Tourism’s digital transformation has reshaped how travelers search for and evaluate destinations. However, relatively little empirical work has examined how user engagement translates into booking intent, especially under the emergent discovery channels mediated by artificial intelligence (AI). This study tests an engagement-driven referral framework using longitudinal behavioral data from a Mediterranean destination portal (April 2022–January 2026; 1.6 million sessions). Engagement depth, measured as average session time, significantly predicts booking intent click rate. Mobile drives 83% of sessions, but desktop users convert at nearly twice the rate (5.69% vs. 3.37%). High traffic, as it turns out, does not equal high commercial intent. Lower-volume international markets routinely outperform the dominant domestic market. The most striking result concerns AI referrals. Traffic arriving from AI assistants converts at 8.26%, more than double the organic search rate of 3.88%, despite shorter sessions, a pattern consistent with compressed decision-making under generative AI. These findings, grounded in real travel portal data, extend engagement theory beyond transactional settings and shed early light on how referrals from AI assistants like ChatGPT or Gemini differ behaviorally from organic search, with practical implications for portal managers, destination marketing organizations (DMOs), and sustainable demand management.

As social media campaigns become increasingly important in grocery and supermarket retail communication strategies, there is little research on how consumers view campaign performance throughout their decision-making process, rather than isolated behavioral outcomes. This study examines how the five-stage decision-making process is influenced by consumer-perceived social media performance effectiveness (CP-SMPE), grounded in consumer decision-making theory and social media performance literature. The study uses a mixed-methods research design, combining qualitative interviews with the consumers and a quantitative survey of 300 grocery shoppers in Greece. Perceived return on investment, revenue contribution, lead generation, engagement, reach, cost efficiency, and quality of electronic word-of-mouth are components of social media performance conceptualized as a multidimensional construct. Exploratory factor analysis and PLS-SEM were employed to analyze quantitative data. The findings show that high perceived social media campaign performance influences all stages of the consumer decision-making process, both directly and indirectly, through sequential intermediate stages. It ultimately enhances purchase decisions and post-purchase outcomes. By adopting a consumer-centric, process-based perspective, this study contributes to research on digitally mediated retail decision-making by demonstrating how effective social media communication can support more informed, structured consumer choices. The findings suggest that social media communication can lead to more informed and potentially responsible consumption choices by improving information environments and decision support, even though sustainability outcomes are not directly measured.

In marketing decision-making, evaluating competing alternatives using both quantitative and qualitative criteria inherently yields a complex, multidimensional process. The PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) is a powerful multicriteria decision-making (MCDM) methodology that evaluates and ranks options through head-to-head comparisons, accounting for decision-makers’ preferences. A crucial part of the PROMETHEE approach involves selecting appropriate preference functions and defining indifference and preference levels that translate performance disparities into comprehensible degrees of preference. This paper investigates the role of PROMETHEE preference functions in marketing decision-making and explores practical strategies for determining threshold values that faithfully represent managerial perspectives. The study explains the method’s utility across various marketing scenarios. In particular, it discusses brand assessment, ad campaign selection, customer satisfaction measurement, market segmentation, pricing strategies, and product line evaluation. By combining quantitative performance metrics with subjective managerial preferences, PROMETHEE provides a clear, adaptable framework that improves decision quality across diverse, sometimes conflicting, evaluation criteria. The research offers actionable recommendations for selecting preference functions and threshold values, helping marketing executives and scholars construct more reliable and comprehensible decision-making aids.

The decision-making process for global marketing is becoming more complex, as it entails balancing economic performance with environmental and social sustainability across diverse international markets. The focus of this study is sustainability-oriented and green marketing decisions, as it reviews and analyzes, using bibliometric methods and PROMETHEE-based applications, marketing research published between 2015 and 2025. In accordance with the PRISMA 2020 guidelines, 42 peer-reviewed articles were analyzed using transparent, reproducible methods. The findings indicate a substantial use of PROMETHEE in selecting markets, developing products and services, planning channels and retail locations, pricing, and benchmarking customer satisfaction. Importantly, the study finds that sustainability is an integral part of marketing decision-making rather than a separate research stream, revealing that sustainability criteria are often applied implicitly, despite limited explicit framing in green marketing theory. The results suggest that hybrid decision-support approaches are increasingly being integrated, highlighting PROMETHEE's potential to support the evaluation of sustainability-related trade-offs and to identify research gaps.

This study assesses the effectiveness of social media advertising campaigns in the supermarket sector by combining managerial insights with multi-criteria decision analysis (MCDA) to support informed, sustainable decision-making. Considering the ever-increasing complexity of digital communication and the growing need for sustainable marketing resources, supermarkets require effective methods to evaluate social media platforms beyond isolated metrics. The study employs the Visual PROMETHEE program, an MCDA that incorporates qualitative insights from 27 supermarket managers in Northern Greece, along with the PROMETHEE II multi-criteria decision analysis method. At the outset, managers evaluated the importance of thirty-four social media performance factors with a five-point scale. Seven core evaluation criteria are identified by aggregating importance ratings and qualitative analysis: return on investment, revenue contribution, lead generation, engagement, cost efficiency, feedback, electronic word of mouth (eWoM), and reach. The use of these criteria later led to the evaluation of seven major social media platforms. A transparent ranking of platforms is presented, based on the results. The ranking highlights significant performance differences across financial, engagement, and reputational dimensions. The findings demonstrate the importance of integrating managerial guidance with multi-criteria analysis to inform long-lasting and evidence-based marketing decisions in retail.

This chapter aims to give a brief overview of the most recent trends and patterns of green and sustainable entrepreneurship in the global fashion market by outlying the potential and future trends of smart fashion consumer products. Issues pertaining to green and sustainable entrepreneurship are discussed, with the goal of achieving growth and profitability while also taking into account the importance of protecting the environment and minimizing climate change. Research and development of green technologies and practices is the key to achieving this through appropriate investments. The chapter explains how PROMETHE's methodology tackles and solves marketing problems related to global fashion challenges by suggesting the best alternative decisions for marketing tactics and strategic actions. In addition, the analysis highlights new trends and challenges that marketer and decision-makers in the fashion industry are facing.

This research presents a short review of Multiple Criteria Decision-Making (MCDM) methods and research in various fields, including marketing and business management. The academic literature shows that MCDM methods in the area of marketing are used by academics to solve problems related to the positioning of products and services, market segmentation, brand management, promotion and advertising strategies, product development and market entry strategies, customer relationship marketing and channel distribution. With regard to business and management domains they are used to prioritize various decision-making aspects, like project assessments, resource allocation, strategic planning, risk management, performance evaluation, supplier and vendor selection, human resource management and strategic investment decisions. We can claim that in both domains, MCDM brings a systematic and transparent approach to decision-making, helping marketing managers to make more informed and objective choices. In summary, the continual refinement of these methods and the integration of cutting-edge technologies hold promise for further enhancing the effectiveness and efficiency of decision-making processes in the dynamic landscape of business and management. Further, the analysis highlights emerging trends and challenges for the future of MCDM research.

The Machine Passport represents a transformative digital framework for managing industrial equipment data throughout the entire machine lifecycle. By consolidating data from diverse sources-ranging from design and manufacturing to use and repair/reuse/recycling-the platform addresses challenges of fragmented information and limited interoperability. Crucially, it aims to foster collaboration among lifecycle stakeholders by enabling secure and standardized data sharing within these networks. The platform not only ensures comprehensive information management but also facilitates advanced analytics through integrated Artificial Intelligence (AI) and Explainable AI (XAI) techniques. This paper presents the Machine Passport's architecture, outlines its key components, and demonstrates how the system supports decision-making, operational optimization, collaboration, data sharing, and sustainability in industrial settings. We discuss the challenges of data integration and sharing across heterogeneous systems and collaborative networks, highlighting the benefits of a standardized, AI-enhanced approach and illustrating its potential impact through an industrial use case.

This paper explores the application of multiple machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Boosted Gradient (BG), and Classification and Regression Trees (CART), in the context of predictive maintenance for industrial machinery. Utilizing a comprehensive dataset specific to machine operations and maintenance requirements, we aim to identify the most effective model for predicting maintenance needs. Post-modeling, we employ SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) to evaluate and compare the significance of various features in each model. This comparative analysis seeks to provide insights into the effectiveness of these feature importance techniques in the realm of predictive maintenance, thus contributing valuable knowledge to the field of industrial machine learning applications.

Digital Product Passports (DPPs) are emerging as pivotal tools driving the digital transformation across industries by providing standardized, interoperable, and detailed digital records of products. This enhances tracking, transparency, and lifecycle management, fostering sustainable and efficient digital ecosystems. While the potential of DPPs is recognized, existing reviews often lack a specific focus on their role as active enablers of industrial digital transformation. This paper addresses that gap by presenting a comprehensive review of the state-of-the-art in DPP research and applications, critically examining how they facilitate this transformation. We outline the methodology used to gather the literature, propose a taxonomy of DPP types based on technology and function (illustrated visually and summarized in a comparative table), present an evaluation framework with relevant metrics, and analyze their implications across various industrial sectors. Key contributions include this structured taxonomy, the evaluation framework, and a critical discussion of challenges (e.g., interoperability, standardization, readiness gaps) and opportunities, offering recommendations for leveraging DPPs effectively.

Through the lens of established marketing and technology adoption theories, this paper examines the complexities involved in marketing smart consumer products to global millennial consumers. It discusses the key challenges and opportunities that marketers face, which are supported by academic literature. Digital interconnectivity, data abundance, AI integration, and evolving consumer expectations are the driving forces behind the complexity of marketing smart consumer products, as demonstrated in the review. To manage this complexity, marketing practices must be strategically, data-informed, and human-centered. Further, the findings highlight the significance of cultural sensitivity, global-local positioning, and adaptive marketing strategies that cater to the distinct desires and expectations of millennials in diverse regions. To advance both theoretical and practical understanding, future research should investigate the longitudinal effects, cross-cultural dynamics, and ethical considerations in AI-driven marketing.

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality.

With the increasing prevalence of AI, significant advancements have been made across various domains, such as healthcare, learning, industry, etc. However, challenges persist in terms of trusting and comprehending the outcomes generated by these technologies. Specifically in the language learning domain, teachers face challenges regarding the classification of the students’ learning capabilities and build the appropriate learning path for them. To address these challenges, the concept of Explainable Artificial Intelligence (XAI) was adopted, which is a set of processes and methods that allows human users to interpret, understand and trust the results derived from machine learning models. In this study, we adopt two well-known XAI algorithms, PFI and SHAP in a proposed Knowledge Generation Model equipped with ML models to derive hidden knowledge. The whole framework has been applied and evaluated on the Language Learning Classification of Spanish Tertiary Education Students acquired from the CEDEL2 database. The analysis concludes that in terms of explaining the black-box models, the SHAP model-agnostic method is the most comprehensive and dominant for visualizing feature interactions and feature importance and be applicable to any type of data.

In recent years the digital landscape has been rapidly evolving as the application of artificial intelligence (AI) becomes increasingly important in shaping search engine optimization (SEO) strategies and revolutionizing the way websites are optimized for search engines. This research aims to explore the influence of AI in the field of SEO through a literature review that is conducted using the PRISMA framework. The study delves into how AI capabilities such as generative AI and natural language processing (NLP) are leveraged to boost SEO. These techniques in turn allow search engines to provide more accurate, user-centric results, highlighting the importance of semantic search, where search engines understand the context and intent of a user’s search query, ensuring a more personalized and effective search experience. On the other hand, AI and its tools are used by digital marketers to implement SEO strategies such as automatic keyword research, content optimization, and backlink analysis. The automation offered by AI not only enhances efficiency but also heralds a new era of precision in SEO strategy. The application of AI in SEO paves the way for more targeted SEO campaigns that attract more organic visits to business websites. However, relying on AI in SEO also poses challenges and considerations. The evolving nature of AI algorithms requires constant adaptation by businesses and SEO professionals, while the black-box nature of these algorithms can lead to the opaque and unpredictable evolution of SEO results. Furthermore, the power of AI to shape online content and visibility raises questions about equality, control, and manipulation in the digital environment. The insights gained from this study could inform future developments in SEO strategies, ensuring a more robust, fair, and user-centric digital search landscape.

The aim of this chapter is to explicate the fundamental advantages of MCDM methods in addressing diverse marketing challenges through the use of statistical techniques. The list of examples included household and consumer panel data on product purchases and survey data and demand models based on micro-economic theory. Integrating MCDM methods with statistical techniques in the metaverse can lead marketers to better outcomes in their marketing efforts by making more informed and robust decisions. Although the MCDM process is generally similar to those of other approaches, there are differences in how information on alternatives, criteria, and the relative importance (weight) of criteria is provided, identified, and analyzed. It could be said that the MCDM process consists of a series of stages from defining the problem to identifying the best alternatives.

In the realm of industrial energy management, the steel industry stands as a significant consumer, necessitating innovative approaches to optimize energy usage. This paper presents a comprehensive study leveraging machine learning models to analyze energy consumption data from DAEWOO Steel Co. Ltd in Gwangyang, South Korea. Utilizing datasets encompassing daily, monthly, and annual electricity usage, this research deploys various Naive Bayes models (Gaussian, Multi-nomial’ Complement, Bernoulli) to predict and analyze energy consumption patterns. Key to this study is the use of Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP) for feature importance analysis. With a particular focus on an advanced extension of SHAP that enhances the interpretability of model predictions by introducing a weighting factor based on feature importance derived from the model itself, Weighted SHapley Additive exPlanations (Weighted SHAP). This extension allows for a more nuanced assessment of feature contributions to the energy consumption patterns. By aligning with such advanced data exchange protocols, the comparative analysis of these models, enriched with the sophisticated SHAP methodology, provides valuable insights, contributing to a more efficient and sustainable energy management strategy in the steel industry. This alignment ensures that the energy management solutions proposed can be seamlessly integrated, furthering the advancements in smart manufacturing and Industry 4.0.

Growth hacking is an experiment-driven technique to determine the most effective ways of growing a business. In order to grow a successful business, entrepreneurs need to adopt various growth hacking techniques. While some of these techniques have been around for a while, others are new to the market. The process involves a mix of marketing, development, design, engineering, data, and analytics. This paper presents a literature review on entrepreneurial marketing techniques that implement new technologies and identifies smarter and inexpensive alternatives to traditional marketing that can boost startup sales, such as content and viral marketing. In addition, this paper presents the results of an empirical study conducted among 83 digital startuppers from Greece regarding the digital marketing techniques they have used. Based on the findings, we propose the framework StUpGrowthPath including a nine-step growth hacking marketing plan for the first phases of a digital startup with tools and digital marketing techniques that could be effective and low-cost.

Developing a personal brand in the world of social media is a new trend. Existing literature examines how professionals, actors, athletes, influencers can utilize social media platforms to build strong personal brands. However, the phenomenon of positioning brand for politicians, online, in a race to gain the middle voter with limited ideological differences between the two main political parties is rarely studied, despite its growing importance, especially during the election period. This research seeks to fulfill this gap and tries to understand what kind of personal branding can be formed successfully on YouTube for politicians' campaigns. The analysis explores visual storytelling of brand-building of the two political leaders in Greece in the 2019 European Parliament pre-election period. The results revealed that a visual storytelling with emotional symbols, authentic appearances and universal slogans were the main points of difference for politicians to position effectively. The results could help the growth of political-personal branding theory and practitioners for political marketing strategies on YouTube. Directions for future research are discussed as well.