Organizational Differences among Universities in Three Socioeconomic Contexts: Finland, Spain and Ecuador. Relational Coordination Approach

: The knowledge of local culture is essential to establish competitive strategies in higher education. The objective of this research was to identify the organizational differences among three universities with different international contexts and satisfaction level. An approach was made regarding Relational Coordination (RC) attributes: accurate, frequent and problem-solving communication, shared knowledge, mutual respect and shared goals, by discriminant analysis method. A random sample of 300 students, 100 belonging to each university, was surveyed on the 23 RC variables in 2017–2018. First, the RC variables were evaluated by general linear model (GLM). The three universities—Arcada University of Applied Science (ARCADA) in Finland, University of Cordoba (UCO) in Spain and Agricultural Polytechnic of Manabi “MFL” (ESPAM) in Ecuador—and the two levels of student satisfaction—Low and High—were used as ﬁxed factors. Second, a discriminant model was built with RC variables. A higher level of RC practices concerning to accurate, frequent and problem-solving communication achieved higher levels of satisfaction, regardless of the universities’ socioeconomic context. RC differentiation among three universities showed that shared goals with lecturers and administrative ofﬁcers and problem-solving communication among classmates were the variables with the highest discriminant power. Two clusters were obtained, where UCO was the most differentiated university. In conclusion, organizational practices made a difference among the three universities. Discriminant analysis can be adapted and extended to different universities to improve quality.


Introduction
As a key to value creation in modern societies, improvement in higher education has received considerable attention from policy makers [1,2]. The way Higher Education Institutions have put into practice organizational learning is considered a key element [3]. Organizational learning is interpersonal and relational, and it has often involved learning to coordinate work in new ways [4]. Coordination has been explained by organization design and contingency theorists such a Kundu et al. [5], as an information-processing problem. Some authors such a Faraj and Sproull [6] and Margalina et al. [7] have perceived organizational differences through discriminant analysis. A comparison among universities allows identifying the key organizational factors that differentiate them. This can enable the design of strategic measures oriented to improve the quality of performances.
We pose the following research questions: (RQ1) Do universities located in developed countries showed a higher level of RC? (RQ2) Do the most satisfied students have a higher level of RC? (RQ3) Is it possible to build an organizational model that differentiates the three institutions?
Therefore, the objective of this study was to identify the organizational differences among three universities with different socioeconomic contexts and two levels of student satisfaction, from the RC perspective. The cases of Arcada University of Applied Science (ARCADA) in Finland, University of Cordoba (UCO) in Spain and Agricultural Polytechnic of Manabi "MFL" (ESPAM) in Ecuador with different satisfaction levels and socioeconomic contexts were selected. The analysis was developed in two stages. In the first stage, considering the 23 organizational variables proposed, those variables with significant differences among the three universities and between the two levels of satisfaction by general linear model (GLM) were identified. In the second stage, the organizational differences among the three universities were explained by discriminant analysis.

Socioeconomic Contexts
The knowledge of local culture and the socioeconomic situation are essential to establish competitive educational leadership and management strategies [28]. In this research, three universities were selected as representative instruments of three international socioeconomic contexts with different organizational structures. ARCADA represented Finland, which has a high Gross Domestic Product (GDP) ( Table 1). This university is in the position 18 out of 35 in the ranking of Finnish Universities [29]. UCO represented Spain, which has a medium GDP (Table 1). UCO is a non-private university and appears in position 53 out of 120 in the ranking of Spanish universities [30]. Ecuador presented the lowest GDP (Table 1) and was represented by ESPAM. This is university is in the position 41 out of 61 universities in Ecuador [31]. In Table 1, the main differences between the three socioeconomic contexts and the positions in the higher education rankings of the three universities were shown.
The three countries were compared using the Program for International Student Assessment (PISA) report. This study carried out by the countries belonging to the Organization for Economic Cooperation and Development (OECD), measures the academic performance of students according to subjects such as mathematics, science and reading. Finland and Spain showed their data in the regular PISA report [32,33], while Ecuador presented their data in the "PISA for development" or PISA-D report [34], an OECD initiative for low-and middle-income countries. Ranking Web of Universities showed the position of each university worldwide [35]. Arcada University of Applied Science (ARCADA) is a private university located in Finland, in Northern Europe. It is composed of 2443 students, 165 employees, 4 educational departments, 17 grade programs and 10 Master programs. U-Multirank ranking evaluates five dimensions of higher education centers (teaching and learning, research, knowledge transfer, international orientation and regional engagement). ARCADA overall profile showed higher performance on several indicators, with "A" scores (very good) overall. According to U-Multirank [36], ARCADA was strongest in Regional Engagement. ARCADA was in position 4589 out of 30,585 in the Ranking Web of Universities [35]. Within the three world rankings that classify the top 1000 universities, University of Cordoba (UCO) was ranked at position 800 in The Higher Education World Universities Ranking [37], position 700-800 in the Shanghai Ranking [38] and position 101-150 in QS World Universities Ranking [39]. It presented a medium size dimension with 21,000 students, 1200 lecturers, 700 workers, 47 undergraduate studies and more than 50 postgraduate studies. Finally, it was classified in the position 686 out of 30,585 in the Ranking Web of Universities [35]. Moreover, Agricultural Polytechnic of Manabi, ESPAM, is a public institution located in Ecuador. It was graded with "C" category by the Council of Evaluation, Accreditation and Quality Assurance of Upper Education (CEAACES) [40]. This is a ranking applicable to Ecuadorian universities exclusively. This classification is distributed in a decreasing way from category "A" to "D". In this case, 2811 students and 176 employees compose ESPAM, and it offers 8 grade programs. In addition, it was ranked 15,330 out of 30,585 in the Ranking Web of Universities [35].

Data Collection and Survey
A stratified random sample composed of 300 surveyed students, 100 from each university, was collected during the period 2017-2018. The initial data started from a database for each university, made up of 200-1000 data each one. Incomplete surveys and those that showed logical inconsistencies were deleted. Finally, a group of 100 surveys from each university was randomly selected with the random function of the spreadsheet software, making up the complete database with 300 surveys. The survey included 33 items: 4 socioeconomic (age, gender, character public/private, size), 6 of students' perceived satisfaction and 23 related to RC. The survey's reliability was verified by means of Cronbach's alpha, with values greater than 0.7, acceptable to confirm internal consistency: communication dimension (0.703), relationship dimension (0.831) and satisfaction (0.793) ( Table 2). The complete survey showed a Cronbach's alpha of 0.87 [20,41].
The 23 items of the RC model focused on the mechanisms involved in organizational practices are shown in Table 2. 11 variables of the communication dimension, 12 of relationship dimension and 6 related to the level of student satisfaction were used. The students answered each question of the survey (Table S1) as many times as profiles were observed at the university. Then, each relational coordination variable was disaggregated into the following profiles: lecturers, administrative officers, classmates, student representatives and me (myself), as a control variable. A Likert scale metric was used, from 1 (infrequent) to 5 (very frequent). In this case, the intervals between the points on the scale corresponded to empirical observations in the metric sense [42]. A visual analog scale was displayed on each survey question presented to the students. The proposed indicator of satisfaction was based on the student's satisfaction level [7,16,43]. This indicator was obtained from variables 24-29, related to profiles of conferences, student representatives, administrative officers, materials, communication channels, training con-tent. The descriptive statistics of trend, dispersion and position of the satisfaction variable were calculated ( Figure S1). In each university the median ranges between 18-20 points and for the total sample of 19 points. Therefore, two levels were determined: 19 points was used as border: less than 19, "Low satisfaction" and more than 19, "High satisfaction" [19,20]. Later, satisfaction level was understood as fixed or independent variable.

Statistical Analysis
The normality of the data distribution was evaluated using the Kolmogorov-Smirnov test (with the Lilliefords correction) and a Levene test was used to evaluate the homogeneity of variance. For those variables that did not show a normal distribution, the Bartlett test was applied to assess if the data had equal variances.
In the first stage, to answer RQ1 and RQ2, the RC variables influenced by the university (socioeconomic context) and the level of satisfaction were identified. 23 variables of RC were compared using the general linear model (GLM). The three universities (ARCADA, ESPAM and UCO) and two satisfaction levels (Low and High) were used as fixed factors. The interactions between both factors were also considered [43]. Three levels: * p-value < 0.05; ** p-value < 0.01 and *** p-value < 0.001 were considered. The test allowed determining which pairs of means differed significantly and to study data whose error did not conform to the normal distribution and non-constant variances. The test allowed determining which pairs of means differed significantly and to study data whose error did not conform to the normal distribution and non-constant variances [43].
Secondly, an organizational model was built using a canonical discriminant analysis to answer to RQ3. This analysis allows studying the concrete relationships that exist among discriminated groups (universities) and their degree of association [44]. The coefficients of the discriminant model show the relative contribution of the variables to the model. The higher the value of the F-remove coefficient, the greater the contribution to group discrimination [45,46]. Therefore, variables with a p-value < 0.05 were accepted and a model with highest percentage of correctly classified cases was selected. The most discriminant variables were calculated applying the F of Snedecor, Wilks' Lambda and the 1-Tolerance. High values of F for each variable indicated that the means of each group were widely separated. Small Lambda values showed that the variable discriminates well among groups. Variables with a high percentage of tolerance (1-Toler) were selected [26]. Statistical analyses were performed using the STATISTICA 12.

Results
The three universities showed an average age of students less than 25 years in 86% of the sample (p-value < 0.05). Regarding gender, the distribution was uniform in ARCADA. However, in UCO most of the students were women (p-value < 0.001) and in ESPAM most of the students were men (p-value < 0.05). Regarding the field of knowledge, significant differences were found among the three universities. In ARCADA 100% of the data corresponded to the Social Sciences (p-value < 0.001), in UCO the Health Sciences predominated (90%; p-value < 0.001) and in ESPAM the Engineering Areas obtained the highest percentage (72%; p-value < 0.05). The sociodemographic indicators of the sample are shown in Table 3.

Identification of Organizational Differences
GLM results are shown in Table 4. Significant differences were found in most of the variables of RC by university and satisfaction (p-value < 0.05). 82.61% of the RC variables showed significant differences by university. The highest RC values were observed at ESPAM and UCO, while ARCADA showed lower values. ARCADA showed significant differences in the variables related to solving problem communication and shared knowledge with the administrative officers. In UCO significant differences were found, highlighting the frequent communication, shared knowledge and mutual respect related to classmates. Lastly, ESPAM showed significant differences in accurate and frequent communication variables, and shared knowledge and goals were the variables that stood out, relating to lectures. Significant differences by level of student satisfaction were found in 65.21% of the organizational variables. 26.09% of the variables showed differences according to both criteria ( Table 4). The non-significant variables were those related to the classmates and representatives of the students in the two dimensions of RC. The interactions between university and satisfaction were found in six RC variables. Most of the variables were related to the profile of administrative officers. The interactions found between both factors in these six significant variables are shown in Figure 1.
Educ. Sci. 2021, 11, x FOR PEER REVIEW 9 of 15 university and satisfaction were found in six RC variables. Most of the variables were related to the profile of administrative officers. The interactions found between both factors in these six significant variables are shown in Figure 1.

Discriminant Model Building
Discriminant analysis model among universities was carried out. As predictors, 23 RC variables were used. The eight significant variables, which showed a p-value < 0.05, were selected for the construction of the discriminant model: Three related to the communication and five to the relationship dimension (Table 5). Accurate communication with administrative officers and lecturers, shared knowledge and mutual respect with classmates; and shared objectives with the representatives of the students belonged to the discriminant model. Additionally, shared goals with lectures and administrative officers, and the communication for solving problems among classmates were the variables with the three highest discriminant powers, showing a higher F-remove coefficient.

Discriminant Model Building
Discriminant analysis model among universities was carried out. As predictors, 23 RC variables were used. The eight significant variables, which showed a p-value < 0.05, were selected for the construction of the discriminant model: Three related to the communication and five to the relationship dimension (Table 5). Accurate communication with administrative officers and lecturers, shared knowledge and mutual respect with classmates; and shared objectives with the representatives of the students belonged to the discriminant model. Additionally, shared goals with lectures and administrative officers, and the communication for solving problems among classmates were the variables with the three highest discriminant powers, showing a higher F-remove coefficient. The classification matrix offered a correct ascription percentage of 69.32, obtaining assignment errors only in ESPAM (data not presented). The organizational differences of the three analyzed universities are shown in Figures 2 and 3. In the first one, in which the Mahalanobis distances obtained from the relational coordination indicators were graphically represented, a first cluster grouped ARCADA and ESPAM University and second cluster made up UCO. The students from UCO showed greater separation, and, therefore, greater relational coordination differentiation, due to its lower RC rating. The existence of different relational models for each university were observed in Figure 3, which showed a spatial distribution of each university with overlap of some individuals of ARCADA and ESPAM, but strong distance from UCO showed a clear differentiation. The classification matrix offered a correct ascription percentage of 69.32, obtaining assignment errors only in ESPAM (data not presented). The organizational differences of the three analyzed universities are shown in Figures 2 and 3. In the first one, in which the Mahalanobis distances obtained from the relational coordination indicators were graphically represented, a first cluster grouped ARCADA and ESPAM University and second cluster made up UCO. The students from UCO showed greater separation, and, therefore, greater relational coordination differentiation, due to its lower RC rating. The existence of different relational models for each university were observed in Figure 3, which showed a spatial distribution of each university with overlap of some individuals of ARCADA and ESPAM, but strong distance from UCO showed a clear differentiation.

Discussion
The relational coordination framework provides an excellent basis for investigating the types of organizational models at universities [7,16,17,47]. According to [47], higher levels of relational coordination improve results. RC model can be useful to achieve excellent results in higher education where high levels of task interdependence, uncertainty, time restrictions and tacit knowledge are required [7]. In the case of higher education, it is important to identify best organizational practices to apply at universities, as well as the differences among universities, which contribute to the global knowledge of the importance of RC on the results of the organization [16,20]. The methodology developed in this research has allowed, in a first step, identifying the relational coordination variables that promote differences among universities and satisfaction levels. In a second stage, according to Addi-Raccah and Gavish [25], Lee and Yu [26] and Noël et al. [27], a canonical discriminant function for the ARCADA, UCO and ESPAM universities, in three countries and very different socioeconomics contexts has been built.
RQ1 was not validated in this study. According to De-Pablos-Heredero et al. [19], an improvement in organizational practices leads to an improvement in results regardless of the socioeconomic context. RQ2 was validated, finding a positive relationship between RC and student satisfaction level. In the three universities there is a positive effect between RC and satisfaction. This link is more prominent in the case of ESPAM (Figure 1). In ESPAM, with high levels of RC, the highest values of satisfaction have been obtained. In ESPAM, which is a small size public university in a developing country with low economic growth, the level of satisfaction is very sensitive to the modifications in RC in the administrative officers profile [7]. According to the Pisa-D report [34] Ecuador requires an improvement in digital literacy, so there is a greater dependence on administrative officers [7]. Therefore, the different social contexts could explain part of the differences in organizational patterns [28].
Accurate and solving problem communication, mutual respect and shared knowledge and goals are strategic factors to improve de RC. The results obtained show

Discussion
The relational coordination framework provides an excellent basis for investigating the types of organizational models at universities [7,16,17,47]. According to [47], higher levels of relational coordination improve results. RC model can be useful to achieve excellent results in higher education where high levels of task interdependence, uncertainty, time restrictions and tacit knowledge are required [7]. In the case of higher education, it is important to identify best organizational practices to apply at universities, as well as the differences among universities, which contribute to the global knowledge of the importance of RC on the results of the organization [16,20]. The methodology developed in this research has allowed, in a first step, identifying the relational coordination variables that promote differences among universities and satisfaction levels. In a second stage, according to Addi-Raccah and Gavish [25], Lee and Yu [26] and Noël et al. [27], a canonical discriminant function for the ARCADA, UCO and ESPAM universities, in three countries and very different socioeconomics contexts has been built.
RQ1 was not validated in this study. According to De-Pablos-Heredero et al. [19], an improvement in organizational practices leads to an improvement in results regardless of the socioeconomic context. RQ2 was validated, finding a positive relationship between RC and student satisfaction level. In the three universities there is a positive effect between RC and satisfaction. This link is more prominent in the case of ESPAM (Figure 1). In ESPAM, with high levels of RC, the highest values of satisfaction have been obtained. In ESPAM, which is a small size public university in a developing country with low economic growth, the level of satisfaction is very sensitive to the modifications in RC in the administrative officers profile [7]. According to the Pisa-D report [34] Ecuador requires an improvement in digital literacy, so there is a greater dependence on administrative officers [7]. Therefore, the different social contexts could explain part of the differences in organizational patterns [28].
Accurate and solving problem communication, mutual respect and shared knowledge and goals are strategic factors to improve de RC. The results obtained show that the personalized service to the student is positively valued by considering individual circumstances. Gallego et al. [16,17] and Margalina et al. [7] proofed how in universities with high quality levels, the institutional coordination with students was stronger. Havens et al. [15] and Haider et al. [13] paid attention to the similarities between teamwork quality and RC. Lacayo-Mendoza and De-Pablos-Heredero [48] indicated that the majority of students highly value the facilities provided by educational staff. Finally, results show that other outstanding attributes are shared goals with students' representatives and with administrative officers. Gallego et al. [16,17] and Margalina et al. [7] concluded how in universities exhibiting high quality levels, the institutional coordination with students is strong.
The construction of a discriminant model verified RQ3. Knowing the variables with the greatest discriminant power, it is possible to propose concrete, simple and economic measures to improve educational quality. The results of this research allow establishing the organizational differentiation among three Universities though discriminant analysis. Shared goals, with lectures and administrative officers, and the communication for solving problems among classmates were the variables with the highest discriminant power. UCO was the most differentiated university according to RC (Figures 2 and 3). This differentiation explains the fact that it is the highest ranked university in the world ranking of universities (Table 1).
Three different universities could be discriminated by the organizational model generated. Shared goals are a key piece for university excellence [17], therefore measures that allow sharing the objectives of the students with lectures and administrative officers are crucial. In order to enhance this, improvements are proposed in digital literacy for communication with administrative officers [7] and changes in the teaching guides, where the lectures establish specific objectives for the students in each subject, are welcomed. Solving problem communication shows that the students use the educational ecosystem in moments of lack of information [16,17]. This way, the creation of direct communication mechanisms among students and other profiles is proposed to solve the problems of university life.
Apart from this, it would be of great interest to develop prediction models for each set of organizational variables over satisfaction. This issue could be developed in future research lines by applying structural equation models. This approach could be extended to different universities and contexts.

Conclusions
This research contributes to a novel approach since it allows identifying the organizational differences among three universities with different socioeconomic contexts.
In each university, as the relational coordination dimensions are improved, the level of satisfaction increases. However, an association among universities located in countries with a higher level of economic resources and a higher level of relational coordination, has not been verified. Those universities that implement a program of best practices in relational coordination will achieve higher levels of quality in terms of student satisfaction, regardless the socioeconomic context. The canonical discriminant model built according to the relational coordination dimensions showed that three organizational variables were enough to explain differences among universities. These variables were shared goals, with lectures and administrative officers, and the communication oriented to solve problems among classmates. Therefore, the discriminant analysis is useful for designing the improvement of the relational practices in each university.
The proposed model can easily be adapted and applied to different contexts and, therefore, they can be of great interest for the improvement of quality at universities. The results were validated but are conditioned in each university by its standard of satisfaction values.