Admission criteria and performance predictors at a GCC executive MBA programme

Transcription

1 J. International Business and Entrepreneurship Development, Vol. 8, No. 1, Admission criteria and performance predictors at a GCC executive MBA programme Ibrahim Tabsh College of Business and Management, Fahad Bin Sultan University, P.O. Box 15700, Tabuk 71454, Saudi Arabia iztabsh@yahoo.com Abstract: The identification of reliable factors that help predict students performance is vital for the selection processes at graduate business schools to ensure a higher success rate and improved college reputation. Using data from the University of Sharjah s Executive Master s in Business Administration (EMBA) programme over 2000 to 2007, regression models were developed to determine whether the current admissions criteria and the students profile variables could adequately explain graduates performance. Results showed that English language proficiency, entry GPA and place of residence had the most significant effects. A comparison of performance was also made between various groups based on 14 precedent variables related to the students profile and academic background to shed additional light onto the issue. The quantitative findings were discussed and related to previous research. Keywords: EMBA; performance predictors; admission criteria; Sharjah; UAE. Reference to this paper should be made as follows: Tabsh, I. (2015) Admission criteria and performance predictors at a GCC executive MBA programme, J. International Business and Entrepreneurship Development, Vol. 8, No. 1, pp Biographical notes: Ibrahim Tabsh has been the Dean of the College of Business and Management at Fahad bin Sultan University in Tabuk, KSA for the last four years. He served as the Director of the Executive MBA programme at the University of Sharjah, UAE from Introduction The Executive Master s in Business Administration (EMBA) is a graduate management programme designed for executives of various academic and professional backgrounds. The programme typically attracts executives who want to improve on their management skills or update their knowledge. The programme is also attractive for executives who want to further their professional growth, as many pursue it on the aim of getting promoted or changing their careers or current jobs. Experience with current university graduates showed that EMBA graduates are appreciated by the local UAE market as more than 70% of graduates got promoted or changed to higher positions within two years from graduation. Copyright 2015 Inderscience Enterprises Ltd.

2 80 I. Tabsh Having started in the last half century, the trend of offering EMBA programmes is a fairly new trend at universities worldwide. The first programme debuted in 1943 at the University of Chicago USA, with the trend slowly continuing in the USA and Europe (EMBA Council, 2011). The largest increase in EMBA programmes started in the early 1990s when more than two thirds of the current programmes were established worldwide. In the Arab world, this trend is recent. It is believed that the majority of EMBA programmes started after Currently, it is estimated that there are no more than 50 programmes in the Arab world. There have been many discussions of whether the EMBA programme was much different from a regular MBA. Sheikh (2006) reported that the core course curricula for EMBA programmes significantly differed from the ones commonly found in MBA programmes, with the latter being less concentrated on specific topics than EMBA programme curricula. This was justified given that EMBA programmes admitted older and more experienced students. Other academics believed that the difference did not mainly lie in the programme contents/curricula (e.g., Mintzberg and Gosling, 2002), but in the delivery modalities and in the presentation of knowledge. However, it seems that programmes worldwide have a wide variety in contents and objectives to formulate a unified opinion. What is certain is that EMBA programmes tend to focus more on applications, discussion of work-related case studies, and differ in the timing and delivery of its classes (i.e., mostly condensed modules offered over weekends), and in the characteristics and qualifications of its participants. Typically, participants in an EMBA programme are middle to upper level managers with several years of working experience and an advanced executive standing. The latter encompasses the category of decision makers within both public and private organisations. This was one of the key differentiating factors in admission between a regular MBA and an Executive MBA programme since the latter typically required a substantial minimum professional work experience from their students (Gropper, 2007). This category of students brings exposure and increased networking opportunities for the university with the corporate sector. Universities often look at this programme as a flagship as it allows it to communicate and tap into a new category of education seekers; an older and mature generation, who is professionally successful in business life, and who could offer a great support to the university within the community. Universities have also seen EMBA programmes as one way of community outreaching while serving a new sector of the society rather than being merely another graduate programme in management. However, it is no secret that many EMBA programmes, especially the ones offered at top-tier universities, generate handsome revenues to the universities due to its appeal to good-salaried and often company-sponsored executives. EMBA participants are not similar to other graduate students. While they often bring to class a wealth of experience and up-to-date practices and information, they are highly opinionated, over confident and are hard to convince. As key decision makers within their organisations, they are demanding and have ambitious expectations from their presence at the university and for their money. This is why universities tend to treat these executive students in an entirely different manner than other graduate students. Admission and registration procedures have to be streamlined to simulate a private business environment. Support services have to be professional and treat students like customers in a big service company. Facilities used have to be modern and suitable for executives in large companies similar to what they are used to see in their work

3 Admission criteria and performance predictors 81 environments. Universities often rely on senior faculty members who have a deep conceptual understanding, wide market experience and strong delivery skills. Instructors should also differentiate these executives from other students by being sensitive to their needs and by allowing ample opportunities for the students to have interactive and constructive dialogue in the class. While faculty members enjoy the increased opportunities for networking and exchange of applied experience with these students, they often clash with students on the quantity of work and on work appraisal. At the same time, the students varying academic backgrounds, age, senior positions, work experience and the long absence from a regular study environment, place a huge challenge on programme administrators and faculty members to design and implement a programme that can satisfactorily cater to all these variables and at the same time maintain its standards and quality. When these are added to a rigorous and demanding condensed programme, the real challenge becomes how to select and admit potentially successful students, develop their skills and prevent drop out. An attractive and a good programme is one that offers sufficient flexibility and support to students to allow them to combine their personal, professional, social and academic duties without jeopardising the academic content, integrity and credibility of the programme, and at the same time ensures that the majority of admitted students complete the degree within the allocated timeframe. To reduce the above conflicts, universities usually adopt admission criteria that screen applicants based on a different (than regular MBA) set of admission requirements, which help attract the category of students it wants, given its mission statement and available resources. The above also depends on the market environment the university operates within. programme admission officers must also ensure that the accepted candidates have the highest chance to succeed in the programme. In a typical EMBA programme, work experience, type and quality of executive responsibilities, undergraduate GPA (UGPA) and often GMAT scores constitute the primary criteria for consideration for admission, although the last criterion is becoming less of a requirement in many US schools (EMBA Council, 2003, 2005, 2006; Dogan, 2011). It is also argued that UGPA (entry GPA) may not be a strong predictor of performance for applicants who have left college many years ago or when evaluating applicants with non-management educational backgrounds (Dogan, 2011). The last criterion becomes even less significant when evaluating executives who have proved themselves professionally in the market by holding senior positions. This conclusion was confirmed by many studies that showed a limited predictive ability of entry GPA and GMAT scores in graduate business programmes (Paolillo, 1982; Wright and Palmer, 1994). Furthermore, the UGPA scores may not help form an accurate comparison or show variation between two candidates who graduate from programmes of different rigor or from more selective institutions (Aiken et al., 1994). Admission criteria vary in strictness among North American Universities. While they all agree on senior positions with a minimum working experience between seven to ten years and decision-making duties, some programmes are less demanding in their academic entry levels, especially the applicant s GPA and GMAT scores. The trend is to reduce the entry requirements and compensate the students with additional preparations.

4 82 I. Tabsh Students are usually expected to graduate within 24 months although there are an increasing number of programmes with shorter duration (EMBA Council, 2011). Students are accepted in batches and follow the same series of courses allowing them to graduate in the same group. 2 The UOS EMBA programme The University of Sharjah (UOS) was among the first universities in the Arab world to offer an Executive MBA programme in The admission requirements at UOS is pretty much in line with other graduate programmes in the United Arab Emirates (UAE), focusing more on academic preparations (i.e., higher GPA) and to a lesser extent on work experience. Admission criteria also included English language proficiency, and the admissions committee often looked at the quantity and quality of work experience. The UAE s Ministry of Higher Education and Scientific Research believed that a higher entry GPA would ensure quality in graduate programmes, and that students were likely to be more successful in their graduate programmes in the UAE s academic environment. Given the above, the UOS management established somewhat uniform admission requirements for all graduate programmes, contrary to common international practices, which allocated different requirements for EMBA candidates. To ensure flexibility and attractiveness to remote students, the UOS programme allowed students to follow the programme on a part-time basis given the heavy work responsibility of students and travel difficulties to remote students. The UAE market has its own specialised circumstances. The country is witnessing a rapidly growing economy that has a high need for highly skilled managers. Additionally, there is a strong trend for nationalisation, whereby local skilled managers are replacing expatriate managers. Accordingly, it is not uncommon to see qualified national managers changing senior positions several times in the same year, with companies competing to attract them with lucrative benefits. Also, the interest in graduate education has recently increased due to social reasons (i.e., prestige and social status). In particular, there has been a huge interest among women to advance their managerial skills as the number of newly established senior managerial positions for women highly exceeds that for men, as the society has recently accepted and endorsed a more visible role for women in the corporate sector. Given above, it becomes crucial to analyse the effect of current admission regulations on students performance in order to make any necessary changes to ensure the achievement of the programme objectives and the admission of students who are likely to have a better rate of success in the programme. Additionally, it is useful to relate certain precedent variables (e.g., related to applicants profile) on the students performance in the current setting. 3 Paper objectives The objective of this paper is to determine which of decision variables in the used admissions criteria followed at the UOS EMBA programme are most closely indicative of students performance, i.e., most useful in predicting academic success. Performance is

5 Admission criteria and performance predictors 83 looked at from two perspectives: exit cumulative grade point average (egpa) and the time to finish course work. 1 Additionally, the paper investigates whether additional alumni characteristics could explain the above variables. A comparison between group variations in predictor variables is also made. The importance of this study is that it adds to the little research done on EMBA programmes, and that it is the first of its kind in the Arab world. 4 Literature review It is crucial to select promising students who will succeed in the MBA/EMBA programmes because successful completion of the programme and subsequent job placement directly impacts the reputation of the business school (Dogan, 2011). Students who drop out represent a loss of revenue for the academic institution, and an ineffective time spent by lecturers on students, and may possibly cause damaging perceptions to the institution s image. Consequences of failing or dropping out to the student include lost money and time, and damaged self-esteem (Bisschoff, 2012). A good understanding of the factors influencing students academic performance will help programme administrators to design appropriate academic programmes and supporting activities. In addition, a good understanding of the relation between the students academic performance and its related precedent variables will enhance decision making in the admission process (Yang and Lu, 2001). There are numerous studies that attempted to relate the admissions criteria and certain precedent variables on the student performance for MBA/EMBA programmes as defined and measured by graduate GPA (Ahmadi et al., 1997; Braunstein, 2002; Carver and King, 1994; Gayle and Jones, 1973; Hobbs and Gropper, 2013; Wright and Palmer, 1994, 1997; Yang and Lu, 2001). However, the literature on the prediction of academic performance in graduate management education was not conclusive (Yang and Lu, 2001). Graduate schools of business use several criteria for admitting applicants into their programmes with the intent of screening out those who are unlikely to succeed in their programmes in general, or in the allocated time frame. Dogan (2011) noted that AACSB allowed schools to select promising students in many ways and they were free to choose their selection tools. The mostly considered admission factors included standardised tests like GMAT/GRE, UGPA, and the length and quality of work experience. Siegert (2008) reported that admission officers at most graduate schools of management use a formula score that combines UGPA, GMAT scores, and other quantifiable factors. GMAT is one of the key standardised tests used by graduate schools of business to screen applicants. However, there is some conflicting research on its effectiveness as an admission tool. Siegert (2008) studied the process followed at 22 EMBA programmes, and noted that the GMAT exam could contribute to the admission selection process. Similarly, Talento-Miller and Rudner (2005) found that GMAT quantitative and verbal scores and UGPA correlated well with core course grades with a mean correlation factor of There are some studies with different conclusions on GMAT suitability. Considering the relationships between GMAT scores and GGPA in EMBA programmes, it is often

6 84 I. Tabsh argued that if a student had more work experience and stayed away from school for a longer period of time, he or she would be in a disadvantageous position to earn a higher GMAT score (Dogan, 2011). Gropper (2007) concurred noting that GMAT score declined with a person s age and thus was not a strong predictor of success unless other information about the student was considered. Gropper (2007) concluded that the GMAT did not have a statistically significant relation to overall EMBA programme performance, however the GMAT was found to have a statistically significant, positive relation to students performance in a smaller set of first-year EMBA courses. This was in line with what the Graduate Management Admission Council (GMAC) claims. 2 Data from GMAC showed that average GMAT scores declined with advancing age for the older students that populate EMBA programmes (Gropper, 2007). Zwick (1993) studied 90 US and Canadian schools to investigate the validity of the GMAT for the prediction of grades in doctoral business students. The author found that the UGPA was a more accurate predictor than GMAT score. Including both predictors was even more effective. The value of the GMAT as a predictor of performance for the typical Executive MBA candidate, who is an adult learner over 30 years of age, is less certain. Some EMBA programme administrators felt that implementing GMAT as part of the admission requirements limited their applicant pool. Accordingly, an increasing number of executive programmes did not require any standardised admission test (Siegert, 2008). The author reported that results from the EMBA Council survey indicated that fewer programmes were using the GMAT as a strict admissions requirement. The number of programmes that waived the condition increased from 35% in 2003 to 51% in 2005 (Gropper, 2007). Dogan (2011) concurred noting that many business schools have dropped the GMAT requirement in their EMBA programmes. He added that many Asian business schools used tests other than the GMAT or GRE for student selection, while other schools gave prominence to UGPA and professional work. Ahmadi et al. (1997) and Darling-Hammond (1991) reported that since standardised tests were objective type questions, they did not accurately measure an individual s ability or critical thinking skills, and placed test takers in a passive role, rather than engage their capacities to generate ideas and solve problems. Arnold et al. (1996) reported that many admission officers felt that the UGPA declined in importance as a predictor variable for graduate performance when the time since obtaining the undergraduate degree exceeded ten years. This applies to EMBA candidates who are normally aged between years of age with at least ten years of management experience. Still, Hobbs and Gropper (2013) concluded that UGPAs were a stronger predictor of academic success in the Executive MBA programmes than were GMAT scores or other variables, including age, race, or gender. Christensen and Nance (2012) found that a student who performed well at the undergraduate level continued to perform on average at the same level. However, previous studies of the effect of UGPA on graduate performance in MBA programmes (with younger students) have consistently found a positive relationship (Adams and Hancock, 2000; Anderson and Benjamin, 1994; Arnold et al., 1996; Braunstein, 2002; Carver and King, 1994; Cheung and Kan, 2002; Dogan, 2011; Fairfield-Sonn et al., 2010; Paolillo, 1982). So students with higher undergraduate grades were found to perform better in MBA study than students with lower undergraduate grades.

7 Admission criteria and performance predictors 85 The validity of admission and performance models using only standardised test scores and undergraduate grade point averages for EMBA programmes was questionable, since it was populated by older students. Hence, many EMBA administrators started to place greater weight on more subjective, qualitative factors, characterising work experience, motivation, and business success, even though they had incomplete evidence of their validity (Arnold et al., 1996). As a consequence, many studies (e.g., Christensen and Nance, 2012; Gropper, 2007; Fairfield-Sonn et al., 2010) evaluated major precedent variables and its likelihood to enhance predication of performance and success in these programmes, so as to be considered in the admission process. Over the past years, several researchers presented their cases as to what fuels the student success of MBA/EMBA programmes and many variables have been taken into consideration. One of these factors was gender. Gropper (2007) reported that holding other factors constant, women tend to perform as well or better than men in the EMBA programme. Fairfield-Sonn et al. (2010) concurred noting that exit GPA of female students was significantly higher than that of male students. Deckro and Woudenberg (1997), and Cheung and Kan (2002) found that women performed better academically during the MBA programme than men. Launius (1997) explained this difference by proposing that women put more time and effort into studies than men. Christensen and Nance (2012) studied another factor. They researched the extent to which undergraduate prerequisite courses predicted MBA success, and found the performance in these courses was a good predictor of MBA success. They also discovered that students who were required to take these courses at entry performed at the same level or slightly higher in the MBA programme than those who did not. Another factor was the undergraduate degree. Adams and Hancock (2000), Braunstein (2002) and Truit (2002) examined the effects of students first degrees (i.e., being in business or other majors), and also whether students completed their MBA at the same institution as their undergraduate degree. They concluded that students with a business degree had no advantage over those with a non-business degree. Truitt (2002) found that having a quantitative undergraduate degree affected MBA academic performance positively. Braunstein (2002) found that having a degree from the same institution had no affect on performance, while Adams and Hancock (2000) found that students who did their undergraduate degree at a different institution performed better than those who did it at the same institution. Age was also studied. Wright and Palmer (1994), Adams and Hancock (2000), and Collis et al. (2000) examined the relationship between age and academic performance. Collis et al. (2000) noted that age was expected to increase the students determination, social skills and strategic thinking despite a high variation across age groups. The authors found that age was a significant factor for individuals who scored poorly on the verbal part of the GMAT and for those in the upper portion of the age distribution. This was in agreement with other studies (e.g., Dogan, 2011; Gropper, 2007) mentioned above. Berger (1998) reported that there was little variation in intelligence and cognitive ability with age and it was usually irrelevant before the age of 65. With regard to work experience, it was assumed that people who had worked full time for several years enjoyed richer, more personally relevant learning in the MBA classroom than did their less-experienced counterparts (Dreher and Ryan, 2004). The knowledge they gained on the job helped them understand the interrelatedness of various

8 86 I. Tabsh business functions and enabled them to develop the interpersonal skills needed to navigate competitive, political, and fast-paced business environments. Thus, they should do better academically and be more successful in their subsequent job placement (Dreher and Ryan, 2004). EMBA programmes are often more heavily case-based and may thus draw less on the traditional academic skills measured in the GMAT and more on the quality of past business experience, thus utilising the gains in judgement that hopefully accrue through experience and other factors that lead to success in a business career (Hobbs and Gropper, 2013). The literature, however, often showed varying results. Adams and Hancock (2000) reported that full-time work experience prior to enrolling in a business school programme was a better predictor of graduate school performance (showing a positive correlation) than results of standardised tests (i.e., GMAT) and UGPAs, concluding that there was a strong relation between the number of years of work experience and MBA success. They found that the least and most experienced group of students differed significantly in their graduate GPA. Gropper (2007) found some measures of work experience and career advancement to be significantly related to overall EMBA programme performance rather than the simple quantity of years. Substantial career accomplishment was found to be strongly related to overall success in the EMBA programme. Gropper (2007) added that the quality of work for students who had reached a level of mid-upper management was a good indicator of students success. Reeves and Rimmer (2008) noted that evaluating the quality of experience and its translation to performance was a subjective issue. In their study, they considered the breadth of work experience as reflected in the number of employers rather than the length of the experience, and the sector of employment (i.e., public or private sector). The authors indicated improved results. Conversely, other studies (e.g., Dreher and Ryan, 2004; Kaiser, 1978; Pitcher, 1972; Sternberg, 2004) suggest that GGPA was not significantly related to work experience. Pitcher (1972) and Kaiser (1978) reported that across most student categories, there were no statistically significant relationships between previous work experience and MBA grades. Dreher and Ryan (2000) found that MBA applicants with less than two years of previous work experience were performing as well as their more experienced counterparts by the end of the second semester in graduate school. Also, there was no evidence that more than five years of work experience provided an advantage in either the first or second semester. A study conducted by the GMAC in 2005 also found that previous work experience was poorly correlated with programme success (Hobbs and Gropper, 2013). Graham (1991) found that the length of interruption (time between completion of undergraduate work and enrolling in an MBA programme) was not a useful predictor of first-year grade or graduate GPA. McClure et al. (1986) found it to be positively related to graduate GPA. Another key variable was the English language proficiency, an issue of key importance to programmes offered in English to both native and non-native speakers. Eddey and Baumann (2009, 2011) examined the relationship between English language proficiency and academic achievement in the context of an Australian university that has over 80% international student enrolment in its postgraduate business programmes. The analysis showed a significant statistical association between language proficiency and academic performance. It was found that a student s English ability was an important factor in determining academic performance at the postgraduate level. Students who required a remedial English programme prior to commencing postgraduate study

9 Admission criteria and performance predictors 87 generally achieved lower academic grades than those students who were evaluated as English proficient. Wright and Palmer (1994), and Ahmadi et al. (1997) noted that the overall quality of admission decisions may be enhanced if other factors were considered in conjunction with GMAT scores and UGPAs, and would help select students who were well prepared for success in graduate business programmes. Many researchers (Hobbs and Gropper, 2013; Siegert, 2008) suggested evaluating screening factors such as personal interviews, writing samples, essays outlining educational and occupational objectives, motivation, letters of recommendation, and an analysis of applicants performance in related undergraduate courses. Hobbs and Gropper (2013) noted that written statements from the applicants and their employers, demonstrated career achievements and advancement, letters of recommendation, personal interviews, and applicant résumés may adequately capture the requirements for success in EMBA programmes. Additionally, workload and time management skills were critical aspects of success and completion for these programmes. For three classes of EMBA students, Arnold et al. (1996) found that adding subjective factors such as motivation to succeed (as measured from personal interviews, letters of recommendation, and prior track record), résumé evaluation and level of financial support offered by the applicant s employer added to the predictive power of models. The résumés of Executive MBA students may be more informative with respect to their ability and motivation when compared to the résumé of younger students since the former students have generally had more time to accomplish things in their professional lives (Hobbs and Gropper, 2013). Siegert (2008) noted that interviews were also a critical admission component for many EMBA programmes especially when marginal applications were reviewed. According to the EMBA Council (2003, 2005, 2006), 84% of EMBA programmes required interviews as part of the admission process. The interviews were used to confirm that applicants had the skills they claimed on their application and to determine if the programme is a match to the programme in personality and intellect (Siegert, 2008). Bisschoff (2012) and Siegert (2008) added that interviews should be chaired by experienced personnel such as deans, school directors and senior academic staff. Reeves and Rimmer (2008) looked at the students indicated intentions to join the programme and found that students seeking academic integration performed better than students giving other reasons. Hedlund et al. (2005) proposed that practical intelligence (PI) measures had the potential to improve selection of promising students for graduate business schools. PI measurements included personality, motivation, interpersonal skills and professional work experience or any other non-cognitive factors that affected success. There were many empirical studies combining multiple variables that attempted to predict performance in graduate business schools; notably the MBA programmes, but only few (e.g., Arnold et al., 1996; Reeves and Rimmer, 2008; Siegert, 2008) were on EMBA programmes. Arnold et al. (1996) used data from three cohorts of EMBA students at Tulane University-USA to develop models relating EMBA students performance to traditional quantitative factors (e.g., GPA and GMAT scores, highest degree earned, time since applicant s last academic experience, years of work experience) and subjective factors such as assessment of applicants transcripts, work experience, motivation to succeed and

10 88 I. Tabsh business success. The motivational factors were deduced from the applicants letter of recommendation, the scope of the applicants managerial responsibilities and personal interview assessment, and level of sponsorship. The authors reported that the inclusion of qualitative factors enhanced the model s predictive ability especially the assessment the transcript of the applicants and the results of the personal interviews. Using data collected from a Southeast university, Ahmadi et al. (1997) ran a series of bivariate regression analyses and found that UGPA explained more than 27% of the variability in graduate GPA, whereas GMAT scores explained only 18%. Wright and Palmer (1994) used a sample of 86 MBA students at a small Midwestern university to determine variables that were better predictors of graduate performance. They found that UGPA and GMAT scores were modestly associated with graduate performance across the full range of students, but their model did not discriminate between moderately low and very low performers. They estimated a multiple R 2 factor of 21.1%. Carver and King (1994) investigated the MBA admission criteria for non-traditional students, exploring a number of precedent variables including gender, undergraduate major, age, work experience, duration of formal education, competitiveness of undergraduate institution, UGPA, and GMAT scores. They found that only three variables; GMAT score, UGPA, and work experience, best predicted performance for non-traditional students. They estimated an R 2 of 22%. Paolillo (1982) reported that UGPA and GMAT scores explained slightly less than 17% of variance in graduate GPA. Paolillo s findings were consistent with the findings of Deckro and Woudenberg (1977), who determined that UGPA and GMAT explained less than 15% of the variance in graduate performance. Yang and Lu (2001) analysed the files of 395 MBA graduates at Auburn University, looking at the impact of students native language, UGPA, GMAT total score, gender and age on graduate performance. They found that only students language, GMAT scores, and UGPA showed strong prediction power (with UGPA playing the greatest explanatory role). Their model explained 26% of the variation in academic performance. Eddey and Baumann (2009) investigated 544 graduates of Australian universities with a high number of international students. In this study, they attempted to relate GPA scores to relevant business studies in a prior university degree, proficiency in English, gender, age, and country/region of origin. Using ordinal logistical regression, the best fit model showed that English proficiency, age and origin explained 24% of students GPA results. Business background and gender were not significant. Siegert (2008) used data from the GMAC to study 22 EMBA programmes representing 2,725 students during a period from 2002 to He found strong correlations between GMAT scores and GGPA (Siegert, 2008). The GMAT total scores and quantitative scores succeeded to explain 30% and 36% of the variance in EMBA grades respectively. Average GMAT score of test takers showed a decline over the age of 31, concluding that GMAT scores declined with advancing age for the older students that populate EMBA programmes. This partially explained why GMAT scores poorly correlated to GGPA in EMBA programmes (Gropper, 2007). As a conclusion, previous research showed varying results on predictor variables affecting performance; hence, it was important to identify the variables that are related to the working environment of the studied EMBA programme/university.

11 Admission criteria and performance predictors 89 5 Research methodology 5.1 Data The current study makes use of data and records of 72 EMBA alumni, which represents the complete population of UOS programme graduates from its inception in March 2000 until the end of summer session A total of 19 variables were collected from each student record. Two students had missing or non-comparable data. Alumni were contacted by telephone to update any incomplete information. 5.2 Research variables The variables were related to the alumni profile, admission criteria and performance in the programme. These included nationality, i.e., local or non-local (NATIONAL), gender (GENDER), city of residence, i.e., Sharjah and the neighbouring cities of Dubai and Ajman, which reflects distance travelled to come to class (RESIDENC), level of executive position, i.e., middle or upper level executive (EXECPOST), type of work, i.e., public or private organisation (WORKTYPE), age at start of courses (ENTRYAGE), entry degree GPA (ENTRYGPA), working experience at admission (WORKEXP), years of graduation from previous degree on start of courses (GRADYRS), number of exempted courses at entry (EXEMPTS), sponsorship (SPONSORD), language proficiency, i.e., whether a student was admitted conditionally based on completion of an intensive English language programme (LANGUAGE), specialisation of entry degree, i.e., management-related degree or otherwise (BUSMAJOR), origin of degree, i.e., from international or local university (ORIGNDEG), and position change during studies (CHNGPOST). The dependent variables were EMBA exit GPA (EXITGPA) and time to finish course work (fcourses). Table 1 shows a descriptive analysis for the variables in the data set with mean and standard deviations. Table 1 Descriptive statistics A Ratio (quantitative) variables Mean Standard deviation EXITGPA (out of 4) ENTRYGPA (out of 4) EXEMPTS (no of courses) ENTRYAGE (in years) FCOURSES (time in months) WORKEXP (time in years) GRADYRS (time in years)

12 90 I. Tabsh Table 1 Descriptive statistics (continued) B Nominal variables Mean Standard deviation NATIONALITY (1 = international) NATIONALITY (1 = international) GENDER (1 = female) RESIDENCE (1 = outside Sharjah city area) SPONSORD (1 = sponsored) BUSMAJOR (1 = management related) ORIGNDEG (1 = international) WORKTYPE (1 = private) CHNGPOST (1 = yes) EXECPOST (1 = high executive) The data showed that the average entry GPA was 2.61 out of 4, average entry age was years and the average working experience at entry was 7.59 years with 7.14 years period of interruption between graduation and the EMBA programme entry. Data showed that the average time to complete coursework was months. The means for nominal variables show the percentage distribution, for example, in gender, 42% of graduates were women, similarly, 11.1% were non-nationals, 25% were admitted conditionally upon completion of an intensive English language programme, 25% lived in farther emirates (i.e., other than Ajman, Sharjah city and its west coast and Dubai), 58% had some degree of financial sponsorship (at least 75% in this case), 57% had management-related entry degrees, 35% had international entry degrees, 19% worked in private organisations, 39% changed positions during study, and 57% had senior executive positions. The standard deviation of exit GPA was low; indicating the results were tightly distributed around the GPA mean. 5.3 Main assumptions Two important assumptions were made in this paper: First, UOS students had high interest in academic excellence as it was seen to be linked to salary and career progression, and the majority of students were committed to attain a qualification and to excel. UOS students expressed a high degree of competition for high grades. The second assumption is that teaching and assessment methods in the programme were appropriate to gaining the knowledge as well as providing a fair and consistent assessment of students performance. 5.4 Data analysis and research models The effect of the followed admission criteria as well as other profile variables on exit GPA and time to finish course work were evaluated. Multiple linear regression analysis was used to examine the relationship between the dependent and independent variables.

13 Admission criteria and performance predictors 91 Additionally, several hypotheses were tested to see if there were significant differences in performance based on precedent variables Analysis of predictor variables on performance Linear regression models were developed to analyse the prediction and explanatory ability of alumni-related independent variables on alumni performance. Two models were formulated as follows: A Model 1: Exit GPA Exit GPA = function of (business major degree, city of residence, English language proficiency, entry age, entry GPA, executive position, gender, nationality, number of course exemptions, origin of degree, position change during study, sponsorship, work type, years of graduation before entry, years of work experience). B Model 2: Time to finish course work. Time to finish course work = function of (business major degree, city of residence, English language proficiency, entry age, entry GPA, executive position, gender, nationality, number of course exemptions, origin of degree, position change during study, sponsorship, work type, years of graduation before entry, years of work experience) Research hypotheses Different groups within the alumni were compared for significant differences in the two performance indicators. The hypotheses are stated in null forms as follows: Hypothesis 1 There is no difference in performance between men and women. Hypothesis 2 There is no difference in performance for students living in farther cities as opposed to students living in nearer cities. Hypothesis 3 There is no difference in performance between students with weak English (i.e., conditionally accepted) and those who are accepted unconditionally. Hypothesis 4 There is no difference in performance between sponsored students and non-sponsored students. Hypothesis 5 There is no difference in performance between nationals and non-nationals. Hypothesis 6 There is no difference in performance between students with local entry degrees and students with international degrees. Hypothesis 7 There is no difference in performance between upper executives and middle executives. Hypothesis 8 There is no difference in performance between students working in public institutions and students working in private institutions.

14 92 I. Tabsh Hypothesis 9 Hypothesis 10 Hypothesis 11 There is no difference in performance between students whose positions changed during studies and students who had the same positions. There is no difference in performance between students whose entry GPA s were less than 2.5 out of 4 and students who had higher GPA s. There is no difference in performance between students who received pre-requisite course exemptions and those who did not receive any exemption. Hypothesis 12 There is no difference in performance between students who had five or more years of experience on entry and those who had lesser number of working experiences. Hypothesis 13 There is no difference in performance between students who graduated five or more years before joining the programme and those who graduated at lesser number of years. Hypothesis 14 There is no difference in performance between students whose entry degree was in management-related fields and those who graduated with other fields. 6 Results The data was coded and multiple regression models were run using the SPSS Statistical Package. Parametric analysis was used since the data contained nominal and ratio variables. Two missing data were replaced by mean values. Backward stepwise regression was utilised and a significance level of 95% was used. 6.1 Pearson correlations A correlation matrix was run between the variables. The Pearson-correlation matrix shown in Table 2 shows the variables with correlation that are significant at the 99% significance level (two-tailed). The table shows that language proficiency significantly correlated with the two performance indices ( and 0.346, respectively). The place of residence was significant for the time to finish courses. Other results were expected in magnitude and sign such as the significant correlation between the national students and public type of work (0.608), entry age and both of work experience and years from graduation (0.871 and 0.718, respectively), and students with business majors and number of exempted courses (0.467). Surprisingly, the entry GPA had only a significant correlation with the number of exempted courses.

15 Admission criteria and performance predictors 93 Table 2 Pearson correlations (see online version for colours) language gradyrs entrygpa entryage sponsord busmajor origndeg worktype chngpost execpost fcourses exitgpa national exempts workexp language gradyrs gender entryage residenc sponsord 0.346

16 94 I. Tabsh 6.2 Results of admission criteria on performance Two variables that represent the main admission criteria; work experience and language proficiency, were investigated first for their ability to predict and explain performance. Then two additional variables that represent other admission criteria; the entry GPA and years from graduation were added sequentially to see their marginal effect. The regression models were run first using the enter method to see the effect of the current variables, and then run again using the stepwise method which kept the significant variables by sequentially removing the least significant variable from the model, the two methods produced the same set of significant variables but the values of coefficients slightly changed under stepwise, as the procedure eliminated non-significant variables and re-allocated the variation of change among remaining variables. The results are shown in Table 3 and the optimal model is shown in equation (1). Table 3 Variables in model Coefficients, t-values, R-squared and f-values for various exit GPA model Model 1 Model 2 Model 3 Model 4 B-coef. t-value B-coef. t-value B-coef. t-value B-coef. t-value (Constant) language residence entrygpa gradyrs F-statistic Adjusted R-sq. (%) Marginal adj. R-sq. (%) 24.75% 29.29% 34.16% 36.95% 4.53% 4.87% 2.79% ExitGPA = Language Residence ( 5.24) ( 2.44) Entrygpa Gradyrs (2.92) (2.00) (1) The fourth model, which best predicts and explains exit GPA, contained four significant variables: language proficiency, place of residency, entry GPA and years from graduation with an adjusted R-square of 36.95%. The latter indicates that the four variables were able to explain 36.95% of the variation in exit GPA with language explaining 24.75% and the three other variable explaining 4.53%, 4.87% and 2.795, respectively. Another variable that almost entered the model was sponsorship with a t-value of 1.94 and a coefficient of The F-value (11.40) was significant. Language was a stronger predictor than place of residence, entry GPA and graduation years in predicting EMBA academic performance (standardised regression coefficients were 0.264, 0.123, and 0.011, respectively, for these four predictors).

17 Admission criteria and performance predictors 95 The above model also shows that students weak language and place of residence (far emirates) affected negatively the students exit GPA, while entry GPA and years of graduation from programme s starting date had a positive effect on their exit GPA. Numerically, students whose English was originally weak received on average a final GPA that is points lower than other students, keeping other variables constant. Similarly students who hailed from farther emirates (i.e., other than Ajman, Dubai or Sharjah) had points lower on their GPA as compared to other students. Additionally, each point on the students entry GPA (out of 4) affected their exit GPA by points, while an additional year from graduation prior to joining the EMBA programme had a small effect of points on their exit GPA. 6.3 Admission criteria and time to complete course work The results of the regression are shown in Table 4 and the optimal model to explain time to complete course work is shown in equation (2). Table 4 Coefficients, t-values, R-squared and f-values for various times to finish courses models Variables in model Model 1a Model 2a B-coefficients t-value B-coefficients t-value (Constant) language residence F-statistic Adjusted R-sq. (%) 10.75% 22.87% Marginal adjusted R-sq. (%) 12.12% FCOURSES = Language Residence (4.221) (3.46) (2) The model had two significant variables; language proficiency and place of residence with an adjusted R-square of 22.87%. The latter indicates that the two variables were able to explain 22.87% of the variation in time to finish courses with language explaining 10.75% and the other variable explaining 12.12% of the variation. Two other variables that came close to enter the equation were gender (t-value 1.81 and coefficient of 0.195) and number of exemptions (t-value of 1.78) and B = 0.186). The F-value (11.52) was significant. Table 5 Significant entry requirements in various students performance models Exit GPA Time to finish course work Work experience Not significant Not significant Language proficiency (1 = weak) Significant Significant Entry GPA Significant Not significant Years from graduation Significant Not significant Residence Significant Significant

18 96 I. Tabsh Language proficiency was a stronger predictor than the place of residence in predicting time to finish courses (standardised regression coefficients were and 5.80, respectively).the above model also shows that students with weak language entry level and those who hailed from remote emirates took on average 6.20 and 5.80 more months respectively to complete their course work (with other variables constant). A summary of results on the relevance of admission criteria on students performance is shown in Table Hypothesis testing results The results of hypotheses testing are shown in Table 6. In most cases, it seems that the null hypothesis could not be rejected, i.e., failed to reject the hypothesis that a significant statistical difference exists between the two variable groups based on differences in the alumni profile in terms of performance indicators at a 95% significance level. There were some exceptions. A difference was recognised in English proficiency on both indicators of performance. Additionally, the place of residence did affect the time to finish course work. These differences are highlighted in grey cells in Table 6. Table 6 Results of hypotheses testing Hypothesis: difference between Exit GPA Time to finish coursework 1 Gender: male vs. female Ho cannot be rejected Ho cannot be rejected 2 Live nearby Ho cannot be rejected Ho is rejected Emirates vs. Farther Emirates 3 Conditional upon English Ho is rejected Ho is rejected programme vs. non-conditional 4 Sponsored vs. non-sponsored Ho cannot be rejected Ho cannot be rejected 5 Nationals vs. non-nationals Ho cannot be rejected Ho cannot be rejected Origin of entry degree: Ho cannot be rejected Ho cannot be rejected local vs. international 7 Executive position: Ho cannot be rejected Ho cannot be rejected high vs. medium 8 Work type: Ho cannot be rejected Ho cannot be rejected public vs. private organisations 9 Changed position during study: Ho cannot be rejected Ho cannot be rejected yes vs. no 10 Entry GPA < 2.5 vs. higher Ho cannot be rejected Ho cannot be rejected 11 Received course exemptions vs. Ho cannot be rejected Ho cannot be rejected did not receive 12 Work experience at entry; five Ho cannot be rejected Ho cannot be rejected years or more vs. lesser years 13 Graduation years before enrolment: five or more years vs. less Ho cannot be rejected Ho cannot be rejected 14 Business major entry degree vs. other majors Ho cannot be rejected Note: The grey area shows that there was a significant difference in the effect of the respective factor on performance indicators at a 95% significance level. Ho cannot be rejected

19 Admission criteria and performance predictors Discussion of results Relevance of admission criteria on exit GPA The findings in this study are somewhat consistent with earlier studies on language proficiency (e.g., Eddey and Baumann, 2009, 2011), which showed a significant statistical association between language proficiency and academic performance for non-english native speakers at the postgraduate level. Given that class lectures and exams are held in English, it is not surprising that students with better proficiency performed better ( points on the GPA on average). Entry (undergraduate) GPA had a significant effect on explaining exit GPA. This result agrees with the findings of Hobbs and Gropper (2013), and Christensen and Nance (2012) who found that a student who performed well at the undergraduate level continued to perform well in graduate studies. This confirms the position of the UAE s Ministry of Higher Education and Scientific Research that students with a higher entry GPA were likely to be more successful in their graduate programmes in the UAE s academic environment. The analysis was carried further comparing the performance between groups of students with degrees earned ten years or more with a group with a lesser number of years. The analysis did not show any significant difference (p = 0.072) indicating that undergraduate or entry GPA declined in importance as a predictor variable for graduate performance when the time since obtaining the undergraduate degree exceeded ten years. Another significant variable was the place of residence with students living in farther emirates performing lower ( points on the exit GPA) than students residing in nearby emirates. This could be justified since remote students may be a bit disadvantaged in (or having less of a chance) of getting frequent support from faculty members when needed as compared to students living in near emirates. This also confirms that students from remote emirates had certain challenges and that the decision of the EMBA administrators to allow part-time study was justified to accommodate the needs, time constraints and travel difficulties of various groups. The last significant variable was the time between earning an undergraduate degree and enrolment in the EMBA programme, with the model showing a small but positive correlation (0.011) with the exit GPA. This could possibly be explained by the fact that students who decide to continue after a long interruption period are likely to have more determination, social skills and strategic thinking (Collis et al., 2000). This was in line with the findings of McClure et al. (1986). A surprising result was the non-significant effect of the number of years of experience on exit GPA. This confirms the findings of researchers (Gropper, 2007; Dreher and Ryan, 2004; Kaiser, 1978; Pitcher, 1972; Sternberg, 2004; Reeves and Rimmer, 2008) that the simple quantity of years of full-time work experience was not significant but rather the career accomplishment, quality and breadth of experience. In this regard, it may be useful to find a suitable and relevant measure to assess the quality of work experience of students and include it in the analysis. A further analysis showed that students with ten or more years of experience did better than students with less years of experience (group GPA means of 3.50 and 3.67, respectively with p = 0.02). However, there was no significant difference between the two groups when the cutoff point was five years.

20 98 I. Tabsh The explanatory power of the model in this study was also consistent with the ones discussed in literature (e.g., Braunstein, 2002; Dreher and Ryan, 2000; Arnold et al., 1996; Adams and Hancock, 2000; Ahmadi et al., 1997) as the adjusted R 2 in these models did not exceed 24%. Hence, additional variables have to be investigated to improve the explanatory power of the models Time to finish course work The findings show that students whose English proficiency was weaker took more time (6.17 months) to complete their coursework. This is in agreement with the findings of Eddy and Baumann, (2009, 2011) that found a positive link between performance and English proficiency. Another significant variable was the place of residence with students from remote emirates taking 5.8 more months to complete their coursework. This also confirms that students from remote emirates had certain challenges that prevented them from studying at the same pace as students living nearby. The model had a low explanatory power of 22.87% indicating that additional variables were needed to improve its explanatory power. A longer time to complete coursework could also indicate higher rates of students failure, weak motivation and a larger number of preparatory foundation courses for students with non business major. However, the correlation between none business major students and time to finish course work was non-significant (Table 2). Longer time of study may mean a higher need for faculty resources and more pressure on scheduling of classes, requiring the programme administrators to make better resource allocation decisions Hypotheses testing results The results of the hypotheses testing were generally in agreement with the literature. In terms of gender (Hypothesis 1), the results showed no difference in performance between male and female students (3.54 vs for female and male students, respectively). Gropper (2007) reported that holding other factors constant, women tend to perform as well or better than men in the EMBA programme. Other research on MBA programmes showed better women performance (Fairfield-Sonn et al., 2010; Deckro and Woudenberg, 1997; Cheung and Kan, 2002). As EMBA programmes accommodate an older age of participants, female students in the UAE face more social pressures as they are given more family responsibilities, and when this is combined with an active career, then the pressure on their study performance becomes even greater. There was a difference in the time to finish coursework between students residing in near emirates (23.33 months) and students coming from remote emirates (28.45 months) as tested in Hypothesis 2. It seems that the latter group was not able to match the course load of the former group. No significant difference was noticed in the exit GPA between the two groups. There was a significant difference in performance between groups of students with various English proficiency levels. This is in agreement with literature (Eddey and Baumann, 2009; Yang and Lu, 2001) who noted that students who were English proficient at the time of application were more successful in their postgraduate programme than those who required English language instruction at the beginning of