Journal of Management Information and Decision Sciences (Print ISSN: 1524-7252; Online ISSN: 1532-5806)

Research Article: 2021 Vol: 24 Issue: 4

AHP & PROMETHEE II for the evaluation of websites of mediterranean protected areas??? managing boards

Katerina Kabassi, Department of Environment, Ionian University

Sapfo Mpalomenou, Department of Environment, Ionian University

Aristotelis Martinis, Department of Environment, Ionian University

Citation Information: Kabassi, K., Mpalomenou, S., & Martinis, A. (2021). AHP & PROMETHEE II for the evaluation of websites of mediterranean protected areas’ managing boards. Journal of Management Information and Decision Sciences, 24(4), 1-17.


The role of national parks is multidimensional, diverse, and important, and maybe supported by a good website. However, a website should be evaluated to ensure that the goals of a national park are met. For this reason, an evaluation of the websites of Protected Areas Managing Boards in two Mediterranean countries, Greece and Italy, has been implemented using Multi-Criteria Decision Making models. The paper presents the effective combination of AHP with PROMETHEE II for evaluating environmental websites that contain content about the national parks. The implementation of the evaluation experiment reveals the effectiveness of the combination of AHP with PROMETHEE II in environmental website evaluation and presents the electronic presence of national parks in two Mediterranean countries. The results of PROMETHEE II are combined with another multi-criteria decision-making model called Simple Additive Weighting, which has been effectively used in the past for the evaluation of environmental websites.


AHP; PROMETHEE II; Website evaluation.


Mediterranean countries are well known for their beautiful physical and cultural environment. Several researchers (Romano et al., 2021) have highlighted the economic and social advantages that the National Parks (NPs) and Protected Areas (PAs), in general, offer in a country. Therefore, several countries have founded Management Bodies of Protected Areas (MBPAs). These bodies constitute an important official tool for the management and the protection of natural and cultural heritage (Papageorgiou & Kassioumis, 2005). Indeed, the role of MBPAs in NPs is multidimensional, diverse, and important, and may be supported by a good website. A website can shape the image of the MBPA and may produce a virtual experience for visitors, promote the environmental value of its area, and promote the area as an ecotouristic destination. Reviews of ecotourism literature in national parks mainly focus on political, social, cultural, and economic factors that affect ecotourism in a NP (Rhama, 2020). However, there are no studies focusing on the websites as powerful tools for promoting the ecotouristic value of NPs.

The importance of the websites for promoting environmental information is indisputable and has been mentioned by several researchers (Głąbiński, 2015; Rusielik & Zbareszewski, 2014; Su et al., 2016; Thapa & Lee, 2017; Podawca & Pawlat-Zawrzykraj, 2018). A website being able to meet the multi-dimentional goals of a MBPA is not easy. Therefore, an evaluation experiment should be implemented. Evaluation is an important phase of a website’s life-cycle and the research areas of software engineering and human-computer interaction have paid a lot of attention in different aspects of this phase.

Evaluations are usually complicated procedures that focus on the examination of several different criteria. As a result, different Multi-Criteria Decision Making (MCDM) models have been used for evaluating websites in different domains (Kabassi et al., 2020a; Kabassi et al., 2020b; Kabassi et al., 2019a) as well as websites of environmental content (Kabassi & Martinis, 2020; Kabassi et al., 2019b). Previous work on the evaluation of websites of environmental content (Martinis et al., 2018, Kabassi & Martinis, 2020; Kabassi et al., 2019b) has revealed the criteria and the weights of importance of these criteria using the Analytic Hierarchy Process (AHP) (Saaty, 1980; Saaty & Hu, 1998). In these evaluation experiments, AHP has been implemented solely or combined with different theories such as VIKOR (Vlsekriterijumska Optimizacija I KOmpromisno Resenje) (Opricovic, 1998; Opricovic & Tzeng, 2004; 2007).

In this paper, we present how AHP (Analytic Hierarchy Process) (Saaty, 1980) can be effectively combined with PROMETHEE II (Preference Ranking Organization METHod for Enrichment Evaluations II) (Brans, 1982; Brans & Vincke, 1985) in order to evaluate the websites of MBPA. The criteria used in the evaluation experiment have been selected during previous work and have been used again for the evaluation of websites of environmental content (Martinis et al., 2018; Kabassi et al., 2019b; Kabassi & Martinis, 2020).

The choice of AHP over other MCDM theories is easily made as it presents a formal way of quantifying the qualitative criteria of the alternatives and in this way removes the subjectivity of the result (Tiwari, 2006). Nevertheless, the method has a rather complex procedure of pairwise comparison of the alternatives, which is not preferred in cases where the number of alternatives is very high. Therefore, AHP was selected to be combined with PROMETHEE II. More specifically, PROMETHEE method is software-driven, user-friendly, provides a direct interpretation of parameters, and analyzes the sensitivity of results. PROMETHEE II is a superior method for ranking and selecting from among a finite set of alternative actions while considering several conflicting criteria (Abedi et al., 2012). More specifically, PROMETHEE II outranking method was adopted for this specific evaluation experiment to aggregate the opinions of various decision-makers that comment on websites of environmental content. In view of the above, we show how AHP can be used for calculating the weights of criteria and then combined with PROMETHEE II for evaluating and comparing the websites.

The combination of AHP with PROMETHEE II has been effectively used in different domains (Vahid et al., 2014; Goswami, 2020; Singh et al., 2020) but never before for the evaluation of websites. The scope of this paper is twofold: 1) checking the effectiveness of the combination of AHP with PROMETHEE II for evaluating websites of environmental content and 2) evaluating the electronic presence of MBPAs in two different Mediterranean countries. In order to check the effectiveness of PROMETHEE II for the evaluation of websites of MPBA, we compared the results of PROMETHEE II with Simple Additive Weighting (SAW) (Hwang & Yoon, 1980), which has been used successfully before for the evaluation of environmental websites (Kabassi et al., 2020b).

Protected Areas in Mediterranean and the World

A protected area is a clearly defined geographical space, recognized, dedicated, and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values (Dudley, 2008, ). According to scientists at IUCN and UN Environment's World Conservation Monitoring Centre, there are 239.729 terrestrial protected areas today, covering almost 20 million square kilometers or 15.38% of the world's land, 18.165 marine protected, corresponding to 7.65% of the marine space. (IUCN, 2018; Steven et al., 2013). In the European Union (EU) the Natura 2000 network currently covers more than 27,000 areas covering a total area of around 1.150.000 square kilometers of land and sea areas. The area covered by the Natura 2000 network represents about 18% of the total. The national land cover of the Natura 2000 network ranges from around 9% to around 38%, depending on the country (EU, 2021).

Protected areas are the basis for the conservation of biodiversity while contributing to the improvement of the living standard of the local communities. In recent decades, with the contribution of collective decisions of public bodies and local communities, protected areas have rapid growth around the world (Watson et al., 2014).

Protected areas today have a very important role to play. They were created not only to protect and preserve natural and cultural heritage, terrestrial and marine ecosystems, and endangered flora and fauna but also to contribute to sustainable development, the revitalization of local communities, and the national economy through the development of mild alternatives tourist activities (Robalino & Villalobos-Fiatt, 2015; Saviano et al., 2018; Tomaskinova et al., 2019). Furthermore, protected areas can contribute to reducing climate change and enhance ecosystem services for the environment and society. It is estimated that the global network of protected areas stores at least 15% of the terrestrial carbon.

Mediterranean climates are one of the rarest of the Earth's thirteen terrestrial biomes, covering only 2% of the Earth's surface (Cox & Underwood, 2011). The Mediterranean regions having a mild climate with cool wet winters and hot dry summers, host millions of people and many of the world's largest metropolitan areas, resulting in the constant burden and degradation of terrestrial and marine ecosystems. Although Mediterranean ecosystems are considered very important for biodiversity and are widely recognized as a global conservation priority, the protected areas in the Mediterranean climate formally cover only 4.3% of the whole area (Underwood et al., 2009), which is less than half of the globally accepted ecosystem protection target. So the degradation of the protected areas and the ecosystems in the Mediterranean is quite obvious.

In recent decades, tourism has been a significant source of income and employment in the Mediterranean protected areas, while at the same time having a significant negative impact on nature and biodiversity (Monti et al., 2018). Many species in the Mediterranean region, especially those with significant habitat requirements, come into conflict with humans for space and resources (Buckley et al., 2016). A few years ago, ecotourism emerged, a mildly sustainable activity that combines recreation with respect for the environment and the principles of sustainable development. Today, protected areas are called upon to play an important role in revitalizing local economies, planning sustainable activities, while respecting the natural and cultural heritage of each place (Yergeau, 2020).

It is generally accepted that ecotourism is the best tourism activity for PAs. However, it is estimated that ecotourism is more than a touristic activity; it is a different way of life that satisfies the need of man to be close to nature. In practice, it is a comprehensive process of sustainable development. It is argued that ecotourism is synonymous with "integrated tourism" which "… is part of a comprehensive system that includes the environment, the community, industry, the economy, and the legal environment. Its design must be democratic and combined with relevant design procedures. Its design will help tourism and will contribute to the prosperity of a community "(Diamantis, 2004).

The main tool for succeeding in effective communication, boosting ecotourism, and provide environmental awareness is the Internet. The importance of the websites for creating perception is significant because it influences the visitors, informs them of the characteristics of a PA, the landscape, the culture, the gastronomy, and all the different parameters which constitute its profile. This virtual approach decides to visit an area easier and part of the visit may be implemented through the Internet (Doolin et al., 2002). As a result, the websites of Greek and Italian National Parks were collected. A1-A26 are the websites of Greek MBPAs and B1-B23 are the websites of Italian MBPA. All websites are presented in Table 1 and are evaluated in order to reveal if their electronic presence of Mediterranean MBPAs is satisfactory and which MBPAs have the best electronic image.

Table 1 The Websites of NPS in Greece and Italy
A1 National Park of Schinias-Marathon
A2 National Park of Koronia and Volvi Lakes
A3 Northern Pindos National Park (of Vikos gorge-Aoös river and Pindos)
A4 Messolonghi Lagoon National Park
A5 Kerkini Lake National Park
A6 Dadia-Lefkimi-Soufli Forest National Park
A7 Evros Delta National Park
A8 Amvrakikos Wetlands National Park
A9 National Park of Eastern Macedonia and Thrace (Nestos Delta, Vistonida and Ismarida lake)
A10 Rodopi Mountain Range National Park
A11 Axios Delta National Park
A12 Prespa National Park
A13 Chelmos-Vouraikos National Park
A14 National Marine Park of Zakynthos
A15 National Marine Park of Alonissos and Northern Sporades
A16 Protected Nature Area of Kalamas and Acheron Rivers
A17 Kotychi and Strofylia Wetlands National Park
A18 National Park of Tzoumerka, Peristeri & Arachthos Gorge
A19 Pamvotis Lake Protected Area
A20 Olympus National Park
A21 Protected Nature Area of Karpathos and Saria
A22 Oiti National Park
A23 Lake Karla-Mavrovouni-Kefalovryso-Velestino
A24 Mount Aenos National Park
A25 Parnassos National Park
A26 Samaria National park
B1 Parco Nazionale d’ Abruzzo, Lazio e Molise
B2 Parco Nazionale dell’Alta Murgia
B3 Parco Nazionale dell’appennino Lucano – Val d’Agri-Lagonegrese
B4 Parco Nazionale dell’ Appennino Tosco-Emiliano
B5 Parco Nazionale dell’Arcipelago di La Maddalena
B6 Parco Nazionale dell’Arcipelago Toscano
B7 Parco Nazionale dell’Asinara
B8 Parco Nazionale dell’Aspromonte
B9 Parco Nazionale del Cilento, Vallo di Diano e Alburni
B10 Parco Nazionale delle Cinque Terre
B11 Parco Nazionale del Circeo
B12 Parco Nazionale delle Dolomiti Bellunesi
B13 Parco Nazionale delle Foreste Casentinesi, Monte Falterona e Campigna
B14 Parco Nazionale del Gargano
B15 Parco Nazionale del Gran Paradiso
B16 Parco Nazionale del Gran Sasso e Monti della Laga
B17 Parco Nazionale della Majella
B18 Parco Nazionale dei Monti Sibillini
B19 Parco Nazionale del Pollino
B20 Parco Nazionale della Sila
B21 Parco Nazionale dello Stelvio
B22 Parco Nazionale della Val Grande
B23 Parco Nazionale del Vesuvio

The websites of PAMPBs are considered to be the alternatives in our decision-making problem.

Applying a MCDM Model

The application of any multi-criteria decision-making theory in order to evaluate a website involves the preliminary stages (1-3). The multi-criteria decision-making theories differ in the way the weights of the criteria are calculated, while many theories do not have a predefined way for criteria weights’ calculation. PROMETHEE II does not have a well-defined way for the calculation of the criteria’s weights. Therefore, AHP is used for this purpose and the particular MCDM theory is implemented in the subsequent steps.

The steps (1-3) that are implemented irrelevant of the MCDM model that is applied are:

1. Forming the overall goal: For this study, the overall goal was to evaluate the MBPAs’ websites.

2. Forming the set of evaluative criteria: For this study, the criteria for evaluating environmental websites were selected by the human experts participating in a previous experiment (Martinis et al., 2018) from a pool of criteria previously proposed by Tsai et al. (2010). This process resulted in the following set of criteria and is presented in detail in Kabassi et al. (2019b):

• c1-Quality of content.

• c2-Attractiveness.

• c3-Navigability.

• c4-Relevancy.

• c5- Accessibility.

• c6- Responsiveness.

• c7- Links.

• c8- Multilingualism.

• c9- Quality of mobile interactiveness.

• c10-Services.

Finding the websites to be evaluated: In this step, the websites of the MBPAs that were going to be evaluated were selected. As already mentioned, we are going to evaluate the websites of MBPA of two Mediterranean countries, Italy and Greece. In Greece, twenty-six out of the twenty-eight MBPAs have a website while in Italy 23 out of the 25 MBPA have a website. The evaluation experiment involved all the websites of Greek and Italian MBPAs and these websites are presented in Table 1.

AHP for Calculating Weights of Criteria

AHP aims to analyze a qualitative problem through a quantitative method. According to Zhu & Buchman (2000), after having developed the goal hierarchy, in order to apply AHP one has to set up the pair-wise comparison matrix of criteria. In order to apply AHP, first, we have to form the set of evaluators that would act as decision-makers in the application of AHP for the calculation of the criteria’s weights. Indeed, a correct choice of an expert would give reliable and valid results. For this purpose, both domain experts and software engineers have been selected to participate in the experiment to increase the reliability of the results. This means that the group of evaluators should have both software engineers and domain experts such as environmentalists or ecologists. More specifically, five (5) human experts were used to make the pairwise comparisons of criteria. The group of human experts was formed by two (2) software engineering experts and three (3) environmentalists (one had experience in environmental awareness in National Parks and the other had experience in ecology and ecotourism) so that a diversity of views could have been taken into account.

The steps that need to implement are:

Setting up a pair-wise comparison matrix of criteria: In this step, a comparison is implemented among the criteria of the same level. For this purpose, a comparison matrix is constructed and each one of the decision-makers was asked to complete the comparison matrix by completing the rate that reveals that pairwise comparison of the criterion in the row with the criterion in the column. They are asked to use the values of the nine-point scale for the pairwise comparison presented in Table 2.

Table 2 The Nine-Point Scale for Pairwise Comparison
Importance Definition Explanation
1 Equal importance The importance of two criteria or alternatives is equal
2 Weak
3 Moderate importance A slight favor of one criterion or alternative over another
4 Moderate plus
5 Strong importance A strong favor of one criterion or alternative over another
6 Strong plus
7 Very strong importance A very strong favor of one criterion or alternative over another
8 Very, very strong
9 Extreme importance One criterion or alternative is surely favored over another

As a result, 5 different matrixes were collected. The values in the cells of the final matrix are calculated as a geometric mean of the corresponding values of the cells of the five matrixes. The final pair-wise comparison matrix of criteria is presented in Table 3.

Table 3 Pairwise Comparison of Criteria
Criteria Quality of content Attractiveness Navigability Relevancy Accessibility Responsiveness Links Multilingualism Quality of mobile interactiveness Services  
Quality of content 1.00 5.06 2.22 2.45 4.57 2.26 4.54 4.19 4.62 7.96  
Attractiveness 0.20 1.00 1.28 0.60 1.35 0.46 1.67 1.22 2.30 2.97  
Navigability 0.45 0.78 1.00 0.62 2.09 1.00 1.61 2.08 3.77 3.62  
Relevancy 0.41 1.66 1.61 1.00 3.23 2.88 3.77 3.53 4.21 5.55  
Accessibility 0.22 0.74 0.48 0.31 1.00 0.52 0.75 0.61 0.57 1.18  
Responsiveness 0.44 2.18 1.00 0.35 1.91 1.00 1.91 2.18 4.43 2.72  
Links 0.22 0.60 0.62 0.27 1.33 0.52 1.00 1.23 1.62 1.23  
Multilingualism 0.24 0.82 0.48 0.28 1.63 0.46 0.81 1.00 1.10 1.12  
Quality of mobile interactiveness 0.22 0.43 0.27 0.24 1.76 0.23 0.62 0.91 1.00 2.44  
Services 0.13 0.34 0.28 0.18 0.85 0.37 0.81 0.89 0.41 1.00  

Calculating weights of criteria: After making pair-wise comparisons, estimations are made that result in the final set of weights of the criteria. More specifically, the principal eigenvalue and the corresponding normalized right eigenvector of the comparison matrix that is calculated, provide the relative importance of the various criteria being compared. The elements of the normalized eigenvector were the weights of criteria or sub-criteria. In terms of simplicity, we had used the 'Priority Estimation Tool' (PriEst) (Siraj et al., 2015), an open-source decision-making software that implements AHP, for making the calculations that the theory requires. This process resulted in the following weights for the ten criteria evaluated:











PROMETHEE II for Ranking Websites

The PROMETHEE methods belong to the family of the outranking methods. The PROMETHEE family of outranking methods is one of the most recent MCDM methods and creates a partial pre-order (PROMETHEE I) or a complete pre-order (PROMETHEE II) on the set of possible actions that can be proposed to the decision-maker in order to achieve the decision problem. The steps of PROMETHEE II after having defined criteria and their weights of importance are:

Calculating the values of the criteria. In this step, the evaluators, which in general may be the same as those specifying the weights of the criteria or not, are asked to visit all the websites presented in Table 1. In the specific case, 8 decision-makers provided values to the 10 criteria of the evaluation. Those values were taken from the nine-number scale (Table 2) so the values would be comparable.

As soon as all the values of the 8 decision-makers were collected, the mean value was calculated for the corresponding values of each criterion for each website. The result of this process is presented in Table 4.

Table 4 The Geometric Mean of the Values of the Criteria for all Websites
  C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
A1 3.75 2.88 3.13 3.88 3.75 3.50 3.25 3.50 3.38 3.75
A2 3.88 3.75 4.13 3.88 4.00 3.75 3.50 2.88 3.13 3.63
A3 4.13 4.50 3.63 3.88 4.50 4.00 4.00 4.13 3.63 4.25
A4 3.75 3.13 3.88 3.88 3.88 3.75 3.88 4.75 3.25 3.75
A5 3.88 3.50 3.50 3.88 4.00 3.75 4.00 3.13 2.88 4.50
A6 3.63 3.38 3.88 3.75 3.63 3.75 3.88 2.38 2.75 3.63
A7 3.50 2.75 3.75 3.75 3.50 3.75 3.75 2.63 2.88 3.63
A8 3.75 3.00 3.38 3.88 3.38 3.75 3.13 1.75 2.88 3.38
A9 3.63 3.00 3.38 3.38 3.25 3.38 3.50 4.75 3.50 4.25
A10 4.00 3.75 4.50 3.88 3.38 3.63 4.25 2.38 3.25 4.13
A11 4.50 4.25 4.00 4.00 4.13 4.00 4.13 2.88 3.38 4.25
A12 3.63 3.25 3.88 3.50 3.88 3.75 3.50 2.38 2.63 3.63
A13 3.75 4.25 4.00 3.88 3.88 3.88 3.75 2.38 3.00 3.88
A14 3.50 3.50 3.25 3.13 3.38 3.50 3.25 2.25 3.13 3.38
A15 4.00 3.63 3.75 4.13 4.00 4.00 2.50 1.63 3.25 3.38
A16 3.13 2.50 3.13 3.13 3.25 3.13 3.38 1.25 2.63 3.38
A17 3.63 4.25 3.50 3.63 3.50 3.50 3.63 1.88 2.63 2.88
A18 3.25 3.75 3.63 3.88 4.00 3.88 3.63 2.25 3.25 3.75
A19 3.75 4.00 3.75 3.88 4.00 4.00 3.38 2.25 3.00 3.50
A20 3.75 3.00 3.50 3.38 3.38 3.13 3.75 2.13 3.00 3.38
A21 3.25 2.50 3.25 3.63 3.25 3.00 4.13 2.25 3.13 3.38
A22 3.75 3.75 3.38 3.63 3.63 3.75 2.63 2.25 2.75 2.88
A23 3.50 3.13 3.50 3.38 3.63 3.63 4.25 5.00 3.50 3.88
A24 3.13 2.63 3.38 3.25 3.25 3.38 3.50 2.25 3.38 3.63
A25 4.00 4.50 3.88 3.88 4.25 4.00 4.00 2.75 3.50 3.88
A26 4.00 4.25 4.00 4.00 3.88 3.88 4.13 2.25 3.25 3.50
B1 2.83 3.70 5.34 5.20 5.34 5.34 3.56 6.05 3.56 2.67
B2 2.80 2.80 4.58 4.84 5.33 5.34 3.84 7.22 4.59 2.67
B3 3.19 3.19 3.81 3.44 4.45 4.56 2.81 1.78 3.56 4.45
B4 3.81 2.92 3.56 3.56 5.34 5.09 4.45 4.20 4.45 2.67
B5 3.19 3.31 3.81 3.81 5.34 4.58 3.56 6.23 4.45 3.56
B6 3.81 3.69 2.92 2.19 5.34 3.56 6.36 0.89 4.45 0.89
B7 4.45 3.44 3.44 3.56 5.48 3.56 4.45 0.89 4.47 4.45
B8 5.47 3.44 3.44 3.56 5.34 4.33 3.56 1.78 4.45 3.56
B9 5.47 5.22 4.45 5.59 5.34 6.11 6.23 2.55 6.22 5.34
B10 6.34 5.33 4.45 5.34 5.34 4.45 5.34 4.84 6.23 4.45
B11 4.58 2.81 4.58 5.34 3.56 4.45 4.45 4.84 5.34 3.56
B12 5.34 3.56 4.33 5.22 4.45 5.09 4.45 7.89 6.23 4.45
B13 3.69 5.34 4.58 4.45 5.34 5.34 3.56 3.44 6.23 4.45
B15 4.58 5.34 3.69 5.34 5.47 6.22 4.45 3.31 6.23 2.67
B16 5.47 4.58 5.34 4.45 5.34 6.22 5.34 2.92 6.23 2.67
B17 4.45 5.34 5.34 3.69 5.34 6.20 4.45 5.22 6.23 2.67
B18 6.48 6.36 6.47 5.47 6.09 5.97 6.20 4.45 7.09 3.56
B19 3.44 3.56 4.33 4.47 5.98 4.45 3.56 1.92 1.81 1.78
B20 4.33 4.33 3.56 4.45 7.27 5.23 4.45 2.55 2.69 3.56
B21 3.69 4.45 3.69 3.69 5.34 5.34 3.56 1.80 4.45 3.56
B22 4.45 4.45 6.25 5.34 5.34 5.22 4.45 4.08 4.45 5.34
B23 4.72 6.22 4.58 4.58 5.34 6.23 4.45 5.98 6.23 5.34

Making comparisons and calculate the preference degree. This step computes for each pair of possible decisions and each criterion, the value of the preference degree. Let be the value of a criterion j for a decision a. We noted , the difference of the value of a criterion j for two decisions a and b.


is the value of the preference degree of a criterion j for two decisions a and b. The preference functions used to compute these preference degrees are defined such as:



Aggregating the preference degrees of all criteria for pair-wise decisions. This step consists of aggregating the preference degrees of all criteria for each pair of possible decisions. For each pair of possible decisions, we compute a global preference index. Let C be the set of considered criteria and the weight associated with criterion . The global preference index for a pair of possible decision a and b is computed as follows:


Calculate positive and negative outranking flow. This step, which is the first that concerns the ranking of the possible decisions, consists of computing the outranking flows. For each possible decision a, we compute the positive outranking flow and the negative outranking flow . Let be the set of possible decisions and the number of possible decisions. The positive outranking flow of a possible decision is computed by the following formulae:


The negative outranking flow of a possible decision is computed by the following formulae:


Calculate the net outranking flow. The last step of the application of PROMETHEE II consists of using the outranking flows to establish a complete ranking between the possible decisions. The ranking is based on the net outranking flows. These are computed for each possible decision from the positive and negative outranking flows. The net outranking flow of a possible decision is computed as follows:


Ranking Websites and Analysing the Results

The application of steps of PROMETHEE II resulted in calculating the outranking flow . The higher the value of the net outranking flow for a decision, the better the decision is. As a result, the alternative websites of the parks are ranked taking into account the values of the net outranking flow . The higher the value is, the better the website is. The ranking as well as the values of net outranking flow are presented in Table 5.

Table 5 The Φ(Α) Values of all Alternative Websites
Rank Φ(α) Φ+ Φ-
1 Β17 0.9153 0.9559 0.0406
2 Β22 0.7803 0.8685 0.0883
3 Β10 0.7677 0.8620 0.0943
4 Β9 0.7546 0.8563 0.1017
5 Β15 0.6836 0.8150 0.1314
6 Β23 0.5950 0.7815 0.1865
7 Β14 0.5859 0.7690 0.1832
8 Β21 0.5484 0.7452 0.1968
9 Β16 0.5379 0.7372 0.1993
10 Β12 0.5114 0.7423 0.2309
11 Β13 0.3512 0.6435 0.2923
12 Α11 0.3268 0.6485 0.3217
13 Β19 0.3070 0.6421 0.3351
14 Β11 0.2730 0.6153 0.3423
15 Α3 0.2532 0.6079 0.3547
16 Α25 0.2317 0.5841 0.3524
17 Α26 0.1449 0.5446 0.3997
18 Α10 0.0995 0.5170 0.4176
19 A2 0.0632 0.4995 0.4363
20 B8 0.0617 0.5028 0.4411
21 B7 0.0201 0.4919 0.4718
22 A13 0.0056 0.4577 0.4520
23 B20 -0.0148 0.4672 0.4820
24 B1 -0.0157 0.4820 0.4977
25 A15 -0.0377 0.4606 0.4984
26 A5 -0.0656 0.4377 0.5034
27 A19 -0.1015 0.4078 0.5092
28 B4 -0.1016 0.4240 0.5256
29 B18 -0.1241 0.4308 0.5549
30 A4 -0.1303 0.3892 0.5195
31 B2 -0.1629 0.4106 0.5734
32 B6 -0.2044 0.3820 0.5864
33 B5 -0.2336 0.3591 0.5928
34 A18 -0.2592 0.3386 0.5978
35 A6 -0.2976 0.3269 0.6245
36 A22 -0.3346 0.2956 0.6302
37 A17 -0.3719 0.2900 0.6619
38 A1 -0.3751 0.2793 0.6544
39 A12 -0.3825 0.2836 0.6662
40 A23 -0.4052 0.2794 0.6846
41 B3 -0.4066 0.2923 0.6989
42 A9 -0.4186 0.2648 0.6835
43 A8 -0.4463 0.2327 0.6790
44 A7 -0.4720 0.2456 0.7176
45 A20 -0.4875 0.2219 0.7094
46 A14 -0.6001 0.1800 0.7802
47 A21 -0.7010 0.1319 0.8330
48 A24 -0.7684 0.0981 0.8665
49 A16 -0.8990 0.0341 0.9331

All the websites of the MBPAs, information about its structure, objectives, financial statements, etc. Additionally, all of them contained information about the ecosystem of the PA and gave contact information. The final ranking of the websites of the National Parks shows that almost half of the websites (45%) are considered good. The best one is the website of Della Majiella (B17), which is considered to be much better than the second according to the value calculated by the application of the MCDM model. Then the next three websites (B10 - Delle Cinque Terre, A9 - Del Cilento, Vallo di Diano e Alburni, B22 - Della Val Grande) are considered much better than the following ones. One of the best Greek websites is that of A11 - Axios Delta National Park. The websites of the National Parks that have a value that is lower than zero are not considered very good at promoting environmental information and need a re-design and update of content.

One can easily observe in Table 4 that the first 11 places in the ranking of a website are occupied by the websites of Italian MBPA the Italian websites outbalance the websites of Greek MBPA. Furthermore, the last 8 places in the ranking are occupied by Greek MBPA’s websites. Taking into account these results the websites of the Italian MBPA outrank the websites of the Greek MBPA.

Comparing Results with SAW

In order to check the effectiveness of PROMETHEE II for the evaluation of websites of environmental content, we compared the results of PROMETHEE II with SAW, which has been used successfully before for the evaluation of websites (Kabassi et al. 2020b). For this purpose, we use the values of criteria given by all users that are presented in Table 3. Then we use SAW and calculate the multi-attribute utility function for each one of the 23 websites. More specifically, for each website a multi-attribute utility function is calculated as a linear combination of the values of the 10 criteria:


Where is one alternative website and is the value of the criterion for the alternative.

The data of Table 6 reveals that PROMETHEE II ranks websites in a very similar way with SAW (not identical). However, the similarity in those rankings can only be confirmed by analysis of pair-wise correlation. For this purpose, we use the Pearson Correlation Coefficient on the data of the two rows that represent the ranking of the websites. The Pearson Correlation Coefficient is calculated to 0.967, which reveals a high correlation on the rankings of the two different theories.

Table 6 Values and Ranking for all Websites Using Saw and Promethee II
A1 38 -0.3751 41 3.46
A2 19 0.0632 27 3.77
Α3 15 0.2532 16 4.11
A4 30 -0.1303 31 3.71
A5 26 -0.0656 29 3.72
A6 35 -0.2976 36 3.55
A7 44 -0.472 43 3.38
A8 43 -0.4463 44 3.37
A9 42 -0.4186 40 3.47
Α10 18 0.0995 25 3.83
Α11 12 0.3268 15 4.13
A12 39 -0.3825 38 3.49
A13 22 0.0056 26 3.81
A14 46 -0.6001 46 3.33
A15 25 -0.0377 32 3.67
A16 49 -0.899 49 2.94
A17 37 -0.3719 35 3.55
A18 34 -0.2592 37 3.54
A19 27 -0.1015 30 3.71
A20 45 -0.4875 45 3.36
A21 47 -0.701 47 3.14
A22 36 -0.3346 39 3.48
A23 40 -0.4052 34 3.56
A24 48 -0.7684 48 3.11
Α25 16 0.2317 18 4.00
Α26 17 0.1449 22 3.90
B1 24 -0.0157 17 4.10
B2 31 -0.1629 21 3.92
B3 41 -0.4066 42 3.45
B4 28 -0.1016 24 3.84
B5 33 -0.2336 23 3.84
B6 32 -0.2044 33 3.56
B7 21 0.0201 20 3.93
B8 20 0.0617 14 4.20
Β9 4 0.7546 3 5.29
Β10 3 0.7677 2 5.45
Β11 14 0.273 13 4.26
Β12 10 0.5114 8 4.88
Β13 11 0.3512 11 4.53
Β14 7 0.5859 10 4.81
Β15 5 0.6836 5 5.03
Β16 9 0.5379 9 4.86
Β17 1 0.9153 1 6.11
B18 29 -0.1241 28 3.75
Β19 13 0.307 12 4.37
B20 23 -0.0148 19 4.00
Β21 8 0.5484 7 4.89
Β22 2 0.7803 4 5.23
Β23 6 0.595 6 4.89


The role of MBPAs is multidimensional, diverse, and important, and maybe supported by a good website. A website can shape the image of the MBPA and may produce a virtual experience for visitors, and promote the environmental value of its area. The twenty-three websites of the MBPAs in Italy offer free information to potential visitors. However, the design, the quality of content, and the attractiveness that differentiate the different MBPA’s websites are a subject of evaluation.

Evaluations are usually complicated procedures that focus on the examination of several different criteria. For this purpose, we use MCDM models for combining these criteria. We have used the results of previous work on the evaluation of websites of environmental content (Martinis et al. 2018, Kabassi & Martinis 2020; Kabassi et al. 2019b), in which the criteria and the weights of importance of these criteria using AHP have been defined. The main contribution of the particular paper is that it presents how PROMETHEE II can be combined effectively with AHP for the evaluation of the websites of MBPA in Italy and Greece.

PROMETHEE II is a highly researched and most applied outranking method that was designed to treat multi-criteria problems. The main motivations for applying the PROMETHEE II method include that the specific model could be easily applied in the domain of website evaluation and that all collected information in the decision matrix can be fully and efficiently considered when making the final decision. PROMETHEE II is also a rather simple ranking method in concept and practice when compared with the other MCDM methods (Brans et al., 1985). The results of the evaluation of website evaluation indicate that the PROMETHEE II method can prioritize the websites effectively.

The combination of particular methods and theories makes the experiment more structured and easier to be implemented or repeated by other researchers that want to evaluate websites of environmental content.

The application of the particular theory was compared to the application of SAW, which is a theory that has been used effectively before for the evaluation of websites. The high correlation of the two theories confirms the effectiveness of PROMETHEE II for evaluating not only websites of environmental content but websites in general.

As far as the electronic presence of MBPA concerns, the results of the PROMETHEE II method revealed that about 45% of the websites of the National Parks of Italy and Greece were very good. This means that the general picture of these websites was not generally bad but certainly needs improvement. Findings are in agreement with the results of similar studies (Andreopoulou et al., 2015; Martinis et al., 2018, Kabassi & Martinis 2020) and confirm that internet technologies’ adoption in MBs is still at an initial level. The usage of these technologies can and must constitute a useful tool for promoting National Parks. The evaluation also revealed that the websites of Italian MBPA outranked the websites of Greek MBPA.

It is among our plans to implement this experiment with a different MCDM model and compare the results in order to see if the selection of the MCDM model may differentiate these results or not. Furthermore, the results of the evaluation of the electronic presence of Protected Areas Managing Boards in all Mediterranean countries may provide very interesting results.


This work was funded by the Interdepartmental Postgraduate Program of Studies entitled "New Technologies in Environmental Education and Sustainable Development" (Proj. No. 80511), Research Committee of the Ionian University.


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