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Authors’ own research TABLE 2. Students’ Intentions Post High School

Table 2 Authors’ own research TABLE 2. Students’ Intentions Post High School

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Special Bibliographic Notes related to special journal issues (separates) and indexing/abstracting:
Special Bibliographic Notes related to special journal issues (separates) and indexing/abstracting:
There has been a wealth of research into various forms of educational export such as distance education and study abroad, but this paper focuses on the delivery of foreign- supplied courses and programs within a host country—in this case, China. Key findings include a high level of demand amongst Chinese students for business and management courses and programs delivered by foreign universities and their Chinese partners, ana a marked preference for relatively unadapted foreign programs and, courses, which give the students an idea of what it would be like to study at the foreign university campus-for example, in USA or Canada. These and other findings contained in this pa- per are of value to foreign universities seeking to develop and deliver a range of educa- tional courses and programs to China and other large, emerging education markets.
There has been a wealth of research into various forms of educational export such as distance education and study abroad, but this paper focuses on the delivery of foreign- supplied courses and programs within a host country—in this case, China. Key findings include a high level of demand amongst Chinese students for business and management courses and programs delivered by foreign universities and their Chinese partners, ana a marked preference for relatively unadapted foreign programs and, courses, which give the students an idea of what it would be like to study at the foreign university campus-for example, in USA or Canada. These and other findings contained in this pa- per are of value to foreign universities seeking to develop and deliver a range of educa- tional courses and programs to China and other large, emerging education markets.
Significance level: *** = p < 0.01, **=p < 0.05, *=p < 0.10 level
Significance level: *** = p < 0.01, **=p < 0.05, *=p < 0.10 level
Source: http://aei.dest.gov.au/AEI/MIP/Statistics/Student EnrolmentAndVisaStatisticsRecent.htn Accessed 9/08/2004 TABLE 1. International Students from Top 10 Source Countries 2002-2003
Source: http://aei.dest.gov.au/AEI/MIP/Statistics/Student EnrolmentAndVisaStatisticsRecent.htn Accessed 9/08/2004 TABLE 1. International Students from Top 10 Source Countries 2002-2003
Source: Australian Education International and IDP Education Australia, 2002.
Source: Australian Education International and IDP Education Australia, 2002.
TABLE 3. Choice Criteria Important for Choosing Perth as a Study Destination (Count, %)
TABLE 3. Choice Criteria Important for Choosing Perth as a Study Destination (Count, %)
TABLE 4. Determinants of the Appropriate Dimensionality
TABLE 4. Determinants of the Appropriate Dimensionality
TABLE 5. Relative Contribution to Dimensions
TABLE 5. Relative Contribution to Dimensions
FIGURE 1. Positions of Choice Criteria and Country of Residence
FIGURE 1. Positions of Choice Criteria and Country of Residence
*Significant at p < .05 TABLE 1. Students’ Perceived Level of Importance of Search Criteria for Col leges—by Gender
*Significant at p < .05 TABLE 1. Students’ Perceived Level of Importance of Search Criteria for Col leges—by Gender
*Significant at p < .05 TABLE 2. Parents’ Perceived Level of Importance of Search Criteria fo Colleges—by Gender
*Significant at p < .05 TABLE 2. Parents’ Perceived Level of Importance of Search Criteria fo Colleges—by Gender
Authors’ own research TABLE 1. The High School Seniors’ Choice Survey Design, Attributes, and Levels
Authors’ own research TABLE 1. The High School Seniors’ Choice Survey Design, Attributes, and Levels
* Denotes significant at or below the 0.05 P value. Authors’ own research TABLE 3. Students’ Intentions Post High School; Classification by Gender, Lo- cation, Decile Ratings, and Parent’s Education
* Denotes significant at or below the 0.05 P value. Authors’ own research TABLE 3. Students’ Intentions Post High School; Classification by Gender, Lo- cation, Decile Ratings, and Parent’s Education
Authors’ own research TABLE 4. Model Fit Statistics for the Conditional MNL Model
Authors’ own research TABLE 4. Model Fit Statistics for the Conditional MNL Model
Authors’ own research TABLE 5. Maximum Likelihood Estimates for the Conditional MNL Model of Tertiary Education Attributes Only, Not Including the Column Heading (Univer- sity Names) Dummy Variables
Authors’ own research TABLE 5. Maximum Likelihood Estimates for the Conditional MNL Model of Tertiary Education Attributes Only, Not Including the Column Heading (Univer- sity Names) Dummy Variables
These two options are: Calculated probabilities of choice for the 35 options in the survey show that option 6 has the highest overall probability of being chosen 0.45, while option 22 is the next highest 0.36. Thaca tun anntinne ora: While these are the two options with the highest probabilities of be- ing chosen, not all of the attributes within them may be contributing to choice preference. The choice may be based on the column heading, i.e., the university name, each of which is repeated a number of times in he design, and then the best combination of attributes under that name, or just the best combination of attributes, or some combination of these wo approaches by the students. However, it is the parameter estimate values which show the importance of, and what attributes are, driving he choices. On this basis the overall preferences for these two options appears to be driven by the perceived brand of the two universities and he presence of attributes that score the highest among the parameter es- imates [Table 5], i.e., that employers recruit from these universities along with fully subsidized accommodation as well as overall costs higher than other universities.
These two options are: Calculated probabilities of choice for the 35 options in the survey show that option 6 has the highest overall probability of being chosen 0.45, while option 22 is the next highest 0.36. Thaca tun anntinne ora: While these are the two options with the highest probabilities of be- ing chosen, not all of the attributes within them may be contributing to choice preference. The choice may be based on the column heading, i.e., the university name, each of which is repeated a number of times in he design, and then the best combination of attributes under that name, or just the best combination of attributes, or some combination of these wo approaches by the students. However, it is the parameter estimate values which show the importance of, and what attributes are, driving he choices. On this basis the overall preferences for these two options appears to be driven by the perceived brand of the two universities and he presence of attributes that score the highest among the parameter es- imates [Table 5], i.e., that employers recruit from these universities along with fully subsidized accommodation as well as overall costs higher than other universities.
Authors’ own research TABLE 6. Model Fit Statistics for the Stepwise Model
Authors’ own research TABLE 6. Model Fit Statistics for the Stepwise Model
Authors’ own research TABLE 7. Analysis of Maximum Likelihood Estimates for the Stepwise Model In: cluding the Dummy Variable University Names, Heading the Survey Option Columns
Authors’ own research TABLE 7. Analysis of Maximum Likelihood Estimates for the Stepwise Model In: cluding the Dummy Variable University Names, Heading the Survey Option Columns
TABLE 2. Focus Group Representation
TABLE 2. Focus Group Representation
TABLE 3. Factor 1—-Variables and Loadings, Education Standards and Facilities (UNISAT1) TABLE 4. Factor 2—-Variables and Loadings, Communication and Guidance (UNISAT2)
TABLE 3. Factor 1—-Variables and Loadings, Education Standards and Facilities (UNISAT1) TABLE 4. Factor 2—-Variables and Loadings, Communication and Guidance (UNISAT2)
TABLE 5. Factor 3—Variables and Loadings, Customer Value and Study Out comes (UNISATS3) TABLE 6. Factor 4—Variables and Loadings, Image/Prestige, and Recognition (UNISAT4)
TABLE 5. Factor 3—Variables and Loadings, Customer Value and Study Out comes (UNISATS3) TABLE 6. Factor 4—Variables and Loadings, Image/Prestige, and Recognition (UNISAT4)
TABLE 7. Eigenvalues, Variance of Factors, and Reliability Alpha
TABLE 7. Eigenvalues, Variance of Factors, and Reliability Alpha
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converge: in 7 iterations
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converge: in 7 iterations
FIGURE 1. A Model of University Organisation in Zimbabwe There are four church-related universities in Zimbabwe developed since 1990. Their key unifying character is that they highlight an evangelization mission immersed in values of pan-Africanism based on a vision to create a new leadership for the post colonial dispensation. The Vice Chancellors of Africa University (AU) captured this notion clearly as he explained the mission of his institution:
FIGURE 1. A Model of University Organisation in Zimbabwe There are four church-related universities in Zimbabwe developed since 1990. Their key unifying character is that they highlight an evangelization mission immersed in values of pan-Africanism based on a vision to create a new leadership for the post colonial dispensation. The Vice Chancellors of Africa University (AU) captured this notion clearly as he explained the mission of his institution:
TABLE 1. A Model of University Marketing Organisation (PR = Public Rela- tions; SWOT = Strengths, Weaknesses, Opportunities, and Threats) organisation was only viewed to exist within the adult-centred univer- sity which had a marketing department and a senior university manager appointed to oversee the marketing aspect of this university.
TABLE 1. A Model of University Marketing Organisation (PR = Public Rela- tions; SWOT = Strengths, Weaknesses, Opportunities, and Threats) organisation was only viewed to exist within the adult-centred univer- sity which had a marketing department and a senior university manager appointed to oversee the marketing aspect of this university.
TABLE 2. Marketing Effectiveness Based on Perceptions of Access to Informatior ((*) = Figures in this row do not add to 100 due to rounding off) The picture relating to the Place element was slightly different. Almos a fifth (19%) of pupils indicated that they had access to information tha enabled them to compare the quality of institutional infrastructure includ- ing campus attractiveness. The research did not specifically find out wha this information source was. The assumption made was that their teachers made this information available to pupils. Despite this, about two-thirds (67%) still indicated that they did not have access to information tha would facilitate their choice and decision processes.
TABLE 2. Marketing Effectiveness Based on Perceptions of Access to Informatior ((*) = Figures in this row do not add to 100 due to rounding off) The picture relating to the Place element was slightly different. Almos a fifth (19%) of pupils indicated that they had access to information tha enabled them to compare the quality of institutional infrastructure includ- ing campus attractiveness. The research did not specifically find out wha this information source was. The assumption made was that their teachers made this information available to pupils. Despite this, about two-thirds (67%) still indicated that they did not have access to information tha would facilitate their choice and decision processes.
TABLE 3. Importance Attached to Elements of the Marketing Mix by Sixth Form Pupils based on several factors. Such factors could include the competencies of the institution, the type of market targeted by the institution and the needs of targeted customers. The idea behind manipulating these ele- ments of the marketing mix is to create a differential advantage or com- petitive edge. A key objective for organisations operating in a competitive environment is to create uniqueness, “something potential customers will recognize value and use as their reason for preferring one product over others” (Brassington and Pettit, 2000:28).
TABLE 3. Importance Attached to Elements of the Marketing Mix by Sixth Form Pupils based on several factors. Such factors could include the competencies of the institution, the type of market targeted by the institution and the needs of targeted customers. The idea behind manipulating these ele- ments of the marketing mix is to create a differential advantage or com- petitive edge. A key objective for organisations operating in a competitive environment is to create uniqueness, “something potential customers will recognize value and use as their reason for preferring one product over others” (Brassington and Pettit, 2000:28).
TABLE 1. U.S. News & World Report Ranking Criteria for MBA Schools Paradoxically, the introduction of the rankings as an attempt to quantify and therefore objectify academic performance arguably has had an unwitting, opposite effect in that the rankings have forced schools to play what amounts to a game of illusion with very tangible results . . . playing that dangerous game has produced an environment where substance is no longer seen as sufficient, merely necessary and image increasingly is coming to dominate . . . (2000, 332)
TABLE 1. U.S. News & World Report Ranking Criteria for MBA Schools Paradoxically, the introduction of the rankings as an attempt to quantify and therefore objectify academic performance arguably has had an unwitting, opposite effect in that the rankings have forced schools to play what amounts to a game of illusion with very tangible results . . . playing that dangerous game has produced an environment where substance is no longer seen as sufficient, merely necessary and image increasingly is coming to dominate . . . (2000, 332)
large = 475 or more students medium = 275-474 students small = 175-274 students very small = 174 or fewer students TABLE 2. All Schools
large = 475 or more students medium = 275-474 students small = 175-274 students very small = 174 or fewer students TABLE 2. All Schools
TABLE 3. Do U.S. News Rankings Measure the Academic Excellence of the Schools They Rank?
TABLE 3. Do U.S. News Rankings Measure the Academic Excellence of the Schools They Rank?
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