1 Introduction

The employment of modern technology in the field of education is an essential factor for success of the e-learning system in educational institutions. Although technology alone is not sufficient to adopt the e-learning system, in that the need is necessary to develop skills to deal with technology, which represents e-readiness to adopt the e-learning system in higher education institutions (Budur et al., 2021). Assessing the ability of educational institutions aims to employ technologies in a thoughtful, planned and effective manner, as well as determine the level of potential of e-learning tools to improve educational outcomes, in addition to being a necessary step to ensure the quality of the e-learning system by identifying and addressing deficiencies, development and innovation to contribute to improving the education system (Abu Rawi et al., 2020). Moreover, e-learning readiness would refer to an organization's capacities as well as that of the educational authorities which represented by stakeholders, key figures, faculty members, and learners to utilizing electronic media in an effective and efficient manner (Machado, 2007). Since the emergence of the Covid pandemic, the stereotype educational has changed in all educational institutions, as many schools and universities have closed and have been keen to ensure access to their educational services, by rapidly shifting to e-learning without medium or long-term planning for that transformation that ensures the quality of e-learning provided by those educational institutions (Janssen & Kirschner, 2020; Sjolie et al., 2022).

The great pressure of Covid-19 in the first half of 2020 on the Iraqi education system was the complete closure of educational institutions, which impeded the progress of the educational process completely, therefore, Iraqi universities were keen to face this difficult challenge by finding alternative means to complete educational process and benefit from advanced technologies for distance education and adoption e-learning as an alternative system that it was a complementary way to traditional education before the pandemic, in accordance to statistics, the students were attending the virtual courses in Iraq are approximately, in that, the students of different educational stages were attending the virtual courses in Iraqi universities to continue their education process.

Educational institutions were gradually reopened after preventive procedures and measures to mitigate the impact of the emerging Covid-19 prevailed, Iraqi universities have adopted blending learning during 2021 and 2022, but the uncertainty about the situation of the pandemic that may recur or the country is exposed to another similar crisis affecting the functioning of educational process, it is better to take a state of certainty and build on facing more difficult challenges. Therefore, it is necessary to assess the capacity of Iraqi universities and genuine potentials to adopt the e-learning system as an alternative educational system, as the assessment process reveals failures and weaknesses, planning and initiative for continuous improvement and development.

To success the e-learning system, it is so important to analyze many aspects of e-readiness, such as network components, human and organizational readiness in that authorities and organizers can adopt the most relevant policies and design suitable development strategies which produce well-balanced and integrative media that would achieve successful adoption of e-learning by assessing the level of e-learning readiness (Kaur & Zoraini, 2004). Therefore, Dutta & Geiger (2010) indicated that the e-learning readiness assessment is a tool to improve the educational institution's environment to adopt the e-learning system. The readiness of e-learning is represented by the readiness of the educational institution through the infrastructure, the regulatory environment, and the electronic readiness of students and instructors (McKenney, 2013). Therefore, it is necessary to know the extent of the educational institution's ability to provide an educational environment that adopts an advanced strategy for the continuity of the education process (Goh et al., 2020; Goni et al., 2020). The substantial need for readiness assessment of higher education institutions for e-learning requires design and develop a suitable models according to country environment to measure readiness for e-learning adoption (Edralin & Pastrana, 2021).

A review and comparison among the different models applied to assess e-learning readiness, the basis for designing a new model for assessing e-learning readiness in Iraqi universities and clarifying the mechanism of the fuzzy delphi method used to prove the validity of the proposed model will be in the next section of the paper, followed by a section, the experts stage for the validation of the proposed model, analyze the data and discuss the results, and in the final section, set the appropriate model for assessing e-learning readiness in Iraq as approved by the experts and validated using the fuzzy delphi method.

1.1 Problem statement

Overcoming the challenges and influences that restrict and affect the implementation and effectiveness of e-learning is essential for the typical implementation and the ongoing efficacy of e-learning in Iraqi universities. Therefore an assessment of the educational institution's e-readiness is necessary to determine the success or failure of the continuous implementation of the e-learning system (Dehghan et al., 2022). Despite the consideration that the Iraqi government, stakeholders and researchers are given this regard, all attempts were restricted to single assessments (case studies) of limited aspects (content, material or human readiness) and for one part of the educational process (the learners or the faculty members) such as (Mohammed, 2019) which was case study, developed an assessment model consists of five factors of readiness: psychological, technological, content, culture and demographics, especially for medical students at University of Fallujah in Iraq depended on reviewing assessment models for some developing countries, in (Basha, 2015), a model of e-learning readiness for instructors has been proposed in Iraqi Public Universities based on the nine dimensions: technological skills, equipment/infrastructure, online learning style, attitude, human resources, cultural, environmental, financial, and engagement readiness.

In addition, the level of readiness cannot be assessed through deterministic methods or through random processes. In fact, due to the multiple dimensions and factors that affect the e-learning readiness and randomness of the data, accurate and precise information cannot be accessed. Most of researchers did not take into consideration the fuzzy readiness values which are between the level of sureness and unsureness to accurately determine the level of readiness except (Alshaher, 2013) study which used fuzzy logic to analyze the data. The studies refer to the results of the readiness assessment of the institution itself, each using different assessment factors specific to the needs of the institution instead of the standard model that other education institutions can use. (Budur et al., 2021) study which developed a reliable evaluation criterion that represented a questionnaire to assess the readiness for online education universities readiness in Kurdistan Region of Iraq, moreover other studies used a standard models such as (Abdullah & Toycan, 2017) study applied two-step methodology in private Universities of Northern Iraq by using a hypothesized model of Technology Acceptance Model (TAM). Mousa et al., (2020) gave an investigation using the TAM for university users (instructors and students) towards measuring their readiness in the higher education institutions in Iraq of the adoption and interactivity with e-learning system.

This study designs a comprehensive model for assessing e-learning readiness to capture starting point conditions for helping Iraqi universities to increase the capacity to ensure and develop level of readiness to adoption e-learning system. So, the research aims to determining dimensions, factors and measures which affected e-learning readiness in Iraqi universities. In contrast to other studies, the fuzzy delphi method investigated all the criteria in this study. This study sought to answer the following research questions:

  • ○ RQ1: “What are the dimensions, factors and potential measures that comprise the designed e-learning readiness assessment model for Iraqi universities?”.

2 Literature review

2.1 E-learning readiness

The adoption process of e-learning in higher education institutions as a result of global trends and the great development of Information and Communications Technology (ICT), require a ready environment due to the fact that readiness contributes to supporting the success of e-learning implementation in higher education institutions (Rohayani, 2015). As well as understanding the level of maturity contributes to demonstrating the ability, quality and consistency of e-learning and helps universities to know the level of maturity of using ICT for students in addition to identifying required information to enable universities for adopting an appropriate strategy that improves the e-learning process (Durek et al., 2018). E-readiness regarded as a capacity degree to follow the identified appropriate chances (Issa et al., 2022).

E-readiness is ability of the country/ organization to create, and use the e-information to improve economic activity (Borotis & Poulymenakou, 2004). E-learning readiness is the most important aspect for the successful achievement of e-learning programs in higher education. Recognizing the role and importance of e-learning readiness helps universities to effectively adopt the e-learning system (Clark & Mayer, 2016). During 2000’s, the concept of readiness grew to form a framework for assessing the level of digital use between developing and developed countries (Mutula & Van, 2006). In that e-readiness is a relatively modern concept that has been expanded due to the rapid spread of Information Technology (IT) and the great progress in the business and industry sector (Choucri et al., 2003). The e-learning readiness is defined as the efficiency in using of e-learning system and its technological mechanisms, this term has resulted from the necessary need to know the level of technological, organizational and social readiness of users to implement the e-learning system (Farazkish & Montazer, 2019). According to (Odunaike et al., 2022) e-learning readiness is considered the intellectual and structural readiness to adopt the e-learning system in educational institutions.

2.2 E-learning readiness models

The assessment process of the e-learning readiness level educational institutions consider as a guide for innovation and future progress for the institutions and educational systems (Budur et al., 2021). Several models and tools have been proposed to assess the readiness of e-learning in various institutions readiness, despite the diversity of methods and mechanisms assessment and the diversity of dimensions and factors which affecting the readiness of e-learning as can be seen in the Table 1 due to the difference in the application environment and the specifications of the concerned organization with the assessment, but most of the models participate in technological readiness, human resources readiness, and financial readiness, culture and content.

Table 1 A review and comparison of various e-learning readiness models
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The comparison of the various models for assessment of e-learning readiness clarified that the readiness factors can be classified into many aspects: infrastructure/ technological, organizational, cultural, psychological and content aspect as well as human resources.

2.3 Proposed new model for assessing e-learning readiness

After the literature review of several models for assessing the readiness of e-learning environments shown in Table 1 the following factors must be taken into consideration as criteria for assessing the readiness to adopt the e-learning system in various institutions, namely technology, policy, human resources, support, management, content, evaluation, and organizational readiness, in that the importance of these factors varies according to the assessment models and the application environment for each model. In view of the conceptual and functional relationship as well as the level of homogeneity of factors with each other, these factors have arranged, distributed and classified into three main dimensions infrastructure, human and organization. Based on the logic process in the assessment which clarified the necessity for readiness in five basic dimensions to achieve an appropriate level for the application of the e-learning system in institutions (Farazkish & Montazer, 2020; Hansen, 2005), which are the dimension of "the organization inputs readiness", "the human resources readiness", "the organization readiness", "infrastructure readiness", and the dimension of "teaching readiness".

Given these five dimensions of the process logic as a basis for the proposed model, the organization’s input readiness factors such as policy, support and finance and factors of the organization’s readiness dimension such as management and policy are linked to a functional and conceptual relationship, therefore, these factors have been included within the organization's readiness dimension in the new proposed model. As for the teaching orientation dimension, which is the most important output of the e-learning system in the logic of the process and represents the educational content, and since the preparation, design and planning of educational content appropriate for the goals of the educational process is a process in which both the learner and the instructor participate, but it is a responsibility whose policy measures fall on the organization that helps to ensure and maintain the quality course development processes, educational content and learning activities, so educational content was included as a factor of readiness under the institution dimension in the proposed readiness model.

The proposed model consists of 3 main dimensions and 13 factors which were distributed as follows:

  1. a.

    Three factors, technological, communication network and security are included under the infrastructure dimension.

  2. b.

    Four factors include human resources, culture, psychological and educational ethics for the human dimension that represent learners, instructors and technicians as the main users of the e-learning system and those responsible for the success of the application in the organization, in that the human and cultural readiness is responsible for the success and failure of the adoption of e-learning system in institutions, compared to the readiness of technological tools and equipment (Lucero et al., 2022; Rohayani, 2015; Schmidt et al., 2017).

  3. c.

    Six factors include management, content, support, policy, financial, and evaluation included within the organization dimension as the main processes of the organization.

The meaning and purpose of each dimension in this study are clarified as follows:

2.4 Infrastructure dimension

Infrastructure has a significant and positive impact on the process of adopting and developing the e-learning system, in that the availability of appropriate infrastructure and appropriate software devices and equipment with an accurate security strategy is a necessary and important matter for the success of the e-learning system in educational institutions (Garad et al., 2021; Nasiri et al., 2014). The basic requirements for the infrastructure to implement e-learning include access to the network and ease of communication, continuous supply of electric power, flexible access to e-learning management system environments and protection of e-learning management systems and technical support (Kayange, 2019). To assess the infrastructure readiness of educational institutions, appropriate measures must be selected for each of the three specific factors of the infrastructure dimension. Hence, a number of measures were identified for the infrastructure readiness components based on previous studies in the process of assessing the readiness of institutions to adopt the e-learning system as shown in Table 7.

2.5 Human dimension

Efficiency and ability to use technology and deal with technological systems is significant and necessary for the adoption of an e-learning system (Farazkish & Montazer, 2019), in that it is necessary for learners, instructors and staff to have a knowledge about e-learning and its advantages, technical skills for using e-learning programs, knowledge of using computers and other electronic devices, in addition to psychological and ethical readiness.

To assess the level of human readiness, the readiness of human resources, culture, psychological and educational ethics must be assessed by examining the measures of each of these factors, with regard to knowledge of the use of devices, programs, e-learning systems, network, technical and educational skills, acceptance and motivation (Darab & Montazer, 2011, Al-Samarrai et al., 2017) as cleared in Table 8.

2.6 Organization dimension

The readiness of the educational institution represents the organizational efficiency and the ability to adopt the e-learning system (Malkawi, 2022; Manjeese, 2022). To assess the readiness level of the organization, the readiness of the organization’s management should be assessed in terms of the effective participation in the process of adopting and developing the e-learning system, the extent of obligation to implementing the principles of proven policy, technical support, financial allocation, the process of designing and transferring educational content, and the diversity of teaching strategies. Table 9 clarifies the measures of each factor which should be assessed.

In this regard it needs to be pointed out that due to unstable political situations for Iraq in particular which hindered the progress of e-learning system in the country as well as the delay in developing strategies for using of digital technology in the education system due to the deficiency in the basic foundations of the e-learning system work for instance, electrical energy and the weak Internet which resulted in a little awareness and interest of the e-learning system in Iraqi universities (Ibrahim et al., 2019). But the world's exposure to Covid-19 which assured that e-learning is an irreversible educational system (Budur et al., 2021), in that the Iraqi government and academics decision-makers have focused on adoption and development e-learning system in Iraqi universities, for the same reasons, it seems that a proposed model including all of the required factors for the adoption of e-learning system, in which the impact ratio of every single measure is clearly identified, will play a significant role in guiding the academics decision-makers of the Iraqi higher education for better application of information technologies at the universities.

In light of the above background, the initial proposed model of e-learning readiness assessment can be perceived as clarified in Fig. 1.

Fig. 1
figure 1

The initial proposed e-learning readiness assessment model

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2.7 Fuzzy Delphi Method

The Fuzzy Delphi Method (FDM) introduced more than three decades ago by (Murphy et al., 1998; Murray et al., 1985) in that the FDM combines the standard delphi method with the fuzzy theory (Saffie et al., 2016). The method was developed to eliminate ambiguity from the panel agreements used in the Delphi method and minimize inquiry times. FDM is used to derive accurate and trustworthy statistical conclusions from qualitative data (Bui et al., 2020). Delphi approach relies on group dynamics rather than statistical power to bring experts together in an agreement (Okoli & Pawlowski, 2004). Multiple opinions of researchers about the sample size of experts, in that which stated that the number of responsive experts should be 10 to 50 experts (Jones & Twiss, 1978; Yusof et al., 2022). Gedera (2014) indicated the numbers of selected experts should be 15 to 35 experts while Rowe and Wright (2001) identified 5 to 20 experts as a sample size on the delphi method as well as Okoli and Pawlowski (2004) recommended 10 to 18 experts. In fuzzy delphi method, the choice criteria of experts for an investigation are education level, field of expertise, experience, and the readiness for participation in the respective study field (Benssam et al., 2016; Berliner, 2004; Buckley & Doyle, 2016; Bui et al., 2020; Rahman et al., 2021). The essential steps of the FDM are input preparation, data analysis and final decision. Input preparation comprises collecting data, creating questionnaires, and choosing experts. Three procedures make up data analysis: converting qualitative scale to a fuzzy scale, figuring out the threshold value and agreement % and defuzzification. Based on the findings of the data analysis stage, the final decision is taken (Saffie & Rasmani, 2016). A threshold value of 0.2 or less, an agreement percentage of 75% or more, and a defuzzification value of 0.5 or more are prerequisites in the data analysis. FDM has been applied in some previous studies, employed to screen the e-readiness assessment indicators (Al-araibi et al., 2019; Habibi et al., 2015; Jafari & Montazer, 2008; Jaya et al., 2022; Khalli et al., 2022; Marlina et al., 2022; Masouleh et al., 2014; Sulaiman et al., 2020).

3 Research method

The current study was performed in three stages: literary review, expert assessment, and analysis of FDM. The study method is clarified in Fig. 2.

Fig. 2
figure 2

Research method

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3.1 Literary review stage

The first stage aimed to identify the dimensions, factors and potential measures that effect e-learning readiness depend on literary reviewing to various readiness models. A Literary review was performed via the Scopus, Elsevier, Springer Link, Science Direct, AIS e-library, research gate and Google scholar databases.

3.2 Experts validation stage

The fuzzy delphi method was employed to assess the dimensions, factors and measures of e-learning readiness, in that the fuzzy delphi method includes two key steps are: design a questionnaire and analyze the data to reach expert agreements. The questionnaire was structured based on previous empirical studies in that included the dimensions, factors and measures that were identified based on the literature review and used a five-point linguistic scale as shown in Table 2. The questionnaire was reviewed by 4 experts to ensure that content validity, wording clarity and structure integrity.

Table 2 Fuzzy numbering for linguistic scale
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The questionnaire was distributed among experts in the fields of e-learning and the results are analyzed in FDM. This study employed 45 experts, according to Delbecq et al., (1975); Okoli & Pawlowski (2004); Harteis (2022); Yusof et al., (2022) who explained that the experts' number of FDM ranged from 10 to 50 experts. The experts were selected based on the academic qualification, the expertise and contributions to e-learning system. The experts involved in this study, 42 experts came from government and non-government universities in Iraq and 3 experts from Iran. The experts’ demographic information is listed in Table 3.

Table 3 Experts’ demographic information
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3.3 Analysis of FDM stage

The last stage which followed distributing the questionnaire, was analyzing the results. The analysis of FDM comprised three basic steps (Manakandan et al., 2017; Marlina et al., 2022; Sensuse et al., 2018):

  • a. Converting of the linguistic variables into fuzzy numbers

The Triangular Fuzzy Number (TFN) considers the key notion in fuzzy delphi method and the rationale to state the fuzziness or an inexactitude of expert’s opinion. A fuzzy number is a generalization of a regular, real number in that it refers to a connected collection of potential values instead of a single value, where each potential value has a weight between 0 and 1 (Anand & Bharatraj, 2017; Khalli et al., 2022; Nagi et al., 2011), in that every expert's response had a degree of ambiguity which can't process by a fixed score scale (Alghawli et al., 2022; Manakandan et al., 2017; Marlina et al., 2022).

Experts responses translated into three fuzzy values to compose a triangular fuzzy numbers as clarified in Table 2. The conversion process of linguistic scale into three fuzzy numbers values: the minimum (n1), the reasonable (n2), and the maximum (n3). The average of the three fuzzy values were calculated and indicated by the values of m1, m2, and m3 as shown in Tables 4 and 5.

  • b. Items admissibility

Table 4 Experts’ agreement results of the proposed model dimensions
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Table 5 Experts’ agreement results of the proposed model factors
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The three FDM requirements for admissibility of the items are:

  • A threshold value for identifying the agreement degree of experts which should be ≤ 0.2. A threshold value for all items was found, by calculating the difference between average of fuzzy values and the expert response fuzzy numbers through applying the following formula (Chen, 2000; Khalli et al., 2022; Manakandan et al., 2017; Marlina et al., 2022; Sulaiman et al., 2020):

    $$\mathrm{Threshold value }\left(\overline{m },\overline{n }\right)=\sqrt{{\frac{1}{3}[(m1-n1)}^{2}+{(m2-n2)}^{2}+{(m3-n3)}^{2}]}$$

  • (2) The percentage of an expert agreement which is represented the percentage of the frequency of accepted values (threshold value ≤ 0.2), should be ≥ 75% (Chen, 2000; Khalli et al., 2022; Marlina et al., 2022; Sulaiman et al., 2020).

    $$The\;percentage\;of\;an\;expert\;agreement = \frac{\mathrm{The\;frequency\;of\;threshold\;values\;}\le 0.2 }{\mathrm{The\;number\;of\;experts\;}} \mathrm{x }100$$

  • (3) Arrangement the items (Defuzzification process).

  • c. The process of defuzzification

The defuzzification process is the priority identification process of items through establish the weights or importance level of the items and ranking the items to reporting whether to approved or disapproved by employing the following formula (Jaya et al., 2022; Khalli et al., 2022; Manakandan et al., 2017; Marlina et al., 2022; Sulaiman et al., 2020):

$$\mathrm{Defuzzification value }=1/3*(\mathrm{m}1+\mathrm{m}2+\mathrm{m}3)$$

If the fuzzy number average has defuzzification value ≥ value α cut (0.5), the item will be approved (Jaya et al., 2022; Khalli et al., 2022; Marlina et al., 2022; Sulaiman et al., 2020).

4 Results

The results included in the Tables 4, 5 and 6 were obtained from the fuzzy delphi method analysis, in that the assessment data of the 45 experts who participated in this study, to the questionnaire in which comprised the dimensions, factors and potential measures of the proposed model. All the three dimensions scored high positive assessments ranging between “agree” to “strongly agree”. The linguistic scale was then converted into a fuzzy scale in which has employed 5 linguistic variables are: “strongly agree”, “agree”, “not sure”, “disagree”, and “strongly disagree”, while the 13 factors scored assessments ranging among (not sure, agree, and strongly agree), the measures of the factors scored varied assessments ranging the five linguistic variables.

Table 6 Experts’ agreement results of the proposed model measures
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A typical and approved item is the one that achieves the FDM requirements: a threshold value ≤ 0.2, expert agreement percentage ≥ 75% and defuzzification value ≥ 0.5.

From the Table 4 the three requirements were fulfilled in that all the three dimensions had threshold value ≤ 0.2, the experts’ agreement ≥ 75% and defuzzification value ≥ 0.5.

Table 5 illustrated that the 13 factors were achieved the requirements of FDM, in that the experts agreement for (11) factors ranging from 91 to 100% except technological factor 76% and finance factor 89%.

As for the result analysis of infrastructure dimension measures as shown in Table 6 appeared that five measures of technological factor, four measures of communication network factor, three measures of security factor have a percentage of experts agreement < 75%.

Experts' agreement is < 75% for five measures of human resources, three measures of culture and psychological factors from the human dimension.

As for the organization dimension: one measure of management and evaluation factors, two measures of content factor, and six measures of finance factor had expert agreement < 75% as clarified in Table 6.

Only the potential measures of support and policy factors of the organization dimension and educational ethics factor of the human dimension were fulfilled the FDM requirement in that the experts' agreement ranging from 93 to 100%.

5 Discussion

The validation process results of the proposed model for assessing e-learning readiness of Iraqi universities have been shown that all three key dimensions which comprised infrastructure, human and organization, a strong evidence by getting the experts’ agreement about its sufficiency and suitability to be the key aspects to e-learning readiness assessment process in the Iraqi universities in that these dimensions accepted with 76% for infrastructure, 98% for human, 100% for organization, and defuzzification values (0.751, 0.698, and 0.716), respectively, that means infrastructure dimension at the first ranking, at the second rank is organization dimension then Human dimension at the third rank among the key dimensions in terms of priority.

The analysis results showed that all the 13 proposed factors have an effect on the readiness level of e-learning, in that have approved with different percentages by the experts, the factors of infrastructure dimension obtained the higher defuzzification values (0.751, 0.724, and 0.724), respectively which make the technological factor at the first rank, communication network and security factors at the second rank among the factors of the preliminary model in terms of effect priority on the e-learning readiness level, two factors of human dimension which were human resources and educational ethics as well as the management factor from organization dimension factors at the third ranking with fuzzy value 0.711 among the proposed model factors but the first level among the factors of its dimensions, that means the most important factor of organization dimension is "Management" which indicates to the formation an active management system and the required principles, while culture and psychological factors of the human dimension, in addition to content and finance factors of the organization dimension achieved a fuzzy value 0.671 which was the minimum of fuzzy values in the analysis results that make these factors at the lowest rank among the model factors in terms of importance and effect level on e-learning readiness, the organization dimension factors which included support and policy obtained 0.684 and 0.680 as fuzzy values that made these factors at the third and fourth ranking respectively in terms of effect priority within the organization dimension and the fifth and sixth rank among the other factors of the proposed model. The final proposed factor which is evaluation achieved 0.707 as defuzzification value that make this factor at the second ranking of importance among organization factors and forth rank among all the proposed model factors based on the experts’ opinion.

The results of the analysis state that all (119) measures achieved requirements which relevance with the threshold value and fuzzy number value but the percentage value of experts’ agreement for 33 measures is less than 75% which means 33 measures have been disapproved due to the failure of achieving the FDM three requirements, the remaining 86 measures achieved the requirements.

The 33 approved measures are as the following and illustrated in the Table 6.

  • • 12 measures from the infrastructure dimension.

  • • 11 measures from the human dimension.

  • • 10 measures from the organization dimension.

The factors of technological, communication network and security are described as a critical factors that effects in e-learning readiness, in that technological factor has (12) measures, five were disapproved, network communication factor has (11) potential measures, four of them have disapproved and security factor has (13) measures, three were disapproved because the experts’ agreement value was < 75%.

Two factors of human dimension, which are culture and psychological, each have three measures with < 75% experts’ agreement, five measures of human resources factor have been disapproved, one of them is related to teachers' knowledge of basic IT skills, has only obtained 71% experts' agreement, and the others achieved less than 70%, in comparison the educational ethics factor has (5) measures that achieved all of the three FDM requirements in the item admissibility process.

At first, the organization dimension included (56) measures, (10) of them were disapproved, in that these measures are specific to the factors of management, content, finance and evaluation as illustrated in Table 6. All the measures of support and policy factors from the organization dimension are approved according to meeting the requirements to getting the experts’ agreement for its suitability to be measures for assessment effect level of the factors on e-learning readiness.

6 Designing the final e-learning readiness assessment model

This study based on process logic (input-process-output) (Hansen, 2005) to contrast an e-learning readiness assessment model in Iraqi universities and fuzzy logic (Markowski et al., 2009) for an accurate analysis process. The prototype model of e-learning readiness included three dimensions: infrastructure, human and organization, in that the model has constructed based on the factors and measures specified in the literary review. The infrastructure dimension comprised three factors: technological, communication network and security, while the human dimension factors included human resources, culture, psychological and Educational ethics, the organization dimension comprised six factors management, content, support, policy, finance and evaluation.

Before the validation process, the proposed e-learning readiness assessment model included three dimensions with (13) factors and (119) measures. Following fuzzy delphi assessment, all the proposed dimensions and factors have validated and approved with identifying the priority of the dimensions, factors and measures while (33) measures in the prototype model did not achieve the assessment requirements, resulting in their removed from the model.

The e-learning readiness model depended on the FDM analysis comprised three dimensions with (13) factors as depicted in Fig. 3 and (86) measures. It is important to notice that there is no difference between the proposed initial model and the final model that has been validated by experts except a number of measures and the arrangement the priority due to the process of identifying the dimensions and factors by the academic researchers who are fully familiar with the reality in addition to the researchers and the experts from one environment.

Fig. 3
figure 3

The e-learning readiness assessment model (IOH)

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7 Conclusion

Today, one of the most important challenges facing Iraqi universities is finding the most appropriate strategies to improve educational process quality, therefore in the post-corona era; the Iraqi Ministry of Higher Education has shown great interest in the e-learning system and the search of the most suitable methodologies for its proper adoption and development in all higher education institutions. This study has designed a comprehensive model which has comprised three key dimensions: infrastructure, organization and human, in that designed based on evaluating various previous models, and analyzing the data that elicited from the experts’ assessment. The FDM analysis results have shown that the experts have agreed upon the suitability of the pre-identified the (3) dimensions and (13) factors comprising technological, communication network, security, management, support, evaluation, policy, content, finance, human resources, educational ethics, culture, and psychological as well as identifying (86) measures for assessing of the (13) factors and exclusion of (33) measures, they didn’t receive expert agreement when assessing the validation. Although previous studies dealt with the subject of e-learning readiness, this study was unique in designing a comprehensive and more accurate model for assessing e-learning readiness in Iraqi universities (IOH), consisting of dimensions, factors and measures that were selected using the fuzzy delphi method and assessed by experts with various expertise in this field to obtain the best quality results and more accurate, to precisely assess the ability of universities and to provide suggestions on the most appropriate plans and procedures to be taken for adopting and developing the e-learning system in Iraqi universities.