Investigating the Effects of Introducing Automated Straddle Carriers in Port Operations with a System Dynamics Model

Port automation has been in the forefront of maritime innovation in the last decade. On that front, Automated Straddle Carriers (ASCs) are increasingly used to move containers efficiently. However, the introduction of ASCs in port operations can be disruptive if not handled properly, especially since the field can face many uncertainties such as increased container trade. The purpose of the paper is to investigate whether the introduction of Automated Straddle Carriers in port operations can improve the overall efficiency. To achieve the objective, a System Dynamics model was developed and tested under different scenarios. The results indicate that the introduction of ASCs is accompanied by an increase in productivity of the vehicles which results in more TEUs serviced. One of the most interesting results of the various scenarios is that for all rates of incoming TEUs, berth productivity is superior when operations are performed with 5 ASCs than with 10 manned vehicles. Finally, another issue that port authorities should always have in mind is the need for coordination among the various sub-processes and optimization of the necessary vehicles in order to avoid under-utilization of resources

A systemic perspective on racism in football: the experience of the BRISWA project

The objective of this paper is to present the process for the development of a causal loop diagram that captures the relevant aspects of racism in football, through a holistic, top-down approach. To do so, a series of workshops/sessions has been organised with experts in the field and with the purpose of designing a tool that could be used to get better insights into how racism in football emerges and where are the potential areas where policymakers could use as leverage for effective counter-measures. The diagram demonstrated the multi-faceted nature of racism, the phenomena that might give rise to it and the elements that could serve as leverage in potential counter-measures. Some of the most interesting results include the following: the power structures of society and football should adapt to represent the actual demographic make-up of each country. Furthermore, policymakers should involve media more directly in every attempt to fight racism. Finally, racism in football is a mirror of racism in society. Hence, any attempt to combat racism in football should be interlinked with corresponding efforts to fight discrimination in society

An innovative game-based approach for teaching urban sustainability

This paper is based on SUSTAIN, an ERASMUS+ project with an innovative perspective on urban transportation, and its target is to promote the importance of sustainability on the everyday problem of urban transportation among the students of higher education (and not only), who are the policy makers of tomorrow. In order to achieve its goals, the research team is currently developing a course that will be based on an interactive serious board game with an analytical style of education. SUSTAIN's purpose is to create a game that will allow students to learn about transportation sustainability and societal metabolism through playing. The project partners develop small and illustrative simulation models, which will make the definitions more concrete and allow students to experiment largely in a consequence-free environment. The simulation models can be used to identify scenario exemplars on how we can achieve sustainable urban transportation and consequently a balanced societal metabolism, while on the same time taking into account formal decision making processes. In this paper, we are going to explain a Stocks & Flows Diagram for the above mentioned model, with a system dynamics approac

Μια νέα παραλλαγή πολυεπίπεδης περιβάλλουσας ανάλυσης δεδομένων για την κατασκευή σύνθετων δεικτών και τη μέτρηση της αειφορίας

In public decision-making factors such as personal values, cultural background and different individual perspectives play a central role in the policy cycle of design, test, implementation and review. To assist policy makers, analysts have used an array of qualitative and quantitative methods to all steps of the cycle.However, the increasing use of sophisticated methods seems not to be always accompanied by an improvement in the quality of policy making; on the contrary, it seems to attract criticism that is focused on their disadvantages. Furthermore, the rise of Artificial Intelligence (AI) and its expanding use in decision- and/or policy- making, has brought forth the issue of interpretability of algorithms and whether their output can be trusted. Questions such as “which specific feature made the model/algorithm reach the specific decision”, hence issues of transparency and interpretability of the methods, are becoming central issues of the critique on quantitative methods and algorithms.This criticism is not without its merits. The complexity of contemporary problems means that there are issues about which an analyst can only make assumptions due to the existence of deep uncertainty. Moreover, in such complexity, the perception of the analyst may limit the view of the policy cycle under study. As a result, the success of a quantitative method relies on all of the above choices to be exactly “correct”.Sustainable development perfectly encapsulates these issues and in order to achieve it, public policies should have economic, social and environmental dimensions, while taking into account the current technological developments, the cultural context and the value system in which they are applied. Thus, sustainable development is a multi- dimensional concept and from early on arose the need to find an appropriate proxy to measure it; that measure was sustainability.In addition to their multi-dimensional nature, both sustainable development and sustainability have been characterized by different perceptions on how to explicitly define them. Complementary to the lack of a unified definition is also the absence of an official and unified methodological framework. Composite indicators have emerged as a suitable means that allow the proper measurement of sustainability.One of the methods that has been proposed and used in the literature for both the measurement of sustainability and the construction of composite indicators is Data Envelopment Analysis. A literature review was performed in the context of the current thesis for the years 2016-2020 and it was discovered that researchers have made an effort to include parameters that represent the social dimension of sustainability (a feature that was missing the previous years), while more dimensions of sustainability are included in recent studies such as technological innovation and advancement, despite the fact that the three-dimensional construct (economy, environment, society) seems to be the preferred one. Moreover, it was discovered that the choice of inputs and outputs (and intermediate measures) despite commonalities is unique to each research work. In addition, the choice of DEA variation and/or combination with other methodologies implies that the perception of each analyst affects the final result of their work.Apart from those identified gaps, the methodology of DEA itself does not come without its own limitations. First, in its traditional form the efficiency of Decision Making Units is calculated with weights that are most favorable to themselves; i.e. each DMU is evaluated under the most favorable weighting scheme with the purpose of maximizing its own efficiency. As a result, the weights that are chosen for one DMU may be completely different from those selected for another. Moreover, DEA needs to be used in the appropriate context, which means that there is the requirement to decide which parameters will best explain different dimensions of sustainability. This is especially important since, the number of inputs and outputs that can be used is limited by the number of DMUs under evaluation for the measurement to be meaningful, otherwise there would be an increased number of efficient DMUs that would result in inconsistencies.As a result, the purpose of the current thesis is to address all the above gaps and more specifically:1) to propose an alternative version of two-stage Data Envelopment Analysis with a different optimization metric that attempts to intervene on the weights of the inputs, intermediate measures and outputs to better reflect their importance for the DMUs by considering positive and negative deviations in the calculations and limiting the distance of these deviations from the maximum and minimum values.2) to propose a computational framework that will attempt to incorporate different perceptions (meaning different combinations of inputs and outputs) and apply it in the measurement of sustainability of the EU 28 countries.To achieve this objective, the framework will rely on Exploratory Modeling and Analysis (EMA). EMA is a school of thought developed at RAND corporation and promotes the exploratory use of quantitative methods despite methodological limitations, uncertainties and different perceptions (meaning different combinations of inputs, intermediate measures and outputs). Employing an exploratory approach to sustainability measurement could reveal unanticipated implications of the initial assumptions regarding inputs and outputs.The thesis is developed in a series of consecutive steps. First, an alternative two-stage DEA model is introduced that employs positive and negative deviational variables both in the objective function (thus altering the optimization metric) and in the constraints. The model attempts to find the best possible weights for the inputs, intermediate measures and outputs, by minimizing the deviations of both the first and second stage of the model. By minimizing simultaneously the deviations of each stage, the efficiencies of both stages are maximized at the same time and no priority is given into which stage should take precedence. Three lemmas and one theorem are proved, and it is proven that the alternative model has a feasible solution that is optimal.The alternative optimization metric, two-stage proposed DEA model is applied in two case studies: one that calculates the environmental performance of European countries and a second to calculate the agricultural sustainability of European countries.Following the definition of the new model, a new computational framework is defined for the construction of composite indicators. The proposed model is used for the calculation of each sub-indicator that the final indicator will consist of. The calculated sub-indicators are then used as parameters in a Benefit-of-the-Doubt (BoD) model that generates the value of the final index. The computational framework is tested two times in the measurement of sustainability of European countries: once with the proposed, alternative, two-stage DEA model and once with the typical two-stage model of Chen et al. (2012).The above calculation of sustainability however is limited by the same notion that was identified in the beginning: since there is no unique, “correct” definition of sustainability, the same indicator can be calculated by using different variations of DEA and/or different combinations of inputs, intermediate measures and outputs.Consequently, there is the need to have an indicator of sustainability that will incorporate all these different perceptions that may arise, where perceptions mean different DEA variation and/or different combination of inputs, intermediate outputs and outputs. The proposed computational framework is based on this principle, and it consists of the following steps:Step 1: Define different perceptions of sustainability and for each perception:a) define how many sub-indicators will be entailed in this perception’s sustainability indexb) define the inputs, intermediate measures and outputs that each sub-indicator will entailc) Repeat for all perceptionsStep 2: Define the variation of DEA that will calculate the value of the sub-indicatorsa) calculate the sub-indicatorsb) calculate the perception’s sustainability index using DEA modelsc) Once all sustainability indices for all perceptions are calculated, calculate the mean value for each country/DMUStep 3: Use machine learning to gain insights into the sustainability of each country under different perceptionsThe proposed computational framework is used with four different versions of two- stage DEA models, and different combinations of inputs, intermediate measures and outputs to the calculation of sustainability of European countries.The final step of the proposed computational framework is to use Machine Learning techniques in the results of the generated computations with the purpose of revealing insights into how the sustainability of countries behaves under different perceptions.Following the logic of EMA, several techniques will be employed in an effort to mitigate intrinsic methodological limitations and find the common, emergent elements that remain robust despite the different methods.The first insights will be revealed by using clustering techniques and more specifically K-Means and Density based clustering (DBSCAN). For the clustering algorithms, the values of the sub-indicators along with those of the sustainability indices under all the computational regimes were used.For the current thesis, three additional techniques were used: Classification and Regression Decision Trees (CART), Random Forests and Boosting Regression.Classification and Regression Decision Trees (CART) since they are not computationally costly, they can be used as communication tools to non-experts and offer deep interpretational capabilities. However, CART trees tend to overfit the data to their training set and are considered weak learners and for that reason two additional ML techniques will be used: Random Forests and boosting regression.Random forests train trees independently using random samples of the available data and the sampling happens with bootstrapping both the sample and the features at every repetition. As a result, they tend to be slower than CART trees, but the generated results are more robust and tend to avoid the pitfalls of overfitting. More specifically, with the random forests 80% of the data will be used for training and the remaining will be used for prediction. Furthermore, for each data row (point) of the remaining data, the contribution of the individual features to the predicted value will be calculated. The average of all the contributions will be plotted in a boxplot to reveal insights on how individual sub-indicators affect the value of the sustainability index.Similarly, boosting regression is also considered a slow learner, but compared to random forests, each tree is generated using information from previous ones. Moreover, the technique will also reveal the relative influence of the individual sub- indicator to the index of sustainability, which could provide further insights into the analysis of the results. Both random forests and boosting regression are more robust than CART trees, but this robustness comes at the detriment of intuitive communication capabilities that are the main characteristic of CART trees. Consequently, the use of all three Machine Learning techniques will limit the methodological weaknesses of each method, while providing results and insights that are robust and independent of the used technique.The final results illustrated that a balance among the performance of various dimensions can be a good policy to achieve sustainable development and when the inclusion of all DEA variations does not alter significantly the mean value of sustainability then the trust in the results increases, thus making them robust.Finally, the blend of DEA with machine learning (applied on the results of DEA for the various scenarios) revealed insights on the areas that policy makers could direct investments to increase sustainability. In addition, the ML applications contributed in the identification of the most important features of sustainability for the various countries something that could have direct implications in the area of EU policy making: for example, countries that share similar features that drive the behavior of sustainability could be grouped together in clusters and policies, laws, regulations etc. could be adapted to those clusters in order to boost the particular features that would increase their sustainability. As a result, policy making has the potential to become customized (adapted to the specifics of each group) without missing its overall and principal theme of pursuing sustainable development. This adaptive and adaptable policy making could greatly be of assistance especially when new countries are negotiating their entry to the Union; based on the features that affect the sustainability of the new countries, they could follow the regulations and laws of the appropriate cluster. Finally, the inclusion of new layers and perceptions renders the algorithms more inclusive and participatory, increasing their transparency, thus improving the trust to the final results.Κατά τη λήψη δημόσιων αποφάσεων, παράγοντες όπως οι προσωπικές αξίες, το πολιτισμικό υπόβαθρο και οι διαφορετικές ατομικές προοπτικές διαδραματίζουν κεντρικό ρόλο στον κύκλο σχεδιασμού, δοκιμής, εφαρμογής και αναθεώρησης της πολιτικής. Για να βοηθήσουν τους υπεύθυνους χάραξης πολιτικής, οι αναλυτές έχουν χρησιμοποιήσει μια σειρά ποιοτικών και ποσοτικών μεθόδων σε όλα τα στάδια του κύκλου αποφάσεων.Ωστόσο, η αυξανόμενη χρήση εξελιγμένων μεθόδων δεν φαίνεται να συνοδεύεται πάντα από βελτίωση της ποιότητας της χάραξης πολιτικής - αντίθετα, φαίνεται να προσελκύει κριτική που επικεντρώνεται στα μειονεκτήματά τους. Επιπλέον, η ραγδαία άνοδος της χρήσης τεχνικών μηχανικής μάθησης και τεχνητής νοημοσύνης και η ολοένα διερευνώμενη χρήση μεθοδολογιών βαθιάς μάθησης στη λήψη αποφάσεων ή/και στη χάραξη πολιτικής, έφερε στο προσκήνιο το ζήτημα της ερμηνευσιμότητας των αποτελεσμάτων των αλγορίθμων αυτών και κατά πόσον τα αυτά τους μπορούν να θεωρηθούν αξιόπιστα και έγκυρα (verified and validated). Ερωτήματα όπως "ποιο συγκεκριμένο χαρακτηριστικό (feature/variable/attribute) ή μετρική (metric) έκανε το μοντέλο/αλγόριθμο να καταλήξει στη συγκεκριμένη απόφαση", άρα ζητήματα διαφάνειας (transparency), ερμηνευσιμότητας (interpretability) και εμπιστοσύνης (trustworthiness) των μεθόδων, καθίστανται κεντρικά ζητήματα της κριτικής θεώρησης στις ποσοτικές μεθόδους και στους αλγορίθμους.Η κριτική αυτή δεν στερείται θεωρητικής και πρακτικής βάσης. Η εγγενής πολυπλοκότητα των σύγχρονων προβλημάτων προκαλεί ερωτήματα για τα οποία ένας αναλυτής μπορεί να κάνει μόνο υποθέσεις λόγω της ύπαρξης πλήρους αβεβαιότητας ή περιβάλλοντος υψηλού ρίσκου. Επιπλέον, σε ένα τέτοιο περιβάλλον πολυπλοκότητας, οι αντιλήψεις και τα στερεότυπα του αναλυτή (cognitive biases) μπορούν να περιορίσουν το οπτικό του πεδίο και τη δυνατότητα ερμηνείας του υπό μελέτη κύκλου πολιτικής. Ως αποτέλεσμα, η επιτυχία μιας ποσοτικής μεθόδου βασίζεται στο ότι όλες οι παραπάνω επιλογές που αφορούν την ανάλυση του προβλήματος θα είναι κατάλληλες.Η έννοια της αειφόρου ανάπτυξης περικλείει αυτά τα ζητήματα και για να επιτευχθεί οι δημόσιες πολιτικές θα πρέπει να έχουν οικονομικές, κοινωνικές και περιβαλλοντικές διαστάσεις, λαμβάνοντας παράλληλα υπόψη τις τρέχουσες τεχνολογικές εξελίξεις, το πολιτισμικό πλαίσιο και το σύστημα αξιών στο οποίο εφαρμόζονται.Εκτός από τον πολυδιάστατο χαρακτήρα τους, τόσο η αειφόρος ανάπτυξη όσο και η αειφορία χαρακτηρίζονται από διαφορετικές αντιλήψεις σχετικά με τον τρόπο που πρέπει να οριστούν. Συμπληρωματικά με την έλλειψη ενός ενιαίου ορισμού είναι και η απουσία ενός επίσημου και ενιαίου μεθοδολογικού πλαισίου. Οι σύνθετοι δείκτες έχουν αναδειχθεί ως ένα κατάλληλο μέσο που επιτρέπει τη σωστή μέτρηση της αειφορίας.Μια από τις μεθόδους που έχουν προταθεί και χρησιμοποιηθεί στη βιβλιογραφία τόσο για τη μέτρηση της αειφορίας όσο και για την κατασκευή σύνθετων δεικτών είναι η Περιβάλλουσα Ανάλυση Δεδομένων (Data Envelopment Analysis, DEA). Στο πλαίσιο της παρούσας διατριβής πραγματοποιήθηκε βιβλιογραφική ανασκόπηση για τα έτη 2016-2020 και διαπιστώθηκε ότι οι ερευνητές έχουν καταβάλει προσπάθεια να συμπεριλάβουν παραμέτρους που αντιπροσωπεύουν την κοινωνική διάσταση της αειφορίας (χαρακτηριστικό που έλειπε τα προηγούμενα χρόνια), ενώ σε πρόσφατες μελέτες περιλαμβάνονται περισσότερες διαστάσεις της αειφορίας, όπως η τεχνολογική καινοτομία και πρόοδος, παρά το γεγονός ότι η τρισδιάστατη προσέγγιση (οικονομία, περιβάλλον, κοινωνία) φαίνεται να είναι η προτιμώμενη. Επιπλέον, διαπιστώθηκε ότι η επιλογή των εισροών και εκροών (και των ενδιάμεσων μέτρων) παρά τις σχετικές ομοιότητες είναι μοναδική για κάθε ερευνητική εργασία. Επιπλέον, η επιλογή της παραλλαγής της DEA και/ή ο συνδυασμός με άλλες μεθοδολογίες συνεπάγεται ότι η αντίληψη κάθε αναλυτή επηρεάζει το τελικό αποτέλεσμα της μελέτης του.Εκτός από τα κενά που διαπιστώθηκαν παραπάνω, η ίδια η μεθοδολογία της DEA δεν είναι απαλλαγμένη από τους δικούς της περιορισμούς. Πρώτον, στην παραδοσιακή της μορφή, η αποδοτικότητα των Μονάδων Λήψης Αποφάσεων (Decision Making Units-DMUs) υπολογίζεται με τα πιο ευνοϊκά για τις ίδιες βάρη, δηλαδή κάθε DMU αξιολογείται με το πιο ευνοϊκό σχήμα στάθμισης με σκοπό τη μεγιστοποίηση της δικής της αποδοτικότητας, κάτι που είναι ως ένα βαθμό αποδεκτό θεωρώντας ότι κάθε DMU δικαιούται το ελαφρυντικό της αμφιβολίας ως προς τους λόγους της μειωμένης αποδοτικότητάς της (benefit of the doubt principle). Ως αποτέλεσμα, τα βάρη που επιλέγονται για μία DMU μπορεί να είναι εντελώς διαφορετικά από εκείνα που επιλέγονται για μια άλλη υπό την έννοια ότι οι αντίστοιχες μονάδες με τις οποίες συγκρίνεται κάθε μία από αυτές μεταβάλλονται ανάλογα με την άριστη λύση του σχετικού μοντέλου ώστε να εντοπιστεί η καλύτερη δυνατή απόδοση αλλά και οι ιδανικοί ομότιμοι προς τους οποίους θα πρέπει να κοιτάξει για να βελτιωθεί (efficient peers). Επιπλέον, η DEA πρέπει να χρησιμοποιείται στο κατάλληλο πλαίσιο, πράγμα που σημαίνει ότι υπάρχει η απαίτηση να αποφασιστεί ποιες παράμετροι θα εξηγήσουν καλύτερα τις διάφορες διαστάσεις της αειφορίας. Αυτό είναι ιδιαίτερα σημαντικό δεδομένου ότι, ο αριθμός των εισροών και εκροών που μπορούν να χρησιμοποιηθούν περιορίζεται από τον αριθμό των DMUs που αξιολογούνται για να έχει νόημα η μέτρηση, διαφορετικά θα υπήρχε αυξημένος αριθμός αποδοτικών DMUs που θα οδηγούσε σε ασυνέπειες.Ως εκ τούτου, σκοπός της παρούσας διατριβής είναι να αντιμετωπίσει όλα τα παραπάνω κενά και πιο συγκεκριμένα:1) να προτείνει μια εναλλακτική εκδοχή της Περιβάλλουσας Ανάλυσης Δεδομένων δύο σταδίων με μια διαφορετική αντικειμενική συνάρτηση που επιχειρεί να παρέμβει στα βάρη των εισροών, των ενδιάμεσων μέτρων και των εκροών ώστε να αντικατοπτρίζει καλύτερα τη σημασία τους για τα DMUs, λαμβάνοντας υπόψη τις θετικές και αρνητικές αποκλίσεις στους υπολογισμούς και περιορίζοντας την απόσταση αυτών των αποκλίσεων από τις μέγιστες και ελάχιστες τιμές.2) να προτείνει ένα υπολογιστικό πλαίσιο που θα επιχειρήσει να ενσωματώσει διαφορετικές αντιλήψεις (δηλαδή διαφορετικούς συνδυασμούς εισροών και εκροών) και να το εφαρμόσει στη μέτρηση της αειφορίας των χωρών της ΕΕ των 28, δηλαδή προτείνεται ένα νέο υπολογιστικό πλαίσιο που στηρίζεται σε τεχνικές μηχανικής μάθησης μέσω του οποίου κατασκευάζονται σύνθετοι δείκτες απόδοσης αειφορίας για κάθε χώρα.Για την επίτευξη αυτού του στόχου, το πλαίσιο θα βασίζεται στη διερευνητική μοντελοποίηση και ανάλυση (Exploratory Modeling and Analysis- ΕΜΑ). Η ΕΜΑ είναι μια σχολή σκέψης που αναπτύχθηκε στο ερευνητικό κέντρο RAND και προωθεί τη διερευνητική χρήση ποσοτικών μεθόδων παρά τους μεθοδολογικούς περιορισμούς τους, τις αβεβαιότητες και τις διαφορετικές αντιλήψεις (δηλαδή διαφορετικούς συνδυασμούς εισροών, ενδιάμεσων μέτρων και εκροών). Η χρήση μιας διερευνητικής προσέγγισης για τη μέτρηση της αειφορίας θα μπορούσε να αποκαλύψει απρόβλεπτες επιπτώσεις των αρχικών υποθέσεων σχετικά με τις εισροές και τις εκροές.Η διατριβή αναπτύσσεται σε μια σειρά διαδοχικών βημάτων. Πρώτον, εισάγεται ένα εναλλακτικό μοντέλο DEA δύο σταδίων που χρησιμοποιεί θετικές και αρνητικές αποκλίνουσες μεταβλητές τόσο στην αντικειμενική συνάρτηση (μεταβάλλοντας έτσι το μέτρο της βελτιστοποίησης), όσο και στους περιορισμούς. Το μοντέλο επιχειρεί να βρει τα καλύτερα δυνατά βάρη για τις εισροές, τα ενδιάμεσα μέτρα και τις εκροές, ελαχιστοποιώντας τις αποκλίσεις τόσο του πρώτου όσο και του δεύτερου σταδίου του μοντέλου. Με την ταυτόχρονη ελαχιστοποίηση των αποκλίσεων κάθε σταδίου, μεγιστοποιούνται ταυτόχρονα οι αποδοτικότητες και των δύο σταδίων και δεν δίνεται προτεραιότητα στο ποιο στάδιο θα πρέπει να υπερισχύσει. Το προτεινόμενο πλαίσιο/μοντέλο στηρίζεται θεωρητικά σε τρία λήμματα και ένα θεώρημα όπου αποδεικνύεται ότι έχει τουλάχιστον μια εφικτή λύση που είναι βέλτιστη.Η προτεινόμενη παραλλαγή της μεθόδου εφαρμόζεται σε δύο μελέτες περίπτωσης: η μία υπολογίζει τις περιβαλλοντικές επιδόσεις των ευρωπαϊκών χωρών και η δεύτερη ελέγχει τη γεωργική βιωσιμότητα των ευρωπαϊκών χωρών.Μετά τον ορισμό του νέου μοντέλου, ορίζεται ένα νέο υπολογιστικό πλαίσιο για την κατασκευή σύνθετων δεικτών. Το προτεινόμενο μοντέλο χρησιμοποιείται για τον υπολογισμό κάθε επιμέρους δείκτη από τον οποίο θα αποτελείται ο τελικός δείκτης. Οι υπολογισμένοι υποδείκτες χρησιμοποιούνται στη συνέχεια ως παράμετροι σε ένα μοντέλο Benefit-of-the-Doubt (BoD) που παράγει την τιμή του τελικού δείκτη. Το υπολογιστικό πλαίσιο δοκιμάζεται δύο φορές στη μέτρηση της αειφορίας των ευρωπαϊκών χωρών: μία φορά με το προτεινόμενο, εναλλακτικό, μοντέλο DEA δύο σταδίων και μία φορά μ

Synergies and Challenges: Exploring Organizational Perspectives on Digital Transformation and Sustainable Development in the Context of Skills and Education

The discourse surrounding digital transformation (DT) and sustainable development (SD) is pervasive in contemporary business and organizational operations, with both processes considered indispensable for sustainability. The success or failure of these endeavors hinges significantly on factors such as the behavior and skill sets of individuals within organizations. Thus, the purpose of the paper is twofold: to investigate the perceptions of organizations on digital transformation and sustainable development with regards to skills and education, and, secondly, to use the insights from these perceptions as a starting point for the use of systems thinking as a tool that could assist in achieving these states. To achieve the objective, a research effort was conducted that included desktop research, interviews with experts, and the development of a survey that was disseminated across Europe with questions on digital transformation and sustainable development. Finally, a general causal loop diagram was designed, illustrating the processes of digital transformation and sustainable development within organizations from a top-down view. The study reveals commonalities between DT and SD, recognizing both processes as advantageous with shared deficiencies in specific skill sets. It highlights a synergistic relationship between initiating DT and fostering SD activities. Furthermore, the research underscores the temporal aspects of these processes, acknowledging delayed positive effects and immediate implementation costs that challenge decision-makers to balance long-term benefits with short-term viability. In conclusion, the exploration emphasizes the dynamic nature of DT and SD, urging continual attention to the evolving landscape and the imperative for a shared understanding within organizational contexts

A Systems Thinking Archetype to Understand, Analyze, and Evaluate the Evolution of International Political Crises

Crises are a relevant element of the modern political, economic, and social landscape. To better understand them and their potential dynamic evolution, and thus allow decision makers in turn to design more effective intervention measures, a more comprehensive understanding of their complexity is necessary. Framing a political crisis, especially one where conflicts might ensue, can be crucial for dealing with it. Consequently, there is the need to adopt a new paradigm that can reveal and contextualize the fundamental factors that can give rise to a political crisis, thus allowing for a more accurate description of it and, in turn, ensuring that every stakeholder will perceive it similarly. The present study proposes such a paradigm, to understand how a political crisis emerges, how it might evolve, and how the intertwined relevant factors can be communicated clearly, and yet be layered, which was the Systems Thinking approach. A set of case studies is presented to demonstrate the added value of such an approach. The performed analysis also draws inspiration from international relations theories, through which the Systems Thinking approach shows its capability in effectively evaluating the potentially underlying dynamics of crises and providing an analytical ground for their management and prevention

An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU

One method that has been proposed for the measurement of sustainability is Data Envelopment Analysis (DEA). Despite its advantages, the method has limitations: First, the efficiency of Decision-Making Units is calculated with weights that are favorable to themselves, which might be unrealistic, and second, it cannot account for different perceptions of sustainability; since there is not an established and unified definition, each analyst can use different data and variations that produce different results. The purpose of the current paper is twofold: (a) to propose an alternative, multi-dimensional DEA model that handles weight flexibility using a different metric (an alternative optimization criterion) and (b) the inclusion of a computational stage that attempts to incorporate different perceptions in the measurement of sustainability and integrates machine learning to explore country sustainability composite indices under different perceptions and assumptions. This approach offers insights in areas such as feature selection and increases the trust in the results by exploiting an inclusive approach to the calculations. The method is used to calculate the sustainability of the 28 EU countries

A Systems Thinking Archetype to Understand, Analyze, and Evaluate the Evolution of International Political Crises

Crises are a relevant element of the modern political, economic, and social landscape. To better understand them and their potential dynamic evolution, and thus allow decision makers in turn to design more effective intervention measures, a more comprehensive understanding of their complexity is necessary. Framing a political crisis, especially one where conflicts might ensue, can be crucial for dealing with it. Consequently, there is the need to adopt a new paradigm that can reveal and contextualize the fundamental factors that can give rise to a political crisis, thus allowing for a more accurate description of it and, in turn, ensuring that every stakeholder will perceive it similarly. The present study proposes such a paradigm, to understand how a political crisis emerges, how it might evolve, and how the intertwined relevant factors can be communicated clearly, and yet be layered, which was the Systems Thinking approach. A set of case studies is presented to demonstrate the added value of such an approach. The performed analysis also draws inspiration from international relations theories, through which the Systems Thinking approach shows its capability in effectively evaluating the potentially underlying dynamics of crises and providing an analytical ground for their management and prevention

An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU

One method that has been proposed for the measurement of sustainability is Data Envelopment Analysis (DEA). Despite its advantages, the method has limitations: First, the efficiency of Decision-Making Units is calculated with weights that are favorable to themselves, which might be unrealistic, and second, it cannot account for different perceptions of sustainability; since there is not an established and unified definition, each analyst can use different data and variations that produce different results. The purpose of the current paper is twofold: (a) to propose an alternative, multi-dimensional DEA model that handles weight flexibility using a different metric (an alternative optimization criterion) and (b) the inclusion of a computational stage that attempts to incorporate different perceptions in the measurement of sustainability and integrates machine learning to explore country sustainability composite indices under different perceptions and assumptions. This approach offers insights in areas such as feature selection and increases the trust in the results by exploiting an inclusive approach to the calculations. The method is used to calculate the sustainability of the 28 EU countries