Decision making on Digital Platforms in Agriculture

Nikola Kadoić*, Katarina Tomičić-Pupek* and Neven Vrček* * University of Zagreb, Faculty of organization and informatics, Pavlinska 2, Varaždin

nkadoic@foi.unizg.hr, ktomicic@foi.unizg.hr, nvrcek@foi.unizg.hr

Abstract - There are many ways to sell and buy products in all fields, using digital technologies. That is the case for the agriculture field as well. Among many possibilities and different ICT solutions in agriculture, selection of the appropriate solution that will result in the satisfaction of both - customers and producers - becomes an important issue, and it is the main research topic of this paper. In this paper, we present the factors that influence the digital platform selection and propose two models for decision making on digital platforms. Decision models can help customers and producers to decide if a specific platform can be useful for their business. The decision model is based on a multi-criteria decision-making method called Dex. Dex is selected because it is very intuitive and less complicated than other multi-criteria methods. On the other hand, Dex can aggregate all crucial factors and include the specifics and subjectivity (perception) of each user (customer or producer). The paper also contains a demonstration of the implementations of two models on the hypothetical cases. In future research, we plan to evaluate the model in practice (and possibly update it if needed) and present it to the scientific and professional community. That can help producers/customers to optimize their business processes but also give directions for the development of new platform functionalities for platform developers.

Keywords – agriculture, digital platform, decision making

I. INTRODUCTION Digital platform as a term refers to an online real-time

accessible digital space similar to a forum where many stakeholders meet in order to communicate, offer, buy, solve, exchange or share ideas, products, services, or other goods. Due to its primary task of facilitating the trade in a virtual environment trust and perception aspects influence the reach of a platform. Another important attribute of digital platforms is the usefulness that depends on expectations and delivered functionalities regardless of industry type the platform is intended for. In the agriculture sector, the variety of value chains, horizontal and vertical integration [1] and the diversity of touchpoints in performing business models creates a high demand and supply on digital platforms. The impact of digital technologies in the agriculture sector has been mostly reported by presenting case studies that incorporate digital technologies in agriculture [2]. Different stakeholders have different roles as platform users and in that sense, the decision on joining or adopting a digital platform becomes significantly important for surviving in a highly competitive environment. Decision making on joining or adopting digital platforms in agriculture is a complex

process by itself that needs to be carried out by actors with usually little digital competencies. In order to ease the selection and decision process, we investigated factors that influence the perceived value or perceived risk and thereby impact the decision on joining and adopting digital platforms. This research was performed within a project which aims to achieve a digital transformation of the food industry in rural areas. The transformation efforts in the area of agriculture and food distribution include a widespread application of knowledge and innovation in the production of agriculture products and services. It is aimed at impacting the readiness of producers for challenges regarding economic growth and increased competitiveness of the rural and agricultural sectors. The project is a combination of a total of 8 research and development project activities whereby following three activities are dealing with: Research of models and development of a platform for the application of IoT in agriculture in rural areas, Software for decision support in managing the adaptability of business systems, and Distribution platform for food products according to the "Platform-based business" model. Within the scope of the project, intuitive multi-criteria decision models for platform selection can be seen as a valuable contribution.

Section II contains the theoretical background of the topic. Section III discusses the selection of the multi-criteria method that is the most appropriate for decision making on the digital platforms in agriculture. Finally, Section IV contains decision models and their demonstration.

II. THEORETICAL BACKGROUND In order to develop a theoretical model for supporting

the decision making on digital platforms in agriculture, we relied on a conceptual model of initial utility perception factors previously designed within the same project [3]. The conceptual model of initial utility perception factors is oriented on food distribution platforms, and for the development of the decision model in this research phase, it is sufficiently complex to test our assumption that a solid decision making model can be designed.

For each actor in the decision making process (consumers and producers), 10 factors have been identified. The factors impact the perception of actors regarding the value gained and risks upon which the utility of a platform is subjectively assessed. The factors are listed in Table I and Table II. The factors are classified within each group (customer or producer) and described.

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TABLE I. FACTORS OF PERCEPTION – CUSTOMERS PERSPECTIVE

Factor Description Eco-friendliness Reducing the carbon footprint [4], avoiding ecologically unsafe additives, but also caring about the

seasonality of products as well as waste load reduction (like packaging options [5]). Location & time (from farm to

fork) The farm-to-fork paradigm consists of the intention and efforts to buy local fresh products in the shortest time possible time from the farm (or field) to use [6].

Relationship history with the producer

Existing experiences relationships with the producer in terms of responsiveness, expertise, flexibility, commitment and other aspects [7], [8]

Payment options Price, price volatility, security issues, avoiding cash, and other attributes of payment processes are necessary but not the most important decisive factors. Additional and hidden costs like fee for platform usage [9] also play a role in weighing benefits and costs.

Comfort & convenience Usefulness and enjoyment, Ease of use [9] affect the subjective sense of value. Also, product information availability can influence the intention to order products and services remotely taking into account benefits like time-saving and reliability of delivery [10].

Recommendations (C2C2C) Previous product reviews can be understood as recommendations or warnings which can affect the buying decisions of consumers when choosing a platform or specifically a supplier within a platform [10]. In social media-based platforms, recommendations should be based on actual experience and can have a vortex similar behavior (Customer-to-Customer-to-Customer, C2C2C).

Community support Local and regional centricity as a sense of affiliation [7] like purchasing from local producers with the goal of supporting the sustainability and development of the local economy, local production, and similar elements related to the purpose like social responsibility according to Almquists elements of value [8].

Producer’s reliability Many elements can be significant when it comes to producers’ reliability. Some of them are related to economic or performance functional elements of value like product quality, delivery time saving, cost reduction [8].

Trust & traceability The role of trust [11] through the whole value chain including preparation, production process, harvesting, delivery, and other elements with an acceptable level of traceability.

Health & safety Even without disruption challenges like epidemics and pandemics health-related awareness of consumers regarding the safety of products and services and their impact on the quality of life loyalty and satisfaction [10].

TABLE II. INITIAL FACTORS OF PERCEPTION – PRODUCERS PERSPECTIVE

Factor Description Sales channels The distinction between customer-preferred sales channels and producer-preferred channels is important for

understanding how these channels intersect and allow touchpoints in terms of collaboration options. This factor includes various aspects from food retailing and restaurants to creating new value chains with customer experience journey management [4].

Health & food safety Health-related awareness of producers regarding the safety of products and services influencing the consumers’ perception of their quality of life are impacting loyalty and satisfaction [10]. The overall quality of products, production, supply chain, and delivery to the end customer should also be traceable.

Production technologies Enhancement of production technologies under the phrase “Green deal” and a smaller carbon footprint. It is oriented towards operational efficiency and effectiveness through the whole production process, including increased control and precision as well as insuring growing potential for more or less complex interfaces in production eco-systems [4].

Product Quality The quality of the production process and the final product should be communicated through the platform in the appropriate form and by enough product information [10].

Resources “Consolidation of the value chain as a single player can do many steps in the value chain” [4] can be achieved by implementing digital technologies to empower and support human labor. Robotics and innovation culture to increase the efficiency of the production process should be available regardless of producers’ digital competencies.

Inbound logistics / Supply Chain “Local Supply Capacity and Constraints to Regional Local Agriculture Expansion” was addressed by Werner et al. [6]. Other aspects of this factor include relationships and channels to suppliers of raw materials (seedlings and seeds, fertilizer), time spent on improving the supply chain in order to respond to growing demands locally.

Innovations The innovation in agriculture can be a factor that according to [12] guarantees adaptation to a new paradigm of the global economy. The lifecycle of innovation should be supported by a distribution platform in order to facilitate the identification of innovation potentials, the innovative design, innovation implementation and monitoring as well as other activities.

Outbound logistics / Distribution chain

Outbound distribution chains have a variety of issues regarding options for ordering, downloading online orders and preparing customer orders, delivery planning, packaging, and the final delivery. Intermediate actors collaborate via other business models like restaurants [6], hotels or tourism enterprises which opens a wide space for improvement potentials.

Incentives and sustainability By understanding customers’ elements of value that trigger them to purchase, long-term sustainability is viewed as an eco-systems and life-changing factor [7]. Incentives for efficiency and capacity increase which come from the governmental or administrational level can affect the readiness of producers to adopt new technologies in their business.

Regulatory compliance According to [13] control is intended to reduce uncertainty by laying structures that increase the likelihood of anticipated outcomes. Regulatory compliance in agriculture from our point of view is relevant in relation to health and safety issues and time spent by the producer to ensure the compliance.

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III. DECISION MAKING ON DIGITAL PLATFORM IN AGRICULTURE

A. Selecting a suitable decision-making method Decision making on digital platforms in agriculture is

undoubtedly a multi-criteria decision-making problem. In the previous section, we presented the list of factors that influence the digital platform selection (i.e. joining or adoption). To be able to successfully deal with ten criteria in the decision-making problem we need a suitable decision-making method. So, a logical research question that appears now is related to the selection of multi-criteria decision-making method that can aggregate all the factors on one side, but on the other side, we need to select a method that is intuitive for the users, not very complex and simple to understand for the users. The idea is to propose a decision-making model that can be successfully used by average agriculture customers and producers who do not have the scientific expertise in the decision-making field.

There are many multi-criteria decision-making methods available for this problem analysis. Some of them are: the analytic hierarchy process (AHP) [14], [15], the analytic network process (ANP) [16], [17], Electre, Topsis, Promethee [18], simple additive weighting (SAW), Vikor [19], Dex [20]–[22], and SNAP [23]–[25]. Due to high-level expertise in multi-criteria decision-making (MCDM) field, we were deeply analyzed those methods.

The results are presented in Table 3. The goal was to select the method that can cover all the factors, but which is as simple for users as possible. Since we knew the average users’ profiles in terms of expertise in decision-making methods and characteristics of the analyzed methods, using the qualitative analysis, we selected the Dex method as the one that can be the most successfully used for modeling the decision-making process of the selection of digital platforms in agriculture.

TABLE III. THE ANALYSIS OF THE MCDM METHODS WITH RESPECT TO APPLICATION IN AGRICULTURE FIELD AT THE LEVEL OF AVERAGE USERS

Characteristics Dex AHP ANP Electre Topsis SNAP Covering all

important aspects of the

problem

+ + + + + +

Qualitative factors

It is purely qualitative method –

supports decision making in agriculture

without need for quantification

Quantification of the qualitative factors using

Saaty scale and pairwise

comparisons

Quantification of the qualitative factors using

Saaty scale and pairwise

comparisons

Quantification of the qualitative factors using

constructed scales which changes the original factors’

values

Quantification of the qualitative factors using

constructed scales which changes the original factors’

values

Quantification of qualitative factors using DEMATEL

scale

Complexity of the method and

decision models for final users

After the models are created, the

user can intuitively use it

After the model is created, the

user must make a high number of

pairwise comparisons

After the model is created, the user must make many

pairwise comparisons

(higher than in AHP)

It covers 10 steps which can be understood by users with high mathematical background;

it does not ensure selection of the best alternative

The method covers many steps

which can be understood by users with high mathematical background

Very complex, no support for

alternatives’ level; need to be combined with e.x. composite

index

Incorporating the subjectivity

(perceived factors)

Highly possible through creating the decision rules and discretization

Possible, but not intuitive

Possible, but not intuitive

Constructed scales are intuitive, but

they limit the factors’ values

Constructed scales are intuitive, but

they limit the factors’ values

Possible, but not at the level of the

alternative

Simulating human

thinking

Highly Medium, since there are many

possible problems in

terms of inconsistency

Medium, since there are many

possible problems in terms of

inconsistency

Creating constructed scales

Creating constructed scales

No support for alternatives’ level

Our choice of applying Dex method is based on following:

• Among the mentioned methods, only Dex is a purely qualitative method and factors that influence the decision on the digital platform in agriculture are in most cases qualitative attributes,

• The methodology of the Dex is still complex, but after we propose the general models for decision-making, the implementation of the models will be much simpler for users than in the case of implementation of models that would be created using other methods

• Using Dex, we can aggregate all the factors. The same can be achieved by using other methods which means

that Dex is equally strong as other methods in terms of aggregating multiple factors into the decision,

• The Dex is more intuitive than the other mentioned multi-criteria decision-making methods,

• The steps of the Dex are more similar to the human thinking than the steps of other mentioned multi-criteria decision-making methods, and that is very important since the decision models are intended to be applicable by not decision-making experts and professionals.

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B. Dex method Decision Expert (Dex) is a qualitative multi-criteria

decision-making method. It is also a hierarchical method (problem structure is similar to the structure in the AHP method). Dex is implemented in a software called Dexi and that software is used in this paper.

There are several main steps in Dex method [21], [26], [27]:

• Creating the hierarchical tree of decision making problem. Here, the model is modeled through tree which has several interpretations: decomposition, dependence, and aggregation. In our case, we will define two hierarchical trees, one for producers, and the other for customers. At the lowest levels of the hierarchical trees, there will be factors identified in the previous section. They will be then grouped into higher-level elements, and finally aggregated into the main element at the highest level of the hierarchical tree.

• Creating the scale for each element in the hierarchy. Here, we will define the qualitative scale for each element in the tree. In most cases, values L (low), M (medium) and (high) are used. However, at the lowest level, it is possible to have only two values (L or H), and at higher and the highest level, it is possible to have more than three values (ex. VL (very low) and VH (very high)). In this step, we can also create the rules which describe how are the values from the natural scale of the factors transformed into qualitative values in the Dex (V, L, H): ex. for the factor Eco-friendliness we have to have the meanings of the V, L, and H on its natural scale. The input values of the alternatives by certain criteria are determined by the discretization of the continuous value space. This process can be done using the threshold or using the percentile.

• Creating the decision rules. Here we have to create the rules that describe the conclusion mechanism for the elements in the tree that are not at the lowest level of the hierarchy. decision-making rules represent the basic mechanism of conclusion and decision-making in the DEX method [28]. Decision rules are defined at the level of aggregated criteria and at root level decisions that describe which value will take the criterion (on its scale) for each combination of criteria values from the level below.

• Once a hierarchical model has been created, and after the rules of decision are defined, the final step is the evaluation of alternatives. In our case, the alternatives are concrete digital platforms in agriculture

The Dex models in this paper can be helpful to decision-makers (produces and customers) in evaluating the platforms and deciding to use some of them or not.

IV. DECISION MODELS AND THEIR IMPLEMENTATION

A. A decision model for producers The hierarchical tree of the decision model for the

producer consists of 14 elements. Ten of them are the factors presented in Section II, and three of them are aggregated elements. The tree has been structured as a

three-level hierarchy, and the main structuring method was the qualitative analysis. The tree is presented in Figure 1.

Figure 1. The hierarchical tree of Dex model (producers)

The values of all factors are set to values L, M, and H. Since each producer can perceive each factor value for some digital platform differently, it was not needed to define the discretization rules for factors’ values. When evaluating the platforms, the user will use its perception to express the factors’ values.

The decision rules for element Product are partly presented in Figure 2. Since that element aggregates four factors, and each of them is described with three values, the number of decision rules is 81 (34).

Figure 2. Decision rules for element Product

Similar decision rules are defined for elements Sales, Distribution, and root element Digital platform in agriculture. Those rules can be modified, but they are recommendations of investigators in the field based on their expert opinions.

Dig

ital p

latfo

rm in

agr

icul

ture

Product

Product quality

Health and food safety

Production technologies

Resources (in production)

Sales

Sales channels

Incentives and sustainability

Regulatory compliance

Distribution

Inbound logistics

Innovations

Outbound logistics

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Finally, in the third step of the Dex method, there is an evaluation of the alternatives. Here, we present the application of the model on three hypothetical cases (evaluation of three platforms, Figure 3).

Figure 3. Evaluation of three digital platforms

The user (producer) has to input the values for the elements at the lowest level of the hierarchy (in Figure 3 those are the ones with three dots in the title), and the software, using the previously defined decision rules, calculates the values of higher-level elements. The digital platform DP3 achieves the qualitative grade high and is the best among the three platforms. It is recommended to use DP3.

B. A decision model for customers The qualitative analysis as the structuring method is

used for creating the hierarchical tree in the Dex model for customers. Again, there are 14 elements of the hierarchy tree. Ten factors from Section II are clustered into three higher-level elements Producer, Product, and Sales. The hierarchical tree of the Dex model is presented in Figure 4.

Figure 4. The hierarchical tree of Dex model (customers)

The values of all factors for customers are set in a same way as in the case of producers. The decision rules for element Product are partly presented in Figure 5. Since that element aggregates three factors, and each of them is described with three values, the number of decision rules is 27 (33).

Figure 5. Decision rules for element Product

Similar decision rules are defined for elements Sales, Producer, and root element Digital platform in agriculture. Those rules can be modified, but they are recommendations of investigators in the field based on their expert opinions. Figure 6 presents the evaluation of three digital platforms from the perspective of the customers.

Figure 6. Evaluation of three digital platforms

The user (customer) has to input the values for the elements at the lowest level of the hierarchy (in Figure 6 those are the ones with three dots in the title), and the software, using the previously defined decision rules, calculates the values of higher-level elements. Here, all platforms achieve the same final value at the root element, so analysis at the second level of the hierarchy is needed. Since the DP3 has better values per two criteria than DP2, DP3 would be a better choice than DP2.

V. LIMITATIONS AND FUTURE RESEARCH The limitations of our research are dealing with

coverage of identified perception factors and the lack of evaluation of our decision model in practice. In future research, the evaluation of two proposed decision models (for producers and for customers) need to be conducted and revised with the inclusion of all relevant actors in order to speed and ease up the decision process and to ensure that the attribute domains (i.e. values) for certain factors cover the lack of experience with a certain platform and decision

Dig

ital p

latfo

rm in

agr

icul

ture

Producer

Producer's reliability

Relationship history

Community support

Recommendations

Product

Eco-friendliness

Trust & Traceability

Health & Safety

Sales

Location & Time (from farm to fork)

Payment options

Comfort & convenience

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framworks. More involvement of scientific and professional community as well as of end-users of digital platforms is vital in order to tie up this research more to architectural design and development of functionalities for platform developers.

VI. CONCLUSION The main goal of this paper was to present the models

which can be helpful in terms of decision making on digital platforms in agriculture. Among many possibilities in that area, both – producers and customers – hardly decide which of the platform to use. Using many (or all) of them is almost always impossible since there is a lot of work in updating the information about the products, quantities, prices, etc.

We analyzed several multi-criteria decision-making methods and the conclusion is that the most appropriate method for creating the decision models is Dex - due to its complexity, level of the intelligibility by not experts in decision-making field and possibility, the individuality of perception and aggregating the multiple factors.

Finally, we presented two models. One can be applied by producers, and the other by customers. They can help them evaluate the platform and decide which platform to use. In future research, we plan to evaluate the model in practice (and possibly update it if needed) and present it to the scientific and professional community. That can help producers/customers to optimize their business processes but also give directions for the development of new platform functionalities for platform developers.

ACKNOWLEDGMENT

This research has been conducted as a part of the wider research within the project Competence Centre for Digital Transformation of the Food Industry in Rural Areas. The project is co-funded by the European Union through the European Regional Development Fund (ERDF).

REFERENCES [1] M. Pesce et al., “Research for AGRI Committee - Impacts of the

digital economy on the food chain and the CAP,” European Parliament, Policy Department for Structural and Cohesion Policies, no. February, 2019.

[2] N. M. Trendov, S. Varas, and M. Zeng, “Digital technologies in agriculture and rural areas,” Briefing Paper FAO, p. 26, 2019.

[3] K. Tomičić-Pupek, I. Pihir, and M. Tomičić Furjan, “The Role of Perception in the Adoption of Digital Platforms in Agriculture,” in Proceedings of Mipro, 2020. /accepted for publishing

[4] M. Chui, M. Evers, J. Manyika, A. Zheng, and T. Nisbet, “The Bio Revolution,” McKinsey Global Institute, no. May, 2020.

[5] A. Mumani and R. Stone, “State of the art of user packaging interaction (UPI),” Packaging Technology and Science, vol. 31, no. 6, pp. 401–419, Jun. 2018.

[6] S. Werner et al., “Prospects for New England Agriculture: Farm to Fork,” Agricultural and Resource Economics Review, vol. 48, no. 3, pp. 473–504, Dec. 2019.

[7] E. Almquist, J. Senior, and N. Bloch, “The 30 Elements of Consumer Value: A Hierarchy,” Harvard Business Review, 2016. [Online]. Available: https://hbr.org/2016/09/the-elements-of-value. [Accessed: 04-Jul-2020].

[8] “What B2B Buyers Really Care About.” [Online]. Available: https://hbr.org/2018/03/the-b2b-elements-of-value. [Accessed: 04-Jul-2020].

[9] H. W. Kim, H. C. Chan, and S. Gupta, “Value-based Adoption of Mobile Internet: An empirical investigation,” Decision Support

Systems, vol. 43, no. 1, pp. 111–126, 2007. [10] A. Tariq, C. Wang, Y. Tanveer, U. Akram, and Z. Akram,

“Organic food consumerism through social commerce in China,” Asia Pacific Journal of Marketing and Logistics, vol. 31, no. 1, pp. 202–222, 2019.

[11] P. Jayashankar, S. Nilakanta, W. J. Johnston, P. Gill, and R. Burres, “IoT adoption in agriculture: the role of trust, perceived value and risk,” Journal of Business and Industrial Marketing, vol. 33, no. 6, pp. 804–821, 2018.

[12] M. de F. Oliveira et al., “Innovations in Sustainable Agriculture: Case Study of Lis Valley Irrigation District, Portugal,” Sustainability, vol. 11, no. 2, p. 331, Jan. 2019.

[13] C. Agyekumhene, J. R. de Vries, A. van Paassen, P. Macnaghten, M. Schut, and A. Bregt, “Digital platforms for smallholder credit access: The mediation of trust for cooperation in maize value chain financing,” NJAS - Wageningen Journal of Life Sciences, vol. 86–87, pp. 77–88, Nov. 2018.

[14] M. G. Marković, N. Kadoić, and B. Kovačić, “Selection and prioritization of adaptivity criteria in intelligent and adaptive hypermedia e-learning systems,” TEM Journal, vol. 7, no. 1, 2018.

[15] N. Kadoic and I. Kedmenec, “Using ICT tools for decision making support in local government units,” in 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2016, pp. 1533–1538.

[16] N. Kadoić, “Characteristics of the Analytic Network Process, a Multi-Criteria Decision-Making Method,” Croatian Operational Research Review, vol. 9, no. 2, pp. 235–244, 2018.

[17] N. Kadoić, N. Begičević Ređep, and B. Divjak, “Decision Making with the Analytic Network Process,” in SOR 17 Proceedings, 2017, pp. 180–186.

[18] N. Kadoić, N. Begičević Ređep, and B. Divjak, “E-learning decision making: methods and methodologies,” in Re-Imagining Learning Scenarios, 2016, vol. CONFERENCE, no. June, p. 24.

[19] C.-H. Liu, G.-H. Tzeng, and M.-H. Lee, “Strategies for improving cruise product sales using hybrid ‘multiple criteria decision making’ models,” The Service Industries Journal, vol. 33, no. 5, pp. 542–563, 2013.

[20] M. Bohanec, N. Kadoić, and N. Begičević Ređep, “Qualitative Multiple Criteria Models with Cycles : A Preliminary Study with Method DEX,” in Scientific program of the 24th International Conference on Multiple Criteria Decision Making, 2018.

[21] M. Bohanec and V. Rajkovič, “DEX : An Expert System Shell for Decision Support,” Sistemica, vol. 1, no. 1, pp. 145–157, 1990.

[22] M. Bohanec, “Qualitative MultiCriteria Modelling Method DEX: Approach, Recent Advances and Applications,” in Book of Abstracts, 16th International Conference on Operational Research, KOI 2016, 2016.

[23] N. Kadoić, B. Divjak, and N. Begičević Ređep, “Integrating the DEMATEL with the analytic network process for effective decision-making,” Central European Journal of Operations Research, vol. 27, no. 3, pp. 653–678, Sep. 2019.

[24] N. Kadoić, N. Begičević Ređep, and B. Divjak, “A new method for strategic decision-making in higher education,” Central European Journal of Operations Research, no. Special Issue of Croatian Operational Research Society and Collaborators, Oct. 2017.

[25] M. Dzeko, N. Kadoic, and Z. Dobrovic, “Metamodeling SNAP, a Multi-Criteria Method for Effective Strategic Decision Making on e-Learning Issues,” in 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2019, pp. 849–853.

[26] M. Bohanec, M. Žnidaršič, V. Rajkovič, I. Bratko, and B. Zupan, “DEX Methodology: Three Decades of Qualitative Multi-Attribute Modeling,” Informatica, vol. 37, pp. 49–54, 2013.

[27] V. Đurek, N. Kadoić, and Ž. Dobrović, “Digital Maturity of Higher Education Institution: A meta model of the Analytical Network Process (ANP) and Decision Expert (DEX),” in Proceedings of Central European Conference on Information and Intelligent Systems 2018, 2018, vol. 1, pp. 223–230.

[28] M. Mihelčić and M. Bohanec, “Approximating incompletely defined utility functions of qualitative multi-criteria modeling method DEX,” Central European Journal of Operations Research, vol. 25, no. 3, pp. 627–649, Sep. 2017.

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