Faculty Commitment to Performance Based Funding for Academic Programs

Faculty Commitment to Performance Based Funding for Academic Programs Author information Janice R. Sandiford, PhD Florida International University College of Education ZEB 366 Miami, FL 33199 305 348-3996 sandifor@fiu.edu Rolando Montoya, Jr. EdD President, Wolfson Campus Miami-Dade College 300 N.E. 2nd Avenue Miami, FL 33132 305 237-7600 rmontoya@mdc.edu Faculty Commitment to Performance Based Funding for Academic Programs Faculty Commitment to Performance Based Funding for Academic Programs Abstract Higher education institutions receiving public financial support are accountable to the governmental bodies providing their funding. The current accountability movement has generated demands for greater effectiveness and efficiency from public higher education institutions. A recent manifestation of this movement is performance-based funding that links budgetary allocations to the attainment of certain indicators. Using a survey, this study explored intrinsic and extrinsic faculty motivators for compliance with performance-based funding indicators. Indicators closely related to the traditional mission of community colleges showed higher level of faculty commitment. Indicators more oriented to State priorities showed lower level of faculty commitment. 1 Faculty Commitment to Performance Based Funding for Academic Programs The accountability movement in elementary and secondary education is spreading to higher education. Policy makers, long frustrated with the slow pace of change in higher education as well as with the sense that colleges and universities are aloof, are demanding higher levels of accountability and responsiveness. (Newman & Couturier, 2003 p. 11). As currently practiced in higher education, government agencies subsidize higher education institutions more by funding the institution rather than the individual, although there is an inkling that vouchers to individuals could be a viable consideration, giving individuals a greater choice. Funding for higher education is a target of state and federal legislation and return on investment is constantly under review. In higher education, funding agencies can set standards and can measure outputs (graduation rates) and outcomes (placement rates, transfer rates) and ask questions regarding cost-benefit. As a result, public colleges and universities and private higher education institutions receiving public financial support are accountable to the governmental bodies providing their funding. Accountability may focus on processes, compliance with standards, outputs, or outcomes (Kells, 1992). The current accountability movement has generated demands for greater effectiveness and efficiency from public higher education institutions. A manifestation of this movement is performance-based funding that links budgetary allocations to the attainment of certain indicators. Early accountability systems focused on auditing for appropriateness of expenditures, internal control and accounting procedures; however, during the 1980s attention changed dramatically toward demonstrating performance (Layzell & Caruthers, 1995). The method for ensuring the new accountability moved progressively during the 1990s from internally assessing results to reporting results to the State (Ruppert, 1997), and most recently to performance funding (Burke, 2002a; Serban, 1998). For Pickens (1982), the philosophical justification for performance-based funding is persuasive since it justifies funding on the basis of educational 2 Faculty Commitment to Performance Based Funding for Academic Programs results, not simply on the basis of activities performed or on the basis of accounting reports. Albright (1998) characterizes performance-based funding as a paradigm shift from entitlement to rewards, from resources to results. The State of Florida joined the current accountability movement in 1991 by establishing, in statue, a systematic process of assessment and reporting, for public colleges and universities, on prescribed performance indicators in the areas of access, diversity, productivity, and quality of undergraduate education (Wright, Dallet, & Copa, 2002). In 1994, performance funding was established with the enactment of the Government Performance and Accountability Act that required, for all state agencies, the phasing in of a system relating state funding to results on indicators closely associated to the agency’s mission (Office of Program Policy Analysis and Government Accountability [OPPAGA], 1997). As a consequence of this law, the State of Florida established performance-based funding programs for community colleges (OPPAGA, 1999). The performance-based funding program for the Associate in Arts (AA) degree was implemented for the first time in fiscal year 1996-1997 with a legislative appropriation of $12 million in incentives for community colleges demonstrating performance on prescribed indicators (Burke & Serban, 1998). During the last seven years, the list of performance funding indicators has been modified with additions and deletions based on State priorities. The current measures refer to program completions in general and by special populations, transfers, job placements, and education acceleration mechanisms. Note that these are clearly measurable outputs and outcomes. They can be summarized into 10 goals: 1. Increase the number of students who graduate with AA degrees. 2. Increase the number of dual enrollment (high school/college) students and credits taken by dual enrollees. 3. Increase the number of graduates with AA degrees among students who required remediation when they started at the college. 3 Faculty Commitment to Performance Based Funding for Academic Programs 4. Increase the number of graduates with AA degrees among students who are classified as economically disadvantaged. 5. Increase the number of graduates with AA degrees among students with disabilities. 6. Increase the number of African-American male students who graduate with AA degrees. 7. Increase the number of graduates with AA degrees among students who originally tested into English as a second language. 8. Increase the number of AA graduates who are placed in full-time jobs earning at least $10 per hour. 9. Increase the number of AA graduates who transfer to the State University System (SUS). 10. Increase the number of AA graduates who complete their degrees with 72 credit hours or less. Since fiscal year 1999-2000, performance-based funds constitute approximately 6.5% of the total State allocation to community colleges (Wright et al., 2002). Florida Statutes (2002) include landmark legislation, effective on January 7, 2003, that requires the State Board of Education to present a proposal to the Legislature for a performance-based funding program that would appropriate at least 10% of the state budget for the Florida education system conditional upon meeting or exceeding performance standards. The recommendation for the community college performance-based funding program was to have been presented in 2004, for consideration by the 2005 Legislature. Data will be collected during academic year 2005-2006 for full implementation in year 2006-2007. Since a community college’s institutional performance on each indicator is measured for the purpose of determining its share of the performance-based funds budgeted by the State for the Community College System and faculty members are deeply involved in and partially responsible for community colleges’ performance, faculty commitment to the indicators established by the 4 Faculty Commitment to Performance Based Funding for Academic Programs State is fundamental. If faculty members are not committed to the performance goals represented by the indicators, they will not do what is necessary to contribute to the achievement of the level of effectiveness and efficiency expected by the State. However, the contribution of individual faculty members to the overall college performance is not measurable. Thus, commitment was selected as the focus of this study because it is a construct correlated with performance that may be measured for each individual faculty member by subjective responses to survey questions (DeShon & Landis, 1997; Hollenbeck, Williams, & Klein, 1989). An underlying assumption of this study is the positive relationship between goal commitment and performance described by the literature (Hollenbeck & Klein, 1987; Klein, Wesson, Hollenbeck, & Alge, 1999; Locke & Latham, 1990; Locke, Latham, & Erez, 1988). A community college with faculty committed to performance-based funding goals is more likely to increase its institutional effectiveness and obtain additional State funds. Thus, the problem addressed by this study is the measurement of commitment. It takes a different approach than that of Middaugh (2002) who studies cost and productivity models or the amount of time faculty are engaged in various activities. Purpose of the Study The backbone for the operations in higher education institutions is the faculty. It is difficult to conceive a revolutionary change in the funding structure for the operations that is introduced without counting on the participation of faculty. Faculty members need to know what are the performance expectations for the institutions in which they work and the institution needs their support in order to be successful. Since commitment seems to affect performance, it is important to know the level of faculty commitment to the performance goals established by the state. This study examined faculty commitment to performance-based funding indicators for academic programs (transferable Associate in Arts degree). Its purpose was to examine the level of self-reported commitment of community college faculty to performance-based funding indicators for academic programs. The study examined the relationship between commitment and 5 Faculty Commitment to Performance Based Funding for Academic Programs two intrinsic variables: (a) self-efficacy to contribute to the achievement of the indicators, and (b) personal financial reward expectation for contributing to the achievement of the indicators. In addition, the study examined the relationship between commitment and three extrinsic variables; (a) gender, (b) academic rank, and (c) types of courses taught. Research Questions The study answers four research questions: 1. What is the overall commitment of community college faculty members to Florida performance-based funding indicators for the AA program? 2. To what extent are community college faculty members committed to each performance-based funding indicator for the AA program? 3. Is faculty commitment to the indicators related to internal variables of self-efficacy and expectation of financial reward? 4. Is faculty commitment to the indicators related to (a) gender, (b) academic rank, and (c) types of courses taught? Statement of Hypotheses It was hypothesized that community college faculty are committed to the performancebased funding. It was also hypothesized that commitment is related to the internal variables of self-efficacy and expectation of financial reward. In addition, it was hypothesized that faculty gender, academic rank and types of courses taught are related to commitment as well. Literature While it could be stated that higher education institutions have become more focused on the market (quality and competition), it is also noted that it is highly regulated. Getting frustrated with higher education’s slow response to change, policy makers are beginning to focus on the power of market forces to leverage reform in higher education. Researchers at the Futures Project support this focus but not without concern. The key they believe, “is finding policy solutions that 6 Faculty Commitment to Performance Based Funding for Academic Programs help steer the market in ways that benefit society and serve the greater public good” (Newman & Couturier, 2002 p. 1). One avenue of research has focused on faculty productivity but researchers are finding difficulty defining the scope of faculty activity because of institutional variation in assignment and common definitions. (Middaugh, 2002; Middaugh, Graham, & Shahid, 2003). The Joint Commission on Accountability Reporting (JCAR) attempted to develop a language of accountability that could be used to describe what higher education does and developed four conceptual frameworks. These are a) student placement rates following degree, b) graduation and transfer rates, c) student charges and costs, and d) faculty activity. Left over from earlier efforts, the accountability movement directs its focus on the first two of these frameworks and thus is the focus of this study. Since the 1960s an accountability movement has flourished and demanded effectiveness and efficiency from publicly funded institutions. The accountability systems for public colleges and universities evolved from control of expenditure appropriateness to the demonstration of performance results (Layzell & Caruthers, 1995). The focus on results moved progressively from assessing to reporting, and most recently to performance funding (Burke, 2002a). Since 1996, Florida community colleges have competed for performance-based funds assigned to the AA program that are distributed based upon each college’s pro-rata share of the collective performance on the indicators. In a period of constrained financial State support for higher education institutions, performance-based funding constitutes an additional source of potential funding. There are plans to extend this type of funding to universities. Faculty members are deeply involved in and partially responsible for community college performance, and their commitment to the goals represented by the indicators facilitates the achievement of the level of institutional effectiveness and efficiency expected by the State. The literature looks first at the accountability movement then the goal commitment theories and finally the variables that might be used to identify those faculty more likely to be more committed to performance based funding. 7 Faculty Commitment to Performance Based Funding for Academic Programs The Accountability Movement The tradition that colleges and universities exercise freedom to manage their own affairs was consolidated in the United States by the 1819 Supreme Court decision supporting the autonomy of Dartmouth College (Rudolph, 1990). For a century and a half this precedent shielded higher education from the type of probing by evaluators and accountability advocates that became common place in public schools and social service agencies. Until the 1960s, the traditional accountability system had a fiduciary orientation focused on the appropriateness of the expenditures and sound comptrollership practices (Layzell & Caruthers, 1995). During the 1960s and 1970s, governments became increasingly attentive to concepts such as effectiveness, efficiency, productivity, and return on investment (Layzell & Caruthers, 1995). This new accountability movement had its first manifestation in state demands for outcome assessments in colleges and universities (Ewell, 1983). Florida’s rising-junior College Level Academic Skills Test (CLAST) introduced in 1982 was an example. In the late 1980s two-thirds of the states had mandated assessment policies, and in a parallel movement, the six regional accrediting agencies also introduced assessment of institutional effectiveness standards to the reaffirmation procedure (Wolff, 1992). The slight attention to accountability and the inability of governmental authorities to compare institutional results motivated the development of a second manifestation of the new accountability movement: performance reporting (Burke, 2002a). Economic, ideological, and sociological factors also brought new urgency to state demands for higher education accountability which were translated to mandated annual performance reports on the indicators. Public annual reports on a common list of statewide performance indicators permitted comparability among institutions of the same type (Burke 2002a) and a response to the accountability concerns of lawmakers, students, parents, employers, and the general public (Christal, 1998; Middaugh, 2002; Middaugh, Graham, & Shahid, 2003). Number of degrees awarded, graduation rates, transfer rates from two-year to four-year colleges, job placements, 8 Faculty Commitment to Performance Based Funding for Academic Programs effectiveness of remediation activities, and pass rates on licensure exams are common indicators prescribed by the states for the purpose of performance reporting (Ruppert, 1997). The indicators used for reporting emphasized results. In Florida, the shift to performance reporting took place in 1991 when the legislature mandated a formal reporting process for community colleges (Florida Statutes, 1991). The law required the Division of Community Colleges to develop objective measures to be used to report annual performance on the following variables: (a) graduation rates of Associate in Arts (AA) and Associate in Science (AS) degree-seeking students; (b) minority student enrollment and retention rates; (c) student performance, including performance on college-level academic skills, mean grade point averages for AA transfer students, and performance on state licensure examinations; (d) job placement rates of vocational students; and (e) student progress by admission status and program. Many authors (Burke 2002a; Ewell, 1997; Ruppert, 1997) indicate that assessing and reporting results alone have a very limited impact on campus behaviors if there are no fiscal consequences tied to the achievement of the indicators. Thus, the next logical stage of the accountability movement was the development of models linking performance to funding. In Florida, OPPAGA (1999) recommended linking accountability and performance funding to demonstrate that the level of performance reported has consequences and to reward those institutions producing better results. Although no formal research has been conducted to measure the opinions of faculty members on performance indicators externally prescribed, there are some illustrations of faculty resentment and outcry when they felt substantive autonomy and academic freedom were being violated. Selingo (1999) describes how several faculty members in the California State University System were disturbed by a new statewide accountability system promising a future in which degrees would be awarded only on the basis of demonstrated learning. Faculty members argued that the final version presented by the Board of Trustees did not incorporate the input provided by 9 Faculty Commitment to Performance Based Funding for Academic Programs the faculty during the earlier stages of the development of the plan. Schmidt (2000) reports how faculty leaders in the University of Texas System protested a Board of Regent’s project to use a system-wide competency test to judge the quality of students and institutions without basing the new assessment instrument either on the advice of faculty members or on the standards already set by regional accrediting agencies. Burke (2002a) reports how some faculty members have responded to what they perceive as an invasion of campus autonomy by externally prescribed indicators with a proposal for candid but confidential self-studies that would help initiate internal reforms on campuses. The practice of linking state funding to campus performance is a recent phenomenon, and its historical predecessors are the outcome assessment and reporting models (Serban, 1998). Performance-based funding consists of the allocation of some proportion of state funds based on performance criteria that emphasize the level of effectiveness as compared to traditional activity criteria that are more oriented to enrollment measurement. The planning for the first comprehensive performance-based funding program started in Tennessee in 1974 with the following two assumptions: (a) funding and educational performance should be linked, and (b) successful performance should not be judged solely by growth in the number of students (Pickens, 1982). The Tennessee program, implemented in 1979, is considered a success, and it is still in operation (Burke & Minassians, 2002). Fisher (1986) states that one of the factors that facilitated the success of the Tennessee program was the significant participation of faculty in developing methodologies, benchmarks, and reporting structures. “The most fruitful assessment programs begin with full involvement of faculty members in the initial design phase, and end with faculty members as active participants in the interpretation and use of the results” (Ewell, 1986, p.115). In Florida, faculty members were not granted the opportunity offered by the Tennessee model. Given the fact that faculty members are at the front line of the operations, it is important to determine their level of commitment to the performance indicators prescribed for community college academic programs. 10 Faculty Commitment to Performance Based Funding for Academic Programs The Move to Performance Funding in Florida The State of Florida utilizes both performance budgeting and performance funding for the allocation of resources to public colleges and universities. Under the performance budgeting model, the governor and the legislators utilize the accountability reports prepared by higher education institutions and state coordinating boards as one of many factors evaluated during the budget preparation process. This loose link between results and budgets is generally overshadowed by a process that tends to rollover the annual budget from year to year with marginal cuts or increments more related to availability of fiscal revenues and political negotiations than to institutional performance. The unclear and subjective connection between budgetary allocations and results is unlikely to influence the performance of higher education institutions. Burke and Serban (1998) clarify the point with the following observation related to the Florida performance budgeting system: “The only obvious link is that the indicators and the allocations usually appear on the same page of an agency’s budget” (p. 32). In Florida, the linking of public funding to community college performance was triggered by the Government Performance and Accountability Act (1994) that applied to all departments and agencies of the State government. A performance incentive funding program for community college academic programs was implemented for the first time in fiscal year 1996-1997. The performance indicators were prescribed by the Legislature based on a recommendation prepared by the Florida Division of Community Colleges (1996) in consultation with campus presidents. During the first three years of operations, the model included indicators for the AA program as well as for the AS and other occupational programs, but effective fiscal year 1999, performance-based funding for occupational programs was transferred to a separate model under the Workforce Development Educational Fund (1997). The performance-based funding indicators used in Florida for higher education programs have been modified periodically by the Legislature. Additional modifications are expected in the near future. This accountability model would be used to appropriate at least 10% of the State 11 Faculty Commitment to Performance Based Funding for Academic Programs budget for the Florida education system conditional upon meeting or exceeding performance standards. Goal Commitment Theories Commitment is the degree of attachment or determination to achieve a goal regardless of whether the goal is self-set, participatively set, or assigned (Locke et al., 1988). Commitment implies the extension of effort over time toward the accomplishment of a goal and the unwillingness to abandon the goal (Campion & Lord, 1982). If an individual is not committed to a goal, the goal will not have a motivational effect (Locke, 1968; Locke & Latham, 1990; Locke, Shaw, Saari, & Latham, 1981). In order to increase funding, community colleges are to view these indicators as performance goals to be maximized. Although every community college’s institutional performance on each indicator is measured for the purpose of distributing the allocated performance-based funds, the contribution of individual faculty members to the overall performance is not measurable. Commitment to the indicators was selected as the focus of this study because of its positive relationship with performance (Klein et al., 1999) and its measurability with a self-reporting survey (DeShon & Landis, 1997; Hollenbeck et al., 1989) for each individual faculty member. An underlying assumption of this study is that goal commitment is positively related to performance. This relationship, well documented by the literature, justifies the use of commitment as the focus of this study. Klein et al. (1999) conducted a meta-analysis of the results of 66 studies measuring the relationship between goal commitment and performance. They found a positive correlation between the two variables for all levels of goal difficulty (high, moderate, and low) that became stronger for higher levels of goal difficulty. This finding is consistent with earlier literature (Locke, 1968; Locke & Latham, 1990; Locke et al., 1981) that describes how difficult and specific goals lead to higher levels of performance when there is commitment to the goals. Since individual faculty in Florida were not included in the development of indicators, a 12 Faculty Commitment to Performance Based Funding for Academic Programs measure of their commitment could give administrators an idea of how faculty value the selected indicators. Higher Education Faculty Commitment Middaugh (2002) suggests the JCAR model in his analysis of faculty activity focusing on output of faculty in “service months” (time) and within area of instruction (discipline). Although some of the measures are similar to this study, Middaugh’s work is clearly a cost-benefit analysis of faculty work load in response to external pressure for disclosure about faculty production, none-the-less, the Delaware Project provides another look at costs and productivity (Middaugh, Graham & Shahid, 2003) and is consistent with Brinkman’s work (Brinkman in Hoenack & Collins, 1990). Burke (1997) classifies performance indicators into three models of excellence based on the goals and objectives that policy makers think higher education institutions should pursue: (a) resource/reputation model, (b) strategic investment/cost-benefit model, and (c) clientcentered model. The resource/reputation model is primarily a traditional faculty-oriented model under which excellence is based on an institution’s resources and on its reputation. The resource/reputation model utilizes indicators such as student academic preparation, spending per student, faculty credentials, library holdings, and institutional rating in guidebooks. The strategic investment/cost-benefit model is primarily a state-oriented model with indicators such as credits at graduation, time to degree, cost of instruction, multi-institutional cooperation, etc. The clientcentered model is primarily a student and other customer-oriented model with indicators such as faculty availability to students, satisfaction survey results, internships, etc. Burke is used as a basis for this study. A very limited number of studies have been conducted in higher education on the subject of faculty commitment. Hollenbeck & Klein (1987) Locke & Latham (1990), Locke, Latham, & Erez, (1988) considered intrinsic variables of self-efficacy and expectation of financial reward. These theories explain how goal commitment is determined by factors affecting the expectancy or perceived ability of attaining the goal and factors affecting the perceived 13 Faculty Commitment to Performance Based Funding for Academic Programs desirability or attractiveness of a goal. Self-efficacy (Bandura; 1982, 1986) is an expectancy factor and expectation of financial reward is an attractiveness factor. Expectancy theory (Dachler & Mobley, 1973; Vroom, 1964) explains how one’s choices, including commitment to goals, are affected by one’s perceived probability of performing well on a task. Locke, Frederick, Lee, and Bobko (1984) found that, in a laboratory setting, individuals with high self-efficacy had higher expectations for achieving difficult goals, and thus higher commitment to the goals, than individuals with low self-efficacy. In a qualitative study of faculty attitudes at the Monterey Bay Campus of California State University, Gonzalez and Padilla (1999) found that faculty members were more committed to organizational reform when they had high expectations that the proposed innovations were feasible. The positive relationship between self-efficacy and goal commitment is also supported by the meta-analysis of Klein et al. (1999) which shows significant positive correlations between goal commitment and self-efficacy and other related variables such as expectancy, ability, past performance, task information, and experience. Expectation of Personal Financial Reward Personal financial reward expectation is the faculty member’s anticipation that he or she will receive higher monetary compensation if performance, as defined by the state indicators, is increased. In the context of this study, the expectation for financial rewards is operationalized as the belief by faculty members that the college’s improved results on the areas measured by the performance-based funding indicators will be reflected in a higher percentage of salary increase. The annual decision about the percentage of salary increase to be received by faculty members and other employees at public community colleges is influenced by State budgetary appropriations. A larger budget due to complementary allocations from performance-based funding enhances discretionary administrative opportunities for salary decisions. More proactively, institutions may tie a portion of the performance-based funds directly to the 14 Faculty Commitment to Performance Based Funding for Academic Programs compensation structure for faculty. For example, North Carolina has authorized community colleges to use performance-based funds for the payment of bonuses to faculty and staff who contributed to the achievement of the goals (North Carolina Community College System, 2000). Within the context of higher education, there are no studies measuring the relationship between financial rewards and faculty commitment to specific priorities or goals. Some related studies illustrate the importance of salaries on faculty attitudes. Hoyle (1990) reported that salaries explained nearly 30% of the variance in faculty members’ morale and 23% of the variance in faculty organizational commitment. Rucker (1993) found that salary increase is one of the strongest incentives reported by community college faculty to improve the quality of teaching. A very limited number of studies have been conducted in higher education on the subject of faculty commitment. Some of them hypothesized a variety of demographic variables as potential factors affecting level of commitment. Gender consistently appeared as a significant factor; academic rank and discipline taught showed conflicting results; and no influence was found by other demographic variables such as age, marital status, years of education, and years of service. Gender, academic rank, and type of courses taught were thus selected as extrinsic predictor variables in this study. Methods The research designs for this study were descriptive and correlational in nature. The data were gathered with a self-reported survey. The descriptive component is the measurement of the level of faculty commitment to each indicator and the composite. The correlational component is the determination of the relationship of commitment as a function of some intrinsic variables (i.e., self-efficacy and expectation of personal financial reward). Context of the Study The participants were community college faculty members. They were teaching in twoyear programs leading to an Associate in Arts degree at a Florida public community college. 15 Faculty Commitment to Performance Based Funding for Academic Programs Students in the AA program take 36 credits of general education courses and 24 credits of elective courses. These credits are transferable as a whole toward a four-year baccalaureate in other universities. In many transferable programs students satisfy the electives by taking occupational courses labeled as transfer prerequisites for the bachelor’s degree (e.g., architecture, business administration, engineering, nursing). Many students in associate in arts programs also take college preparatory and English as a second language courses when they have basic skills and language deficiencies. Population and Sample The participants selected for this study consisted of all the full-time faculty members at a large urban Community College teaching courses taken by students in the AA program. These courses include English as a second language, college preparatory, general education, and occupational courses. With information provided by the academic deans, faculty members on leave or those teaching only courses not related to the AA degree were deleted from the faculty list (LUCC, 2002). This produced a total of 550 faculty members selected to receive the survey. The study college has a diverse full-time faculty corps. The gender composition is 50% female and 50% male. The ethnic classification is 54% white non-Hispanic, 28% Hispanic, 15% black, and 3% other ethnicities. The distribution of academic ranks is 12% instructors, 12% assistant professors, 13% associate professors, 14% senior associate professors, and 49% professors. Degree credentials are distributed as follows: 6% hold baccalaureate or lower degrees, 70% hold master’s degrees, and 24% hold doctoral degrees. For tenure status, 19% of the faculty work under annual contracts, and 81% work under continuing contract (P. Schwartz, personal communication, April 17, 2003). The study college faculty work with a student body representative of the special populations targeted by the indicators: 64% are enrolled in associate in arts programs, 21.5% are non-Hispanic blacks, 58% have a native language different from English (Morris & Mannachen, 16 Faculty Commitment to Performance Based Funding for Academic Programs 2001), 17% are below the poverty threshold, 47% are financial aid recipients (Mannchen, 1999), and 81% need remediation in at least one area of basic skills (Rodriguez, 1999). Conclusions drawn from this study may apply to the other 27 community colleges in Florida, inasmuch as they are subject to the same performance-based funding indicators for academic programs. The conclusions may also apply to community colleges in other states using similar indicators. Instrumentation The main source of data for this study was a questionnaire developed as a measure of commitment to the attainment of each of the ten goals represented in the State performance-based funding indicators for community college AA programs. The theoretical rationale for the measurement of the variables provided substantive validity. Content and face validity were evaluated by a panel of experts who reviewed the content relevance, representativeness, and technical quality of the questionnaire. A principal component factor analysis was conducted to evaluate the factorial structure of the survey. The questionnaire is divided into three parts: (a) a section on general information, (b) a section on demographics, and (c) a section on goal statements. A description of every section is presented below. (See figure 1, Sample of instrument next page). Procedure The survey was delivered to 550 community college faculty members teaching courses taken by students in the AA degree program. The mailed survey packet consisted of a cover letter, the questionnaire, a coded postcard, and a preaddressed returned envelope. There were two follow-up measures subsequent to the initial mailing. A field test was conducted using 27 faculty members who reviewed the questionnaire and determined its usability as a mail questionnaire. The research questions about the extent of community college faculty commitment to the performance-based funding indicators were answered with the computation of the mean and 17 Faculty Commitment to Performance Based Funding for Academic Programs Survey on Florida Performance-Based Funding Indicators for the Associate in Arts Program at Public Community Colleges Part I: Demographic Information 1. What is your gender? (Circle one) 1) Male 2. 2) Female What is your academic rank? (Circle one) 1) Instructor 2) Assistant Professor 3) Associate Professor 3. 4) Senior Associate Professor 5) Professor Which of the following best describes the type of courses you teach? (Circle one) 1) 2) 3) 4) English as a Second Language College Preparatory Professional/Technical General Education/Liberal Arts Part II: Performance-Based Funding Indicators The State of Florida utilizes ten indicators to measure the effectiveness and efficiency of public community colleges’ performance in the Associate in Arts program. In order to increase funding, institutions are to view these indicators as performance goals to be maximized. This survey presents a list of statements for each of the performance indicators. Please indicate by circling the appropriate response your level of agreement or disagreement with the each statement using the following scale: Strongly Agree, Agree, Undecided, Disagree, Strongly Disagree. 18 Faculty Commitment to Performance Based Funding for Academic Programs Goal No. 1: Increase the number of students who graduate with an Associate in Arts degree from the College. Strongly Agree Agree Undecided Disagree Strongly Disagree SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD 4. I think this goal is a good goal to work toward. 5. I am capable of taking actions that will contribute to the achievement of this goal. 6. Quite frankly, I don’t care if I contribute to the achievement of this goal or not. 7. I am committed to contribute to the pursuit of this goal. 8. I am willing to make a great effort to contribute to the achievement of this goal. 9. I believe I can help to overcome barriers to the achievement of this goal. 10. It wouldn’t take much to make me abandon my contributions to the attainment of this goal. 11. I expect there will be higher salary increases if this goal is achieved. Goal No. 2: Increase the number of dual enrollment (high school/college) students and credits taken by dual enrollees at the College. Strongly Agree Agree Undecided Disagree Strongly Disagree SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD SA A U D SD 12. I think this goal is a good goal to work toward. 13. I am capable of taking actions that will contribute to the achievement of this goal. 14. Quite frankly, I don’t care if I contribute to the achievement of this goal or not. 15. I am committed to contribute to the pursuit of this goal. 16. I am willing to make a great effort to contribute to the achievement of this goal. 17. I believe I can help to overcome barriers to the achievement of this goal. 18. It wouldn’t take much to make me abandon my contributions to the attainment of this goal. 19. I expect there will be higher salary increases if this goal is achieved. Figure 1. Sample of instrument. 19 Faculty Commitment to Performance Based Funding for Academic Programs standard deviation of the commitment variable for each indicator and for the composite score. The research questions about the relationship between commitment and the predictor variables (i.e., self-efficacy and expectation of personal financial reward) were answered by developing multiple regression models for each of the indicators and the composite scores. Pearson coefficients of correlation were computed to identify relationships between commitment and the intrinsic variables that were not identified in the regression analysis. Analysis of the data and findings The data collected was stored in a computerized file, transformed and analyzed. Several transformations were applied to the raw data in preparation for the statistical analyses. The first set of transformations was the reversal of the polarity of each of the 20 negatively-stated questions in the commitment scale (2 for each of the 10 goals) to make them parallel to the positive polarity of the other 30 questions in the commitment scale. The second set of transformations was the computation of three separate scores for commitment, self-efficacy, and expectation for higher salary increases for each of the indicators and the total. The commitment score for each of the 10 indicators was computed by averaging the scores of the five questions (3 original and 2 reversed) in the commitment scale. The composite total commitment score for the 10 indicators as a whole was computed by averaging the 50 questions (30 original and 20 recoded) measuring commitment. The self-efficacy score for each of the 10 indicators was computed by averaging the scores of the two questions on self-efficacy. The composite self-efficacy score for the 10 indicators as a whole was computed by averaging the 20 questions measuring self-efficacy. The score measuring the expectation for higher salary increases for each of the 10 indicators was the answer to the item used for that variable. The composite score for the expectation of higher salary increases for the 10 indicators as a whole was computed by averaging the 10 questions measuring this variable. 20 Faculty Commitment to Performance Based Funding for Academic Programs The third set of transformations was the creation of dummy variables for the demographic variables so that they could be used as predictor variables in regression analyses. Under this system, a number of variables equal to the number of categories minus one was generated. The membership in a given group or category was assigned 1, while non-membership in the category was assigned 0. The category that is not represented by a variable was depicted by assigning the code of 0 to each of the dummy variables representing the other categories (Fox, 1997; Pedhazur, 1982). Gender had two categories, thus it required the creation of one dummy variable. Academic rank had five categories, thus it required the creation of four dummy variables. Courses taught were classified into four types, thus three dummy variables were needed. Findings of this study, including the survey response rate, the demographic characteristics of the respondents, faculty perceptions on goal commitment, and the relationships between goal commitment and the intrinsic variables are presented, followed by the extrinsic variables. Description of the Respondents Among the 303 useable returns, representing 55% of respondents, gender was balanced between males and females; respondents were from all academic ranks with a predominance of senior level ranks (60%). Respondents reported teaching different types of courses, the highest being 45% general education and 29% professional/technical. Respondents were representative of the demographics of the faculty at the institution. Commitment Level Findings and Discussion The overall commitment, as well as the commitment for each indicator of community college faculty members to Florida performance-based funding indicators for the AA program was measured by the composite mean and standard deviation for the 10 indicators. A factor analysis permitted the classification of the commitment scores into two factors. The relationships 21 Faculty Commitment to Performance Based Funding for Academic Programs between commitment and the intrinsic predictor variables (i.e., self-efficacy and expectation of personal financial reward) and extrinsic predictor variables (i.e., gender, academic rank and type of courses taught) were examined by applying the multiple regression model to each indicator and to the composite scores. Additional analyses were performed to identify relationships of commitment and the predictor variables beyond the explanation indicated by the simultaneous multiple regression equations (Pearson coefficients of correlation between commitment and the variables) were computed. ANOVAs were conducted to identify significant differences in mean commitment scores based on the categorical intrinsic variables. Ratings of Commitment to the Indicators Table 1 shows the indicators ranked in a descending ordinal scale according to their mean commitment score. Increasing the number of AA graduates from the College who transfer to the SUS was the indicator with the highest mean commitment score (M = 4.35, SE = 0.62). Increasing the number of dual enrollment (high school/college) students and credits taken by dual enrollees at the College was the indicator with the lowest mean commitment score (M = 3.62, SD = 0.87). The mean score for the composition of all the indicators as a whole was 4.07 with a standard deviation of 0.55. Considering that the scale for the commitment score ranged from 1 to 5, the results indicate that the reported level of faculty commitment to the performance-based funding indicators was generally high. This high level of commitment was observed despite the absence of faculty participation in the development of a performance-based funding system that was categorized by Burke (2002b) as mandated/prescribed by the government. The mean commitment scores for the individual indicators ranged from 4.35 to 3.61. The results of a factor analysis permitted the classification of the indicators into two groups. The seven indicators showing higher commitment scores loaded on the first factor. The three indicators showing lower commitment scores loaded on the second factor (See Table 2). These findings seem reasonable when analyzed under the taxonomies developed by Burke (1997) to 22 Faculty Commitment to Performance Based Funding for Academic Programs Table 1 Descriptive Statistics for Commitment to Performance-Based Funding Indicators ______________________________________________________________________________ Rank Performance indicator n M SD ______________________________________________________________________________ Increasing the number of: 1 AA graduates who transfer to SUS 302 4.35 0.62 2 AA graduates among students who are economically disadvantaged 301 4.27 0.61 3 AA graduates 302 4.22 0.68 4 AA graduates among students who originally tested into English as a second language 301 4.21 0.67 AA graduates among students who required remediation when they started 300 4.18 0.72 African-American male students who graduate with an AA degree 301 4.16 0.70 AA graduates among students with disabilities 301 4.13 0.68 AA graduates who are placed in a full-time job earning at least $10 per hour 300 3.88 0.78 AA graduates who complete their degree with 72 credit hours or less 297 3.70 0.93 Dual enrollment (high school/college) students and credits taken by dual enrollees 301 3.62 0.87 5 6 7 8 9 10 Composite of all indicators 303 4.07 0.55 ______________________________________________________________________________ classify higher education performance-based funding indicators. Primary indicator concern: Internal versus external. The State of Florida prescribed performance-based funding indicators for community colleges to satisfy public accountability concerns. Several of these indicators are also related to one of the traditional community college missions: facilitating the completion of AA degrees, especially by students of disadvantaged 23 Faculty Commitment to Performance Based Funding for Academic Programs populations, so they can transfer to senior institutions for the completion of baccalaureate degrees. The seven indicators with reported commitment scores greater than 4.00 and that loaded on the higher commitment factor were precisely the ones related to this traditional internal concern of community colleges: (a) number of AA graduates who transfer to the SUS; (b) number of AA graduates among students who are economically disadvantaged; (c) number of AA graduates; (d) number of AA graduates who originally tested into English as a second language; (e) number of AA graduates who initially required remediation; (f) number of AA graduates who are African-American males; and (g) number of AA graduates among students with disabilities. Table 2 Factors and Loadings for the Scores of Commitment to the Indicators _____________________________________________________________________________ Performance indicator Factor 1 AA graduates who transfer to SUS .61 AA graduates among students who are economically disadvantaged .86 AA graduates .72 AA graduates among students who originally tested into English as a second language .79 AA graduates among students who required remediation when they started .85 African-American male students who graduate with an AA degree .84 AA graduates among students with disabilities .73 Factor 2 AA graduates who are placed in a full-time job earning at least $10 per hour .64 AA graduates who complete their degree with 72 credit hours or less .55 Dual enrollment (high school/college) students and credits taken by dual enrollees .87 _____________________________________________________________________________ 24 Faculty Commitment to Performance Based Funding for Academic Programs Education acceleration mechanisms for reducing costs and increasing the level of employment are two priorities of the State of Florida that constitute external concerns to community colleges. Indicators related to these external concerns showed commitment scores below 4.00 and loaded on the lower commitment factor: (a) number of dual enrollment students and credits; (b) number of AA graduates who complete their degree with 72 credit hours or less; and (c) number of AA graduates who are placed in a full-time job earning at least $10 per hour. These findings reflect some consistency with the opinions expressed by Florida adult education directors (Oroza, 1997). Indicators emphasizing the internal concerns of adult education programs (e.g., quality and graduations) were considered more important than those emphasizing external concerns (e.g., cost efficiency, job placements, and dual enrollments). Policy value emphasized by the indicators. In a nation-wide survey answered by 916 campus administrators and state policy makers, Burke and Serban (1997) found that these stakeholders ranked quality as the highest value that should be reflected by performance funding indicators. The second priority was efficiency, and the last one was equity. Florida performance funding indicators for community college academic programs emphasize the values of efficiency and equity. The Florida model completely disregards the value of quality. The fact that in this study no indicator obtained a mean commitment score close to the highest value of 5.00 might be due to the absence of indicators reflecting quality of education. While there are some consistencies between the findings of this faculty study and the opinions expressed by administrative stakeholders in Burke and Serban’s study (1997), there are some inconsistencies. Faculty members may not be as committed to efficiency as administrators and policy makers, especially when the efficiency indicators relate more to the concerns of external stakeholders than to the traditional mission of community colleges. The three indicators more purely reflecting the value of efficiency (i.e., job placement, graduation acceleration through dual enrollment, and graduation acceleration by completing degree with 72 credits or less) loaded on the lower commitment factor. 25 Faculty Commitment to Performance Based Funding for Academic Programs Relationship Between Commitment and the Independent Variables The second purpose of this study was to examine the relationship between commitment and two intrinsic variables (i.e., self-efficacy and expectation of personal financial reward) treated as quantitative predictor variables and three extrinsic variables (i.e., gender, academic rank, and types of courses taught). To examine these relationships 11 multiple regressions were run, one for each of the 10 indicators and one for the composite score. In each regression analysis the dependent variable was commitment. Self-efficacy and expectation for higher salary increases were treated as quantitative predictor variables. Gender, academic rank, and types of courses taught were treated as categorical predictor variables. Pearson coefficients of correlation and ANOVA tests were applied to the intrinsic and extrinsic variables respectively to identify relationships between commitment and the factors that were not identified in the regression analysis. Findings of the Multiple Regression and Correlation Analyses All the multiple regression equations of commitment scores against the intrinsic variables (i.e., self-efficacy and expectation of financial reward) showed significant partial correlation coefficients for the self-efficacy predictor p<.01. Table 3 shows the Pearson coefficients of correlation between the commitment scores and the intrinsic variables for each indicator and the composite. All the correlations were significant, p < .01. The correlations between commitment and self-efficacy ranged from .66 and .80. The correlations between commitment and financial reward expectation were lower, ranging from .15 to .33. Significant Pearson correlation coefficients (p<.01) confirmed the strong relationship between commitment and self-efficacy that were manifested in the multiple regression analysis. In general, the partial regression coefficients for the other predictors were not statistically significant. The few exceptions listed below showed significance: 26 Faculty Commitment to Performance Based Funding for Academic Programs 1. For commitment to the remediation goal, the partial regression coefficient for the academic rank senior associate professor was significant, t(278) = 2.43, p < .05 (see Table 4). 2. For commitment to the disability goal, the partial regression coefficient for the academic rank senior associate professor was significant, t(275) = 2.35, p <.05 (see Table 5). 3. For commitment to the English as a second language goal, the partial regression coefficient for faculty members teaching professional/technical courses was negative and significant, t(278) = -2.34, p < .05 (see Table 6). Table 3 Correlations Between Commitment and the Intrinsic Variables ______________________________________________________________________________ Commitment to increasing Self-efficacy Financial Reward the number of: Expectation ______________________________________________________________________________ AA graduates .70** .24** Dual enrollment (high school/college) students and credits taken by dual enrollees .74** .33** AA graduates among students who required remediation when they started .76** .15** AA graduates among students who are economically disadvantaged .72** .22** AA graduates among students with disabilities .75** .22** African-American male students who graduate with an AA degree .72** .19** AA graduates among students who originally tested into English as a second language .74** .22** AA graduates who are placed in a full-time job earning at least $10 per hour .66** .26** AA graduates who transfer to SUS .73** .17** AA graduates who complete their degree with 72 credit hours or less .80** .31** Composite of all indicators .76** .25** _____________________________________________________________________________ **p < .01 27 Faculty Commitment to Performance Based Funding for Academic Programs Table 4 Simultaneous Multiple Regression of Commitment to Goal No. 3: Remediation (n = 289) _____________________________________________________________________________ Variable B SEB β _____________________________________________________________________________ Self-efficacy 0.64 0.03 0.78** Salary increase expectation -0.04 0.02 -0.08 Female gender 0.08 0.06 0.06 Instructor 0.08 0.09 0.04 Assistant professor 0.13 0.13 0.04 Associate professor -0.03 0.09 -0.01 Senior associate professor 0.20 0.08 0.10* English as a second language -0.11 0.09 -0.05 College preparatory 0.02 0.09 0.01 Professional/technical 0.02 0.07 0.01 ______________________________________________________________________________ Note. Multiple R = .77; model adjusted R2 = .58; significant ANOVA test for overall regression, F(10, 279) = 41.09, p < .01. B = partial regression coefficient; SEB = standard error of the partial regression coefficient; β = standardized partial correlation coefficient. *p < .05. **p < .01. Table 5 Simultaneous Multiple Regression of Commitment to Goal No. 5: Disability (n = 286) _____________________________________________________________________________ Variable B SEB β _____________________________________________________________________________ Self-efficacy 0.60 0.03 0.75** Salary increase expectation -0.00 0.02 -0.01 Female gender 0.04 0.06 0.03 Instructor 0.13 0.09 0.06 Assistant professor 0.13 0.13 0.04 Associate professor 0.02 0.09 0.01 (table continues) 28 Faculty Commitment to Performance Based Funding for Academic Programs (Table continued) Senior associate professor 0.19 0.08 0.10* English as a second language -0.07 0.09 -0.04 College preparatory 0.01 0.09 0.01 Professional/technical -0.03 0.06 -0.02 _______________________________________________________________________________ Note. Multiple R = .76; model adjusted R2 = .56; significant ANOVA test for overall regression, F(10, 276) = 37.75, p < .01. B = partial regression coefficient; SEB = standard error of the partial regression coefficient; β = standardized partial correlation coefficient. *p < .05. **p < .05. Table 6 Simultaneous Multiple Regression of Commitment to Goal No. 7: English as a Second Language (n = 289) ______________________________________________________________________________ Variable B SEB β ______________________________________________________________________________ Self-efficacy 0.56 0.03 0.73** Salary increase expectation -0.01 0.02 -0.02 Female gender 0.09 0.06 0.07 Instructor 0.03 0.09 0.01 Assistant professor 0.05 0.13 0.02 Associate professor 0.04 0.09 0.02 Senior associate professor 0.14 0.08 0.07 English as a second language -0.08 0.09 -0.04 College preparatory -0.14 0.09 -0.07 Professional/technical -0.15 0.06 -0.10* ______________________________________________________________________________ Note. Multiple R = .75; model adjusted R2 = .54; significant ANOVA test for overall regression, F(10, 279) = 35.40, p < .01. B = partial regression coefficient; SEB = standard error of the partial regression coefficient; β = standardized partial correlation coefficient. *p < .05. **p < .01. 4. For commitment to the job placement goal, the partial regression coefficient for the female gender was statistically significant, t(273) = 2.05, p = .05 (see Table 7). 29 Faculty Commitment to Performance Based Funding for Academic Programs Table 7 Simultaneous Multiple Regression of Commitment to Goal No. 8: Job Placement (n = 284) _____________________________________________________________________________ Variable B SEB β _____________________________________________________________________________ Self-efficacy 0.53 0.04 0.65** Salary increase expectation 0.01 0.03 0.01 Female gender 0.15 0.07 0.10* Instructor -0.02 0.12 -0.01 Assistant professor 0.23 0.17 0.06 Associate professor -0.15 0.11 -0.06 0.03 0.11 0.01 -0.12 0.11 -0.05 0.14 0.11 0.06 Senior associate professor English as a second language College preparatory Professional/technical 0.01 0.08 0.01 ______________________________________________________________________________ Note. Multiple R = .69; model adjusted R2 = .44; significant ANOVA test for overall regression, F(10, 274) = 23.60, p < .01. B = partial regression coefficient; SEB = standard error of the partial regression coefficient; β = standardized partial correlation coefficient. *p < .05. **p < .01. Summary of Findings Faculty members are deeply involved in and partially responsible for their community college’s performance. Their commitment to the goal priorities reflected by the performancebased funding indicators is essential because without commitment the faculty may not exert enough effort to attain the level of effectiveness and efficiency expected by the State (Stengel & Richardson, 1984). There are few studies on faculty commitment to specific priorities. Moreover, it appears that there are no studies on faculty commitment to performance-based funding. The 30 Faculty Commitment to Performance Based Funding for Academic Programs following summary of findings from this study follow. They provide responses to the research questions and address the support, or lack thereof, to the research hypotheses. 1. Community College faculty in this college has significant buy-in to meeting the performance based funding indicators for AA students. They are committed to ensuring the college meets the stated indicators to gain additional funding. For those faculty members who responded the survey, the mean composite commitment score to the performance-based funding indicators was 4.07 in a scale of 1 to 5. 2. Indicators closely related to the traditional mission of community colleges of serving students from disadvantaged populations so they can graduate with an AA degree and transfer to a university showed higher level of faculty commitment. However, preparing students for jobs earning $10.00, a state priority, was only 3.88 in commitment. 3. Indicators more oriented to State priorities, such as education acceleration mechanisms for cost reduction and degree completion with 72 semester credits, showed lower level of faculty commitment. 4. The positive relationship between goal commitment and self-efficacy found by this study provides additional evidence in support of theoretical statements by Hollenbeck and Klein (1987), Locke and Latham (1990) and Lock et al. (1988) that describe the positive relationship between the two variables. Implications for Theory The findings and conclusions of this study have the following implications for theory: 1. This study contributed to confirm the validity and reliability of the goal commitment scale developed by Hollenbeck, Williams, and Klein (1989) and condensed into five items by DeShon and Landis (1997). 31 Faculty Commitment to Performance Based Funding for Academic Programs 2. In general, the findings of this study provide evidence in support of a relationship between self-efficacy and expectation of personal financial reward as commitment to performance-based funding indicators. Implications for Practice The findings and conclusions of this study have the following implications for practice: 1. The mean composite commitment score for faculty members who responded to the survey was 4.07 in a scale of 1 to 5. A score of 4.00 is associated with the “agree” option. The positive attitude towards the indicators reported by many faculty members, contribute to MDCC’s potential to achieve institutional effectiveness and increase revenues from performancebased funding for academic programs. Administrators would be wise to continue to encourage faculty to reach these goals. 2. Florida policy makers in the Division of Community Colleges, the Department of Education, and the Legislature should consider the opinions of faculty members when developing indicators for performance-based funding programs. Faculty members are at the front line of community college operations and they play a fundamental role in the achievement of the performance goals targeted by the State. This study shows that faculty members are more committed to those indicators closely related to the mission of community colleges and to the traditional primary responsibilities of community college faculty. Recommendations for Future Research Based upon the results of this study, the following recommendations for future research are proposed: 1. One of the limitations of this study was its restriction to M-DCC faculty. The study could be replicated to include faculty from other community colleges in the State of Florida who are subjected to the same performance-based funding indicators for academic programs. 32 Faculty Commitment to Performance Based Funding for Academic Programs 2. Further research may be conducted to measure faculty acceptance of performance indicators not currently used in Florida, such as the indicators currently recommended by policymaking committees, indicators described in the performance funding literature, and indicators used in other states and foreign countries. Such a study would provide a more comprehensive view of what types of values faculty members desire to be reflected in performance-based funding indicators. 3. A study may be conducted to determine faculty commitment to performance-based funding indicators for workforce development programs also offered at community colleges. Workforce development includes occupational programs leading to AS degrees, college credit certificates, vocational credit certificates, and adult education completion diplomas. Conclusion Public colleges and universities and private higher education institutions receiving public financial support are accountable to the governmental bodies providing their funding. “Pressures from policy makers and the public to improve teacher education, control costs, and measure learner outcomes have not led to serious change or reform in higher education” (Newman & Couturier, 2003 p. 1). However, “Policy makers, . . . want assurance of quality and want to see the results of their investments” (Newman & Couturier, 2003 p. 6). One accountability movement that has generated demands for greater effectiveness and efficiency from public higher education institutions is performance based funding. It appears it will continue to be required by legislators for reporting accountability to the public and funding programs until something better comes along. It makes sense that policy decisions and solutions are based on research and not just opinion. 33 Faculty Commitment to Performance Based Funding for Academic Programs References Albright, B. N. (1998). The transition from business as usual to funding for result: State efforts to integrate performance measures in higher education budgetary process. Denver, CO: State Higher Education Executive Officers. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122147. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Brinkman, P.T. (1990) Higher education cost functions. In Hoenack, S. A., & Collins, E.L. Eds. The economics of American universities management. Operations, and fiscal environment. Albany, NY: State University of New York Press. SUNY Series, Frontiers in Education. Burke, J.C. (1997). Performance funding indicators: Concerns, values, and models for two-and four-year colleges and universities. Albany, NY: State University of New York, the Nelson A. Rockefeller Institute of Government. (ERIC Document Reproducation Service No. ED407910) Burke, J. C. (2002a). The new accountability. In J. C. Burke (Ed.), Funding public colleges and universities for performance: Popularity, problems, and prospects (pp. 1-18). Albany, NY: Rockefeller Institute Press. Burke, J. C. (2002b). Performance funding indicators: Imperfect necessitites. In J. C. Burke (Ed.), Funding public colleges and universities for performance: Popularity, problems, and prospects (pp.39-60). Albany, NY: Rockefeller Institute Press. Burke, J. C., & Minassians, H. (2002). Performance reporting: The preferred “no cost” accountability program – the sixth annual report. Albany, NY: State University of New York, The Nelson A. Rockefeller Institute of Government. Burke, J. C., & Serban, A. M. (1997). Performance funding and budgeting for public higher education: Current status and future prospects. Albany, NY: State University of New York, The Nelson A. Rockefeller Institute of Government. Burke, J. C., & Serban, A. M. (1998). State synopses of performance funding programs. In J. C. Burke & A. M. Serban (Eds.), Performance funding for public higher education: Fad or trend? (pp. 25-48). San Francisco: Jossey-Bass. Campion, M. A., & Lord, R. G. (1982). A control systems conceptualization of the goal setting and changing process. Organizational Behavior and Human Performance, 30, 265-287. Christal, M. E. (1998). State survey on performance measures: 1996-97. Denver, CO: State Higher Education Officers. 34 Faculty Commitment to Performance Based Funding for Academic Programs Dachler, H. P., & Mobley, W. H. (1973). Construct validation of an instrumentality-task-goal model of work motivation: Some theoretical boundary conditions [Monograph]. Journal of Applied Psychology, 58, 397-418. DeShon, R. P., & Landis, R. S. (1997). The dimensionality of the Hollenbeck, Williams, and Klein (1989) measure of goal commitment on complex tasks. Organizational Behavior and Human Decision Processes, 70, 105-116. Ewell, P. (1983). Information on student outcomes: How to get it and how to use it. Boulder, CO: National Center for Higher Education Management Systems. Florida Statutes, Chapter 240, § 240.324 (1991). Florida Statutes, Title XLVIII, Chapter 1008, § 31 (2002). Fisher, H. S. (1986). Planning and resource allocation at the University of Tennessee, Knoxville: Institutional responses to changes in state funding policies. In T. W. Banta (Ed.), Performance funding in higher education: A critical analysis of Tennessee’s experience (pp. 27-36). Boulder, CO: National Center for Higher Education Management Systems. (ERIC Document Reproduction Service No. ED310655) Fox, J. (1997). Applied regression analysis, linear models, and related methods. Thousand Oaks, CA: SAGE Publications. Gonzalez, K. P., & Padilla, R. V. (1999, November). Faculty commitment and engagement in organizational reform. Paper presented at the Annual Meeting of the Association for the Study of Higher Education, San Antonio, TX. (ERIC Document Reproduction Service No. ED437869) Government Performance and Accountability Act, Laws of Florida, Chapter 94-249, 1994. Hollenbeck, J. R., & Klein, H. J. (1987). Goal commitment and the goal-setting process: Problems, prospects, and proposals for future research. Journal of Applied Psychology, 72, 212-220. Hollenbeck, J. R., Klein, H. J., O’Leary, A. M., & Wright, P. M. (1989). Investigation of the construct validity of a self-report measure of goal commitment. Journal of Applied Psychology, 74, 951-956. Hollenbeck, J. R., Williams, C. R., & Klein, H. J. (1989). An empirical examination of the antecedents of commitment to difficult goals. Journal of Applied Psychology, 74, 18-23. Hoyle, D. E. (1990). The relationship of varying organizational structures with faculty communication satisfaction, job satisfaction, organizational commitment, and morale in academic settings. (Doctoral dissertation, University of Wisconsin, Madison, 1989). Dissertation Abstracts International, 51(5), 1519. Kells, H. (1992). Self-regulation in higher education: A multinational perspective on collaborative systems of quality assurance and control. London: Jessica Kingsley. 35 Faculty Commitment to Performance Based Funding for Academic Programs Klein, H. J., Wesson, M. J., Hollenbeck, J. R., & Alge, B. J. (1999). Goal commitment and the goal-setting process: Conceptual clarification and empirical synthesis. Journal of Applied Psychology, 84, 885-896. Layzell, D. T., & Caruthers, J. K. (1995, November). Performance funding at the state level: Trends and prospects. Paper presented to the Annual Meeting of the Association for the Study of Higher Education, Orlando, FL. (ERIC Document Reproduction Service No. ED391406) Locke, E. A. (1968). Toward a theory of task motivation and incentives. Organizational behavior and human performance, 3, 157-189. Locke, E. A., Frederick, E., Lee, C., & Bobko, P. (1984). Effects of self-efficacy, goals, and task strategies on task performance. Journal of Applied Psychology, 69, 241-251. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Englewood Cliffs, NJ: Prentice Hall. Locke, E. A., Latham, G. P., & Erez, M. (1988). The determinants of goal commitment. Academy of Management Review, 13, 23-39. Locke, E. A., Shaw, K. N., Saari, L. M., & Latham, G. P. (1981). Goal setting and task performance: 1969-1980. Psychological Bulletin, 90, 125-152. Mannchen, M. (1999). Miami-Dade Community College Student Profile. Miami, FL: MiamiDade Community College, Office of Institutional Research. Middaugh, M. F. (2002). Faculty productivity: Different strategies for different audiences. Planning for Higher Education v. 30 no. 3 (spring 2002) p. 34-43. Middaugh, M.F., Graham, R., Shahid, A. (2003). A study of higher education instructional expenditures: The Delaware study of instructional costs and productivity. Research and Development Report: U.S. Department of Education, Institute of Education Sciences. NCES-2003-161 Morris, C., & Mannchen, M. (2001). Fall profile, 2000-2001: Closing fall enrollment analysis (Research Report No. 2001-01R). Miami, FL: Miami-Dade Community College, Office of Institutional Research. Newman, F., & Couturier, L.K., (2002). Trading public good in the higher education market. The observatory on borderless higher education. London: John Foster House. North Carolina Community College System. (2000). North Carolina Community College System performance measures and standards and performance funding. Raleigh, NC: Author. (ERIC Document Reproduction Service No. ED445724) Office of Program Policy Analysis and Government Accountability. (1997, April). Performancebased program budgeting in context: History and comparison (Report No. 96-77A). Tallahassee, FL: Author. 36 Faculty Commitment to Performance Based Funding for Academic Programs Office of Program Policy Analysis and Government Accountability. (1999, March). Program evaluation and justification review: Florida’s community college system (Report No. 9806A). Tallahassee, FL: Author. Oroza, L. (1997). Adult education directors’ perceptions of performance indicators for adult education programs in the state of Florida. (Doctoral dissertation, Florida International University, 1997). Dissertation Abstracts International, 58 (10), 3803. Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction. Pickens, W. H. (1982, December). Performance funding in higher education: Panacea or peril? Paper presented at a Conference on Survival in the 1980’s: Quality, Mission and Financing Options, Tucson, AZ. (ERIC Document Reproduction Services No. ED 236 980) Rodriguez, S. (1999). Basic skills assessment results, Miami-Dade Community College, fall terms 1994 through 1998. (Information Capsule 99-05C). Miami, FL: Miami-Dade Community College, Office of Institutional Research. Rucker, J. A. (1993). Incentives to improve teaching as perceived by community college faculty members. (Doctoral dissertation, University of Texas at Austin, 1992). Dissertation Abstracts International, 53(8), 2637. Rudolph, F. (1990). The American college & university: A history. Athens, GA: The University of Georgia Press. Ruppert, S. S. (1997). Roots and realities of state-level performance indicator systems. In B. A. Jones (Series Ed.) & L. F. Goodchild, C. D. Lovell, E. R. Hines, & J. I. Gill (Vol. Eds.) Public policy and higher education (pp. 315-324). Columbia, MI: University of Missouri, Center for Educational Policy Analysis, ASHE Reader Series. Schmidt, P. (2000, October 6). Faculty outcry gets proposal for competency tests at U. of Texas. The Chronicle of Higher Education. Retrieved February 2, 2001, from http://chronicle.com Selingo, J. (1999, February 5). Plan to reshape California State U. disturbs many faculty members. The Chronicle of Higher Education. Retrieved February 2, 2001, from http://chronicle.com Serban, A. M. (1998). Precursors of performance funding. In J. C. Burke & A. M. Serban (Eds.), Performance funding for public higher education: Fad or trend? (pp. 15-24). San Francisco: Jossey-Bass. Stengel, J. C., & Richardson, R. C. (1984, April). Faculty commitment to administrative priorities. Paper presented at the meeting of the American Educational Research Association, New Orleans, LA. (ERIC Document Reproduction Service No. ED245763) Vroom, V. (1964). Work and motivation. New York: Wiley. Wolff, R. A. (1992). Incorporating assessment into the practice of accreditation: A preliminary report. Washington, DC: Council on Postsecondary Accreditation. 37 Faculty Commitment to Performance Based Funding for Academic Programs Workforce Development Education Fund, Laws of Florida, S. B. 1688, Chapter 97-307, 1997. Wright, D. L., Dallet, P.H., & Copa, J. C. (2002). Ready, fire, aim: Performance funding policies for public postsecondary education in Florida. In J. C. Burke (Ed.), Funding public colleges and universities for performance: Popularity, problems, and prospects (pp. 1937). Albany, NY: Rockefeller Institute Press. 38