In recent years, productivity and development of today’s organizations have been dependent on some competitive opportunities that arise from competitive environments around the organizations. This is because today’s organizations have changed from the point of view of organizational structure, organizational behaviour, and organizational relationship. An organization can get benefit from its competitive opportunities if its outside environment is fully studied and analysed (Mwesigye and Muhangi, 2015). A typical organization, an educational organization like universities, research centres, higher education centres, etc., responds to the outside environment according to the information and recognition obtained about the outside environment. Higher education organizations of a country play significant roles in the country. Although these organizations may be affected by outside environment seriously, they are effective in national economical and cultural issues (Presmus et al., 2003). Based on the literature, outside environment of the higher education sector (in terms of some environmental factors) can seriously influence organizational behaviour in this sector where output and productivity of the employees can be affected by these factors (Robbins and Judge, 2016). Generally, the variables and factors of outside environment of a university are important and the university should be managed and developed according to such environment for achieving the predetermined goals and objectives (Torkzadeh et al., 2019). Therefore, we can claim that the environmental factors of an organization, especially organizations from the higher education sector, are important and crucial in all aspects of the organization, specifically its organizational behaviour.

The literature contains numerous studies about the organizational behaviour of general organizations, academic and higher education institutes, and especially the environmental aspect of such behaviour. The studies of Rojas (2000) and Gita Kumari and Pradhan (2014) have stated that organizational effectiveness is important for the managers. Torkzadeh and Dehghan Harati (2015) have concluded that effectiveness is an important index for assessing performance of organizations. They performed the study on the employees of Shiraz University as case study. Ketkar and Sett (2009) also mentioned that effectiveness can be measured by the employees’ behaviour, financial performance, and operational performance of an organization. Also the works of Nabatchi et al. (2007), Zhang and Lee (2010), and Gita Kumari and Pradhan (2014) studied the importance of organizational behaviour on productivity and effectiveness of an organization. Robbins and Judge (2016) studied the importance of organizational behaviour and described it as a set of equipment for understanding, analysing, describing, and managing the behaviour in organizations. According to the study of Kapoor and Jain (2017), organizational behaviour analyses the impact of people, groups, and structures on improvement and effectiveness of an organization. According to this study, the behaviour in an organization can be studied and analysed in three levels, such as individual, group, and organizational level. Investigating the environmental aspect of organizations and its impact on organizational behaviour is also an important topic which was considered in the studies of Gibson (2007), Burton and Obel (2015), and Makolov (2019). According to the study of Lutans et al. (2021), the environmental aspect of organizational behaviour has forced the managers of universities to change their traditional procedures and be more responsible to their inside and outside environments. The studies of Rizvi (2007) and Mwesigye and Muhangi (2015) stated that higher education institutes and universities, like other organizations, have been significantly affected by recent developments of the organizational behaviour. The study of Daigle and Cuocco (2002) is about general responsiveness in higher education. They studied various responsiveness methods in the universities of United States and claim that it is a challenging issue in those universities. Furthermore, Kreysing (2002) studied the responsiveness and organizational complexity of higher education sector. As a result, they claimed that in order to be more responsive to environmental changes in such organizations, their decentralization level should be increased.

In this study, we focus on the organizational behaviour aspect of higher education sector. This aspect is important in any organization and can help an organization to be successful. The main aim of this study is to analyse the environmental factors which may affect the organizational behaviour in the higher education sector. In this analysis, the aspects, such as importance weights of the factors and their influential impact on each other could be some very important and challenging issues. Therefore, the environmental criteria affecting the organizational behaviour of higher education sector are selected from the literature. We aim to apply the selected criteria and perform a study to determine their effect on the organizational behaviour of higher education sector and prioritize them. This is a new aspect of this field that to the best of our knowledge has not been considered earlier in the literature and can enable the managers to make suitable strategies for managing the organizations. As a solution methodology, some experts from the higher education sector of Iran are selected and are asked to compare the importance of the criteria pair wisely using linguistic terms. Then, in order to respect the uncertain nature of such evaluations, the linguistic terms are converted to interval-valued Pythagorean fuzzy values. Interval-valued Pythagorean fuzzy numbers are used as they keep more information and uncertainty compared to classical fuzzy numbers (see Das and Granados, 2022; Narang et al., 2022; Dinçer et al., 2023; Younis Al-Zibaree and Konur, 2023; Jafarzadeh Ghoushchi and Sarvi, 2023; Rezazadeh et al., 2023). As DEMATEL approach can simultaneously determine importance weight values of the criteria and their influential impact on each other, an interval-valued Pythagorean fuzzy DEMATEL approach is developed for the first time for prioritizing the criteria and performing the causality analysis on them. Finally, the obtained results are interpreted, and some managerial insights are given. In addition, a sensitivity analysis of the proposed approach is performed, and the results are compared to the results obtained by the existing methods of the literature.

The contributions of this study to the literature of the field can be summarized as below:

The rest of this paper is organized in five sections. In Section 2, some basic concepts of fuzzy sets and numbers are presented. The criteria affecting organizational behaviour of the higher education sector is described in Section 3. As solution approach, an interval-valued Pythagorean fuzzy group DEMATEL approach is developed in Section 4. In continuation, a case study is considered to evaluate the criteria of Section 2, and the numerical results and some remarks about the case study are reported in Section 5. Finally, the conclusions are given in Section 6.

Zadeh (1965) introduced fuzzy set theory for the first time. This is a useful theory in order to reflect the uncertain nature of real life systems while modelling them. Therefore, many real life problems are modelled and optimized in a fuzzy based uncertain environment. As the classical fuzzy sets and numbers may have some shortcomings and may not be able to reflect some high degrees of uncertainty, this theory has been developed and modified in the literature (Ali et al., 2023; Naseem et al., 2023; Mahmoodirad and Niroomand, 2023). For this aim, some newer types of fuzzy sets, such as type-2 fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, etc., have been introduced in the literature (Wang et al., 2023; Mishra et al., 2023). These newer types of fuzzy sets and numbers reflect more uncertainty of events and parameters.

Pythagorean fuzzy sets and numbers were introduced by Yager (2013). This type of fuzzy numbers is more flexible and capable to reflect the uncertain nature of an uncertain event. Because of this flexibility and capability, this type of fuzzy numbers are widely used in optimization problems.

Some basic definitions and concepts of Pythagorean fuzzy sets and numbers are given in the rest of this section. These definitions later will be used to construct the solution methodology of this study. Definition 1

The Pythagorean fuzzy set P ˜ with membership function μ P ˜ ( x ) : X → [ 0 , 1 ] and non-membership function ν P ˜ ( x ) : X → [ 0 , 1 ] with the condition 0 ⩽ μ P ˜ ( x ) 2 + ν P ˜ ( x ) 2 ⩽ 1 on set X is defined as below: (1) P ˜ ≅ { ⟨ x , μ P ˜ ( x ) , ν P ˜ ( x ) ⟩ : x ∈ X } .

The environmental aspect of organizational behaviour is an important issue for controlling and effective guidance of the behaviour of members of academic organizations. The managers of academic organizations can recognize and understand the internal behaviour of their organization by focusing on the environmental factors. Assessment of influence of the environmental criteria on the organizational behaviour of academic organizations can be helpful from different points of view, e.g. recognition of the internal behavioural processes of the universities, reaching the goals of universities in organizational behaviour, determining the future goals of universities in organizational behaviour, etc. This might be important to understand the effects of environmental criteria on organizational behaviour in the higher education sector. Therefore, for this aim, the problem of prioritizing and causality analysis of such criteria should be considered. According to the literature of organizational behaviour in higher education sector, the important criteria affecting such organizational behaviour are economic, social, technological, environmental, and professional domain criteria (Torkzadeh et al., 2019), where each of these criteria can be divided into several criteria. Based on the literature, Table 1 represents 36 criteria affecting organizational behaviour of the higher education sector.

As mentioned earlier, it is an important study to evaluate the effect of the environmental criteria (as mentioned by Table 1) on organizational behaviour of the higher education sector. For this aim, a method for evaluating, prioritizing, and causality analysis of these criteria is needed. For this aim, an interval-valued Pythagorean group DEMATEL approach is developed for the first time in the next section, which performs prioritizing and causality analysis of the criteria of Table 1 on organizational behaviour of higher education sector.

In this section, the proposed criteria of Table 1 are analysed and their importance weight values are calculated. There are several methods in the literature that can be used for weight determination of the criteria in MCDM problems (Sahoo and Goswami, 2023). The BWM is a method that determines the criteria weights by comparing the criteria with the best and the worst criteria and then determines all weight values by applying a mathematical model (Rezaei, 2015). The FUCUM (Pamučar et al., 2018) is a subjective method of weight determination in MCDM where the relation between consistency and the required number of the comparisons of the criteria are considered. Žižović and Pamucar (2019) proposed the LBWA method for weight determination purposes. This approach enables the involvement of experts from different fields with the purpose of defining the relations between criteria and providing rational decision making. The DIBR method is another method based on defining the relationship between ranked criteria, i.e. it considers the relationship between adjacent criteria (Pamucar et al., 2021).

Here, a solution methodology is proposed in order to evaluate the effect of the environmental criteria on organizational behaviour of the higher education sector as described in Section 3. For this aim, a solution methodology should be applied with the following properties:

For this aim, the DEMATEL approach (see Alinezhad and Khalili, 2019; Nezhad et al., 2023) is used. The classical form of the DEMATEL approach is represented by the flowchart of Fig. 1. As mentioned earlier, interval-valued Pythagorean fuzzy values represent more uncertain information compared to Pythagorean fuzzy values and some other types of fuzzy values. Therefore, in this section, the DEMATEL approach is extended to an interval-valued Pythagorean fuzzy form (we call it IVPF-DEMATEL) for evaluating the effect of the environmental criteria on organizational behaviour of higher education sector as described in Section 3.

In order to describe the steps of the proposed extended DEMATEL approach with interval-valued Pythagorean fuzzy information (IVPF-DEMATEL), some steps should be followed. These steps are detailed in the rest of this section and depicted in the flowchart of Fig. 2. The notations of this approach are detailed in Table 2 in advance.

Step 1. A set of criteria (each indexed by j , k ∈ { 1 , 2 , … , n }) and a set of experts of the field (each indexed by e ∈ { 1 , 2 , … , E }) are selected.

Step 2. Each expert is requested to complete the linguistic comparison matrix of the criteria (the linguistic terms are shown in the left column of Table 3). Based on the numerical values of Table 3, the linguistic comparison matrix is converted to an interval-valued Pythagorean fuzzy matrix such as A e ˜ = [ a ˜ j k e ] n × n for expert e, where a ˜ j k e = ⟨ ( μ j k , L e , μ j k , U e ) , ( ν j k , L e , ν j k , U e ) ⟩ is the equivalent interval-valued Pythagorean fuzzy value for comparing criterion j to k (importance or influence of j to k).

Step 3. The IVPF matrixes obtained from the experts are integrated into one matrix shown as A ˜ = [ a ˜ j k ] n × n (where a ˜ j k = ⟨ ( μ j k , L , μ j k , U ) , ( ν j k , L , ν j k , U ) ⟩) using the IVPFWG operator described in Section 2 as below: (13) μ j k , L = ∏ e = 1 E ( μ j k , L e ) w e , j , k ∈ { 1 , 2 , … , n } , (14) μ j k , U = ∏ e = 1 E ( μ j k , U e ) w e , j , k ∈ { 1 , 2 , … , n } , (15) ν j k , L = 1 − ∏ e = 1 E ( 1 − ( ν j k , L e ) 2 ) w e , j , k ∈ { 1 , 2 , … , n } , (16) ν j k , U = 1 − ∏ e = 1 E ( 1 − ( ν j k , U e ) 2 ) w e , j , k ∈ { 1 , 2 , … , n } , where w e is the importance weight of expert e.

Step 4. The values of the fuzzy matrix A ˜ = [ a ˜ j k ] n × n are deffuzified using the below equation: (17) a j k = 1 6 ( μ j k , L 2 + μ j k , U 2 + ( 1 − π j k , L 4 − ν j k , L 2 ) + ( 1 − π j k , U 4 − ν j k , U 2 ) a j k = + μ j k , L μ j k , U + ( ( 1 − π j k , L 4 − ν j k , L 2 ) ( 1 − π j k , U 4 − ν j k , U 2 ) ) 1 4 ) , j , k ∈ { 1 , 2 , … , n } . Therefore, the crisp matrix A = [ a j k ] n × n is obtained instead of A ˜ = [ a ˜ j k ] n × n .

Step 5. Each value of the crisp matrix A = [ a j k ] n × n is normalized by the below equation in order to obtain the normalized matrix N = [ n a j k ] n × n : (18) n a j k = a j k max j { ∑ k = 1 n a j k } , j , k ∈ { 1 , 2 , … , n } .

Step 6. The total-relation matrix ( T = [ t j k ] n × n ) is obtained by below equation: (19) T = N ( I − N ) − 1 . In equation (19), I is the unit matrix, and ( I − N ) − 1 is the inverse form of matrix I − N.

Step 7. The causal diagram is constructed in this step. For this aim, for each criterion in the total-relation matrix the sum of row values ( R j ) and the sum of column values ( C j ) are calculated using below equations: (20) R j = ∑ k = 1 n t k j , j ∈ { 1 , 2 , … , n } , (21) C j = ∑ k = 1 n t j k , j ∈ { 1 , 2 , … , n } .

On the causal diagram, a point is depicted for each criterion. The horizontal axis of this diagram shows the importance weights of the criteria, and the vertical axis shows the degree of relation for the criteria. The coordinate of criterion j is obtained as ( R j + C j , R j − C j ). For the case of R j − C j > 0, the criterion is effective and is categorized as cause class. For the case of R j − C j < 0, the criterion is susceptive and is categorized as effect class.

In this section, the solution methodology proposed by Section 4 is implemented to evaluate the environmental criteria influencing the organizational behaviour of academic sector described by Section 3 for the case of higher education sector of Iran. For this aim, the following issues are considered. •

Based on Section 3, number of the environmental criteria is n = 36, which are detailed in Table 1.

In this study, some environmental criteria affecting organizational behaviour of the higher education sector were considered. The aim of the study was to analyse and prioritize these factors for giving some insights to the managers. As a solution methodology, first some experts from the higher education sector of Iran were selected and asked to determine pairwise comparison of the criteria. Then, in order to respect the uncertain nature of the comparisons, the linguistic terms were converted to interval-valued Pythagorean fuzzy values. Interval-valued Pythagorean fuzzy numbers were used as they keep more information and uncertainty compared to classical fuzzy numbers. Then, an interval-valued Pythagorean fuzzy DEMATEL approach was developed for the first time for prioritizing the criteria and performing the causality analysis on them. Finally, the obtained results were interpreted, and some managerial insights were given. According to the obtained results, most of the economic, political, and professional domain criteria were selected to be of the cause category. According to the obtained results, the managers can improve the organizational behaviour of their organizations by focusing on the cause category of the criteria. On the other hand, there were some limitations for performing this study. A limitation is selecting suitable and experienced people for criteria comparison step. Another limitation was reflecting the uncertainty that may happen in comparing the criteria that was solved by linguistic terms and their equivalent fuzzy values.

This study can be extended by considering more range of criteria other than the environmental criteria. Also, other types fuzzy sets and numbers can be considered for reflecting the uncertain nature of the problem.