Int Rev Public Nonprofit Mark (2010) 7:21–36 DOI 10.1007/s12208-009-0042-9 O R I G I N A L A RT I C L E
Understanding university image: a structural equation model approach Paulo O. Duarte & Helena B. Alves & Mário B. Raposo
Received: 20 October 2009 / Accepted: 29 November 2009 / Published online: 15 December 2009 # Springer-Verlag 2009
Abstract Over the last years, institutional image management has become a critical element to the competitiveness of higher education institutions. The aims of this study are to review the organizational image construct; explore the process of image formation and analyze the impact of the different source factors on university’s image. A sample of 1024 university student was surveyed to test a conceptual model of university image formation trough structural equations. The results show that, nevertheless all factors being significant to image formation, the university social life atmosphere and employment opportunities are the most important predictors. As a result, this study is important for researchers and practitioners, as it presents a different approach to image measurement, it provides a new framework to assess university image formation and, at the same time, helps managers to understand that educational related factors aren’t the only ones where they should focus to successful differentiate their institutions and remain competitive. Keywords Institutional image . University image . Image sources . Higher education . Marketing strategies
1 Introduction In the last two decades the Portuguese higher education industry, similar to what happened in the United States and in the remaining countries of Europe, has suffered P. O. Duarte (*) : H. B. Alves : M. B. Raposo NECE—Research Unit in Business Sciences, Business and Economics Department, University of Beira Interior, Estrada do Sineiro, 6200-209 Covilhã, Portugal e-mail:
[email protected] H. B. Alves e-mail:
[email protected] M. B. Raposo e-mail:
[email protected]
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quite profound changes. Currently, higher education institutions face more competitive market structures that threaten the survival of some institutions, which are now forced to compete with scarce resources and, at the same time, attract more potential candidates, which are heavily disputed among the several competing institutions. It is expected that, in the future, this competitive scenario will become even more, unpredictable and intense in consequence of the agreement foreseen in the Bologna Convention, for the harmonization of the academic degrees in the European Union. With the harmonization of the different academic degrees, the mobility and employability of students, professors, researchers and technicians will be greater, and less competitive universities may come to lose part of their students and their knowledge capital. This scenario of increasing competitiveness, together with the growing limitation of public resources for higher education and the social debate about the need for universities to improve their ability to generate their own income (Binsardi and Ekwulugo 2003; Marginson 2006), makes image an essential part of modern strategic management in these institutions (Luque-Martínez and Barrio-García 2009). These changes have compelled universities to become more involved in marketing activities to create and sustain strong brands in order to enhance awareness and differentiate themselves and their courses from the vast array of offers (McPerson and Shapiro 1988; Ali-Choudhury et al. 2009). Similarly, Smith (2001) state that, it has become increasingly important for organizations to differentiate themselves in order to remain competitive, especially where they appear to provide the same service, which is the case of universities. The image of universities is currently receiving much more attention as universities recognize the importance of building a distinctive favorable image to attract the best students, staff and potential funding sources (Bok 1992; Parameswaran and Glowacka 1995; Theus 1993; Treadwell and Harrison 1994; Wilson 1999). This increased interest in the role of university image is reflected in the studies developed a considerable number of researchers (e.g. Bok 1992; Gose 1994–1995; Immerwahr and Harvey 1995; Phair 1992; Theus 1993; Ivy 2001; Kazoleas et al. 2001; Palacio et al. 2002 and Arpan et al. 2003). For University administrators concerned with the success of their enrolment practices, the long-term effectiveness and survival of their institutions, understanding the multidimensional nature of the image construct is crucial to the development of a marketing strategy, assuring that its image reflects the current institution identity (Belanger et al. 2002; Terkla and Pagano 1993). The purposes of this study are to review the organizational image construct; explore the process of image formation and analyze the impact of the different source factors on university’s image.
2 Organizational image concept and value Images represent people’s perceptions of their own reality (Gotsi and Wilson 2001). Ditcher (1985) define image as “the total impression an entity makes on the minds of others”. Therefore, image represents a simplification of the combination of a large
Understanding university image: a structural equation model approach
23
number of associations and pieces of information connected to an object, person, organization or place. According to Dobni and Zinkhan (1990) image is a perceptual phenomenon that is formed by the sum of beliefs, attitudes and impressions that a person or group has of an object. Consequently, university image can be defined as the sum of all the beliefs an individual has towards the university (Landrum et al. 1998; Arpan et al. 2003). Jenkins (1991) emphasizes the need for organizations to have a visual identity, as a way to project their self-image. Elements like its name, logo, tagline, color palette, facilities, former students, course offer, academic reputation, and university’s public behavior, are some of the elements that contribute to university image representation (Alessandri 2001). Every institution has an image, and whether planned or not a good image can offer much to an organisation’s success (Gregory 1999). A favorable corporate image is an important resource as it provide organizations with a competitive advantage by stimulating potencial publics. Sung and Yang (2008) reviewed the academic literature and found several studies showing that corporate image is important to attract potential publics, enhance buying intentions and satisfaction, develops loyalty and increase sales. Although the study of corporate image from a business perspective has long attracted the attention of researchers, according to several authors much less attention has been paid to aspects concerning the image of non-profit organizations such as universities (Arpan et al. 2003; Kazoleas et al. 2001). However, university’s image could be a critical factor influencing students’ choice process (Nguyen and LeBlanc 2001; Weissman 1990). Higher education choice process is not easy, in large because it is an important and complex decision for students, not only in economic terms, but also because is a long-term decision which affects their future life (Litten 1980; Yost and Tucker 1995). It can influence students’ future career, friendships, future place of living and personal satisfaction (Kotler and Fox 1995). Furthermore, is one kind of decision that in many cases is unique in life and often involves many costs besides monetary (Smith and Cavusgil 1984). Previous research on organizational image could be divided in four categories: 1. 2. 3. 4.
Research Research Research Research
examining the sources of organizational image; measuring the multi-dimensional image construct; addressing the way different people generate different images; assessing the implications of organizational image.
Is this research, only the first two categories will be discussed.
3 Sources of university corporate image It is not an easy task to identify all the sources of an image, especially when different people rely on different combinations of elements to build it. Even if one could identify all the sources and elements of image, still it would very difficult modelling all the relationships and interactions, mostly because image is a dynamic and complex construct, and the same institution has the power to generate distinct
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images in specific groups of audience members (Sung and Yang 2008). This happens because images are the result of choices, actions and social interactions of the involved stakeholders (Barich and Kotler 1991). To increase the complexity of the problem, some confusion has accompanied the concept of image since several studies interchangeably use the term with corporate identity and reputation (Abratt 1989). Gotsi and Wilson (2001) revised the literature and found two schools of thought. One consider corporate reputation as a synonymous for corporate image, and the other, (they name it the differentiated school of thought) regard themselves as different. On this specific issue, we agree with Barich and Kotler (1991), for whom, corporate reputation may be considered as a dimension of corporate image. For Kennedy (1977) image has two components: a functional and an emotional. The functional component comprise tangible stimulus that can be easily measured, like physical properties, while emotional component is related to psychological conditions that become perceptible in feelings and attitudes. Several studies (e.g. Dobni and zinkhan 1990; Keaveney and Hunt 1992; Stern and Krakover 1993) point out the importance of using both components to assess image. However, Bagozzi and Burnkrants (1985) argued that they should be treated separately in order to get better behavioral predictions, but it is crucial not to forget that they are interrelated and both shape the overall image (Baloglu and Brinberg 1997). Consensus on the importance of both components has not yet been achieved, so it remains important to study the effect of each component separately and together to foster research and discussion. In this way, in this study the focus was placed on the cognitive component. According to Treadwell and Harrison (1994) the pre-entry images of institutions are also the result of interaction with the institutional literature and communication materials.
4 Factors that influence university image Several authors have investigated which factors influence university image. For example Treadwell and Harrison (1994) identified: Commitment to academic excellence; Being a well-regarded business school; students friendships environment; whether graduates are proud of their education; School national image; Faculty research image, cultural contribution to community; whether students party too much; adequate facilities; problems with athletes' academic performance; homogeneity of the student population and; academic reputation and costs. For its turn Theus (1993) found that: the size of the institution; location; appearance; scope of offerings; excellence of faculty; extent of endowments; diversity of students; campus morale; athletic prowess; service to the community; institutional visibility and institutional prestige, were all sources of overall university image. Kazoleas et al. (2001) and Arpan et al. (2003) conducted the foremost significant studies on university image. The first study found seven components (e.g. overall image; program image; teaching and research emphasis; quality of education; environmental factors, financial reasons and sports programs) of university image that explained 54.75% of the total variance.
Understanding university image: a structural equation model approach
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The Arpan et al. (2003) study replicate and extend Kazoleas et al. (2001) research on the evaluation of the factors associated with university image held by different publics. Using data of all participants (students and non-students), they found that academic and athletic ratings and news coverage were significant to explain global image rating. Both studies stressed that, factors controlled by the university were stronger predictors of overall image valuations than were characteristics of respondents or factors related to the location, costs and admissions standards. Another factor that stands out distinctly was personal experiences with the university, which appear to have a greater impact on overall image than did media exposure. However, Arpan et al. (2003), contrary to Kazoleas et al. (2001) results, found a significant direct relationship between news coverage and image evaluation. The most recent study reviewed was conducted by Luque-Martínez and BarrioGarcía (2009). They reported the institution’s services to society, its teaching activity, its administrative management and the physical and technological infrastructure as the dimensions that influence the image of the University of Granada formed by teaching and research staff, in decreasing order. Specifically, the factors with the strongest influence on image formation were the institutional presence in society and on Internet, the cultural offer, the improvement in management of administrative processes such as the introduction of e-administration and the efforts to find jobs for graduates. Given the high number of factors involved in the university image formation, they were grouped according to their nature. To achieve this objective, guidelines from (Gutman and Miaoulis 2003) were followed and we came up with four categories: institutional; academic; social and personal. Table 1 presents the factors found for each category in the literature.
5 Image construct measurement Even with the high number of factors identified after several years of research, there are little evidences on what precisely compose students' image of a university (Yugo and Reeve 2007). For itself, this fact imposes increased difficulties to the task of measuring university image. Measuring image is not an easy and consensual task, mostly due to the multi-dimensional and subjective nature of the image construct, especially in service industries, because of the lack of objectivity and tangible attributes to measure (LeBlanc and Nguyen 1995). The consensus around the idea that organizational image can only be interpreted and assessed by its stakeholders was emphasized by Arpan et al. (2003) from previous studies. They point out that, independently of the signals or messages emanating from an institution, it is most likely that different stakeholders groups generate different images, as they use different criteria when evaluating organizational communication (Williams and Moffitt 1997; Arpan et al. 2003). To Leister and MacLachlan (1975) an image is an aggregative concept, resulting from the various possible combinations of image source elements, and therefore must be measured using several factors at once, in order to obtain meaningful and accurate information. Keaveney and Hunt (1992) and Stern and Krakover (1993),
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Table 1 Factors that influence university image Category/Factor
Study
Institutional Geographic location
Soutar and Turner (2002), Ivy (2001), Kazoleas et al. (2001), Gutman and Miaoulis (2003), Arpan et al. (2003)
Number of years
Soutar and Turner (2002), Palacio et al. (2002)
Size
Palacio et al. (2002), Gray et al. (2003), Arpan et al. (2003)
Facilities
Palacio et al. (2002), Belanger et al. (2002), Gray et al. (2003)
Popular vs Elitist
Palacio et al. (2002)
Cost
Palacio et al. (2002), Gray et al. (2003)
Administrative processes
Belanger et al. (2002)
International alliances and student exchange programs
Gray et al. (2003), Ivy (2001)
University success
Gray et al. (2003)
Sports programs
Kazoleas et al. (2001), Arpan et al. (2003)
Institutional communication
Arpan et al. (2003)
Academic Academic Reputation/prestige
Soutar and Turner (2002), Palacio et al. (2002), Belanger et al. (2002), Gray et al. (2003), Ivy (2001), Gutman and Miaoulis (2003), Arpan et al. (2003)
Teaching Quality
Soutar and Turner (2002), Palacio et al. (2002), Ivy (2001), Kazoleas et al. (2001)
Employment opportunities
Soutar and Turner (2002), Palacio et al. 2002 ;Gray et al. (2003), Ivy (2001)
Image of the course
Soutar and Turner (2002), Kazoleas et al. (2001)
University atmosphere
Soutar and Turner (2002), Palácio et al. (2002), Belanger et al. (2002), Gray et al. (2003), Gutman and Miaoulis (2003), Arpan et al. (2003)
Range of courses
Palacio et al. (2002), Belanger et al. (2002), Gray et al. (2003), Ivy (2001), Arpan et al. (2003)
Quality of teaching staff
Palacio et al. (2002), Ivy (2001), Gutman and Miaoulis (2003), Kazoleas et al. (2001)
Theortical/pratical
Palacio et al. (2002)
Difficulty level
Palacio et al. (2002), Belanger et al. (2002)
Student oriented
Palacio et al. (2002), Belanger et al. (2002), Ivy (2001)
Study Resources
Belanger et al. (2002), Gray et al. (2003), Price et al. (2003)
Research visibility
Ivy (2001), Kazoleas et al. (2001)
Course accreditation
Ivy (2001)
Social Family and friends opinion
(Soutar and Turner 2002)
Distance from companies
Palacio et al. (2002), Ivy (2001)
Distance from society
Palacio et al. (2002), Arpan et al. (2003)
Security
Belanger et al. (2002), Gray et al. (2003)
Social atmosphere
Gutman and Miaoulis (2003), Kazoleas et al. (2001)
Personal Personal aspirations
O’Mahony et al. (2001), Belanger et al. (2002)
Professional aspirations
O’Mahony et al. (2001)
Understanding university image: a structural equation model approach
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after having studied image in different contexts, reinforce this position, claiming that, to fully capture the richness of the image construct both the cognitive and affective components mentioned by Kennedy (1977) must be included. To Fram (1982) university image is a Gestalt (organized whole). As a result university image is often composed of ideas about faculty, the curriculum, the teaching quality and the tuition-quality relationship. There are many ways by which to measure image. Carlivati (1990) distinguish two broad classes of image measurement techniques: judgment and response methods. In generic terms, in the first, the user provides a judgment of a stimulus using semantic differential scales and in the second, the researcher use multidimensional scaling to obtain the information needed to evaluate the image. Apart from the techniques mentioned above to measure image, few studies have attempted to create and validate a scale to reliably measure university image. A review of those studies shows that their measures exhibit a lack of internal consistency or conceptual structure. They use a variety of different set of variables, ranging from brand personality to satisfaction, passing by reputation, prestige and physical environment (Arpan et al. 2003; Belanger et al. 2002; Kazoleas et al. 2001; Nguyen and LeBalanc 2001; Palacio et al. 2002; Vaughn et al. 1978). A recent discussion of this problem was made by Sung and Yang (2008). However, their paper mislead readers, since it does not deal directly with image neither provide any significant contribution on how to measure it. Rather, they propose and test a model of students suportive attitudes. Based upon the works of Kazoleas et al. (2001) and (Palacio et al. 2002), this paper used a multi-item scale to measure image and test the relationship between some of the major sources of image found in the literature. The review of the literature has provided support to the belief that there is a direct link among course image, communication, job opportunities, university social atmosphere and university overall image. However, as stated before, few studies have attempted to obtain a reliable measure for image, and ever fewer have tried to estimate the strength of the relationships between university image and image sources. This paper makes a preliminary attempt to address these issues by proposing a framework for evaluating university image and sources. The research model used is presented in Fig. 1.
6 Research methodology 6.1 Design and sample This research used a survey approach to collect data from freshman students on several images related attributes and image source constructs variables. The sample comprised 1024 first year full-time students from University of Beira Interior. Taking as frame of reference the studies of Kazoleas et al. (2001), Palacio et al. (2002) and Arpan et al. (2003) and all the contributions referenced in Table 1, a set of factors reflecting several dimensions and sources of university image were selected.
28 Fig. 1 Research model. Note: Values in parenthesis are p values indicating the statistical significance based on a based on t(199), two-tailed test
P.O. Duarte et al.
Communi cation
0.141 (0.02)
0.143 (0.02)
Courses
Image Rsq=0.947 Social life
Job Opport.
0.550 (0.00)
0.173 (0.048)
A global image indicator was not included in the analysis, because the theoretical research has shown that image is a multi-dimensional construct, so rather than use a single indicator to describe it, this study used a set of indicators identified in the literature to reflect overall image. 6.2 Questionnaire design The questionnaire consisted of four sections. The first section focused on students’ background, and included 7 items to obtain information about demographic characteristics and enrolment profile. The second section examined the information sources. This section had 5 items to quantify the relative importance of the information sources used to gain knowledge about the university. The third section addressed the geographic location evaluation. This section comprises 10 items. The fourth and last section comprises 23 statements used to assess the image construct and sources. From those, four items reflected the overall image, two items addressed the job opportunities perception, three items focused on the university communication, two items reflected the social life and lastly, four items were designed to assess course image (see Table 4 for a description of constructs and indicators). The remains items of fourth section serve other research purposes and were not used. Variables for sections 2, 3 and 4 were all measured on a 1 to 7 Likert-type scale. 6.3 Data analysis The data collected was analyzed through SPSS 17.0 for descriptive statistics. SmartPLS 2.0 M3 was used to test the theoretical model and evaluate the impact of different source factors on university’s image. The choice of Partial Least Squares path modeling technique was due to its ability to simultaneously test the quality of the measures and judge the explanatory power of the relations between the different constructs. Partial Least Squares (PLS) path modelling is a structural equation modelling technique (SEM) that can simultaneously test the measurement model (relationships between indicators or manifest variables and their corresponding constructs or latent variables), also called the outer model, and the structural model (relationships between constructs) also called the inner model. The estimation of the model was carried out in two stages, as recommended by Anderson and Gerbing (1988). In the first stage, the measurement model is and
Understanding university image: a structural equation model approach
29
evaluated in order to estimate the structural model in the second stage. To ensure that the measures used are valid and that they adequately reflect the underlying theoretical constructs is crucial to accurately estimate and test the theoretical model. The strength of the measurement model for constructs with reflective measures is assessed by looking at: individual item reliability, internal consistency and discriminant validity.
7 Results 7.1 Descriptive analysis The final sample was composed by 1024 students (597 males and 427 females), with the majority of respondents in the 18–20 years of age category. Looking at the distribution by scientific area, we noticed that the faculty of social sciences area had the highest number of respondents and the faculty of engineering the lowest. Health, arts and social sciences represent 73.2% of the respondents. It was also confirmed if that current sample distribution was similar to the population distribution by scientific area, and it was. The distribution of the respondents with regard to age and scientific area of the University is showed in Table 2. Students’ first choice is seen as an important indicator of the university attraction power, and consequently of its good or bad image. The results show that for 602 students this university was their first option and for 627 the current course was also their first choice (Table 3). Students’ information sources and their credibility have also been regarded as crucial elements to form university image. There is currently great interest in identifying students’ information sources and evaluate their importance, given the popularity of the new sources of information, namely the web social networks. The results reveal that students’ relies particularly on the internet (importance mean rating of 5.09) and family and friends opinion (with a mean rate of 4.52) to gather the information needed. In accordance with previous findings, it appears that institutional communication and advertising has only a moderate importance (mean rate of 3.5) as source of information, which may be comprehensible, as institutional communication reflects the university’s own vision. To evaluate the university major associations, a top of the mind analysis was conducted using an open-ended question with three answer options to check for spontaneous associations. Academic life ranks in first place in all three answers Table 2 Respondents distribution by gender and scientific area Scientific area
Total
Gender
Engineering
Math Sciences
Health
Arts
Social and Economics Sciences
Female
37
91
153
149
167
Male
96
50
53
94
134
427
Total
133
141
206
243
301
1024
597
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P.O. Duarte et al.
Table 3 Course option by scientific area Course Option Scientific area
1st option
Total 2nd option
Other
Social and human Sciences
195
38
65
298
Arts
140
50
53
243
Health
136
23
46
205
Math Sciences
47
23
69
139
Engineering
109
14
9
132
Missing
–
–
–
7
Total
627
148
242
1024
options, followed by facilities, university prestige and quality of teaching in the first answer option. The global evaluation shows that academic life, the quality of teaching and facilities are the most salient aspects of the image students’ have of the university. These results are consistent with those of other studies that stressed the importance of these factors (Kallio 1995; Lin 1997; Donnellan 2002; Holdswoth and Nind 2005) and suggest that, nevertheless, students use several aspects to form image, within this specific sample, those related to university social life, and the quality of teaching seems to stand out. 7.2 Image model evaluation As previously mentioned the estimation of the model should be carried out in two stages. In the first stage, the measurement model was analyzed to assess the quality of the measures used. The results are in Table 4. As we can see in Table 2, all items loadings were well above the minimum level (0.5) for acceptability (Chin 1998; Barclay et al. 1995). The significance of loadings was verified through a bootstrap procedure (200 sub-samples) for obtaining t-statistic values. All loadings were significant at 0.999 level (based on t(199), two-tailed test). The examination of the Cronbach’s alpha and the composite reliability (Fornell and Larcker 1981) reveals good internal consistency for all constructs since all have measures of internal consistency that far exceed the minimum benchmark of 0.7 proposed by (Nunnaly and Bernstein 1994) and Hair et al. (1998) (See Table ). This ensures that the occurrence of random error of measures has been minimized. In the same way, Average Variance Extracted (AVE) (Fornell and Larcker 1981) by the constructs is, in all cases, well above the minimum threshold of 0.5, meaning that 50% or more variance of the indicators is accounted for. To test for discriminant validity, the correlations matrix of latent variables, with the squares roots of AVE as diagonal elements was checked and no problem was detected. This suggests good discrimination validity.
G4_16
National academic reputation
G4_06
Graduate employment prospects
G4_08
Quality of university advertising
G4_19
Availability of sport and recreational activities
G4_02
G4_03
G4_04
Quality /up-to-date courses
Adequacy to labour market needs
Courses are valued by companies
(*) based on t(199), two-tailed test
G4_01
Range of courses
Course image
G4_18
Perception of campus social life
University Social life
G4_07
Personal knowledge of the institution
Communication
G4_05
University employment programs
Job opportunities
G4_14
G4_15
Teachers reputation
G4_13
Global teaching quality
Being a well known university
G4_12
Var
0.895
0.895
0.966
0.943
0.968
0.970
0.937
0.931
0.968
0.967
0.924
0.931
0.951
0.921
0.909
Loadings (O)
Measure model statistics
Physical facilities
Image
Latent Variables/Manifest Variables
Table 4 Indicators for measure model evaluation
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Sig. (*)
0.943
0.935
0.854
0.931
0.959
Cronbach’s Alpha
0.959
0.969
0.932
0.967
0.969
Composite Reliability (rc)
0.856
0.939
0.872
0.936
0.860
Average Variance Extracted (AVE)
Understanding university image: a structural equation model approach 31
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P.O. Duarte et al.
Having analyzed the reliability and validity of the measures, the model was estimated to assess the explanatory power of the independent constructs and the strength and significance of the path coefficients, which represent the relationships among the latent variables or constructs. In PLS, the hypotheses are tested by evaluating the path coefficients and their significance levels. Following Chin (1998), a bootstrapping (with 200 resamples) was performed to obtain estimates of t-statistic values to test the statistical significance of path coefficients. As can be seen in Fig. 1, the model shows good results. The proposed constructs explain 94.7% of the variance in overall image. Nevertheless, a note of caution has to be made concerning the fact that just one university was used and surely not all dimensions were included, thereby a more extensive study is advisable is order to substantiate the good results obtained. The analysis of the magnitude and significance of path coefficients give support to the hypotheses of the research model, by confirming the direct links among all the hypothesized image source constructs and the overall university image. To obtain measures of overall goodness of fit beyond R2 the Goodness of Fit (GoF) index (Duarte and Raposo 2009) and the Stone-Geisser cross-validation test were computed (Table 5). The GoF measure is the geometric mean of the average communality and the average R2. Its value ranges from 0 to 1, where greater values indicate better predictive ability. For our model, the GoF was 0.914, as can be seen in Table 5. The Stone-Geisser test of predictive relevance was also used as an additional assessment of model fit. According to Chin (1998) the Q2 statistic is a jackknife version of the R2 statistic, and represents a measure of how well observed values are reconstructed by the model and its parameters estimates. Models with Q2 greater than zero are considered to have predictive relevance and models with higher positive Q2 values are considered to have more predictive relevance. From Table 5 it is clear that university social life atmosphere presents the strongest effect in overall image, which is in consonance with the results of the top of the mind analysis performed. This result clearly suggests that University of Beira Interior is best known by its social life, and that factor is the main predictor of university overall image.
Table 5 Effects on image Construct
Effect on image (Path coefficient)
Sig
Q2
Communality
Job opportunities
0.173
0.048
0.935
0.936
Communication
0.143
0.023
0.872
0.872
Social life
0.550
0.000
0.857
0.939
Course image
0.141
0.021
0.726
0.856
Image
–
–
0.859
0.860 GoF
R2
0.947 0.914
Understanding university image: a structural equation model approach
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8 Conclusions Much has been investigated and written on corporate image. However, the difficulty to identify its sources, to accurately measure the effect of every factor and the difficulty to deal with receiver subjective evaluation and anticipate a stable outcome, stresses the need to extend this line of research to cope with the diversity of institutions. The objectives of this research were to review the organizational image construct, to understand the process of image building and to analyze the impact that the various sources have on the university’s image. The review of the literature indicated consensus regarding the multidimensional nature of image construct, but, at the same time, has revealed a lack of consensus on what factors contribute to image formation, their weight, and how to measure them. On the process of image formation, the studies are unanimous in agreeing that image has two components, one cognitive and affective, and that is build by the receiver from a variety of inputs. Based upon the works of Kazoleas et al. (2001), (Palacio et al. 2002) and Arpan et al. (2003), this study used a group of indicators to measure the four main dimensions identified as predictors of university image, and successfully show that they have a statistical significant effect on image. Despite the accepted importance of university image to the competitiveness of higher education institutions, the review also uncovers the limited research that has been undertaken on this subject. Surely, in this context of limited research, students’ information sources and their importance are two issues that deserve a more profound and detailed investigation. Present results point to the need to redesign the communication strategies and models used, placing more emphasis on on-line communication and public relations with families, friends and especially with current students, since they represent the university’s voice and face and are responsible, in large scale, for the institution image. It was also possible to conclude that the university overall image is connected to aspects related with the education itself, but also with issues not so central to the education service, such as academic life and facilities. Through this research, it was possible to conclude that university communications, degrees, employment opportunities and academic life have the capability to explain 94.7% of image variance, once again demonstrating the multidimensional nature of the image. However, despite all these influences proved to be significant, the academic life and employment opportunities construct are the ones that most affect the university image in this research, with path coefficients of 0.55 and 0.17, respectively. This seems to indicate that students are very much sensitive and influenced by the expectations and beliefs regarding the experiences of the academic life, as well as how easily will be to find a job after finish the course. Other important issue that deserves a detailed attention is that the communication, job opportunities and course constructs, all have almost the same influence on image. From a practical standpoint, this means that universities managers should pay attention to all factors instead of investing a great deal of resources and efforts just on one dimension.
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Finally, the main implication of this research is that universities should focus on these appeals, since it seems that the quality of education is taken for granted by students and, as such, may not be a differentiating factor.
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