










































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 2 (2024), pp. 218-244 

 

218 
 

HOW DOES THE (DE)CENTRALİZATİON OF SECONDARY 

EDUCATİON AFFECT İNDİVİDUAL İNNOVATİVENESS? EVİDENCE 

FROM AZERBAİJAN 

 

E. NURALIZADE 

 

Ehtiman Nuralizade 

Doctoral School of Public Administration Sciences, National University of Public Service, 

Budapest, Hungary. 

https://orcid.org/0009-0005-3822-3685, E-mail: nuralizade.ehtiman@stud.uni-nke.hu  

 

Abstract: This paper explores the relationship between centralization of decision-

making in secondary education and individual innovativeness in developing countries, 

specifically within the context of Azerbaijan. The research examines to what level centralized 

decision-making can hinder or promote individual innovativeness, utilizing secondary data 

and quantitative analysis. The outcomes of this study, even if a significant difference was not 

found, indicate the need for a broader lens that considers various factors affecting individual 

innovativeness. This bears significant implications for politicians, policymakers, and other 

stakeholders in Azerbaijan and other developing countries to reconsider their current form of 

decision-making structure in educational systems.   

Keywords: centralization, secondary education, individual innovativeness, Azerbaijan 

 

INTRODUCTİON 

The education system plays an important role in the overall development of a country. 

It not only shapes the future of individuals but also influences the economy and society as a 

whole (Hanushek & Woessmann, 2015). It is a known fact that education can impact an 

individual's cognitive abilities, attitudes, and overall well-being (Binder, 2013). In recent 

decades, there has been an increasing interest in the role of innovation in education. 

Innovativeness is the ability to generate and implement new ideas and is seen as a critical driver 

of economic growth and social development (Jukneviciene, 2019). 

Innovation as a desired attribute significantly triggers the development of society, and 

often challenges norms and inspires new ideas (Rogers, 2003). Yet, a crucial question arises 

when innovation encounters centralized decision-making systems. Current research delves into 

this dynamic, focusing on its effect on people's innovative capacities. Particularly, I assume 

that individuals educated under a centralized system may demonstrate lower levels of 

innovativeness than their counterparts from decentralized educational structures. To test this 

hypothesis, we need to explore how creativity, problem-solving skills, and ultimately 

innovation were influenced within certain educational contexts due to centralized decisions 

(Tarman, 2016). 

This research underscores the urgent need to scrutinize whether top-down policy 

formulations stifle creativity in contrast to bottom-up structures. Both of these frameworks are 

prevalent worldwide, and assessing their effect on innovation will yield significant insights 

https://orcid.org/0009-0005-3822-3685
mailto:nuralizade.ehtiman@stud.uni-nke.hu


Ehtiman NURALİZADE 

219 
 

(Caldwell, 2009). Many developing countries are under research on this theme due to their 

significant educational reforms (World Bank, 2010). Such reforms often involve a transition 

from centrally-administered to decentralized education administration at different levels 

(Winokur, 2014). To properly evaluate these changes, our inquiry will also touch on the 

educational background of these developing countries. This exploration involves 

understanding why certain countries prefer highly-centralized education systems while others 

adopt more decentralized models. Furthermore, this raises a relevant question: which system 

is more conducive to student success, and is there any correlation between the chosen system 

and the students' creative capacities? 

 

1. Concepts 

1.1. Individual innovativeness 

Individual innovativeness is defined as “developing, adopting, or implementing an 

innovation” (Yuan & Woodman, 2010). Rogers (2003) states that “in individual innovativeness 

theory, there is always new information within the social system and that this new information 

is processed by adopters”. Put simply, innovativeness is the ability to generate and implement 

new ideas, which is seen as a key driver of economic growth and social development. The 

question is under what circumstances can this innovation, which encompasses new ideas and 

knowledge, emerge and serve as a key driver of economic growth and social development?  

Hayek (1945) argues that knowledge is decentralized. In his essay "The Use of 

Knowledge in Society" Hayek (1945) discusses the concept of decentralization of knowledge, 

which refers to the idea that individuals in a society are better equipped to make decisions and 

solve problems than a centralized authority. This is because individuals have access to unique 

and local knowledge that a centralized authority may not have, and they can use this knowledge 

to make decisions that are better suited to their particular circumstances. 

Studies indicate that various factors contribute to individual innovativeness, including 

individual traits, cultural norms, resource availability, and institutional environment (Scott & 

Vincent-Lancrin, 2014). Current study will focus on a particularly influential aspect – the 

centralization of decision-making within educational settings which could potentially stifle or 

foster innovativeness. Analyzing the implications of centralized decision-making on 

innovativeness can shed light on how best to structure educational institutions to promote 

individual innovativeness. (Rubalcaba, 2022) 

The focus on secondary education, specifically high schools, in this case study is 

purposeful for multiple reasons. First, high school is the stage where students begin to develop 

advanced cognitive and creative skills that are instrumental to individual innovativeness. This 

period of education often includes exposure to a wider range of subjects and more complex 

problem-solving activities, fostering critical thinking and creativity. Secondly, secondary 

education is the final compulsory stage of schooling in many countries, including Azerbaijan. 

Therefore, it represents the educational experiences of most individuals in the population, 

providing a more generalized perspective on the effects of the education system. Lastly, the 

centralization of decision-making is particularly salient in secondary education where 

curriculum, teaching methodologies, and assessment strategies are often dictated by a central 

authority. Thus, high schools provide an excellent unit of analysis to study the impact of 

centralized decision-making on individual innovativeness (Hofman et al., 2013). 



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1.2. Centralization of Decision-Making in Education 

One of the well-known principles of the modern administrative system is centralization. 

Simply, centralization is the concentration of power and authority in one center.  According to 

White (1965, p.41), centralization is “the process of transfer of administrative authority from a 

lower level to a higher level of government is called centralization”.  

The concept of decision making is the major element of centralization. It is very 

important to define who has authority to make decisions in the administrative system. It is the 

process by which choices are made to change (or leave unchanged) an existing situation and to 

choose the most appropriate course of action to achieve the desired goal while minimizing risk 

and uncertainty to the extent possible. (Carvalho, 2013) 

In this modern and rapidly changing world, education has evolved beyond the mere 

imparting of knowledge on specific subjects or skills. The focus now lies on cultivating 

students' abilities and knowledge to prepare them for success in their future lives (Hendarman 

& Cantner, 2018). However, with an ever-growing population, providing high-quality 

education to every individual presents a significant challenge. A crucial issue among these 

challenges is related to the decision-making process employed in the education system 

(Caldwell, 2009). The decision-making process within the educational sector can either be 

centralized or decentralized, depending on who holds power over it. Many centralized countries 

across the globe, particularly developing countries face centralization-related obstacles 

throughout its decision-making procedures (Hawkins, 2000). Analyzing this relationship's 

dynamics provides valuable insights into countries grappling with similar dilemmas concerning 

educational matters that require critical decisions. It is essential that we delve deep into this 

matter because centralizing control over pivotal aspects of education may streamline processes 

but also stifle innovation and creativity emanating from both individual learners as well as 

educators (Caldwell, 2009). 

The standard method for making decisions in education has long been the top-down 

approach (Moe, 2003). Nevertheless, this style of decision-making has elicited criticism due to 

its negative impact on creativity and critical thinking within the educational system. Relying 

exclusively on a top-down approach can limit the progression of a well-rounded education 

system (Mok, 2004). One crucial component that is often disregarded in top-down decision-

making is student involvement. When teachers and administrators neglect the voices and 

opinions of students, they forfeit important insights into what works best for individual learners 

(Yilmaz et al., 2014). Ultimately this lack of consideration toward students stifles their creative 

expression while hindering their capacity to think critically. Moreover, when decisions are 

made from just one perspective without considering diverse viewpoints or alternative ideas, 

there remains little space for innovation or experimentation with new methods or techniques. 

As a result, this restrains not only student creativity but also teacher ingenuity as they may feel 

restricted by strict guidelines imposed by the administration. To create an effective education 

system that stimulates growth across all areas - including creativity and critical thinking - 

educators must embrace more comprehensive approaches to decision-making that value 

diversity of thought inside classrooms as well as outside them (Amalia et al., 2020). 



Ehtiman NURALİZADE 

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1.3. (De)centralization and individual innovativeness 

The form of decision-making in education has its upsides like better efficiency, 

consistency, equity of outcomes, streamlined processes and more opportunities for 

collaboration among stakeholders. However, it also carries potential drawbacks such as 

reduced flexibility to respond to local needs, loss of local autonomy, increased bureaucracy 

and an augmented chance for the misuse of power. (Nurakhir, 2021) This research scrutinizes 

the impact that centralizing decisions can have on two primary areas – improved equity of 

outcomes and streamlined procedures. However, before delving into these topics we need to 

grasp how centralization influences internal functioning; whether positively or negatively 

depending on multiple factors mentioned above. This study contends that while certain aspects 

may improve through centralization initiatives in the right decision-making structure - such as 

streamlining procedures or boosting equity - proper planning must take place beforehand if 

these improvements are going to outweigh any negative externalities caused by them over time 

such as lost local autonomy (Welsh & McGinn, 1999).  

Centralization offers significant benefits, one of them is amplified efficiency that results 

from having a distinct chain of command. (Bray, 1999) This ensures everyone knows who is 

in charge of making decisions, which simplifies communication across departments and 

mitigates the possibility of errors or misunderstandings. Another key advantage to 

centralization is consistency. All choices are made using the same set of criteria leading to an 

approach that's more standardized. Notably, collaboration becomes more productive when 

decision-making power resides in one place since it promotes teamwork among employees 

with shared objectives. Caldwell argues that centralizing decision-making may enhance 

efficiency, consistency, and collaboration" (2009). Providing clarity on roles and 

responsibilities within an organization's hierarchy through centralized-decision-making 

policies can help people see how their contributions fit into overall objectives while also 

promoting synergy between teams working towards shared goals. In sum, centralizing 

decision-making not only leads to better communication but also promotes trust among team 

stakeholders by ensuring transparency in organizational operations - ultimately resulting in 

higher-quality work output and greater satisfaction levels among stakeholders. 

Organizations may suffer significant drawbacks when decision-making is centralized. 

One downside is the loss of local autonomy which can lead to decreased creativity and 

innovation at lower levels. Central decision-making leaves lower-level employees feeling 

disconnected from their work, resulting in reduced morale and productivity (Nurakhir, 2021). 

Moreover, centralization restricts flexibility as it neglects regional differences or changes in 

circumstances that require unique solutions. This rigidity causes suboptimal outcomes when 

enforcing universal policies across varying geographical regions or situations. Ultimately, 

there's a risk of power misuse by those who possess decision-making authority; centralizing 

power offers little oversight on how decisions are executed at lower levels where actual 

implementation takes place. Given these negative consequences for organizational 

performance and dynamics, leaders must explore alternatives that balance centralized 

directives with devolved responsibilities concerning planning, budgeting, and staffing among 

others depending on specific organizational needs (Bray, 1999). 

The centralization of decision-making could lead to streamlined procedures while 

improving the equity of outcomes. This is because when decisions are made by a centralized 



HOW DOES THE (DE)CENTRALİZATİON OF SECONDARY EDUCATİON AFFECT 

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body, they're consistent across the organization, creating uniformity in rules and regulations 

for everyone. However, on the other hand, such an unvarying organizational structure could 

stifle innovation at lower levels of management. To support this assertion further, Caldwell 

(2009) defended that centralized decision-making can be essential for some functions that 

require tight control or coordination, but it also may restrict employees' discretion. Thus while 

there may be advantages to centralizing decision-making in specific situations, weighing them 

against likely downsides becomes mandatory. Let us consider an illustration where a retail 

chain decides to hoard its purchasing decisions for all stores nationwide. While this would 

result in more consistent pricing and inventory levels across locations - thereby improving 

equity of results - it could as well hinder each store's ability to cater quickly enough to local 

customer needs or preferences, which ultimately reduces overall efficiency. Therefore, 

organizations must critically evaluate potential benefits before implementing any centralized 

decision-making structure. In conclusion, evaluating the impact of centralizing decision-

making requires carefully weighing out pros against cons since rushing into such structures 

without giving due consideration might end up curtailing employee discretion even though 

doing such might be essential for achieving strict controls over corporate operations, as 

highlighted by Caldwell (2009). 

To draw to a close, the form of structure in education may offer numerous advantages 

and disadvantages. It's crucial to weigh both sides before deciding whether or not your 

organization should adopt centralization policies. Evaluate how much autonomy you're willing 

to sacrifice when considering the impact on streamlining processes and improving equity of 

outcomes. Ultimately institutions need to make sure any decisions made relating to 

centralization align with their mission and values because there are bound to be tradeoffs 

involved during this process. Careful consideration will allow institutions more effective 

methods that result in better overall effectiveness levels within their organizations. 

Literature review indicates that centralization in decision-making processes plays a 

crucial role in shaping individual innovativeness within the educational sphere, with both direct 

and indirect factors contributing to its impact. Direct factors (Table 1), such as curriculum 

development, teacher training, assessment and evaluation, may have a more immediate 

influence on the design and delivery of educational content, thereby directly affecting the 

innovative capabilities of students (Stevenson & Baker, 1991). 

 

Table 1. Centralization vs individual innovativeness 

Form of 

centralization 
Description Effect on individual innovativeness 

Direct effects 

Centralization of 

Curriculum 

Development 

A centralized curriculum means that 

decisions about what is taught are made 

by a single authority, usually the 

government or a national organization. 

This may limit teachers' and students' ability 

to adapt and innovate based on local needs or 

unique situations, as they are required to 

follow a standardized curriculum. 

Centralization of 

teacher training 

Teacher training is directed by a central 

authority, with uniform standards and 

methods applied to all teachers in the 

system. 

With standardized training methods, teachers 

may have limited exposure to diverse 

teaching styles or innovative practices, 



Ehtiman NURALİZADE 

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reducing their potential for innovation in the 

classroom. 

Centralization of 

Assessment and 

Evaluation 

A centralized authority designs and 

implements standardized assessments 

and evaluations for teachers and 

students, which may include testing, 

grading, and performance reviews. 

This may discourage individual 

innovativeness, as teachers and students 

might focus on meeting standardized criteria 

rather than exploring new and creative ways 

of learning and teaching. 

Indirect effects 

Centralization of 

resource allocation 

A centralized authority determines how 

resources, such as funding, materials, 

and personnel, are allocated to schools 

and districts. 

Centralized resource allocation may limit the 

flexibility for individual schools and teachers 

to pursue innovative projects or ideas, as they 

must rely on the centralized authority for 

resources. 

Centralization of 

policy and 

regulation 

Centralized policies and regulations are 

established by a single authority that all 

schools and districts must adhere to. 

Strict adherence to centralized policies and 

regulations may hinder innovation by 

discouraging experimentation and the 

development of localized solutions to unique 

challenges. 

Centralization of 

parents and 

community 

involvement 

A central authority oversees and directs 

the extent and nature of parents' and 

community members' involvement in 

schools. 

By limiting the autonomy of local 

communities and parents, this centralization 

can hinder the exchange of diverse ideas and 

grassroots innovation that could otherwise 

benefit schools and students. 

Source: Author’s own completion. 

 

In contrast, indirect factors (Table 1), including resource allocation, policy and 

regulation, as well as parents and community involvement, may not have an immediate bearing 

on individual innovativeness but still hold a significant effect on the overall educational 

environment, indirectly on individual innovativeness (Ragmoun & Alfalih, 2021). A full 

understanding of this process is essential to fostering a more innovative and adaptable 

educational landscape, which can, in turn, enable dynamic teaching and learning experiences 

for all involved (Model 1). 

 

Figure 1. (De)centralization and individual innovativeness 

 

Source: Author’s own creation 

   Centralization Individual 

Innovativeness 

Directly 

Indirectly 

Curriculum Development 

Teacher Training 

Assessment and Evaluation 

Resource Allocation 

Policy and Regulation 

Community Involvement 

   



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2. Literature Review 

How (de)centralization in education affects individual innovativeness 

Innovation in education systems cannot always be achieved through centralized 

decision-making. Instead, as Amalia et al. (2020) point out, this style of leadership structure 

may pose a significant obstacle to those who seek to innovate and expand their horizons within 

their respective fields. The rigidity inherent in centralized decision-making limits opportunities 

for experimentation or exploration by creative thinkers seeking fresh solutions to complex 

problems. Decentralized approaches offer a more effective framework for fostering innovation 

among educators. By empowering teachers with autonomy and agency over curriculum 

development and implementation, administrators encourage them to experiment with new 

methodologies that can enhance student outcomes while remaining true to their areas of 

expertise. In essence, educational leaders must recognize the crucial importance of cultivating 

an environment where creativity flourishes without fear of retribution from central authority 

figures. Only then will teachers and students alike have greater opportunities for learning while 

pushing our education system forward toward greater effectiveness and success as a whole. 

In today's world, education systems are trending toward decentralization. The reason 

for this shift is rooted in the belief that it can lead to better educational outcomes and promote 

innovation at an individual level. That’s why many developed countries have joined this trend 

by implementing various policies aimed at granting schools more autonomy in their decision-

making processes (Payne, 2008). Research indicates that such decentralization can indeed have 

a positive impact on innovativeness levels among individuals. A study conducted by Pollock 

(2008) found that decentralized school management positively influences teachers' innovative 

behavior. This discovery should not come as a surprise since people tend to take ownership of 

their responsibilities when given more authority over their work environment, making them 

more likely to experiment with new ideas.  

However, some experts caution against embracing this change too quickly without 

weighing its consequences fully. Critics argue that there may be downsides to education system 

decentralization, too - such as disparities between regions or schools within countries- which 

could harm students' learning outcomes overall (Fiske, 1996). Therefore, policymakers must 

be careful not to tip the scale too far in either direction when considering these changes. They 

need to balance centralized power enough so all students receive a quality education while still 

providing decentralized autonomy for schools where experimentation might be beneficial. The 

research concludes that educational decentralization promotes innovativeness amongst 

teachers and individuals alike; however, policymakers must consider all implications before 

implementing any significant changes because they can have both positive and negative effects 

on society as a whole if not done correctly (Brown & McIntyre, 1981). 

Additionally, studies such as Winokur (2014) indicate that centralization of decision-

making in education can have a positive impact on individual innovativeness if they consider 

innovativeness as an important element in the curriculum, but it is not the sole determinant.  

Other factors, such as adequate resources and support for teachers, also play a crucial role in 

fostering innovation in the education sector. If the necessary conditions are met, centralization 

of decision-making in the education system may enhance individual innovativeness among 



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students (Winokur, 2014). However, more research is needed to understand the underlying 

mechanisms and the potential limitations of this approach. 

 

3. Data and research methodology 

3.1. Research question and hypothesis 

The main aim of this study is to investigate the relationship between the centralization 

of decision-making in the education system and the level of individual innovativeness, with a 

specific focus on Azerbaijan. This research has the potential to contribute to our understanding 

of how educational systems can foster or hinder individual innovativeness. By examining the 

case of Azerbaijan, this research can provide insights into how centralized decision-making in 

education affects individual innovativeness in a specific context (Guliyev, 2016). The findings 

of this research have implications for educational policy and practice, as well as for our 

understanding of the relationship between educational systems and individual innovativeness 

more broadly. 

The main research question of the study is as follows:  

How does the centralization of decision-making in secondary education affect the level 

of individual innovativeness? 

In the study, I hypothesize that:  

H: Individuals who have received education in a system with centralized decision-

making will have lower levels of innovativeness compared to those who have received 

education in a system with more decentralized decision-making. 

Through this study, I hope to contribute to the ongoing debate on the role of education 

in fostering individual innovativeness. By providing empirical evidence on the relationship 

between the centralization of decision-making in education and individual innovativeness, this 

research can inform discussions on how educational systems can best support the development 

of innovative individuals (Goldsmith & Foxall, 2003). Ultimately, this research aims to provide 

insights that can help improve educational systems and support the development of innovative 

individuals. Briefly, this study has two main ambitions. The first is to test the proposed 

hypothesis, while the second is to generate new hypotheses that can guide subsequent 

exploration of this topic. 

 

3.2. Case selection 

The unit of analysis in the case is high schools (public and private) in terms of individual 

innovativeness. The population of the case mainly covers Azerbaijan, but also this case can be 

applied as an example for former SOVIET member countries and other developing countries 

in which similar situation are experienced. The case to explain the effectiveness of 

centralization of decision-making in education system is Azerbaijan as a developing country. 

If we consider that it has gained its independence since 1991 and had a transition period from 

the SOVIET management system to the education system, it will be useful to investigate the 

centralization of decision-making in the education system and its effectiveness in terms of 

individual innovativeness in Azerbaijan during its independence (1991-2022).  

The other factor which makes this case unique is Baku, the capital city of Azerbaijan 

remains the only capital city in the Council of Europe area with no directly elected governance 

(’’Council of Europe”, 2021). Baku is not the only city in Azerbaijan that experiences this, but 



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also other big cities of Azerbaijan remain the same. In Azerbaijan, only small areas like villages 

and settlements have municipalities and directly elected officials but they have no competences 

to intervene any issue of local education. This also demonstrates that there is no possibility for 

decision-making in education at local government level. There is a centralized education 

system in this case which makes it specific enough to be investigated.  

In terms of innovation, Azerbaijan holds the 93rd position among the 132 economies 

featured in the Global Innovation Index (GII) 2022, reflecting a need for improvement in 

fostering a culture of innovation within the nation. Interestingly, the country performs better in 

innovation inputs (79th) than innovation outputs (110th), although both rankings have seen a 

decline since 2021 and 2020. This disparity suggests that while resources and infrastructures 

for innovation are present, the country is facing challenges in translating these inputs into 

tangible results. As Azerbaijan seeks to strengthen its position in the GII, it becomes essential 

to harness individual innovativeness and inspire a national mindset that encourages creativity 

and risk-taking to boost the translation of innovation inputs into successful outputs (Table 2). 

 

Table 2. World Intellectual Property Organization, 2022 

 
 

The focus on secondary education, specifically high schools, in this case study is 

purposeful for multiple reasons. First, high school is the stage where students begin to develop 

advanced cognitive and creative skills that are instrumental to individual innovativeness. This 

period of education often includes exposure to a wider range of subjects and more complex 

problem-solving activities, fostering critical thinking and creativity. Secondly, secondary 

education is the final compulsory stage of schooling in many countries, including Azerbaijan. 

Therefore, it represents the educational experiences of most individuals in the population, 

providing a more generalized perspective on the effects of the education system. Lastly, the 

centralization of decision-making is particularly salient in secondary education where 

curriculum, teaching methodologies, and assessment strategies are often dictated by a central 

authority. Thus, high schools provide an excellent unit of analysis to study the impact of 

centralized decision-making on individual innovativeness.  

Regardless of the result, whether centralization of decision-making in the education 

system negatively affects individual innovativeness or not, it is worth investigating the case of 

high schools in Azerbaijan. Briefly, the case will help us to observe a proper example of a 

highly centralized system looking to improve its contribution to innovativeness. 

 

3.3. Data collection and variables 

This data is the result of a survey on innovativeness and entrepreneurship potential 

among high school students in Azerbaijan (Gasimov et al., 2021). It is an unpublished dataset 

that is being used as secondary data. Dataset includes high school students selected from private 

and public schools. Gasimov et al. (2021) have been cautious while choosing public schools to 



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maintain public-private balance in quality of staff and target audience. Such homogeneity is 

required to reveal actual difference due to (de)centralization.  

Individual Innovativeness Index (III) is a scale developed by Hurt, Joseph and Cook 

(1977) to evaluate how innovative people generally are.The original model of the scale contains 

20 statements (Annex 1) that describe the features of people in five different classes, from 

highly innovative to very traditional. Each statement related to individual innovativeness was 

initially scored using a 7-point Likert scale, from "Strongly Disagree" to "Strongly Agree". The 

final version of the scale uses a 5-point Likert scale. There are 12 positive (1, 2, 3, 5, 8, 9, 11, 

12, 14, 16, 18, 19) statements and 8 negative (4, 6, 7, 10, 13, 15, 17, 20) statements in the scale. 

The innovativeness score is determined by adding 42 points to the difference between the total 

positive and negative scores. The lowest possible score is 14, and the highest is 94. It is 

calculated as follows: 

1. Calculate sum of numbers for positions 4, 6, 7, 10, 13, 15, 17, and 20. 

2. Calculate sum of numbers for positions 1, 2, 3, 5, 8, 9, 11, 12, 14, 16, 18, and 19. 

3. Use the following formula to find II: 

II = 42 + total sum from stage 2 - total sum from stage 1 

Individuals can be classified according to their innovativeness on the basis of their 

scores. They can be considered as "Innovators" if their II score is above 80, "Early Adopters" 

if the II score is between 69 and 80, "Early Majority" if the II score is between 57 and 68, "Late 

Majority" if the II score is between 46 and 56, and "Laggards/Traditionalists" if the II score is 

below 46. These scores can also be used to provide an overall evaluation of a person's level of 

innovativeness. In general, individuals who score above 68 are considered as highly innovative, 

whereas those who score below 64 are considered as low in innovativeness. (Hurt, Joseph & 

Cook, 1977) 

 

Table 3. Summary of data 

Variable name Abbreviation Meaning Measure Source 

Individual 

Innovativeness (II) 

Index 

Score_All It is a scale to evaluate how innovative people 

(students) generally are. 

Continuous Own 

calculation 

II Index of public 

school students 

ScoreC It represents the II index of students from 

centralized public schools. 

Continuous Own 

calculation 

II Index of private 

school students 

ScoreD It represents the II index of students from 

decentralized private schools. 

Continuous Own 

calculation 

Type of schools 

(public or private) 

School_Type It represents the type of schools either it is 

public or private. 

Nominal Survey 

All schools (public 

and private) 

School_ALL It represents both centralized public schools 

and decentralized private schools. 

Nominal Survey 

Public schools SchoolC It represents centralized public schools. Nominal Survey 

Private schools SchoolD It represents decentralized private schools. Nominal Survey 

Gender Gender_All It represents the gender of students from both 

public and private schools. 

Nominal Survey 

Family income Income_All It represents the family income of students 

from both public and private schools. 

Ordinal Survey 

Family business Business_All It represents whether families of students have 

a business or not. 

Nominal Survey 



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3.4. Data analysis 

In the process of data analysis, quantitative methods were employed to assess the 

research outcomes.  

a)      One-way Analysis of Variance (ANOVA) 

ANOVA was utilized to assess the variations between and within different school 

groups. The dependent variables "ScoreC" and "ScoreD" denoting centralized public schools 

and decentralized private schools respectively were investigated. The results of the ANOVA 

test displayed key statistics such as the sum of squares, degrees of freedom (df), mean square, 

F-value, and significance (Sig.). The F-value, a statistic used to interpret the significance of the 

group differences, and the significance value, which shows the statistical validity of these 

differences, were particularly instrumental in the analysis. 

There are the mean differences between different pairs of schools using a Tukey HSD 

post-hoc test. This test is useful in comparing all possible pairs of means to understand 

significant differences between group means after conducting an ANOVA. These tables 

present statistics such as mean differences, standard error, significance, and 95% confidence 

intervals for each pair of schools, which were analyzed to understand the relative performance 

of the schools. 

By observing these quantitative measures, the significance level of the results, and 

comparing this with the conventional threshold of 0.05, an understanding of the statistical 

significance of the differences between the school groups was derived. This led to the 

conclusion that while most schools performed at similar levels, certain significant outliers 

existed that require more focused examination. 

b)     Multiple regression 

Multiple regression is a statistical method used to analyze the connection between one 

dependent variable and several independent variables. In this case, this method was used to 

predict an outcome (innovativeness score of students) based on various predictor variables 

(such as family business, gender, type of school, and family income). It creates a model to 

estimate how these variables collectively influence the dependent variable. The method 

includes assessing the strength of the relationship, measuring how much variability in the 

outcome can be explained by the predictors, adjusting for the number of predictors, checking 

the model's overall significance, examining the contribution of each predictor individually, 

evaluating model assumptions, and diagnosing issues such as multicollinearity (high 

correlation among predictors). It's a comprehensive process aimed at accurately forecasting the 

outcome variable using the selected predictors. 

 

4. Results 

The Republic of Azerbaijan sees education as a strategic priority and the Constitution 

guarantees the right of all citizens to education. The state plays a significant role in controlling 

the education system, setting minimum educational standards, and providing free compulsory 

secondary education. All decision-making processes for secondary education take place at the 

central level mainly by the Ministry of Education in Azerbaijan. This body is responsible for 

setting educational standards and curriculum, managing teacher training, and regulating the 

establishment and operations of schools. This centralized system allows for a unified 



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educational framework across the country but may also present challenges, such as potential 

lack of local autonomy and flexibility. An example of central decision-making is the 

government's strategy to realign the country's education system with global standards by 

improving the quality of skilled workers and providing equal opportunity in education at all 

levels. As part of the country's National Development Strategy 2020, the government’s strategy 

is to extend primary and secondary education to 12 years, making attendance obligatory for 

students up to the age of 16. The central government's decision-making and policy-making in 

secondary education highlights the centralization of the education system in Azerbaijan (Asian 

Development Bank, 2015).  

 

4.1. Descriptive statistics of variables 

In the year 2021, an online survey was conducted with the participation of 335 

respondents who were all high school students from the capital city of Baku (Gasimov et al., 

2021). Those students were studying in the 10th and 11th grades from both public and private 

schools. However, the distribution of students was somewhat skewed from these schools, with 

252 students, or 75.2% of the total, attending public schools, while the remaining 83 students, 

representing 24.8% of the total, came from private schools (See Table 4). 

 

Table 4. School_Type 

 Frequency Percent Valid Percent Cumulative Percent 

Valid public 252 75.2 75.2 75.2 

private 83 24.8 24.8 100.0 

Total 335 100.0 100.0  

Source: Author’s own completion 
 

The gender distribution of the respondents is nearly balanced, 183 of the participants, 

accounting for 54.6% of the total, are males, while females make up the remaining 152 

participants, or 45.4% of the total (See Table 5). 

 

Table 5. Gender_All 

 Frequency Percent Valid Percent Cumulative Percent 

Valid male 183 54.6 54.6 54.6 

female 152 45.4 45.4 100.0 

Total 335 100.0 100.0  

Source: Author’s own completion 

 

Table 6 provides a distribution of income levels of families of 335 students. The largest 

proportion, 36.1%, earn between 0 to 500, while 15.8% earn from 501 to 1000. The next income 

bracket, 1001 to 2000, constitutes 20.3% of the population. Those earning between 2001 to 

3000 make up 13.7% of the total, followed by 7.2% earning 3001 to 5000. The smallest group 



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is individuals earning over 5000, representing 6.9% of the population. The cumulative 

percentage column reflects the growing total proportion as each income group is sequentially 

added, reaching 100% by the final group. 

 

Table 6. Income_All 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 0-500 121 36.1 36.1 36.1 

501-1000 53 15.8 15.8 51.9 

1001-2000 68 20.3 20.3 72.2 

2001-3000 46 13.7 13.7 86.0 

3001-5000 24 7.2 7.2 93.1 

5000+ 23 6.9 6.9 100.0 

Total 335 100.0 100.0  

Source: Author’s own completion 
 

Table 7 shows the distribution of “Business_All” which represents whether students' 

families possess their own business or not. Out of a total of 335 students, 119, or 35.5% 

responded with "Yes", while 216, which is 64.5% of the respondents, answered "No". 

 

Table 7. Business_All 

 Frequency Percent Valid Percent Cumulative Percent 

Valid Yes 119 35.5 35.5 35.5 

No 216 64.5 64.5 100.0 

Total 335 100.0 100.0  

Source: Author’s own completion 

 

4.2. Comparative statistics of public or private schools 

Table 8 provides insights into the key differences between public and private schools. 

In terms of gender demographics, public schools appear to have a higher proportion of female 

students compared to private schools. It's also noteworthy that a larger percentage of students 

attending private schools come from families owning a business and those with higher income 

brackets. Additionally, both school types exhibit some differences in the II index, but they are 

relatively close in their mean, median, and standard deviation values. 

 

Table 8. Comparative statistics of public or private schools 

 Public Private 

No. of observations 252 83 

Females (%) 48.8 34.9 



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Family business (%) 25.4 66.3 

Family income (%) 

(low [0-500 AZN], high [2000+ AZN])  

 

45.6; 16.7 

 

7.2; 61.5 

II Index (mean, median, Std. dev, [min-max]) 67.57; 68; 9.001 [37-90] 65.06; 65; 8.45 [38-85] 

Source: Author’s own completion 

 

4.3. ANOVA 

The analysis (See Table 9) displays the results of the ANOVA test conducted on the 

dependent variable, ScoreC, which represents centralized public schools. The test is used to 

examine the differences between group means and their associated procedures. It is organized 

into three sections: between groups, within groups, and total. The 'Between Groups' section 

represents the variance between different groups, while the 'Within Groups' section represents 

the variance within each group. The 'Total' section represents the sum of these variances.  

H0: There is no significant difference between and within public schools. 

H1: There is a significant difference between and within public schools. 

Based on the table, the variability in the 'ScoreC' variable is not significantly explained 

by the group variable, as the p-value (.117) is greater than the commonly used significance 

level of .05. This suggests that the means across the different groups are not significantly 

different from each other. The F-value is 1.865, but due to the high p-value, the null hypothesis 

of equal group means is accepted. 

 

Table 9. Public Schools’ Group Means (ANOVA) 

ScoreC   

 Sum of Squares df Mean Square F Sig. 

Between Groups 596.089 4 149.022 1.865 .117 

Within Groups 19737.625 247 79.909   

Total 20333.714 251 
   

Source: Author’s own completion 

 

Table 10 shows the results of the Tukey HSD post-hoc test for the dependent variable 

"ScoreC", which represents centralized public schools, across five different schools (SchoolC 

1-5). It indicates that none of the mean differences between the schools' scores reached 

statistical significance, as indicated by the Significance (Sig.) values all exceeding the typical 

threshold of 0.05. For example, the mean score difference between School 1 and School 2 is -

2.295 with a Sig. of .716, showing no significant difference. This pattern is consistent across 

all school comparisons. Consequently, the conclusion from this data is that there are no 

statistically significant differences in ScoreC between the five schools based on this analysis. 

 

Table 10. Public Schools’ Multiple Comparisons  
Dependent Variable:   ScoreC   
Tukey HSD   



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(I) SchoolC (J) SchoolC 

Mean Difference 

(I-J) Std. Error Sig. 

95% Confidence Interval 

Lower Bound Upper Bound 

1 2 -2.295 1.822 .716 -7.30 2.71 

3 2.085 1.527 .650 -2.11 6.28 

4 2.089 1.518 .643 -2.08 6.26 

5 2.580 2.412 .822 -4.05 9.21 

2 1 2.295 1.822 .716 -2.71 7.30 

3 4.380 2.001 .187 -1.12 9.88 

4 4.384 1.994 .184 -1.10 9.86 

5 4.875 2.737 .387 -2.65 12.40 

3 1 -2.085 1.527 .650 -6.28 2.11 

2 -4.380 2.001 .187 -9.88 1.12 

4 .005 1.728 1.000 -4.75 4.75 

5 .495 2.550 1.000 -6.51 7.50 

4 1 -2.089 1.518 .643 -6.26 2.08 

2 -4.384 1.994 .184 -9.86 1.10 

3 -.005 1.728 1.000 -4.75 4.75 

5 .491 2.544 1.000 -6.50 7.48 

5 1 -2.580 2.412 .822 -9.21 4.05 

2 -4.875 2.737 .387 -12.40 2.65 

3 -.495 2.550 1.000 -7.50 6.51 

4 -.491 2.544 1.000 -7.48 6.50 

Source: Author’s own completion 
 

Table 11 presents the results of an ANOVA (analysis of variance) test conducted on the 

dependent variable, ScoreD which represents decentralized private schools.  

H0: There is no significant difference between and within private schools. 

H1: There is a significant difference between and within private schools. 

The test shows the difference between the groups is statistically significant. The sum 

of squares between groups is 1159.478, resulting in a mean square of 165.640, while the within 

groups sum of squares is 4695.221, with a mean square of 62.603. The F statistic is 2.646, and 

the significance level (p-value) is .017. Because the p-value is less than .05, the null hypothesis 

is rejected, meaning the differences between the groups are not due to chance, and there are 

significant differences in scores between the groups. 

 

Table 11. Private Schools’ Group Means 

ScoreD   

 Sum of Squares df Mean Square F Sig. 

Between Groups 1159.478 7 165.640 2.646 .017 

Within Groups 4695.221 75 62.603   

Total 5854.699 82    

Source: Author’s own completion 
 

The Table 18 shows the results of the Tukey HSD post-hoc test conducted on the 

dependent variable, ScoreD, to analyze the mean differences between various public schools 

(SchoolD). The majority of pairwise comparisons showed non-significant differences, with p-

values above 0.05. However, one comparison stood out with a statistically significant 



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difference: School 6 had a higher ScoreD compared to School 3 (mean difference = 14.247, p 

= .009). This suggests that students at School 6 scored significantly higher than students at 

School 3. All other comparisons did not reveal significant differences in scores across the 

schools (See Annex 3). 

Table 12 displays the results of the ANOVA test conducted on the dependent variable, 

ScoreALL which represents both public and private schools.  

H0: There is no significant difference between and within schools (both public and 

private). 

H1: There is a significant difference between and within schools (both public and 

private). 

The test shows that the observed F-value is 5.006 with a significance level (p-value) of 

.026. This means there is a statistically significant difference between the groups being 

compared, as the p-value is less than the common threshold of 0.05. Thus, the alternative 

hypothesis (H1) is accepted which means there is a difference between groups. The high 

between-groups sum of squares relative to the within-groups sum of squares further supports 

this conclusion. Briefly, we can see a significant difference between schools.  

 

Table 12. All schools’ group means  

ScoreALL   

 Sum of Squares df Mean Square F Sig. 

Between Groups 393.724 1 393.724 5.006 .026 

Within Groups 26188.413 333 78.644 
  

Total 26582.137 334 
   

Source: Author’s own completion 
 

4.4. Multiple regression 

Table 13 is a summary output of a multiple regression analysis, where the dependent 

variable 'Score_All' represents the individual innovativeness score of students and independent 

variables 'Business_All', 'Gender_All', 'School_Type', and 'Income_All' represent accordingly 

family business, gender, type of the school and family income of students. The model provides 

a weak fit for the data. The multiple correlation coefficient (R) is low at .146, suggesting a 

weak correlation between the predicted and observed values of the dependent variable 

'Score_All'. The coefficient of determination (R Square) is .021, indicating that a mere 2.1% 

of the variability in 'Score_All' can be explained by the independent variables 'Business_All', 

'Gender_All', 'School_Type', and 'Income_All'. The adjusted R Square further reduces to .009, 

pointing towards the potential presence of irrelevant predictors in the model. The standard error 

of the estimate stands at 8.879, which measures the variability of the predictions, and appears 

high. On a positive note, the Durbin-Watson statistic is 1.888, showing no significant 

autocorrelation in the model, a desirable attribute. However, the overall model's lack of 

statistical significance is evidenced by the Sig. F Change value of .129, which exceeds typical 

significance levels such as .05. Hence, based on these results, the model in its current form 

seems insufficient to accurately predict 'Score_All' using the chosen independent variables. 



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Source: Author’s own completion 

 

Table 14 represents an Analysis of Variance (ANOVA) for a multiple linear regression 

model which shows that the set of predictors - 'Business_All', 'Gender_All', 'School_Type', and 

'Income_All' - may not significantly predict the dependent variable 'Score_All'. The sum of 

squares for regression, which measures the variation explained by the model, is 566.421, while 

the residual sum of squares, indicating the variation is quite high at 26015.717. The F statistic, 

a measure used to determine if the model significantly explains more variance than residuals, 

is 1.796. However, the p-value, which estimates the likelihood that the observed data could 

have occurred if there were no relationship between the predictors and dependent variable, is 

0.129. This value is greater than the commonly used threshold of 0.05, indicating that we 

cannot reject the null hypothesis at this significance level. In other words, this set of predictors 

may not significantly predict the 'Score_All' based on these results. 

 

Table 14. ANOVAa 

Model Sum of Squares df Mean Square F Sig. 

1 Regression 566.421 4 141.605 1.796 .129b 

Residual 26015.717 330 78.836   

Total 26582.137 334    

a. Dependent Variable: Score_All 

b. Predictors: (Constant), Business_All, Gender_All, School_Type, Income_All 

Source: Author’s own completion 
 

Table 15 presents the results from a multiple regression analysis, focusing on the 

variable Score_All as the outcome. The Unstandardized Coefficients section provides the 

change in Score_All for each unit increase in the predictor variables. However, none of these 

predictor variables - School_Type, Gender_All, Income_All, and Business_All - showed a 

significant association with Score_All, as indicated by p-values greater than 0.05 in the Sig. 

column. These p-values reflect the probability that the observed relationships could have 

occurred by chance. The t-values in the table, calculated as the ratio of departure of an estimated 

parameter from its notional value to its standard deviation, were used to derive these p-values. 

Comparatively, the Standardized Coefficients (Beta) show the relative importance of each 

predictor when the variances of dependent and independent variables are standardized to 1. 

Lastly, the Collinearity Statistics (Tolerance and VIF) suggest that there is no significant issue 

Table 13. Model Summaryb 

Model R 

R 

Square 

Adjusted R 

Square 

Std. Error of the 

Estimate 

Change Statistics 

Durbin-

Watson 

R Square 

Change 

F 

Change df1 df2 

Sig. F 

Change 

1 .146a .021 .009 8.879 .021 1.796 4 330 .129 1.888 

a. Predictors: (Constant), Business_All, Gender_All, School_Type, Income_All 

b. Dependent Variable: Score_All 



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of multicollinearity among the predictor variables, meaning they are not highly correlated with 

each other. This is further confirmed by the fact that all VIF values are below 5, which means 

there is no serious issue of multicollinearity. 

 

Table 15. Coefficientsa 

Model 

Unstandardized Coefficients 

Standardized 

Coefficients 

t Sig. 

Collinearity Statistics 

B Std. Error Beta Tolerance VIF 

1 (Constant) 72.880 3.514  20.743 .000   

School_Type -2.172 1.301 -.105 -1.669 .096 .746 1.341 

Gender_All .266 .988 .015 .269 .788 .973 1.028 

Income_All -.495 .394 -.088 -1.256 .210 .608 1.644 

Business_All -1.409 1.215 -.076 -1.159 .247 .695 1.438 

a. Dependent Variable: Score_All 

Source: Author’s own completion 
 

The collinearity diagnostic table (Table 16) presented represents a test of 

multicollinearity, which is the statistical phenomenon where predictor variables in a model are 

highly correlated. The table shows that the model contains five predictors: School_Type, 

Gender_All, Income_All, and Business_All, all used to predict the dependent variable 

Score_All. The condition index is greater than 5 for dimensions 2, 3, 4, and 5, which may 

indicate potential issues with multicollinearity. More specifically, the predictor "Business_All" 

shows high variance proportions on the 5th dimension with a condition index of 17.819, 

signifying it might be the source of high multicollinearity in the model. This could impact the 

reliability of the model, as it suggests that Business_All might be linearly predictable from the 

other predictors, thus potentially inflating the variance of its estimated regression coefficient.  

 

Table 16. Collinearity Diagnosticsa 

Model Dimension Eigenvalue 

Condition 

Index 

Variance Proportions 

(Constant) School_Type Gender_All Income_All Business_All 

1 1 4.522 1.000 .00 .00 .00 .01 .00 

2 .305 3.852 .00 .01 .04 .32 .04 

3 .090 7.091 .00 .12 .72 .04 .11 

4 .069 8.098 .00 .66 .05 .45 .16 

5 .014 17.819 .99 .20 .19 .18 .68 

a. Dependent Variable: Score_All 

Source: Author’s own completion 
 

Table 17 represents residuals and predicted values for a model with a dependent 

variable "Score_All". With a sample size (N) of 335, the predicted value ranges between 63.01 

and 69.34 with a mean of 66.95 and a standard deviation of 1.302. The residual values vary 



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between -29.928 and 22.072 with a mean of 0 and a standard deviation of 8.826. The 

standardized predicted values and residuals exhibit similar characteristics; they both center 

around 0 with standard deviations close to 1. These data suggest a reasonably well-fitted model 

with some outliers. 

 

Table 17. Residuals Statisticsa 

 Minimum Maximum Mean Std. Deviation N 

Predicted Value 63.01 69.34 66.95 1.302 335 

Residual -29.928 22.072 .000 8.826 335 

Std. Predicted Value -3.022 1.833 .000 1.000 335 

Std. Residual -3.371 2.486 .000 .994 335 

a. Dependent Variable: Score_All 

Source: Author’s own completion 

 

From the plot for the distribution of the dependent variable “Score_All” which 

represents the II index (See Graph 1), it seems it is fairly close to normal distribution which 

can allow to continue with a simple OLS regression.  

The simple OLS (Ordinary Least Squares regression) model can be as follows in this case: 

Score_All = β0 + β1Business_All + β2Gender_All + β3School_Type + β4Income_All + ε 

Where: 

● Score_All is the dependent variable. 

● Business_All, Gender_All, School_Type, and Income_All are the independent 

variables. 

● β0 is the y-intercept (the value of Score_All when all independent variables are 0). 

● β1, β2, β3, and β4 are the coefficients of the independent variables, which represent the 

expected change in the dependent variable for a one-unit change in the corresponding 

independent variable, holding all other independent variables constant. 

● ε represents the error term (residuals), which captures the variation in Score_All not 

explained by the independent variables. 

G

r

a

p

h 

1 



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To summarize, the analysis indicates that the residuals of the regression model are quite 

normally distributed. This is a positive outcome as it suggests that the model adequately 

captures the underlying patterns in the data. Additionally, the absence of high VIF scores 

indicates that multicollinearity is not a significant issue, which strengthens the reliability of the 

results. 

The p-value of 0.129 for the overall model suggests that the model's explanatory power 

is not considered statistically significant. This means that the model doesn’t explain the amount 

of the variance in the dependent variable, individual innovativeness, but it cannot be considered 

statistically significant at the conventional significance level (e.g., p < 0.05). 

One possible explanation for the lack of significant results could be the small sample 

size of 335. With a small sample, the statistical power to detect significant effects may be 

limited. Therefore, caution should be exercised when interpreting the overall significance of 

the model. 

Moving on to the independent variable 'School_Type,' its p-value is just below 10%, 

which indicates that there may be a marginally significant relationship between school type 

and individual innovativeness. Although it does not meet the conventional threshold for 

significance, this result suggests that the type of school attended by students might have some 

influence on their level of innovativeness. 

Furthermore, the negative beta parameter associated with the 'School_Type' variable 

indicates that there is a negative relationship between school type and individual 

innovativeness, which contradicts the initial hypothesis. This finding suggests that students 

attending a certain type of school (as represented by the 'School_Type' variable) may exhibit 

lower levels of innovativeness compared to other types of schools. 

In essence, the multiple regression analysis shows that the overall model explains a 

moderate amount of variance in individual innovativeness but is not statistically significant. 

This lack of significance may have different reasons; it may occur because more variables 

would explain more of the dependent's variance or the small sample size of 335 may not be 

sufficient to predict the dependent variable.  

 

5. Discussion 

The development and success of individuals are inextricably linked to education. But, 

as educational systems become increasingly centralized, questions arise about their impact on 

creativity and problem-solving skills (Tether et al., 2005). Centralized decision-making is a 

top-down approach where higher authorities make decisions instead of grassroots-level 

participation. This type of decision-making can have lasting impacts on how people perceive 

the challenges they face in their lives. Research conducted by Topsakal et al. (2022) shows that 

people prefer autonomy when it comes to seeking new information or taking risks for greater 

creativity. When decision-making processes are centralized, this ability may be limited, leading 

to rigid thinking patterns which stifle innovation. Moreover, homogenization across different 

regions or countries could result from centralized decision-making in education settings. This 

would mean students receive similar educational experiences irrespective of their background 

or location - hindering diversity and cross-pollination of ideas among learners. Policymakers 

should seek the balance between centralization and decentralization for consistency while 

preserving unique learning environments where diverse ideas breed excellence among all levels 



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of society regardless of jurisdictional differences. (Hanson, 1998) In essence, understanding 

the effects centralized decision-making has on human development requires an investigation 

into its influence on individual creativity and problem-solving abilities. Policymakers should 

create balanced policies that cater towards consistency whilst nurturing unique learning 

environments for fostering creative expression amongst learners at all levels within varied 

contexts without impeding upon academic standards. 

The literature review demonstrated a correlation linking the centralization of decision-

making in education to individual innovativeness. Exploring the effects of centralized decision-

making on student creativity and problem-solving skills has considerable consequences for 

policymakers, educators, parents, and other stakeholders involved in creating educational 

policies. It indicates that delegating control over decisions is beneficial since it can lead to 

greater inventiveness and imagination as well as improved problem-solving abilities among 

pupils (Brown & McIntyre, 1981). Moreover, this highlights the requirement for future studies 

to understand how different forms of decentralization impact student results across varied 

cultural contexts. More investigation needs to be conducted so we may better understand how 

choices are made within schools and their effects on academic achievements. To sum up - 

whilst there may be some upsides when it comes down to uniformity or standardization 

practices within regions or areas, some studies indicate its negative influence on children's 

creative reserves must not be disregarded either. Therefore, it is vital educators find effective 

ways they can create safe spaces for kids where innovative ideas are encouraged without 

restraint - something which could potentially stimulate greater levels of innovative thinking 

with time (Amalia et al., 2020).  

Whereas the results of this study did not show a significant difference in individual 

innovativeness between public (centralized) and private (decentralized) schools, and the 

outcome of the multiple regression which analyzed the innovative innovativeness by 

considering several independent variables including the type of school, gender, family income, 

and family business, cannot be considered statistically significant, the study can be considered 

useful for further investigation.  

One possible explanation for the lack of a significant difference between public and 

private schools is that despite the differences in governance, there might be different factors 

such as student selection, family income, etc, leading to similar outcomes in terms of individual 

innovativeness. (Yilmaz et al., 2014) It is also possible that the measures of innovativeness 

used in the study were not sufficiently sensitive or comprehensive to capture the other factors 

affecting individual innovativeness. 

Even if the study's findings do not provide definitive evidence for the hypothesis, they 

do contribute to a broader understanding of the complex interplay between centralization, 

decentralization, and individual innovativeness in education. Future research might benefit 

from a more in-depth investigation of the specific factors that influence individual 

innovativeness in different educational contexts, as well as the potential mediating or 

moderating variables that could be impacting the relationship between centralization and 

individual innovativeness. For instance, researchers could explore how the degree of autonomy 

granted to teachers and school administrators, the flexibility of curricula, and the availability 



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of resources for experimentation and creativity vary across different systems, and how these 

factors might interact with centralized or decentralized decision-making structures. 

Additionally, longitudinal and cross-national comparative studies could be conducted 

to examine the long-term effects of centralization or decentralization on individual 

innovativeness, as well as to explore the generalizability of the findings to other countries and 

contexts. Such research could provide further insight into the potential benefits and drawbacks 

of different governance models in education, helping policymakers and practitioners make 

more informed decisions regarding the design and implementation of education systems that 

best foster innovation and creativity. 

In light of the findings from this study, it is clear that the relationship between 

centralization of decision-making in education and individual innovativeness is not 

straightforward, and that further research is needed to fully understand the complex dynamics 

at play. However, the expert opinions gathered in this research suggest that decentralized 

systems may hold promise for fostering greater individual innovativeness, by allowing for more 

flexible, adaptive, and context-specific educational experiences. As the world continues to 

evolve and the need for innovative, adaptable individuals grows ever more critical, it is 

essential for education systems to adapt and evolve as well, fostering the skills and mindsets 

needed to thrive in a rapidly changing global landscape. This research contributes to the 

ongoing conversation around the best ways to achieve that goal, highlighting the importance 

of considering the impact of governance structures on the development of individual 

innovativeness within education systems. 

 

6. Limitations 

The present study is subject to a number of limitations that should be considered when 

interpreting the findings. These limitations pertain to the data sources, sample size, and 

generalizability of the results. The subsequent paragraphs detail these limitations and their 

potential impact on the study. 

Firstly, this study relies on a secondary dataset collected in 2021 by independent 

researchers. The use of this data poses several limitations. For one, it covers only students in 

the 10th and 11th grades, which may not be representative of the broader student population. 

Additionally, the data is limited to the best schools in Azerbaijan, and was collected exclusively 

in the capital city, Baku. This geographical constraint may limit the generalizability of the 

findings to other regions or types of schools within the country. 

Secondly, the cross-sectional nature of the data poses another limitation. As the data 

was collected only once from the students, it is not possible to compare their innovativeness 

before and after joining the schools in question. This precludes any conclusions about the 

causal effects of the school environment on individual innovativeness, and may lead to an over- 

or underestimation of the relationship between centralization of decision-making and 

innovativeness. Future research would benefit from a longitudinal study design, which could 

provide insights into the temporal dynamics of this relationship. 

Consequently, the limitations of this study should be acknowledged when interpreting 

the findings. The reliance on a secondary dataset with a restricted scope and the cross-sectional 

nature of the data may all have implications for the generalizability and validity of the results. 

Despite these limitations, the study provides a valuable starting point for future research on the 



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relationship between centralization of decision-making in education and individual 

innovativeness in Azerbaijan. Further investigation, employing longitudinal data and more 

diverse samples would contribute to a deeper understanding of this important issue. 

 

CONCLUSİONS 

This research paper aimed to explore the impact of the centralization of decision-

making in education on individual innovativeness, specifically focusing on the case of 

Azerbaijan. While my hypothesis suggested a greater level of innovativeness in individuals 

educated within a decentralized system, our quantitative analysis did not yield a significant 

distinction between students from centralized and decentralized schools in Azerbaijan. This 

discrepancy, however, does not detract from the value of the research but rather illuminates the 

intricacies that underlie the relationship between centralization and individual innovativeness. 

The difference between the hypothesis and findings demonstrates the need for a broader lens 

that takes into account the various factors that influence individual innovativeness. It 

underscores the necessity of more in-depth, longitudinal, and cross-national comparative 

studies, which might reveal the long-term and far-reaching consequences of centralization 

versus decentralization in education. This research, although not affirming the initial 

hypothesis definitively, contributes to a nuanced understanding of the complexities between 

educational governance and individual innovativeness, which is invaluable for future 

exploration and decision-making. It sets the stage for ongoing dialogue about how education 

systems can best foster the innovativeness required in our rapidly changing global society. 

There are some hypotheses driven by this study that can be investigated in the future: 

H2: Decentralization of decision-making in education might have a positive impact on 

individual innovativeness in rural areas. This study is primarily urban-centric, focusing on 

schools in the capital, Baku. There might be significant differences in rural areas that were not 

accounted for in this study. 

H3: Decentralization of decision-making on individual innovativeness might have a 

positive impact in the long term. A longitudinal study might reveal that the impact of 

centralization or decentralization on individual innovativeness becomes more evident over 

time. The cross-sectional nature of this study may have limited its ability to detect the long-

term effects of the educational system on innovativeness. 

H4: The centralization of decision-making might have a more significant effect on the 

innovativeness of the educators rather than the students. Teachers and administrators who have 

more autonomy might be more innovative themselves, which could indirectly affect the 

innovativeness of their students. 

H5: The level of resources available to a school could mediate the impact of 

centralization or decentralization on individual innovativeness. Schools with more resources 

might be better able to foster innovativeness regardless of the degree of decision-making 

autonomy. 

H6: The effects of centralization or decentralization on individual innovativeness might 

be less significant than the effects of other factors, such as teaching quality or curriculum 

relevance. This study did not find a significant difference in innovativeness between 

centralized and decentralized systems, suggesting other factors might be more important. 



Ehtiman NURALİZADE 

241 
 

H7: Centralized decision-making might lead to less innovation in the short term but 

provide a solid foundational knowledge base that enables greater innovation in the long term. 

It's possible that a more structured, centralized approach initially provides a solid base of 

knowledge upon which students can later innovate. 

Additionally, the measures of individual innovativeness might be more sensitive to 

changes in the education system in different cultural contexts. This study focused more on the 

individual innovativeness index, but the relationship between centralization and innovativeness 

might look different in Azerbaijan or countries with different cultural attitudes toward 

innovation and education. Thus, it can be beneficial studying the interaction between cultural 

aspects and institutional aspects of the innovation ecosystem. 

 

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Appendix 1. Multiple Comparisons 
Dependent Variable:   ScoreD   
Tukey HSD   

(I) SchoolD (J) SchoolD 

Mean Difference 

(I-J) Std. Error Sig. 

95% Confidence Interval 

Lower Bound Upper Bound 

1 2 .228 2.733 1.000 -8.29 8.75 

3 3.713 2.998 .918 -5.63 13.06 

4 -4.272 3.705 .942 -15.83 7.28 

5 -.748 2.787 1.000 -9.44 7.94 

6 -10.534 3.498 .066 -21.44 .37 

7 -6.705 3.977 .696 -19.11 5.70 

8 .395 3.705 1.000 -11.16 11.95 

2 1 -.228 2.733 1.000 -8.75 8.29 

3 3.485 3.141 .953 -6.31 13.28 

4 -4.500 3.822 .936 -16.42 7.42 

5 -.976 2.940 1.000 -10.14 8.19 

6 -10.762 3.622 .073 -22.05 .53 

7 -6.933 4.086 .689 -19.67 5.81 

8 .167 3.822 1.000 -11.75 12.08 

https://www.learntechlib.org/p/187548/
https://www.learntechlib.org/p/187548/
https://unesdoc.unesco.org/ark:/48223/pf0000120275
https://doi.org/10.1108/S1479-367920140000024013
https://doi.org/10.1108/S1479-367920140000024013
https://openknowledge.worldbank.org/handle/10986/2460
https://openknowledge.worldbank.org/handle/10986/2460
https://openknowledge.worldbank.org/handle/10986/2460
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_2000_2022/az.pdf
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_2000_2022/az.pdf
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_2000_2022/az.pdf


HOW DOES THE (DE)CENTRALİZATİON OF SECONDARY EDUCATİON AFFECT 

İNDİVİDUAL İNNOVATİVENESS? EVİDENCE FROM AZERBAİJAN 

 

244 
 

3 1 -3.713 2.998 .918 -13.06 5.63 

2 -3.485 3.141 .953 -13.28 6.31 

4 -7.985 4.016 .496 -20.51 4.54 

5 -4.461 3.188 .855 -14.40 5.48 

6 -14.247* 3.826 .009 -26.18 -2.32 

7 -10.418 4.268 .237 -23.72 2.89 

8 -3.318 4.016 .991 -15.84 9.20 

4 1 4.272 3.705 .942 -7.28 15.83 

2 4.500 3.822 .936 -7.42 16.42 

3 7.985 4.016 .496 -4.54 20.51 

5 3.524 3.861 .984 -8.51 15.56 

6 -6.262 4.402 .844 -19.99 7.46 

7 -2.433 4.791 1.000 -17.37 12.51 

8 4.667 4.568 .970 -9.58 18.91 

5 1 .748 2.787 1.000 -7.94 9.44 

2 .976 2.940 1.000 -8.19 10.14 

3 4.461 3.188 .855 -5.48 14.40 

4 -3.524 3.861 .984 -15.56 8.51 

6 -9.786 3.663 .148 -21.21 1.63 

7 -5.957 4.122 .833 -18.81 6.90 

8 1.143 3.861 1.000 -10.90 13.18 

6 1 10.534 3.498 .066 -.37 21.44 

2 10.762 3.622 .073 -.53 22.05 

3 14.247* 3.826 .009 2.32 26.18 

4 6.262 4.402 .844 -7.46 19.99 

5 9.786 3.663 .148 -1.63 21.21 

7 3.829 4.633 .991 -10.62 18.27 

8 10.929 4.402 .219 -2.80 24.65 

7 1 6.705 3.977 .696 -5.70 19.11 

2 6.933 4.086 .689 -5.81 19.67 

3 10.418 4.268 .237 -2.89 23.72 

4 2.433 4.791 1.000 -12.51 17.37 

5 5.957 4.122 .833 -6.90 18.81 

6 -3.829 4.633 .991 -18.27 10.62 

8 7.100 4.791 .815 -7.84 22.04 

8 1 -.395 3.705 1.000 -11.95 11.16 

2 -.167 3.822 1.000 -12.08 11.75 

3 3.318 4.016 .991 -9.20 15.84 

4 -4.667 4.568 .970 -18.91 9.58 

5 -1.143 3.861 1.000 -13.18 10.90 

6 -10.929 4.402 .219 -24.65 2.80 

7 -7.100 4.791 .815 -22.04 7.84 

*. The mean difference is significant at the 0.05 level. 

 Source: Author’s own completion 
 


