Πλοήγηση ανά Επιβλέπων / ουσα "Manolopoulos, Dimitrios"

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Τώρα δείχνει 1 - 3 από 3

Business model innovation (BMI): adjacent consequences
(2025-02-26) Pavlidis, Alexandros; Παυλίδης, Αλέξανδρος; Magkoutas, Konstantinos; Mamakou, Xeni; Manolopoulos, Dimitrios
Στόχος της παρούσας μεταπτυχιακής διατριβής ήταν η διερεύνηση των παραγόντων που επηρεάζουν την καινοτομία του επιχειρηματικού μοντέλου (BMI), εστιάζοντας στις στρατηγικές, τις ηγετικές ομάδες και τις οργανωτικές δομές των εταιρειών. Για την επίτευξη αυτού του στόχου, εφαρμόστηκε η μεθοδολογία της ποσοτικής έρευνας, η οποία επιλέχθηκε για την αντικειμενική καταγραφή των δεδομένων, χρησιμοποιώντας το ερωτηματολόγιο ως ερευνητικό εργαλείο, ενώ η στατιστική επεξεργασία επέτρεψε τη διερεύνηση των υποθέσεων μέσω γραμμικών και πολλαπλών παλινδρομήσεων. Συγκεντρώθηκε ένα συνολικό δείγμα 85 συμμετεχόντων, οι οποίοι ήταν ανώτατα στελέχη (ανώτατες διοικητικές ομάδες) και προέρχονταν κυρίως από μεγάλες εταιρείες-πολυεθνικές, με έμφαση στη βιομηχανία δομικών υλικών, αλλά και σε άλλους τομείς υπηρεσιών. Τα ευρήματα αυτής της μελέτης έδειξαν ότι η ποικιλομορφία και η συνοχή των ομάδων ανώτατης διοίκησης έχουν θετική επίδραση στην ικανότητα των επιχειρήσεων να καινοτομούν, ενώ ταυτόχρονα η ένταση του ανταγωνισμού και οι δείκτες απόδοσης βρέθηκαν να σχετίζονται σημαντικά με την επιτυχία των νέων επιχειρηματικών μοντέλων. Τα κύρια συμπεράσματα στα οποία κατέληξε η μελέτη αφορούσαν την ανάδειξη της σημασίας της ευέλικτης οργανωτικής δομής αλλά και της στρατηγικής πειραματισμού και των κατάλληλων ηγετικών χαρακτηριστικών για τη διατήρηση του ανταγωνιστικού πλεονεκτήματος. Οι διαπιστώσεις από τα αποτελέσματα αυτής της μελέτης ενισχύουν την προηγούμενη βιβλιογραφία σχετικά με την αυτονομία της βιβλιογραφίας των επιχειρηματικών μοντέλων και θέτουν τις βάσεις για περαιτέρω έρευνα σε διαφορετικούς κλάδους και αγορές.
The impact of AI-driven decision-making on the strategic ecosystem of firms
(2026-02-13) Papingkis, Vyron; Παπίνγκης, Βύρων; Skarmeas, Dionysis; Argouslidis, Paraskevas; Manolopoulos, Dimitrios
Artificial Intelligence is increasingly used in managerial decision-making and is changing the way firms organize and innovate. This study explores how AI-driven decision-making is connected with business model innovation, organizational capabilities, experimentation practices and firm performance. A quantitative research approach was followed using a structured questionnaire completed by senior representatives from different firms (N = 66). The analysis includes descriptive statistics, reliability tests, correlations and regression models. The findings show that AI-driven decision-making does not appear to have a strong relationship with firm performance on its own. Instead, performance differences seem to depend more on how AI is integrated into organizational routines, particularly through capability development, experimentation and business model changes. Overall, the results suggest that AI mainly supports innovation-related processes within firms.
Can AI create emotions? a marketing experiment
(2026-02-20) Sakas-Daikos, Georgios; Σακάς-Δαΐκος, Γεώργιος; Skarmeas, Dionysios; Kaminakis, Konstantinos; Manolopoulos, Dimitrios
This master thesis examines how consumers respond to AI-generated advertisements in comparison to traditionally produced advertisements, focusing on the psychological and demographic determinants that shape ad evaluation. As artificial intelligence increasingly transforms advertising production, understanding whether and how consumers differentiate between AI-created and human-created content has become both theoretically and practically significant. The theoretical framework of the study draws upon research in advertising effectiveness, personality psychology, and transgenerational theory. In particular, the thesis reviews the literature on ad likability as a key outcome variable, the Big Five personality traits as stable individual differences influencing evaluative judgments, and generational cohort theory as a lens for understanding cohort-based variations in technology perception and communication preferences. By integrating these streams of research, the study addresses a gap in the literature concerning the combined role of personality traits and generational differences in shaping responses to AI-generated advertising. Methodologically, the study employed an experimental design in which participants were split in two sub samples and each sub sample was exposed to either a conventionally produced advertisement or an AI-generated advertisement and was assigned to evaluate it. In both subsamples, data were collected using the same structured questionnaire, which included scaled items measuring advertisement likability (the dependent variable) and the Big Five personality traits (the independent variables). The instrument also gathered demographic information, including age, which was subsequently used to categorize participants into generational cohorts. To examine whether the determinants of ad likability differed across advertising formats, separate regression models were estimated for each subs ample. The empirical results demonstrate that personality traits exert a substantially stronger influence on advertisement likability than demographic characteristics across both advertising formats. Contrary to the study’s initial expectations, generational belonging and age do not significantly differentiate responses to conventional and AI-generated advertisements. Instead, variation in ad likability is explained primarily by selected dimensions of the Big Five personality framework. Agreeableness emerges as the only consistent predictor across both models, positively influencing advertisement evaluations regardless of creative origin, with a stronger effect observed in the AI-generated condition. In contrast, openness to experience is negatively associated with likability in the conventional advertisement model and does not significantly affect evaluations of the AI-generated advertisement. Overall, the findings suggest that stable psychological characteristics—rather than demographic factors—constitute the primary determinants of advertising evaluation in both traditional and AI-mediated contexts.