Research background: Chatbots represent valuable technological tools that allow companies to improve customer experiences, meet their expectations in real time, and provide them with personalized assistance. They have contributed to the transformation of conventional customer service models into online solutions, offering accessibility and efficiency through their integration across various digital platforms. Nevertheless, the existing literature is limited in terms of exploring the potential of chatbots in business communication and studying their impact on the customer's response. Purpose of the article: The main objective of this study is to examine how consumers perceive chatbots as customer service devices. In particular, the paper aims to analyze the influence of the dimensions of “Information”, “Entertainment”, “Media Appeal”, “Social Presence” and “Risk for Privacy” on the “Customer Experience” and the latter on the “Purchase Intention”, under the consideration of the Uses and Gratifications Theory. Moderations due to Chatbot Usage Frequency for some of the relationships proposed are also analyzed. Methods: An empirical study was performed through a questionnaire to Spanish consumers. The statistical data analysis was conducted with R software through the lavaan package. To test the hypotheses from the conceptual model a structural equation modelling approach was adopted. Findings & value added: The results obtained identify the main characteristics of chatbots that can support brands to effectively develop their virtual assistants in order to manage their relational communication strategies and enhance their value proposal through the online customer journey. Findings demonstrate the contribution that chatbot dimensions make to the online consumer experience and its impact on the purchase intention, with the consideration of the moderating effect exercised by the user's level of experience (novice vs. experienced) with the use of chatbots. Regarding managerial implications, this research offers recommendations for e-commerce professionals to manage chatbots more effectively. The “Entertainment” and “Social Presence” dimensions can be operationalized at a visual (e.g., appearance of the avatar and text box, use of designs aligned with the website) and textual level (e.g., style and tone of voice, use of expressions typical of the target audience) to generate a feeling of proximity with the chatbot and facilitate its adoption. “Media Appeal” requires that the chatbot be easy to use, effective, and accessible, to facilitate its usability. Finally, mitigation of “Privacy Risk” concerns should be achieved by presenting an appropriate privacy policy and requesting permission for the use of customers’ private information.

Research background: Consumers frequently exchange personal data for limited benefits from digital services, despite privacy concerns. This data enables digital providers to tailor content and enhance marketing communication, and digital services' effectiveness and efficiency. Purpose of the article: Utilizing the principles of privacy calculus theory, this study aims to identify how attitudes towards advertising (ATT), perception of advertising credibility (CRE), consumer susceptibility to interpersonal (II) and reference group influence (RGI), social isolation (SI) and social anxiety (SA) influence the willingness of Generation Z to disclose personal information on social media derived from the Generation Z privacy concerns (PC) and develop a prediction model for such behavior. Methods: Data was gathered using an online self-administered questionnaire from a sample of 451 Generation Z individuals. A non-random convenient sampling technique and binary logistic regression were used to quantify the influence of selected independent variables on the dispersion of values in the dependent variable under investigation. Findings & value added: The results highlight that Generation Z's self-disclosure willingness on social media is significantly influenced by attitudes toward advertising and consumer susceptibility to reference group influence. The effect of social isolation was also close to the required level of statistical significance. It means that positive attitudes towards advertising and high susceptibility to influence from reference groups enhance the probability of personal information disclosure. Being one of the few studies to address factors that influence the willingness of Generation Z to disclose personal information on social media, this study stands out for its holistic approach. Thus, combining various interconnected elements provides a fresh perspective to comprehend the intricate dynamics of Generation Z's relationship with privacy on social media.

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

Research background: This study identifies the key factors influencing environmental health across a global panel of countries, focusing on protection from environmental hazards, as informed by the existing literature, while also shedding light on novel aspects of these causal relationships. Purpose of the article: This study aims to reveal, through a comprehensive review of the relevant literature, the underexplored phenomena of spatial diffusion and contagion of national environmental behaviors and the nonlinear dynamics between environmental performance and its determinants, acknowledging the significant diversity in the characteristics and behaviors of the countries studied. Methods: Spatial analysis and econometric methods, including spatial panel regression alongside dynamic panel models using threshold techniques, were employed to meet the study’s objectives. Findings & value added: This study’s major finding is that environmental performance across nations shows significant clustering influenced by economic and institutional factors. This clustering effect arises from spatial contagion and diffusion processes, as evidenced by spatial panel regression analysis. Furthermore, this study demonstrates that variations in environmental behavior can be attributed to differing levels of development and specific internal conditions within countries. Notably, a country’s gross domestic product and the proportion of industries in its economy have a substantial effect on its environmental health practices, establishing distinct impact thresholds. This research enriches academic dialogue by illustrating, through these thresholds, that in less developed countries, an increased industrial share leads to environmental degradation. Moreover, the influence of the other examined factors varied depending on the category of the country under review, highlighting the nuanced effects of economic and institutional variables on environmental outcomes.

Research background: The sustainability of small- and medium-sized enterprises (SMEs) represents a significant scientific and professional problem in the current turbulent period because these enterprises play an important role in any country’s economic and social systems. Purpose of the article: This paper aimed to define the significant sustainability factors of small and medium-sized enterprises and to quantify their impact and importance on the sustainability of SMEs. The areas of Human Resource Management, Corporate Social Responsibility, and financial management were defined as significant sustainability factors. Methods: Empirical research, on which the scientific hypotheses were formulated and evaluated, was conducted in June 2022 in V4 countries (Czech Republic, Slovak Republic, Poland, and Hungary) using a structured questionnaire. The study accumulated a sample of 1398 respondents. Data collection was conducted through an external agency, MN FORCE, operating in Central European countries. The Computer Assisted Web Interview (CAWI) method was used to record respondents’ perceptions. Descriptive statistics, correlation analysis, and linear regression analysis were used to evaluate the hypotheses. Findings & value added: The research showed that all defined factors in the areas of Human resource management (HRM), Corporate social responsibility (CSR), and financial management of the firm had an impact on defined sustainability attributes. The greatest impact was found on the firm’s financial management, followed by CSR and HRM. The empirical results confirm that the intensity of the independent variables varies across the V4 countries. These results also show that the intensity of the selected HRM, CSR, and financial management factors of a firm is higher in the integrated models than in the models for individual V4 countries. The research results have shown that a range of factors determine the right attitude towards the sustainability of companies. In this context, economic policymakers and entrepreneurs must perceive sustainable growth as complex and apply a systemic approach to its design and implementation.

Research background: Many authors emphasize that successful human resource management (HRM) practices align with employees’ needs associated with the construct of employees’ interests. In particular, the importance of considering employees’ interests is emphasized in the process of shaping the architecture of Socially Responsible Human Resource Management (SR-HRM) systems. Purpose of the article: The aim of the article is to contribute to understanding employees’ interests by designing and validating a measure to recognize these interests. Methods: Through the use of literature sources and expert opinions, the authors developed a list of employee interests. Empirical data collected via the survey method in Poland was used to statistically verify the measurement scale. In particular, exploratory factor analysis and exploratory structural equation modelling were applied. Findings & value added: This article shows that it is important to create a comprehensive list of interests, as well as validate the research tool used. The newly developed scale has 22 items and five dimensions: support and development at the level of the enterprise, employee participation, support and development at the departmental level, employment security, working conditions and remuneration. It may be used in a variety of companies, as well as in complex research models, and developed further taking into consideration the context of other countries.

Research background: On the basis of an analysis of the current situation and expectations in the field of implementation of the elements of the Industry 4.0 concept, the purpose of this paper is to identify the effects on the labor market in large manufacturing enterprises in the Slovak Republic. Purpose of the article: The presented work has a theoretical-empirical nature and consists of a theoretical section and a practical section, which includes statistical indicator analysis and quantitative research. In the theoretical section, the paper discusses the issue of Industry 4.0 in general, with a focus on its impact on the labor market, thus laying the groundwork for future research on the subject. Methods: The output of this work is an analysis of selected indicators of the manufacturing industry sector in the Slovak Republic, based on the most recent employment data analysis in the first stage and quantitative research survey in the second stage, with the respondents being manufacturing industry companies operating in the Slovak Republic, and whose primary objective is to determine the current status of the implementation of the elements and technologies of Industry 4.0 in production companies in the Slovak Republic, as well as the factors influencing this situation, such as digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms. Findings & value added: The research findings indicate that the degree of digitization adopted by businesses in the Slovak Republic is comparatively less robust and more sluggish to adapt. This is primarily attributable to the underdeveloped educational system, population reluctance, self-actualization, and inadequate state support. Recommendations for the Slovak market aim to increase the digital proficiency of businesses and of the general populace through various means, such as reforming legislation, enhancing state support for entrepreneurs, and modifying the education system, constituting the added value of the work.

Research background: Digital ecosystems in Europe are heterogenous organizations involving different economies, industries, and contexts. Among them, Digital Innovation Hubs (DIHs) are considered a policy-driven organization fostered by the European Commission to push companies’ digital transition through a wide portfolio of supporting services. Purpose of the article: There are DIHs existing in all European economies, but literature needs more precise indications about their status and nature. The purpose is to study a distribution of DIHs and differences in portfolios of DIHs’ services across European economies. Therefore, the paper wants to deliver more precise data on effects on national and European policies. This is required to define their final role and scope in the complex dynamics of the digital transition, depending on regional context and heterogeneity of industries. Methods: Data on 38 economies was collected from the S3 platform (on both existing and in preparation DIHs) and further verified by native speaking researchers using manual web scrapping of websites of DIHs identified from S3. To find potential similarities of digital ecosystems in different economies as emanated by the existence of DIHs, clusterization (Ward’s method and Euclidean distances) was applied according to the services offered. Economies were clustered according to the number of DIHs and the spread of DIHs intensity in different cities. The results were further analyzed according to the scope of the provided services. Findings & value added: The applied clustering classified European economies in four different sets, according to the types of services offered by the DIHs. These sets are expression of the different digitalization statuses and strategies of the selected economies and, as such, the services a company can benefit from in a specific economy. Potential development-related reasons behind the data-driven clustering are then conjectured and reported, to guide companies and policy makers in their digitalization strategies.

Research background: Protected areas (PAs) play a fundamental role in the maintenance of ecosystem processes and in the flow of ecosystem services (ESs) they provide. However, the management of PAs is complex due to the existence of different stakeholders with disparate and, often, opposed preferences and valuations. The sociocultural assessment of ESs contributes to optimizing the management of scarce resources based on the preferences of the different stakeholders, taking into account the economic, environmental and social dimensions of the analysed area. Purpose of the article: In this work, a sociocultural assessment of the ESs provided by a PA in southeast Spain is carried out. The objective is to identify which the various ESs provided by this PA are and to establish their degree of importance for all the stakeholders involved. Methods: For this, different complementary methodologies have been used in successive phases, both qualitative and quantitative. Specifically, a literature review, in-depth interviews and an assessment questionnaire were used. Findings & value added: Based on the results obtained, a series of measures are proposed to improve the sustainable management of the PA and the socioeconomic development of its environment. The results of this study may be useful for PAs whose management tries to find a balance between conservation measures and the design of models that contribute to the socioeconomic development of their area of influence.

Research background: To achieve the targets for carbon peak and air quality improvement, local governments should propose environmental targets and develop realization paths that are tailored to their unique local conditions. They then promote low-carbon development through the implementation of multiple measures. Purpose of the article: As the government performance appraisal system im-proves, the question arises as to whether governments take the initiative to com-bine environmental policies with government target constraints to reduce carbon emissions. Methods: The announcement of environmental target constraints by local governments in government work reports is considered a quasi-natural experiment. This study examines the effect of government environmental target constraints (GETC) on carbon emissions (CEs) using differences-in-differences (DID), propensity score matching-DID (PSM-DID), and spatial-DID (SDID) with data from 241 Chinese cities from 2003 to 2019. Findings & value added: The results demonstrate that GETC can effectively reduce local CEs, with the inhibitory effect being most effective in the first two years after setting environmental targets, but diminishing in the third year. GETC can reduce local CEs through three paths: reducing energy consumption, promoting industrial structure optimization, and encouraging green technology innovation. Spatial spillover effects show that GETC reduces local CEs while exacerbating CEs in neighboring cities, indicating a beggar-thy-neighbor effect in conventional environmental regulation policy. This effect is observed mainly in the geographic matrix and the economic-geographic matrix, but not in the economic matrix. According to heterogeneity analysis, GETC in the eastern and central cities can significantly reduce CEs. The inhibitory effect of GETC on local CEs is stronger in cities where secretaries and mayors have longer tenures and higher levels of education. The paper's theoretical value lies in exploring the reduction of CEs through the combination of government self-restraint and environmental policies, providing a new solution for local governments to achieve CEs reduction. Furthermore, it offers practical insights into the improvement of the Chinese government assessment system.