Explore the vast range of titles available.

The role of new technologies in tourism is changing rapidly, leading to the development of customer relationships through the use of virtual reality in the marketing of tourist destinations. In addition to focusing on the influence of travel intentions that has prevailed in practice so far, the use of VR is expected to have an impact on the travel experience on the spot. This exploratory research study was conducted with 824 respondents to identify the role of virtual reality in choosing a tourist destination, as well as the expectations of potential customers that could rekindle the tourism industry for a post-pandemic world. The results pointed out that highly used virtual reality applications for destination marketing aim to create a certain image for a tourist destination and to communicate this to the outside world in a consistent and coordinated manner. The findings also reinforce the importance of developing future scenarios for virtual reality as a decisive factor for strategic planning in the tourism sector.

Online consumers are increasingly looking for more convenient ways to purchase products and services, and chatbots are becoming increasingly popular in multichannel environments due to their ability to provide an efficient service. In this context, managing digital complexity with the help of artificial intelligence and supporting decisions in a multichannel context is an appealing perspective for the retailer, who must find the right strategy to win and keep customers online. The present empirical study aims to better understand consumer behaviour in the multichannel environment in the context of four categories of products and services (retail banking, mobile communications, fashion, and consumer electronics) from the perspective of identifying determinants of channel selection when the consumer uses chatbots. Data were collected from 936 respondents with multichannel retail experience to conduct an empirical investigation on social media platforms, including Twitter, Facebook, and Instagram; these data were then analysed using structural equation modelling (SEM). We found that the online consumer’s multichannel behaviour was not only a reality in the field of broad purchasing decisions but already a norm, and consumers had good reasons to use more channels in the context of chatbots. Research results suggest that chatbots can represent a decision-making aid for managers in retail companies who want to develop an efficient and optimal logistics service strategy in multichannel environments.

The purpose of this research study is to examine and explain whether there is a positive or negative linear relationship between sustainability reporting, inadequate management of economic, social, and governance (ESG) factors, and corporate performance and sustainable growth. The financial and market performances of companies are both analyzed in this study. Sustainable growth at the company level is introduced as a dimension that depends on sustainability reporting and the management of ESG factors. In order to achieve the main objective of the paper, the methodology here focuses on the construction of multifactorial linear regressions, in which the dependent variables are measurements of financial and market performance and assess corporate sustainable growth. The independent variables of these regressions are the sustainability metrics and the control variables included in the models. Most of the existing literature focuses on the causality between sustainability performance and financial performance. While most impact studies on financial performance are restricted to sustainability performance, this study refers to the degree of risk associated with the inadequate management of economic, social, and governance factors. This work examines the effects of ESG risk management, not only on performance, but also on corporate sustainable growth. It is one of the few studies that addresses the problem of the involvement of companies in controversial events and the way in which such events impact the sustainability and sustainable growth of the company.

Artificial intelligence (AI) is now part of the daily routine in many universities. It shows up in learning platforms, digital assessments, and even student services. But despite its growing presence, institutions still face the challenge of making sure it is used in ways that respect legal and ethical boundaries. This research explores how university settings that prioritise knowledge—real, shared, and thoughtfully managed—can help students become more aware of these dimensions. A total of 270 students took part in the study. We used a structural equation model to look at the links between knowledge-based practices, institutional governance, and students’ understanding of AI’s legal and ethical sides. The results show that when knowledge is genuinely valued—not just stored or repeated—governance practices around AI tend to develop more clearly. And this, in turn, makes a difference in how students relate to AI systems. Rather than teaching ethics directly, governance shapes the environment where such thinking becomes part of the everyday. When students see that rules are not arbitrary and that transparency matters, they become more cautious, but also more confident in navigating technology that does not always make its logic visible.

The transition to a low-carbon economy is profoundly reshaping European labour markets, creating both opportunities for sustainable employment and challenges for regions reliant on carbon-intensive sectors. Assessing how prepared EU Member States are for this shift remains difficult due to the lack of unified evaluation tools. This study introduces the Green Labour Market Readiness Index (GLMRI)—a composite measure assessing the adaptability of national labour markets to the energy-driven green transformation in nine EU countries: Germany, France, Sweden, Spain, Italy, Greece, Poland, Romania, and the Czech Republic. The index integrates five dimensions—education and skills, investment and infrastructure, policy and institutional quality, labour market structure, and innovation—based on harmonized data from 2010 to 2024. Panel econometric models (Fixed and Random Effects), combined with Hausman tests, are used to examine how structurally independent external energy-system characteristics, institutional capacity, and macro-structural labour-market conditions are associated with observed variation in labour-market readiness, as captured by the GLMRI composite outcome. Machine learning algorithms (Random Forest, XGBoost, LSTM) are employed to forecast readiness trajectories until 2040 under alternative policy scenarios. Results reveal persistent asymmetries between Northwestern and Southeastern Europe, showing that successful energy transition is closely associated not only with investment and innovation but also with human capital and governance quality. These associations are interpreted as diagnostic rather than causal, highlighting how external structural conditions shape the translation of energy-transition pressures into differentiated labour-market outcomes. The GLMRI provides a methodological and policy-relevant framework, helping decision-makers prioritize resources and design measures that make Europe’s energy transition sustainable, inclusive, and equitable.

Digital marketing has given new life to healthcare services by enhancing their visibility in the online space. People choose online healthcare services because they can receive instant answers and communicate with specialists in their comfortable environment at the right time. The purpose of this study was to understand the impact of virtual communities on the intention to use telemedicine. The model is based on a combination of consumer desire (psychological objective) and loyalty through promotional formats (economic objective), as well as data collected from 442 respondents analyzed using structural equation modeling. The research results show that by analyzing target groups in social networks, content can be individualized, and an accurate measurement of e-patient satisfaction must be conducted in order to improve the experience of future consumers of telemedicine services. The results of this study explain what makes people want to use digital healthcare services and can serve as a guide for people who run virtual communities and help digital healthcare service providers figure out how to market their services.

This paper addresses the challenge of segmenting consumer behavior in contexts characterized by both numerical regularities and semantic variability. Traditional models, such as RFM-based segmentation, capture the transactional dimension but neglect the implicit meanings expressed through product descriptions, reviews, and linguistic diversity. To overcome this gap, we propose a hybrid clustering algorithm that integrates numerical and semantic distances within a unified scalar framework. The central element is a scalar objective function that combines Euclidean distance in the RFM space with cosine dissimilarity in the semantic embedding space. A continuous parameter λ regulates the relative influence of each component, allowing the model to adapt granularity and balance interpretability across heterogeneous data. Optimization is performed through a dual strategy: gradient descent ensures convergence in the numerical subspace, while genetic operators enable a broader exploration of semantic structures. This combination supports both computational stability and semantic coherence. The method is validated on a large-scale multilingual dataset of transactional records, covering five culturally distinct markets. Results indicate systematic improvements over classical approaches, with higher Silhouette scores, lower Davies–Bouldin values, and stronger intra-cluster semantic consistency. Beyond numerical performance, the proposed framework produces intelligible and culturally adaptable clusters, confirming its relevance for personalized decision-making. The contribution lies in advancing a scalarized formulation and hybrid optimization strategy with wide applicability in scenarios where numerical and textual signals must be analyzed jointly.

Cable-driven robotic systems are widely used in applications requiring lightweight structures, large workspaces, and accurate force regulation. In such systems, the mechanical behavior of cable-driven actuators is strongly influenced by the elastic properties of the cable, transmission elements, and supporting structure, leading to an effective stiffness that varies with pretension, applied load, cable length, and operating conditions. These stiffness variations have a direct impact on force control performance but are often implicitly treated or assumed constant in control-oriented studies. This paper investigates the effects of operating-point-dependent (incremental) cable stiffness on actuator-level force control performance in cable-driven robotic systems. The analysis is conducted at the level of an individual cable-driven actuator to isolate local mechanical effects from global robot dynamics. Mechanical stiffness is characterized within a limited elastic domain through local linearization around stable operating points, avoiding the assumption of global linear behavior over the entire force range. Variations in effective stiffness induced by changes in pretension, load, and motion regime are analyzed through numerical simulations and experimental tests performed on a dedicated test bench. The results demonstrate that stiffness variations significantly affect force tracking accuracy, dynamic response, and disturbance sensitivity, even when controller structure and tuning parameters remain unchanged.

The global employment landscape will continue to change due to new technologies, in particular automation, online collaboration tools, and artificial intelligence. The shortage of skilled workers and the growing jobs of e-tourism employees are a challenge for maintaining day-to-day operations. It is crucial to develop a digital recruitment strategy and communicate a good employer brand, supported by targeted digital advertising. This study aims to identify the impact of social networks on the effectiveness of digital human resources recruitment strategies in tourism. On the basis of a sample of 620 respondents who had experience of the digital recruitment of human resources in the tourism sector, the collected data was analyzed using structural equation modeling. The results underline the high relevance of building these strategies, as professional communication in social networks is the key to successful work in sustainable human resources practices.

Considering the worldwide evolutionary stage of Industry 4.0, this study wants to fill in a lack of information and decision-making, trying to answer a question about the level of preparation of Romanian Small and Medium-sized Enterprises (SMEs) regarding the implementation of the new technology. The main purpose of this article is to identify the opinions and perceptions of SME managers in Romania on the drivers and barriers of implementing Industry 4.0 technology for business development. The research method used in the study was analyzed by sampling using the questionnaire as a data collection tool. It includes closed questions, measured with a nominal and orderly scale. 176 managers provided complete and useful answers to this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. Major contributions from research have highlighted the fact that Romania is in a full transition process from industry 2.0 to industry 4.0. There was also a high level of knowledge of the new Industry 4.0 technology, and a desire to implement it in the Romanian SMEs, as well as the low level of resources needed to implement it.