofit organizations, Duncan et al. (1999) use the questionnaires to examine the effects of church size and denomination on internal controls in the U. S. They find that evaluation scores of internal controls vary significantly with church size and the polity and hierarchical structure of denomination. Bowrin (2004) examines the 5

internal control systems in Trinidad and Tobago religious organizations by interviewing the chief financial officers of each religious organization. He finds that religious organizations normally have inadequate and patchy internal control systems and that the comprehensiveness of internal control across all the religious organizations is at best moderate. Gallagher and Radcliffe (2002) use a case study to investigate the rationale behind a massive fraud of the Ohio Division of the American Cancer Society in the U. S. They further make recommendations for designing internal control systems with higher quality to detect such fraud as early as possible. These recommendations include background checks of all employees, separation of duties, a system of authorizations, formation of an audit committee, removal of overreliance on one individual, board oversight, and a fraud response plan. While their suggestions also apply to other nonprofit organizations, internal controls of universities are not examined. In addition, prior research has not examined the association between internal controls and operating performance either. Related to operating performance of universities, prior studies use DEA 3 to evaluate the relative efficiency of university departments (Johnes and Johnes 1995) or the university as a whole (Abbott and Doucouliagos 2003; Ahn et al. 1988; Anthanssopoulous and Shale 1997; Avkiran 2001; and Kao 1994). Of relevance to this paper is Ahn et al. (1988) who use data of public and private universities in the U. S. from 1984 to 1985, and find that having medical school or not is a crucial factor 3

In DEA, among all possible production mixes, production frontier is produced by the most favorable production mixes; hence, a decision making unit (DMU) on the production frontier is “efficient”. In Farrell (1957)’s setting, there are only two inputs and one output, and every DMU is under the assumption of constant return of scale. Based on Farrell (1957) measurement, Charnes, Cooper, and Rhodes (1978) extend the setting to multiple inputs and multiple outputs, and the model is still with the assumption of “constant return of scale”. This model is generally used by the literature and called CCR model. Banker, Charnes, and Cooper (1984) release the “constant return of scale” assumption and construct a model to measure relative efficiency assuming DMU is with variable return of scale. This model is called BCC model. Under BCC model, inefficiency of DMU may come from the operation with different return of scale, so the technical efficiency can be further divided into pure technical efficiency and scale efficiency. 6

affecting the efficiency of a university. Kao (1994) uses data of 11junior colleges of technology in Taiwan to measure efficiency of each college, and compares the results to the evaluation by the government that is conducted every three years. His results show that quantitative evaluation by DEA is highly correlated with that of the government evaluation. Kuo and Ho (2008) use stochastic frontier analysis (SFA) to measure efficiency of 34 public universities from 1992 to 2000 to evaluate the impact of implementing the university operation fund system (UFO) in Taiwan. The results show that the adoption of UFO has a significant and negative impact on cost efficiency, which suggests that the traditional budget regime governing public universities is helpful to limit wasteful spending. Hypotheses Development The first research question in this study is whether there is an association between internal control implementation and operating efficiency in universities. Extant literature does not directly examine the empirical relationship between them in the setting of universities. Nevertheless, in light of the literature on internal controls (e.g., COSO report) suggesting that an important objective of internal controls is to improve operating efficiency, we propose the first research hypothesis: Hypothesis 1: Internal control implementation is positively associated with university-wide technical efficiency (overall efficiency). According to Perrow (1970), the effects of the controls depend on the task characteristic such as the degree of routineness. Routineness can be further classified by applying the two dimensions: task analyzability and the number of exceptions. When there exist more well-established techniques to perform the task (high task analyzability), and fewer exceptions, tasks are classified routine. Otherwise, tasks like research and development are normally non-routine and less repetitive. Similarly,

7

Hofstede (1981) suggests that control types should be adjusted for the characteristics of activities, and these characteristics include four criteria: “are objectives unambiguous?”, “are outputs measurable?”, “are effects of interventions known?”, and “is the activity repetitive?” Since the learning effect occurs when employees conduct repetitive activities, these activities can be easily predicted and controlled. Therefore, when objectives are unambiguous, outputs are measurable, and effects of interventions are known, repetitive activities are better controlled by “routine control”, while non-repetitive activities are better controlled by “expert control”. Abernethy and Brownell (1997) use Perrow’s model of technology and structure to examine the effectiveness of three forms of controls, including accounting controls, behavior controls, and personnel controls in research and development organizations. Their empirical results show that when task characteristics do not fit accounting-based controls, personnel controls especially contribute to the effectiveness. Davila (2000) investigates the drivers which influence the design of management control systems in new product development of the medical devices industry. He finds that the use of information obtained from control systems to reduce uncertainty varies with uncertainty types and product strategy. He further finds that uses of cost and customer information, but not time information, are positively associated with performance. Ditillo (2004) investigates uncertainty and management control systems in knowledge-intensive firms, such as accounting firms, computer consultancy organizations, and research centers. He uses knowledge complexity as a proxy for uncertainty, and examines the impact of knowledge complexity on coordination and knowledge integration, and then on management control systems. Since the tasks of knowledge-intensive firms are typically uncertain and unpredictable, different forms of knowledge integration are adopted according to the types of knowledge complexity, such as computation, technical, and cognitional complexity. Moreover, various 8

management controls are used for the three types of knowledge complexity. Computational complexity can be well regulated by procedures, actions and rules, whereas cognitional complexity should be controlled by self or group controls because this kind of activity needs more flexibility. Related to procedural controls as opposed to self (or group) controls, Adler and Borys (1996) divide uses of management control systems into coercive and enabling uses. Coercive uses emphasize centralized management and usually adopt conventional top-down management. On the other hand, enabling uses focus on delegation to the employees and allow the employees to directly deal with any contingencies at their positions. Ahrens and Chapman (2004) use a field study to compare the differences between coercive and enabling uses of control systems. Furthermore, they examine the impact of control systems on the flexibility and efficiency of an organization. Their studies show that proper enabling uses of control systems result in the efficiency and flexibility of the organization. In the specific case of universities, Chung et al. (2009) use structural equation models to examine the interdependencies in organization design of universities. They find a significantly positive relation between strategic emphasis on service innovation and the extent of structural autonomy granted to academic units of universities. Their implications further suggest senior management of universities to delegate more decision-making authorities of service innovation to the chairpersons of academic units and hold them accountable for their own decisions through performance measurement systems. The results of extant research show that task characteristics including routineness, repetitiveness, uncertainty (or predictability), and analyzability determine the use of various controls, and that the uses may have effects on performance at the project or organizational levels. Universities primarily engage in teaching and research activities. Teaching activities are relatively more programmable, repetitive, and routine, but are less subject to exceptions and uncertainty. These activities are more amenable to the 9

requirements of rules and procedures prescribed in internal controls. As such, implementation of internal controls will have a positive effect on the efficiency of teaching related activities. We therefore propose the following hypothesis: Hypothesis 2: Internal control implementation is positively associated with teaching-related efficiency. However, research activities are less repetitive, routine, and analyzable, but are more subject to uncertainty and exceptions. These activities are less amenable to rigid rules and procedures. When research activities are controlled through “routine control”, researchers will face more restrictions and limitations, which is detrimental to generating creative and innovative ideas. Moreover, these rules and procedures may prolong the progress of research. For example, internal controls for the procurement of research equipment and facilities may require preparing documents and going through the bidding procedures, which may delay the ongoing projects. 4 More serious is that internal controls can be an end themselves without any substantive functions. In fact, prior research has suggested that research activities are less suitable for the use of accounting control (Abernethy and Brownell 1997), and more suitable for the enabling use of controls (Ahrens and Chapman 2004) and for delegating controls to researchers (Chung et al. 2009). As such, we expect that internal control implementation will have a negative effect on the efficiency of research-related activities in universities. We develop the third research hypothesis as follows: Hypothesis 3: Internal control implementation is negatively related to research-related efficiency. Private not-for-profit organizations have to seek funds themselves, and therefore

4

This is the case that a professor at National Taiwan University raised in a faculty meeting, where he complained that the prolonged procurement procedures had a negative effect on the timeliness and innovativeness and therefore competitiveness of his research. 10

have more incentive than public ones to operate efficiently. Lindsay (1976) compares efficiencies of public sectors and private counterparts, and finds public sectors are “unobservably” less efficient. The empirical results using hospital data supports his argument. Sisk (1981) extends the argument to higher education, and suggests that private universities are more responsive to students’ needs and produce more graduates with earned degrees. By examining the efficiency of public and private universities in the U. S., Rhodes and Southwick (1986) argue that public universities rely on the support from the government while private universities need to meet the test of market place. Therefore, public universities are less efficiency conscious and have less incentive than private universities in efficiently managing the universities. Their results show that efficiency rating of public universities is lower than that of private universities. Based on the same argument, however, Ahn et al. (1988) find mixed results depending on which measure of efficiency and whether the universities have medical schools or not. Moreover, compositions of board of trustees are different between public and private universities. In U. S., board of trustees of public universities may be responsible for overseeing one or many universities, and the extent differs among states (Toma 1990). 5

Board of trustees in private universities usually consists of representatives of

alumni or donors whereas members of board of trustees in public universities largely are representatives assigned by the government and in some cases the faculty members. Alumni or donors represent the interest that is specific to the university under control while the members assigned by the government represent the interest of a diffuse group. Therefore, the former have more incentives than the latter in the implementation of 5

Board of regents is another institution used in some universities in U.S. (e.g., University System of Georgia). In some countries (e.g., Taiwan), public universities do not even establish board of trustees. The overseeing of operations ultimately rests on the university faculty meeting, which may further weaken the association between internal controls and efficiency as faculty members are themselves subject to the requirements imposed by internal controls. 11

internal controls to increase efficiency. Moreover, when faculty members are included as the members, they may not be as interested as alumni or donors in internal controls as they themselves are subject to internal control requirements or constraints. Toma (1990) finds that public universities can function more like private universities if public universities are under separate board of trustees, suggesting the role of incentive specificity in overseeing a university. Lowry (2004) examines the relation between university governance and the quality of undergraduate education. His results show that when faced with little competition and relying more on government subsidies relative to tuition, public universities tend to put emphasis on undergraduate enrollments and research. On the other hand, private universities place more emphasis on quality of undergraduate education. Thus, extant research has suggested that due the sources of funding and the structure and functions of board of trustees, public and private universities differ in efficiency. While these studies do not further examine how the universities differ in implementing internal controls to achieve efficiency, given that internal controls are designed to enhance efficiency, we posit that private universities will have more incentives to implement internal controls to increase efficiency than their public counterparts. Thus, the association between internal control implementation and efficiency will differ between these two types of universities. Specifically, we propose the following hypotheses: Hypothesis 4: The association between internal control implementation and university-wide efficiency will be more pronounced in private universities than in public universities. Hypothesis 5: The association between internal control implementation and teaching-related efficiency will be more pronounced in private universities than in 12

public universities. Hypothesis 6: The association between internal control implementation and research-related activities will be more pronounced in private universities than in public universities. III. RESEARCH METHOD Overview and Data Sources This study first measures internal control implementation of universities in Taiwan. Due to data are not available in the field, we rely on questionnaire survey to collect data in this regard. We then measure operating efficiency by applying DEA, which is followed by Tobit regressions to examine the association between internal control implementation and operating efficiency. Data sources for inputs and outputs in DEA and for variables in Tobit regressions come from financial statements of the universities, ISI Web of Science database (February, 2008), 6 and the websites of National Science Council and Ministry of Education in Taiwan. Detailed explanations on the sample, design of questionnaires, inputs and outputs in DEA, and Tobit regression models are presented in order. Sample At the end of 2005, we distributed 160 questionnaires to 55 public and 105 private universities in Taiwan. With the support of the Ministry of Education, we obtained 121 responses (response rate is 75.63 percent), among which seven responses were discarded due to a large number of missing answers. We further excluded 15 responses

6

ISI Web of Science is provided by Thomson Reuters. The database provides access to the world's leading citation, and it covers over 10,000 of the highest impact journals worldwide. We use this database to obtain the numbers of papers published by each university. See: http://thomsonreuters.com/.

13

due to lack of complete data on the inputs and outputs for DEA and on the variables for the Tobit regressions. The final sample includes 99 universities of which 35 come from public universities and 64 from private universities. Table 1 shows detailed information on how we arrive in the final sample. [Insert Table 1 here] Measure of Internal Control Implementation Due to lack of field archival data on internal controls of universities, we designed a questionnaire as the instrument. In so doing, we referred to the internal control questionnaires used by universities in the U. K. and the U. S., 7 the laws and regulations in Taiwan, 8 and the COSO report (1992). The questionnaire consists of three parts, and except for Part 3, respondents are asked to express the extent to which they agree with each statement. A response of “1” represents “completely disagree” whereas “7” represents “completely agree”. Part 1 pertains to control environment and the overall internal control design and implementation in the university. An example of questions in Part 1 is “Internal controls in my university facilitate efficiency of the university’s operations.” Part 2 contains questions regarding internal control design, and implementation in specific divisions or offices in the university. The head in each of the related divisions or offices is asked to answer the questions regarding internal controls in his/her division or office. Due to differential coverage of activities, the number of questions varies with divisions or offices. The nine involved divisions or offices, and the number of questions for each (in parenthesis and for public universities) are: Presidential Secretary (7), Personnel (8), General Affairs (16), Cashier (34),

7

8

For example, we refer to the functions of audit committee of University of Oxford (http://www.ox.ac.uk/), and internal control questionnaire at the University of North Carolina, Wilmington ( http://www.uncw.edu/). Laws and regulations include, for example, University Act, Private School Law, Government Procurement Act, and Accounting Law. 14

Procurement (7), Property Management (11), Accounting (8), Internal Auditing (17), and Computer and Information (20). Part 3 includes the questions with regard to demographic information of the respondents, such as age, education level, and years of service. It is noted that there are slight differences between the questions for public universities and for private ones because the internal control requirements by laws and regulations differ between public and private universities. To ensure the quality of the responses, we adopted the following procedures. Before sending the questionnaires, we sought for the support from the Ministry of Education in Taiwan. 9 The Ministry of Education issued an official document to all the universities, and asked the universities to answer the questionnaire seriously. Since the Ministry of Education in Taiwan is the highest authority in education, we believe that this step will enhance the quality of data as manifested in the responses by the universities. To further ensure the quality of data, we sought for the experts’ opinion about the responses. These experts are certified public accountants and had audited the financial statements of the private universities in our sample. They indicated that the responses were consistent with their observation on internal controls of these universities. 10 Furthermore, we examined the reliability and validity of our scale for measuring the internal control implementation. We used the principal component analysis and extracted factors with eigenvalues greater than 1. As there are more than one factor with an eigenvalue greater than 1, we used the orthogonal rotation and grouped into a factor the measurement items (i.e., questions) whose loading on that factor being greater than 0.5. The resulting factors are then used for the subsequent analysis. Each factor represents a control. For the questionnaire sent to public

9

10

We thank Mr. San-Chi Liu and Ms. Shu-Fend Cheng’s support for this study. In 1990, Ministry of Education in Taiwan required that financial statements of private universities should be audited by independent certified public accountants. 15

universities, there are six factors (controls) extracted from the measurement items in Part 1 of the questionnaire. Similarly, 27 factors (controls) are extracted from the measurement items in Part 2. In total, there are 33 factors. For the questionnaire sent to private universities, there are also 33 controls in total. We take the average of the responses to the measurement items constituting a factor to represent the extent of implementing that particular control. The Cronbach’s α of the 33 control scales ranges from 0.5 to 0.98. Nunnally (1967) suggests that Cronbach’s α of 0.50-0.60 is acceptable for the newly developed scales. Thus, our measurement of internal control implementation has moderate to high reliability. Panels A and B of Table 2 present the 33 controls, the number of measurement items (i.e., questions) constituting each control, and Cronbach’s α for each control for public and private universities, respectively. [Insert Table 2 here] Measure of Operating Efficiency Operating efficiency can be measured through input-orientation or output-orientation. Since universities have more control over input variables, and it does not seem that all universities are with constant returns of scale, we adopt the input-oriented BCC model to measure operating efficiency of universities. EMS (Efficiency Measurement System) version 1.3 software is used. Extant research on university efficiency uses non-current assets, and operating expenditures excluding faculty salaries as input variables (Abbott and Doucouliagos 2003; Ahn et al.1998; Athanassopoulos and Shale 1997). Moreover, other scholars have used number of academic staff and non-academic staff (Abbott and Doucouliagos 2003; Athanassopoulos and Shale 1997; Avkiran 2001) as inputs. In selecting output variables, previous studies use the number of undergraduate students or graduate students enrolled or the number of graduates (Abbott and Doucouliagos 2003; Athanassopoulos and 16

Shale 1997; Kuo and Ho 2008), number of papers published in academic journals (Johnes and Johnes 1995; Kao 1994), and research funding form the government (Abbott and Doucouliagos 2003; Ahn et al. 1998). As we would like to examine the effect of internal control implementation on teaching-related efficiency as well as on research-related efficiency, our output variables consist of teaching-related and research-related variables. Teaching-related output variables include number of graduates including number of students conferred bachelor degrees (BACH), master degrees (MAS), and doctoral degrees (DOC), respectively. Research-related output variables measure the research outcomes of the faculty and students in a university; and we use number of papers published in SCI, SSCI, ACHI, and TSSCI (Taiwan SSCI) journals (PAPER), and research grant obtained from National Science Council in Taiwan (SUB). 11 When measuring overall efficiency, we use all the five variables as output variables. To fulfill both the teaching-related and research-related purposes, every university must invest in capital, equipment, and labor. Therefore, our input variables consist of fixed assets (FA), operating expenditures excluding faculty salaries (EXP), number of full-time faculty members and number of non-academic staff (STAFF). Since faculty members with Ph.D. degrees tend to receive more academic research training and such training may be conductive to research achievement, we further divide the number of faculty members into those with Ph.D. degrees (PHDF) and those without such degrees (NPHDF). We use these five variables as input variables when measuring overall efficiency, teaching-related efficiency, and research-related efficiency. When applying DEA, the number of DMU should be at least twice of the aggregated number of input and output variables (Golany and Roll 1989). Our DMU

11

Publications in these journals are used in Taiwan for evaluating research performance of faculty members and universities. 17

includes 99 public and private universities in Taiwan, which is tenfold of the number of input and output variables. Moreover, input variables and output variables should follow the “isotonicity” assumption (Golany and Roll 1989), which means an increase in any input variable should not result in a decrease in any output variable. Panel A of Table 3 presents the Pearson correlation coefficients of input and output variables, and it indicates that a positive correlation exists between any pair of input and output variable. Thus, our DMU number is large enough and the variables follow the isotonicity assumption, suggesting that the use of the DEA model is appropriate. [Insert Table 3 here] Tobit Regression Model Since efficiency estimated by applying DEA always equals or is less than 1, we use Tobit regression to analyze the censored data for the association between internal control implementation and efficiency. Our model is described as follows: EFF = α 0 + α1IC + α 2 PUB + α 3TECH + α 4 NOR + α 5 HIS + α 6 PHDCR + α 7CSIZE + α8 SEA + ε1

(1)

EFFTEA = β 0 + β1 IC + β 2 PUB + β3TECH + β 4 NOR + β5 HIS + β 6 PHDCR + β7CSIZE + β8 SEA + ε 2

(2)

EFFRES = γ 0 + γ 1 IC + γ 2 PUB + γ 3TECH + γ 4 NOR + γ 5 HIS + γ 6 PHDCR + γ 7CSIZE + γ 8 SEA + ε 3

(3)

E =1 if z ≥ 1 E=z if z < 1 E = EFF , EFFTEA, EFFRES z is the value of actual efficiency.

Where, IC is the degree of internal control implementation; PUB is an indicator variable and is equal to 1 when a university is a public one; otherwise 0; TECH is another indicator variable and is equal to 1 for a technology-oriented university, and 0 otherwise; NOR is an indicator variable and is equal to one when a university is a normal university focusing on cultivating students as junior high or high school teachers, and 0 otherwise. HIS is the age of a university. PHDCR is the percentage of

18

Ph.D. courses as measured by the number of Ph.D. courses divided by the total number of courses offered in a university. CSIZE denotes the size of class and is measured by the total number of students divided by the number of classes. SEA measures a university’s academic orientation toward science and engineering, and is measured by the number of departments in natural science, medical science, agriculture, and engineering divided by the total number of departments in a university. E is efficiency and can be overall efficiency of university (EFF), teaching-related efficiency (EFFTEA), or research-related efficiency (EFFRES). Further explanations on the independent variable of primary interest (IC) and control variables are presented next. Internal Control Implementation (IC) IC in the Tobit regression model represents the degree of internal control implementation in a university. As mentioned earlier, controls 1 through 6 are extracted from questions in Part 1, and controls 7 through 33 are from Part 2. For Part 1, nine chief directors of nine divisions or offices are asked to answer same questions; we thus use mode of their responses to represent a university’s answer to each question. 12 We then add the answers in Part 1 to the answers in Part 2 provided by a university to represent that university’s overall implementation of internal controls. Since the questions for public universities are somewhat different from those for private universities, we standardized the sum responses when conducting Tobit regression analysis. According to our hypotheses, the sign of the coefficient for IC should be

α1 > 0 (Equation 1), β1 > 0 (Equation 2), and γ 1 < 0 (Equation 3). Control Variables PUB controls for the effect of ownership of universities. Extant research does not discuss much about the impact of ownership on university efficiency. Ahn et al. (1988) 12

In robustness tests, we use mean and median of their responses to perform the analysis and obtain essentially the same results. 19

compare efficiency of public and private universities in the U. S. from 1984 to 1985. Their results are mixed and depend on which measures of efficiency are used and whether the universities have medical schools or not. Since the association between ownership and university efficiency is not clear, we do not make directional prediction. TECH and NOR control for the types of universities. The purpose of technology universities is to cultivate students to be high-level technicians for the needs of industrial and economic development. As a result, these universities emphasize teaching and training. The purpose of normal universities is to cultivate students as teachers for junior and senior high schools, and therefore these universities are more teaching-oriented. We predict that TECH and NOR are both positively associated with teaching-related efficiency (EFFTEA), but negatively related to research-related efficiency (EFFRES), which means β3 > 0 , β 4 > 0 , γ 3 < 0 , and γ 4 < 0 . HIS controls for the effects of history on efficiency, and is measured by the number of years since a university was founded. Due to the experience and learning effect, universities with longer history result in higher efficiency (Besanko, Dranove, and Shanley 1996). However, a university with longer history may have more senior faculty members and non-academic staff as well, which will increase expenditures on salaries and fringe benefits, and in turn will decrease efficiency. We therefore do not predict the sign for α 5 , β5 , and γ 5 . PHDCR measures the proportion of Ph.D. courses. A university with higher proportion of Ph.D. courses tends to be research oriented, and in turn will have more published papers. We predict α 6 > 0 , and γ 6 > 0 . CSIZE measures the size of class. Other things being equal, larger class size may mean that there will be less faculty members required for the same number of students, which in turn will increase efficiency, especially teaching-related efficiency. We thus 20

predict α 7 > 0 , and β7 > 0 . SEA measures the academic orientation toward science and engineering. On one hand, universities with such orientation will have higher amount of expenditure on equipments and facilities for experiments and simulations. On the other hand, these universities may obtain more research grants, which will increase research outputs. As a result, we do not predict the sign for α8 , β8 , and γ 8 .

IV. EMPIRICAL RESULTS Descriptive Statistics Panels A and B of Table 2 present the 33 controls, the number of measurement items comprising each control, and descriptive statistics for each control for public and private universities, respectively. Table 2 indicates that the average implementation of internal controls across the 33 controls is 5.97 (on a 1-7 scale) for public universities, and 5.87 for private universities. For the public universities, control 15 (records of usage of petty cash, bank deposits, and checks) is implemented most extensively (6.88), followed by control 20 (property procurement procedures; 6.83) and control 22 (general procedures for recording goods and properties; 6.82). Meanwhile, the three controls that are implemented the least are control 19 (construction procurement management; 3.05), control 7 (activities of controlling staff members; 5.18) and control 31 (uses of information systems; 5.25). For private universities, the control that is implemented most extensively is control 1 (understanding of functions of internal controls; 6.83), followed by control 8 (design of human resources management; 6.82), and control 15 (rules for checks usage; 6.79), whereas the controls that are implemented the least include control 19 (construction procurement management; 2.99), control 27 (internal audits of deposits and securities; 4.61), and control 25 (design and promotion of 21

internal control systems; 4.74). It is noted that, for both public and private universities, there are more than 23 controls of which the degree of implementation is 1. This further ensures the quality of data as no one would answer 1 if they were self-serving. Next, Panel B of Table 3 presents the descriptive statistics of the input and output variables for DEA. Fixed assets (FA) of universities range from US$2.53 million to US$481.82 million, with the average of US$76.97 million. After excluding faculty salaries, operating expenditure (EXP) ranges from US$0.82 million to US$211.52 million, with the average of US$15.45 million. The average number of full-time faculty members with Ph.D. degrees (PHDF) is 165, whereas the average number of non-academic staff (STAFF) is 151. The average number of students conferred bachelor degrees (BACH), master degrees (MAS), and doctoral degrees is 1,859, 252, and 13, respectively. The average number of papers published in SCI, SSCI, ACHI, and TSSCI journals (PAPER) is 133. And the average amount of research grant from National Science Council is about US$2.75 million.. Panel A of Table 4 presents the descriptive statistics of variables used in Tobit regression. Our dependent variables include three measures of operating efficiency: overall efficiency of universities (EFF), teaching- related efficiency (EFFTEA), and research-related efficiency (EFFRES). The average is 0.83, 0.78, and 0.48, respectively. Standardized internal control implementation (IC) ranges from -2.60 to 1.98. Furthermore, among the 99 universities, 35 (35 percent) are public (PUB), 56 (55 percent) are technology-oriented (TECH), and 6 (6%) are normal (NOR) universities. The history (HIS) of all universities ranges from 4 to 123 years, with the average of 38 years. The average proportion of Ph.D. courses is 3 percent, with the minimum of zero and maximum of 19 percent. The average class size is 11.12 students. Panel B of Table 4 shows three measures of efficiency of public and private universities separately. EFF and EFFTEA of public universities are both lower than those of private universities, 22

and the differences are not significant. EFFRES of public universities is higher than that of private universities, and the difference is statistically significant. The Pearson correlation coefficients of variables used in Tobit regression are presented in Table 5. Our independent variable, IC, is positively correlated with EFF and EFFTEA, but negatively correlated with EFFRES, which is consistent with our predictions. [Insert Table 4 and Table 5 here] Tobit Regression Results Table 6 shows the results of Tobit regressions. The LR statistics are significant for all models, suggesting that the null hypothesis that all the coefficients of independent variables are zero is rejected. The variance inflation factor (VIF) of all models is lower than 2.30, which indicates that multicollinearity is not a serious concern. Panel A indicates that IC is positively but insignificantly associated with EFF and EFFTEA, and that IC is negatively and significantly associated with EFFRES. Thus, H1 and H2 are not supported, while H3 is supported. For the control variables, public universities tend to have lower overall efficiency but higher research-related efficiency than private universities; meanwhile, normal universities tend to have higher overall efficiency and teaching-related efficiency than other types of universities. Universities with longer history tend to be lower than younger universities in the three measures of efficiency, while those offering more proportion of Ph.D. courses tend to be higher in the three measures of efficiency. Universities with larger class size tend to be more efficient across all activities as well as in teaching- related activities. Universities with higher tendency toward science and engineering tend to have lower teaching-related efficiency but higher research-related efficiency. We further divide the sample into public and private universities, and present the 23

Tobit regression results in Panels B and C of Table 6. For public universities, IC is not significantly related to any of the three measures of efficiency. However, for private universities, IC is significantly and positively associated with EFFTEA, while significantly and negatively associated with EFFRES. This result is consistent with our expectation that the association between IC and EFF (and EFFTEA as well as EFFREA) is more pronounced for private universities than for public universities. Thus, H5, and H6 are supported. [Insert Table 6 here] Sensitivity Analysis To check the robustness of out results, we perform sensitivity analyses, including using different input and output variables when applying DEA, and different measures of internal control implementation. Regarding input variables, we replace the number of faculty members with Ph.D. degrees and the number of faculty members without Ph.D. degrees by the number of full-time faculty members (Abbott and Doucouliagos 2003; Athanassoulos and Shale 1997; Avkiran 2001). With respect to output variables, we replace research grant obtained from National Science Council with the number of research projects obtaining grant from National Science Council. We repeat the DEA and Tobit regression, and the results are essentially unchanged. We further replace mode with mean (and median) of the responses from nine directors to the questions in Part 1, and perform the DEA and Tobit regression. The results are also essentially unchanged.

V. SUMMARY AND DISCUSSIONS This study measures internal control of universities, and examines the relation between internal control implementation and operating efficiency. An analysis on the 24

data of internal control implementation collected through a questionnaire survey with public and private universities shows that the 99 universities in our sample generally have a high level of internal control implementation. The results of data envelopment analysis (DEA) show that the average efficiency of universities is 0.83, and that the average efficiency of teaching-related activities and research-related activities is 0.78, and 0.48, respectively. Tobit regression for the full sample indicates that internal control implementation is positively but insignificantly associated with overall efficiency and teaching-related efficiency. But, internal control implementation is negatively and significantly related to research-related efficiency. Dividing our sample into public and private universities to perform Tobit regression for each suggests that, consistent with our expectations, the associations between internal control implementation and efficiency measures are more pronounced for private universities than for public universities. That is, for public universities, internal control implementation does not have significant associations with any of the three measures of efficiency. But, for private universities, there is a positive and significant association between internal control implementation and teaching-related efficiency, whereas there is a negative and significant association between internal control implementation and research-related efficiency. Our results suggest that while the average degree of internal control implementation in our sample universities is high, the effect of internal control implementation varies with types of university activities (i.e., teaching or research), and ownership of universities (i.e., public or private). Such findings suggest that in designing internal controls for universities, one should take into account the characteristics of activities. This is especially important for knowledge-intensive organizations like universities because rigid and routine controls may be detrimental to

25

the generation of creative solutions to problems, and may also prolong the progress of research projects. On the other hand, teaching activities are more programmable and repetitive and thus more amenable to routine controls. Internal control implementation will enhance teaching-related efficiency. Our findings also indicate the effect of differential incentives on using internal controls to improve efficiency, and suggest that the current funding system is not effective for public universities to improve efficiency (especially teaching-related efficiency) through internal controls. This further suggests the importance of designing an incentive scheme for public universities to rely on control systems in improving efficiency. Our study contributes to the literature in the following ways. First, while there are studies on determinants and consequences of internal controls; they largely focus on business organizations. Thus, our study adds to the literature by examining internal controls in universities. Second, in examining internal controls of universities, we investigate the performance effect of internal controls. We further consider types of activities and the incentive difference (due to ownership), and their effects on the association between internal controls and operating efficiency. In view of no prior research in this area, our study is the first to investigate these issues in the setting of universities. This study has the following limitations and suggestions for future studies. First, the levels of internal control implementation are based on self-evaluation. While we took multiple procedures to ensure the quality of the data, subjective responses may be open to biases. Future research may consider using the evaluation by independent institutions as the measure of internal control implementation. Second, our sample comes from 99 universities in Taiwan. The globalization of higher education has been on the rise in recent years; nevertheless the generalizability of our findings remains to

26

be examined. Finally, our study focuses on the performance effect of internal controls. Future research may consider studying the effect of internal controls on transparency of financial reports of universities. Moreover, in view of the importance of corporate governance, future research may examine the association between university governance and internal controls.

27

REFERENCES Abbot, M. T., and C. Doucouliagos. 2003. The efficiency of Australian universities: A data envelopment analysis. Economics of Education Review 22 (1): 89-97. Abernethy, M. A., and P. Brownell. 1997. Management control systems in research and development organizations: the role of accounting, behavior and personnel controls. Accounting, Organizations and Society 22 (3): 233-248. Adler, P., and B. Borys. 1996. Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly 41 (1): 61–90. Ahn, T., A. Charnes, and W. W. Cooper. 1988. Some statistical and DEA evaluations of relative efficiencies of public and private institutions of higher learning. Socio-Economic Planning Sciences 22 (6): 259-269. Ahrens, T., and C. S. Chapman. 2004. Accounting for flexibility and efficiency: a field study of management control systems in a restaurant chain. Contemporary Accounting Research 21 (2): 271–301. Ashbaugh-Skaife, H., D. W. Collins, and W. R. Kinney, Jr. 2007. The discovery and reporting of internal control deficiencies prior to SOX-mandated audits. Journal of Accounting and Economics 44: 166-192. Athanassopoulos, A. D., and E. Shale. 1997. Assessing the comparative efficiency of higher education institutions in the UK by means of data envelopment analysis. Education Economics 5 (2): 117-134. Avkiran, N. K. 2001. Investigating technical and scale efficiencies of Australian Universities through data envelopment analysis. Socio-Economic Planning Sciences 35: 57-80. Banker, R. D., A. Charnes, and W. W. Cooper. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30 (9): 1078-1092. Besanko, D., D. Dranove, and M. Shanley. 1996. The Economics of Strategy. New York: John Wiley and Sons, Inc. Bowrin, A. R. 2004. Internal control in Trinidad and Tobago religious organizations. Accounting, Auditing & Accountability Journal 17 (1): 121-152. Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6): 429-444. Chung, T. K. J., G. L. Harrison, and R. C. Reeve. 2009. Interdependencies in organization design: A test in universities. Journal of Management Accounting Research 21: 55–73. Committee of Sponsoring Organizations of the Treadway Commission (COSO). 1992. Internal Control: Integrated Framework. New York, NY: AICPA. Davila, T. 2000. An empirical study on the drivers of management control systems' 28

design in new product development. Accounting, Organizations and Society 25: 383-409. Ditillo, A. 2004. Dealing with uncertainty in knowledge-intensive firms: the role of management control systems as knowledge integration mechanisms. Accounting, Organizations and Society 29: 401-421. Doyle, J. T., W. Ge, and S. McVay. 2007a. Determinants of weaknesses in internal control over financial reporting. Journal of Accounting and Economics 44: 193-223. _______, _______, and _______. 2007b. Accruals quality and internal control over financial reporting. The Accounting Review 82 (5): 1141-1170. Duncan, J. B., D. L. Flesher, and M. H. Stocks. 1999. Internal control systems in US Churches: An examination of the effects of church size and denomination on systems of internal control. Accounting, Auditing & Accountability Journal 12 (2): 142-163. Farrell, M. J. 1957. The measurement of productivity efficiency. Journal of the Royal Statistical Society 3: 253-281. Gallagher, M., and V. S. Radcliffe. 2002. Internal Controls in Nonprofit Organizations: The Case of the American Cancer Society, Ohio Division. Nonprofit Management & Leadership 12 (3): 313-325. Golany, B., and Y. Roll. 1989. An application procedure for DEA. OMEGA: International Journal of Management Science 17 (3): 237-250. Hofstede, G. 1981. Management control of public and not-for-profit activities. Accounting, Organizations and Society 6 (3): 193-211. Johnes, J., and G. Johnes. 1995. Research funding and performance in U. K. university departments of economics: A frontier analysis. Economics of Education Review 14 (3): 301-314. Kao, C. 1994. Evaluation of junior colleges of technology: The Taiwan case. European Journal of Operational Research 72: 43-51. Kaplan, R. S. 1989. Connecting the research-teaching-practice triangle. Accounting Horizons (March): 129-132. Kinney, W. R., Jr. 1989. The relation of accounting research to teaching and practice: A “positive” view. Accounting Horizons (March): 119-124. Krishnan, J. 2005. Audit committee quality and inter control: An empirical analysis. The Accounting Review 80 (2): 649-675. Kuo, J. S., and Y. C. Ho. 2008. The cost efficiency impact of the university operation fund on public universities in Taiwan. Economics of Education Review 27: 603-612. Lindsay, C. M. 1976. A theory of government enterprise. Journal of Political Economy 84 (5): 1061-1077. Lowry, R. C. 2004. Markets, governance, and university priorities: evidence on 29

undergraduate education and research. Economics of Governance 5: 29-51. Nunnally, J. 1967. Psychometric Theory. McGraw-Hill. Perrow, C. 1970. Organizational Analysis: A Sociological View. New York: Tavistock Publications. Rhodes, E. L., and L. Southwick Jr. 1986. Determinants of Efficiency in Public and Private Universities. Mimeo, School of Environmental and Public Affairs, Indiana University, Bloomington. Sanchez, A. M., and M.P. Perez . 2002. R&D project efficiency management in the Spanish industry. International Journal of Project Management 20(7): 545-560. Securities and Exchange Commission (SEC). 2002. Final rule: Certification of Disclosure in Companies' Quarterly and Annual Reports. Release Nos. 33-8124. Available at: http://www.sec.gov/rules/final/33-8124.htm. Securities and Exchange Commission (SEC). 2004. Final rule: Management's Report on Internal Control Over Financial Reporting and Certification of Disclosure in Exchange Act Periodic Reports. Release Nos. 33-8238. Available at: http://www.sec.gov/rules/final/33-8238.htm. Sisk, D. 1981. A theory of government enterprise: university Ph. D. production. Public Choice 37: 357-363. Sunder, S. 1997. Theory of Accounting and Control. South Western Publishing. Toma, E. F. 1990. Boards of trustees, agency problems, and university output. Public Choice 67: 1-9.

30

TABLE 1 Breakdown of the Sample Public universities

Private universities

Total

Number of questionnaires distributed (A)

55

105

160

Number of responses (B)

45

76

121

Minus: incomplete responses (C)

(3)

(4)

(7)

Effective responses (D)=(B)-(C)

42

72

114

76.36%

68.57%

71.25%

(7)

(8)

(15)

35

64

99

Effective responses ratio (E)=(D)/(A) Minus: data for DEA and Tobit regression are not available (F) Final sample (G)=(D)-(F)

31

TABLE 2 Elements of Controls and Descriptive Statistics Panel A: Public Universities # of questions Mean

Controls

STD

Min. Max.

Cronbach's α

Control 1: Understanding of functions of internal controls

4

6.74

0.44

6.00

7.00

0.85

Control 2: Understanding of objectives and control

4

6.36

0.83

3.00

7.00

0.67

Control 3: Procedures for internal controls

4

5.37

1.35

1.00

7.00

0.65

Control 4: Understanding of the complete set of procedures

4

6.40

0.85

3.00

7.00

0.51

Control 5: Understanding the division in charge of the

3

5.27

1.74

1.00

7.00

0.64

2

6.03

0.71

4.00

7.00

0.57

Control 7: Activities of controlling staff members

7

5.18

1.63

1.00

7.00

0.87

Control 8: Procedures for evaluation and termination of

3

5.74

1.77

1.00

7.00

0.70

Control 9: Documentation of timesheets

3

5.76

1.69

1.00

7.00

0.81

Control 10: Recording and filing of payrolls

2

6.65

0.71

4.00

7.00

0.95

Control 11: Internal control of general affairs

6

5.30

1.50

1.00

7.00

0.89

Control 12: Records and custody of stamps usage

4

6.55

0.87

1.00

7.00

0.68

Control 13: Compliance with laws for general affaires

3

5.42

1.69

1.00

7.00

0.74

Control 14: General affairs management

3

5.37

1.58

1.00

7.00

0.71

Control 15: Records of usage of petty cash, bank deposits,

23

6.88

0.52

1.00

7.00

0.98

Control 16: Recording and filing of payment vouchers

6

5.62

2.26

1.00

7.00

0.78

Control 17: Rules for checks usage

3

6.18

1.73

1.00

7.00

0.51

Control 18: Follow-up of payments and reconciliations of

2

5.74

1.78

1.00

7.00

0.68

Control 19: Construction procurement management

4

3.05

1.93

1.00

7.00

0.85

Control 20: Property procurement procedures

3

6.83

0.50

4.00

7.00

0.72

Control 21: Recording and filing of goods and fixed assets

9

6.29

1.27

1.00

7.00

0.94

Control 22: General procedures for recording goods and

2

6.82

0.54

3.00

7.00

0.77

6

5.39

1.60

1.00

7.00

0.81

activities of the university

design and implementation of internal control Control 6: Identifying the events which are obstacles of achieving the objectives

employees

and checks

cash receipts and disbursements

properties Control 23: Internal control of accounting-related policies

(continued on next page) 32

TABLE 2 (continued) # of questions Mean

Controls

STD

Min. Max.

Cronbach's α

Control 24: Procedures for collateral of loans

2

5.84

1.78

1.00

7.00

0.85

Control 25: General procedures for internal audits

4

6.64

0.70

4.00

7.00

0.71

Control 26: Internal audits of properties

4

5.92

1.60

1.00

7.00

0.70

Control 27: Internal audits of deposits and securities

3

5.57

1.79

1.00

7.00

0.86

Control 28: Internal audits of general rules and procedures

4

5.51

1.81

1.00

7.00

0.66

Control 29: Internal audits of procurements

2

6.54

0.99

1.00

7.00

0.64

Control 30: Development and maintenance of information

8

5.63

1.26

1.00

7.00

0.90

Control 31: Uses of information systems

5

5.25

1.42

1.00

7.00

0.90

Control 32: Security maintenance of information systems

3

6.18

0.99

2.00

7.00

0.71

Control 33: General procedures for information systems

4

5.51

1.17

2.00

7.00

0.77

149

5.97

1.51

1.00

7.00

systems

management Total

Panel B: Private Universities Controls

# of questions Mean

STD

Min. Max.

Cronbach's α

Control 1: Understanding of functions of internal controls

4

6.83

0.37

6.00

7.00

0.85

Control 2: Understanding of objectives and control

4

6.49

0.78

4.00

7.00

0.67

Control 3: Procedures for internal controls

4

5.78

1.26

1.00

7.00

0.65

Control 4: Understanding of the complete set of procedures

4

6.50

0.87

2.00

7.00

0.51

Control 5: Understanding the division in charge of the

3

5.52

1.71

1.00

7.00

0.64

2

5.52

1.71

1.00

7.00

0.57

Control 7: Activities of controlling staff members

5

5.06

1.61

1.00

7.00

0.87

Control 8: Design of human resources management

5

6.82

0.69

1.00

7.00

0.72

Control 9: Recording and filing of payrolls

2

5.23

2.08

1.00

7.00

0.52

Control 10: Internal control of general affairs

6

5.07

1.65

1.00

7.00

0.88

Control 11: General affairs management

5

4.93

1.90

1.00

7.00

0.83

Control 12: Records and custody of stamps usage

4

6.59

1.09

1.00

7.00

0.84

activities of the university

design and implementation of internal control Control 6: Identifying the events which are obstacles to achieving the objectives

(continued on next page) 33

TABLE 2 (continued) # of questions Mean

Controls Control 13: Records of usage of petty cash and payments of

STD

Min. Max.

Cronbach's α

7

6.26

1.61

1.00

7.00

0.88

Control 14: Recording and filing of receipts

5

6.59

1.10

1.00

7.00

0.58

Control 15: Rules for checks usage

4

6.79

0.60

4.00

7.00

0.73

Control 16: Rules for custody of bank deposits and checks

5

6.46

1.19

1.00

7.00

0.62

Control 17: Custody of securities and procedures to

2

5.15

2.28

1.00

7.00

0.61

Control 18: Property procurement procedures

3

6.74

0.52

4.00

7.00

0.80

Control 19: Construction procurement management

3

2.99

1.97

1.00

7.00

0.72

Control 20: Validity of the procurements

2

6.77

0.54

4.00

7.00

0.58

Control 21: General procedures for recording goods and

5

5.94

1.60

1.00

7.00

0.85

Control 22: Recording and filing of goods and fixed assets

4

6.67

0.99

1.00

7.00

0.52

Control 23: Compliance with laws for hiring and evaluating

3

5.14

1.91

1.00

7.00

0.75

Control 24: Procedures for collateral of loans

2

6.50

1.59

1.00

7.00

1.00

Control 25: Design and promotion of internal control

2

4.74

1.97

1.00

7.00

0.65

Control 26: General procedures for internal audits

6

5.97

1.52

1.00

7.00

0.78

Control 27: Internal audits of deposits and securities

2

4.61

2.34

1.00

7.00

0.75

Control 28: Internal audits of property and procurement

3

5.80

1.57

1.00

7.00

0.56

Control 29: Validity of custody of deposits or properties

3

5.80

1.57

1.00

7.00

0.62

Control 30: General procedures for information systems

8

5.74

1.26

1.00

7.00

0.93

5

5.74

1.21

1.00

7.00

0.90

Control 32: Uses of information systems

4

5.37

1.46

1.00

7.00

0.76

Control 33: Security maintenance of information systems

3

6.50

0.80

3.00

7.00

0.78

129

5.87

1.62

1.00

7.00

payrolls

collateralize

properties

the personnel

systems

management Control 31: Development and maintenance of information systems

Total

34

TABLE 3 Pearson Correlation Coefficients and Descriptive Statistics of Input and Output Variables for DEA Panel A: Pearson correlation coefficients FA

EXP

PHDF

NPHDF

STAFF

BACH

MAS

DOC

PAPER

SUB

FA

1.0000

EXP

0.6942

1.0000

PHDF

0.7421

0.9630

1.0000

NPHDF

0.4627

0.1620

0.1778

1.0000

STAFF

0.8060

0.9123

0.9407

0.3007

1.0000

BACH

0.6182

0.2310

0.3071

0.7970

0.3787

1.0000

MAS

0.5959

0.9251

0.9434

0.0018

0.8549

0.1388

1.0000

DOC

0.6161

0.9677

0.9168

0.1367

0.8694

0.1472

0.9157

1.0000

PAPER

0.6620

0.9673

0.9187

0.1610

0.8647

0.1518

0.8909

0.9644

1.0000

SUB

0.6485

0.9764

0.9165

0.1461

0.8637

0.1502

0.8748

0.9702

0.9840

1.0000

Panel B: Descriptive Statistics of Input and Output Variables for DEA Variables

N

Mean

STD

Min.

Median

Max.

FA (US$000)

99

76,969.70

68,181.82

2,533.33

65,853.37

481,818.18

EXP (US$000)

99

15,454.55

25,121.21

821.21

8,788.38

211,515.15

PHDF

99

165.43

201.24

0.63

114.18

1560.04

NPHDF

99

131.34

72.85

10.65

125.80

296.84

STAFF

99

151.14

142.09

12.00

117.00

1169.00

BACH

99

1859.12

1126.42

0.00

1763.00

5598.00

MAS

99

252.66

475.79

0.00

25.00

2990.00

DOC

99

13.77

48.47

0.00

0.00

383.00

PAPER

99

133.13

377.28

0.00

19.00

3077.00

SUB

99

2,746.43

8,644.95

0.00

551.82

75,326.67

Input variables

Output variables

(continued on next page)

35

TABLE 3 (continued) Variable Definitions: FA

The amount of fixed assets as of December 31, 2005 (in US$000)

EXP

Total operating expenditures (excluding salaries paid for the faculty) in 2005 (in US$000)

PHDF

Number of full-time faculty members with Ph. D. degrees

NPHDF

Number of full-time faculty members without Ph. D. degrees

STAFF

Number of non-academic staff

BACH

Number of students conferred a bachelor degree

MAS

Number of students conferred a master degree

DOC

Number of students conferred a doctor degree

PAPER

Number of papers published in SCI, SSCI, ACHI, and TSSCI journals

SUB

Amount of research grant obtained from National Science Council in Taiwan (in US$000)

36

TABLE 4 Descriptive Statistics of Variables in Tobit Regressions Panel A: Full sample Variables

N

Mean

STD

Min.

Median

Max.

EFF

99

0.83

0.16

0.43

0.87

1.00

EFFTEA

99

0.78

0.19

0.33

0.83

1.00

EFFRES

99

0.48

0.24

0.15

0.41

1.00

99

0.00

0.99

-2.60

0.12

1.98

PUB

99

0.35

0.48

0.00

0.00

1.00

TECH

99

0.56

0.50

0.00

1.00

1.00

NOR

99

0.06

0.24

0.00

0.00

1.00

HIS

99

38.76

25.01

4.00

39.00

123.00

PHDCR

99

0.03

0.05

0.00

0.00

0.19

CSIZE

99

11.12

4.27

1.54

11.16

35.06

SEA

99

0.43

0.29

0.00

0.40

1.00

Independent Variables

Independent Variable IC Control Variables

Panel B: Efficiency of Public and Private Universities Public N=35

Private N=64

Difference (p-values)

Mean

0.8004

0.8485

-0.0481

[STD]

[ 0.1832 ]

[ 0.1500 ]

(0.1624)

Mean

0.7552

0.8006

-0.0453

[STD]

[ 0.2071 ]

[ 0.1817 ]

(0.2616)

Mean

0.6042

0.4051

0.1992

[STD]

[ 0.2730 ]

[ 0.1971 ]

(0.0001)

Variables EFF

EFFTEA

EFFRES


37

TABLE 4 (continued) Variable Definitions: EFF

Overall efficiency of universities (with both teaching-related and research-related output variables of DEA)

EFFTEA

Teaching-related efficiency of universities (with only teaching-related output variables)

EFFRES

Research-related efficiency of universities (with only research-related output variables)

IC

Standardized internal control implementation

PUB

Equals 1for a public university; otherwise 0

TECH

Equals 1for a technology-related university; otherwise 0

NOR

Equals 1for a normal university; otherwise 0

HIS

History of universities (= number of years since a university was established)

PHDCR

Proportion of Ph. D. courses (= number of Ph. D. courses divided by total number of courses offered by a university)

CSIZE

Class size (= number of students divided by number of classes)

SEA

Academic orientation toward science and engineering (= number of departments in the field of natural, medical and agricultural sciences and engineering divided by total number of departments in a university)

38

TABLE 5 Pearson Correlation Coefficients of Variables in Tobit Regressions EFF

EFFTEA

EFFRES

IC

PUB

TECH

NOR

HIS

PHDCR

CSIZE

EFF

1.0000

EFFTEA

0.8601

1.0000

EFFRES

0.4193

0.2069

1.0000

IC

0.1496

0.1137

-0.0432

1.0000

PUB

-0.1415

-0.1139

0.3908

0.0000

1.0000

TECH

-0.0140

0.0977

-0.3433 -0.0422

-0.3165

1.0000

NOR

-0.0010

0.0597

0.0070 -0.0730

0.3435

-0.2840

1.0000

HIS

-0.0911

-0.0860

-0.1165 -0.0574

0.3342

-0.2408

0.3802

1.0000

PHDCR

0.2983

0.1903

0.1000

0.4502

-0.5086

0.0589

0.1290

1.0000

CSIZE

0.2426

0.2989

-0.2212 -0.0690

-0.3330

0.5525

-0.1954

-0.0053

-0.2906

1.0000

SEA

0.1581

-0.0690

-0.0778

0.1557

-0.2851

-0.0540

0.1733

0.2360

0.6067 0.3060

0.1016

SEA

1.0000

Variable Definitions: EFF


EFFTEA


EFFRES

Research-related

IC


PUB


TECH


NOR


HIS

History of a university (= number of years since a university was established)

PHDCR

Proportion of Ph. D. courses (= number of Ph. D. courses divided by total number of courses offered by a university)

CSIZE


SEA

Academic orientation toward science and engineering (= number of departments in the field of natural, medical and

efficiency of universities (with only research-related output variables)

agricultural sciences and engineering divided by total number of departments in a university)

39

TABLE 6 Results of Tobit Regressions Panel A: Full sample (N=99) Model (1) EFF Variable

coefficients

p-value

Model (2) EFFTEA

Model (3) EFFRES

coefficients p-value


0.0212

( 0.2650 )

0.0262

( 0.2210 )

-0.0362

( 0.0640 )

PUB

-0.0905

( 0.0590 )

-0.0675

( 0.2130 )

0.1451

( 0.0040 )

TECH

-0.0197

( 0.7120 )

0.0337

( 0.5790 )

-0.0844

( 0.1170 )

NOR

0.1470

( 0.1030 )

0.1915

( 0.0630 )

0.0146

( 0.8750 )

HIS

-0.0014

( 0.0900 )

-0.0018

( 0.0690 )

-0.0032

( 0.0010 )

PHDCR

2.5833

( 0.0000 )

2.5116

( 0.0000 )

2.5881

( 0.0000 )

CSIZE

0.0236

( 0.0010 )

0.0301

( 0.0000 )

0.0014

( 0.8100 )

-0.0134

( 0.8500 )

-0.2018

( 0.0130 )

0.2464

( 0.0010 )

CONSTANT

0.6393

( 0.0000 )

0.5703

( 0.0000 )

0.4206

( 0.0000 )

LR (p-value)

40.4400

( 0.0000 )

38.1900

( 0.0000 )

76.3100

( 0.0000 )

IC

SEA

Panel B: Public universities (N=35) Model (1) EFF Variable

Model (2) EFFTEA

Model (3) EFFRES



coefficients

p-value

IC

0.0268

( 0.3560 )

0.0055

( 0.8680 )

-0.0264

( 0.5210 )

TECH

0.1351

( 0.1140 )

0.2209

( 0.0310 )

0.0653

( 0.5840 )

NOR

0.2708

( 0.0050 )

0.2967

( 0.0080 )

0.0398

( 0.7490 )

HIS

-0.0023

( 0.0350 )

-0.0022

( 0.0900 )

-0.0037

( 0.0190 )

PHDCR

2.2906

( 0.0000 )

2.7171

( 0.0000 )

2.8270

( 0.0010 )

CSIZE

-0.0011

( 0.9440 )

-0.0094

( 0.6100 )

-0.0209

( 0.3570 )

SEA

0.1870

( 0.0900 )

0.0368

( 0.7700 )

0.1929

( 0.2200 )

CONSTANT

0.6750

( 0.0000 )

0.6959

( 0.0010 )

0.7579

( 0.0020 )

LR (p-value)

28.7600

( 0.0002 )

24.4400

( 0.0010 )

27.2400

( 0.0003 )


40

TABLE 6 (continued) Panel C: Private universities (N=64) Model (1) EFF Variable

Model (2) EFFTEA

Model (3) EFFRES



coefficients

p-value

0.0268

( 0.2450 )

0.0504

( 0.0490 )

-0.0456

( 0.0320 )

TECH

-0.0535

( 0.4500 )

-0.1122

( 0.1590 )

-0.1310

( 0.0430 )

HIS

-0.0003

( 0.7980 )

-0.0011

( 0.4100 )

-0.0021

( 0.0690 )

PHDCR

3.6285

( 0.0280 )

-0.7100

( 0.6740 )

2.9121

( 0.0590 )

CSIZE

0.0254

( 0.0020 )

0.0373

( 0.0000 )

0.0027

( 0.6170 )

-0.1214

( 0.1960 )

-0.2554

( 0.0160 )

0.3066

( 0.0010 )

CONSTANT

0.6395

( 0.0000 )

0.6120

( 0.0000 )

0.3726

( 0.0000 )

LR (p-value)

21.4500

( 0.0015 )

28.6100

( 0.0001 )

35.8900

( 0.0000 )

IC

SEA

Variable Definitions: EFF


EFFTEA


EFFRES

Research-related

IC


PUB


TECH


NOR


HIS

History of a university (= number of years since a university was established)

PHDCR

Proportion of Ph. D. courses (= number of Ph. D. courses divided by total number of courses

efficiency of universities (with only research-related output variables)

offered by a university) CSIZE


SEA

Academic orientation toward science and engineering (= number of departments in the field of natural, medical and agricultural sciences and engineering divided by total number of departments in a university)

41