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Article

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

by
Cristina Checa-Morales
1,2,3,*,
Carmen De-Pablos-Heredero
2,4,
Angela Lorena Carreño
5,
Sajid Haider
6 and
Antón García
3
1
International Doctoral School (EID), Rey Juan Carlos University, 28008 Madrid, Spain
2
Department of Business Economics (Administration, Management and Organization), Applied Economics II and Fundamentals of Economic Analysis, Rey Juan Carlos University, 28032 Madrid, Spain
3
Animal Science Department, University of Cordoba, Rabanales University Campus, 14071 Cordoba, Spain
4
Area of Business Economics, ESIC University, 28223 Madrid, Spain
5
Faculty of Administrative and Economic Sciences, Technical University of Manabí, Portoviejo 130105, Ecuador
6
Department of Management Sciences, COMSATS University Islamabad, Vehari Campus, Vehari 61100, Pakistan
*
Author to whom correspondence should be addressed.
Educ. Sci. 2021, 11(8), 445; https://doi.org/10.3390/educsci11080445
Submission received: 22 July 2021 / Revised: 16 August 2021 / Accepted: 18 August 2021 / Published: 20 August 2021
(This article belongs to the Special Issue Assessment and Evaluation in Higher Education)
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Abstract

:
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.

1. 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 coordination as shared understanding of work, and the context in which the activity is carried out has been defined as a relational process. According to Fu et al. [8], coordination is an important management strategy which helps organizations to improve efficiency and effectiveness.
Gittell [9] defined relational coordination (RC) as a mutually reinforcing process of communicating and relating for the purpose of task integration. RC is a mechanism based on human factors. Even though excellence is measured by results, it is generated in intangible, personal and human processes [10]. The RC model could become a helpful tool to measure and encourage effective coordination. RC is based on human relationships among emotional beings [11]. For this reason, it attempts to group together all the connections between them, not merely as tasks, tools or technical needs, but rather in real ways to make it possible for people to work efficiently. RC has been built around work coordination, by considering all aspects of team’s relationships. It recognizes, therefore, the need for the relational side of coordination to achieve organizational effectiveness [12,13].
Gittell et al. [14] structured the RC model around two dimensions: communication and relationships. The communication dimensions are: (i) frequent communication helps to establish relationships via roles through the proximity generated because of repeated interaction; (ii) timely communication, communication provided on time; (iii) accurate communication, in the context of relevant information, this plays a critical role in the effectiveness of a group’s tasks performance; (iv) problem-solving communication, referred to effective coordination to solve problems. The relationship dimensions are: (i) shared goals: these play a key role in the coordination of highly interdependent tasks; (ii) shared knowledge: communication among those involved in the various tasks that constitute a process is not always effective because of different social backgrounds, training and experience; (iii) mutual respect, that generates an effective coordination, because participant’s profiles in the same process value the contribution of others and consider the impact of their own actions in others too.
Understanding the RC factor relationships helped to know how resources can be organized best in order to maximize an institution’s performance [10,11]. Existing research showed that RC was positively linked to organizational performance in several sectors. Gittell et al. [11] applied it to different medical units inside hospitals and observed that units with higher levels of RC produced best performance. Havens et al. [15] explained higher levels of job satisfaction, work engagement and decrease of burnout from the RC perspective. Haider et al. [13] applied the RC to the banking industry to explain the relationship between high performance work systems and job satisfaction. Gallego et al. [16,17] and Margalina et al. [7] applied the RC model to explain best results in online systems in higher education. The model was also applied to face-to-face learning. At Quevedo State Technical University (Ecuador), a typology of organizational models was built [18]. In addition, the level of quality in education of Agricultural Polytechnic of Manabi “MFL” and Quevedo State Technical University [19] was estimated. Furthermore, Checa et al. [20] located RC factors oriented towards sustainability in higher education.
Quality is an important performance indicator for education, and it is one of the main issues examined by modern scholars and practitioners from the international education market [21]. The main problem lies in the subjectivity of the concept of quality, which makes it difficult to measure [22]. According to Gallego et al. [16], an indicator to measure the quality was the degree of students’ satisfaction. Satisfaction showed a customer orientation, linking what it is expected from one student with the obtained result [23]. Student’s perceived satisfaction showed the efficiency of organizations at different areas of activity: Academic services, administrative services, teaching staff, training programs, etc. [24]. However, there is still a lack of empirical research that examines the relationship between RC and students’ satisfaction to solve some questions such us: How deep is that relationship? Does an improvement in RC increase student satisfaction? Furthermore, in the current literature, the findings are based on cases that can hardly be extended to other universities. Addi-Raccah and Gavish [25], Lee and Yu [26] and Noël et al. [27] identified 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].

2. Materials and Methods

2.1. 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 content. 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.

2.2. 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.

3. 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.
The three universities reached relational coordination values close to the average (69.87 ± 0.78; CV = 0.19). Regarding satisfaction, UCO obtained the lowest level (18.25 ± 0.44; CV = 0.24) and ARCADA (19.44 ± 0.53; CV = 0.27), the highest one. The dispersion coefficient was low in the three universities (data not presented).

3.1. 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.

3.2. 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 Figure 2 and Figure 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.

4. 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 (Figure 2 and Figure 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.

5. 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.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/educsci11080445/s1, Figure S1: Statistical parameters of the student satisfaction value, Table S1: Relational coordination survey.

Author Contributions

Conceptualization and methodology, all authors. Formal analysis, software, data curation, data processing, A.L.C.; statistical analysis, C.C.-M. and S.H.; validation and investigation, C.D.-P.-H. and A.G.; supervision, A.G., C.D.-P.-H. and C.C.-M.; project administration, C.D.-P.-H. and A.G.; data acquisition, A.L.C. and A.G. All authors have been involved in developing, writing, commenting, editing and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

This is not applicable as the data are not in any data repository of public access, however if editorial committee needs access, we will happily provide them, please use this email: [email protected].

Acknowledgments

We want to thank the RED-RC, the open research and innovation network for the improvement in higher education among Quevedo State Technical University (Ecuador), University of Cordoba (Spain) and Agricultural Polytechnic of Manabi “MFL” (Ecuador). The authors are thankful for the funding provided to the research project by the competitive scientific and technological research funds of Agricultural Polytechnic of Manabi “MFL” (Ecuador).

Conflicts of Interest

The authors declare no conflict of interest.

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Education 11 00445 g001 550
Figure 1. Interactions between university and level of satisfaction.
Figure 1. Interactions between university and level of satisfaction.
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Education 11 00445 g002 550
Figure 2. Cluster from Mahalanobis distances for three universities.
Figure 2. Cluster from Mahalanobis distances for three universities.
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Education 11 00445 g003 550
Figure 3. Plot of the individual observation discriminant scores obtained with the canonical discriminant function for three universities.
Figure 3. Plot of the individual observation discriminant scores obtained with the canonical discriminant function for three universities.
Education 11 00445 g003
Table
Table 1. Socioeconomic contexts (pre-COVID 19).
Table 1. Socioeconomic contexts (pre-COVID 19).
Characteristics of the CountryPISA Report [32,33]/PISA-D Report [34]Ranking 3
CountryPopulationGDP 1Public univ.Non-public univ.Political
System		Mathematics and science average (pts.)High performance students (%)Socioeconomic impact (%) 2Student performance trendStaff and resources qualityQualified teaching by type of centre (%)Bullying cases (%)Lifestyle satisfaction (%)Student growth mindset (%)
Finland5,515,52548,2801322Parliamentary republicMathem. 520
Science 552		1211DecreaseRemainFavoured: 94
Disadvantaged: 91		187867ARCADA = 4589
Spain46,797,75429,3506456Parliamentary monarchyMathem. 481
Science 483		410StableShortageFavoured: 94
Disadvantaged: 98		177462UCO = 679
Ecuador17,084,35960903031Presidential constitutional republicMathem. 377
Science 399		1.425DecreaseShortageNo dataNo data87No dataESPAM = 15,330
1 Gross Domestic Product: $/person/year. 2 Status students over performance. 3 Ranking web universities [35].
Table
Table 2. Relational coordination and satisfaction variables.
Table 2. Relational coordination and satisfaction variables.
Dimensionα CronbachCodeQuestion/Variable
COMMUNICATION0.703ACCURATE COMMUNICATION: Do the people who belong to these areas have the need to offer you information at certain times?
1. ACCUAdminAccurate communication with administrative officers
2. ACCULectAccurate communication with lecturers
3. ACCUClassAccurate communication with classmates
FREQUENT COMMUNICATION: Do people who belong to the following work areas communicate with you frequently?
4. FREQAdminFrequent communication with administrative officers
5. FREQLectFrequent communication with lecturers
6. FREQClassFrequent communication with classmates
SOLVING PROBLEM COMMUNICATION: When any type of problem appears (study, logistics, documentation…), how much did the following profiles help you to solve your problem?
7. SOLPROMyselfProblem-solving communication with myself
8. SOLPROLectProblem-solving communication with lecturers
9. SOLPRORepresProblem-solving communication with students’ representatives
10. SOLPROAdminProblem-solving communication with administrative officers
11. SOLPROClassProblem-solving communication with classmates
RELATIONSHIP0.831SHARED KNOWLEDGE: How well do the following profiles know about your role in the university and the problems that arise?
12. SKNOWLectShared knowledge with lecturers
13. SKNOWRepresShared knowledge with students’ representatives
14. SKNOWAdminShared knowledge with administrative officers
15. SKNOWClassShared knowledge with classmates
MUTUAL RESPECT: How much do the following profiles respect your role at the university?
16. RESPELectMutual respect with lectures
17. RESPERepresMutual respect with students’ representatives
18. RESPEAdminMutual respect with administrative officers
19. RESPEClassMutual respect with classmates
SHARED GOALS: How well do the following profiles share your goals at the university?
20. SHARGOALLectShared goals with lecturers
21. SHARGOALRepresShared goals with students’ representatives
22. SHARGOALAdminShared goals with administrative officers
23. SHARGOALClassShared goals with classmates
SATISFACTION0.793STUDENT SATISFACTION: Indicate your degree of satisfaction with the following profiles.
24. SATISLectSatisfaction with lectures
25. SATISRepresentSatisfaction with students’ representatives
26. SATISAdminSatisfaction with administrative officers
27. SATISMaterialsSatisfaction with materials
28. SATISCommunicSatisfaction with communication channels
29. SATISContentsSatisfaction with training contents
Table
Table 3. Sociodemographic distribution of the sample (%).
Table 3. Sociodemographic distribution of the sample (%).
AgeGenderField of Knowledge
<25≥25p-ValueMaleFemalep-ValueSocial sciencesEngineeringHealth sciencesp-Value
ARCADA8614ns5446ns100--***
UCO8812ns3070***-1090***
ESPAM8317ns5644*157213*
TOTAL8614ns4654***382735***
* p-value < 0.05; ** p-value < 0.01; *** p-value < 0.001; ns = not significantly different.
Table
Table 4. Relational coordination by university and satisfaction level (Mean ± SE).
Table 4. Relational coordination by university and satisfaction level (Mean ± SE).
VariableUniversity (A)Satisfaction Level (B) p-Value
ARCADAUCOESPAMLowHighUniversity (A)Satisfaction level (B)Interactions (A × B)
1. ACCUAdmin2.75 a ± 0.102.60 a ± 0.103.38 b ± 0.102.67 ± 0.093.15 ± 0.08******ns
2. ACCULect3.57 a ± 0.093.77 ab ± 0.093.95 b ± 0.093.60 ± 0.073.92 ± 0.07******
3. ACCUClass3.67 a ± 0.103.76 a ± 0.103.84 a ± 0.103.67 ± 0.083.84 ± 0.08nsnsns
4. FREQAdmin2.54 b ± 0.091.99 a ± 0.092.81 c ± 0.102.16 ± 0.082.74 ± 0.07********
5. FREQLect3.44 a ± 0.093.54 a ± 0.093.83 b ± 0.093.36 ± 0.083.84 ± 0.07*****ns
6. FREQClass4.08 ab ± 0.094.30 b ± 0.094.00 a ± 0.094.08 ± 0.084.17 ± 0.07nsnsns
7. SOLPROMyself4.20 ab ± 0.084.43 b ± 0.084.18 a ± 0.084.29 ± 0.074.25 ± 0.07nsnsns
8. SOLPROLect3.04 a ± 0.083.04 a ± 0.083.11 a ± 0.082.87 ± 0.073.26 ± 0.07ns***ns
9. SOLPRORepres2.13 a ± 0.112.51 b ± 0.112.47 b ± 0.112.18 ± 0.092.56 ± 0.09***ns
10. SOLPROAdmin2.50 b ± 0.092.05 a ± 0.092.46 b ± 0.092.09 ± 0.082.59 ± 0.07******ns
11. SOLPROClass3.43 b ± 0.103.78 c ± 0.102.95 a ± 0.103.37 ± 0.083.40 ± 0.08***nsns
12. SKNOWLect3.04 ab ± 0.112.85 a ± 0.103.16 b ± 0.102.81 ± 0.093.23 ±0.08ns**ns
13. SKNOWRepres2.36 a ±0.122.21 a ± 0.112.51 a ± 0.112.23 ± 0.102.48 ± 0.09nsns**
14. SKNOWAdmin2.40 b ± 0.101.73 a ± 0.102.47 b ± 0.101.96 ± 0.092.44 ± 0.08*********
15. SKNOWClass3.35 a ± 0.114.02 b ± 0.103.21 a ± 0.103.47 ± 0.093.59 ± 0.08***nsns
16. RESPELect3.30 ± 0.113.62 ± 0.103.58 ± 0.103.27 ± 0.093.73 ± 0.08ns*****
17. RESPERepres2.52 a ± 0.122.96 b ± 0.123.06 b ± 0.122.60 ± 0.103.09 ± 0.09*******
18. RESPEAdmin2.65 b ± 0.122.33 a ±0.113.00 c ± 0.112.33 ± 0.102.99 ± 0.09********
19. RESPEClass3.42 a ± 0.104.15 b ± 0.093.53 a ± 0.093.62 ± 0.083.78 ± 0.07***nsns
20. SHARGOALLect2.84 a ± 0.103.09 a ± 0.103.43 b ± 0.102.83 ± 0.083.41 ± 0.08******ns
21. SHARGOALRepres2.46 a ± 0.122.65 ab ± 0.122.85 b ± 0.122.33 ± 0.102.97 ± 0.09ns****
22. SHARGOALAdmin2.55 b ± 0.101.77 a ± 0.102.85 c ± 0.102.09 ± 0.082.68 ± 0.08*******
23. SHARGOALClass3.46 a ± 0.104.03 b ± 0.103.46 a ± 0.103.59 ± 0.083.71 ± 0.08***ns**
* p-value < 0.05; ** p-value < 0.01; *** p-value < 0.001; ns = not significantly different. a, b Within row, averages with different superscript differ significantly.
Table
Table 5. Discriminant function for the organizational variables of three universities (ARCADA, UCO and ESPAM).
Table 5. Discriminant function for the organizational variables of three universities (ARCADA, UCO and ESPAM).
VariableWilks’PartialF-Removep-ValueToler1-Toler
2. ACCULect0.4910.9743.088*0.6030.397
3. ACCUClass0.4940.9674.029*0.6810.319
11. SOLPROClass0.5160.9279.279***0.7370.263
15. SKNOWClass0.4930.9713.568*0.5490.451
19. RESPEClass0.4940.9673.960*0.6430.357
20. SHARGOALLect0.5180.9249.726***0.5580.442
21. SHARGOALRepres0.4910.9733.290*0.4570.543
22. SHARGOALAdmin0.5500.86917.672***0.4800.520
* p-value < 0.05; *** p-value < 0.001.
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Checa-Morales, C.; De-Pablos-Heredero, C.; Carreño, A.L.; Haider, S.; García, A. Organizational Differences among Universities in Three Socioeconomic Contexts: Finland, Spain and Ecuador. Relational Coordination Approach. Educ. Sci. 2021, 11, 445. https://doi.org/10.3390/educsci11080445

AMA Style

Checa-Morales C, De-Pablos-Heredero C, Carreño AL, Haider S, García A. Organizational Differences among Universities in Three Socioeconomic Contexts: Finland, Spain and Ecuador. Relational Coordination Approach. Education Sciences. 2021; 11(8):445. https://doi.org/10.3390/educsci11080445

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Checa-Morales, Cristina, Carmen De-Pablos-Heredero, Angela Lorena Carreño, Sajid Haider, and Antón García. 2021. "Organizational Differences among Universities in Three Socioeconomic Contexts: Finland, Spain and Ecuador. Relational Coordination Approach" Education Sciences 11, no. 8: 445. https://doi.org/10.3390/educsci11080445

APA Style

Checa-Morales, C., De-Pablos-Heredero, C., Carreño, A. L., Haider, S., & García, A. (2021). Organizational Differences among Universities in Three Socioeconomic Contexts: Finland, Spain and Ecuador. Relational Coordination Approach. Education Sciences, 11(8), 445. https://doi.org/10.3390/educsci11080445

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