Social media allows customers and prospects to communicate directly to your brand representative or about your brand with their friends. However, the obvious question is: who are the people interacting online and how engaged are they in online activities? This paper aims to answer this question based on a study regarding the online activities of 236 social media users, by identifying different types of users, a segmentation of these users and a linear model to examine how different predictors related to social networking sites have a positive impact on the respondents’ perception of online advertisements. The answer can help discover how to engage with different types of audiences in order to maximize the effect of the online marketing strategy.

Customer engagement has emerged as a vital component in social media marketing strategies, prompting considerable interest from both marketers and academics. This study investigates customer engagement (CE) in a framework that includes three antecedents and a main outcome (loyalty). Based on the survey method, we test a proposed model on social media users. The data analysis focuses on exploratory and confirmatory factor analyses, as well as structural equation modeling to test the hypotheses, and a multi-group analysis. The results validate CE as a multidimensional construct and support all hypotheses of the conceptual model. As key contributions, the study reveals involvement as the strongest antecedent of customer engagement and emphasizes the importance of CE in predicting and fostering customer loyalty. This paper adds to the existing body of knowledge on customer–brand relationships by establishing the main drivers for customer engagement in digital settings, with valuable theoretical and practical perspectives. Finally, our findings provide managerial recommendations on the development of social media marketing strategies conveyed to enhance customer engagement.

The COVID-19 pandemic has impacted consumers’ lives and their shopping patterns, leading them towards mobile commerce. To address current shopping circumstances, an adapted UTAUT2 model aims to integrate trust and perceptions of the effects of the COVID-19 pandemic on consumers’ behavioral intention to rely on m-commerce during this period. The study proposes a research model which is investigated with the help of structural equation modelling in AMOS. Using the framework of a cross-sectional study, data were collected from an emerging market in Romania, where internet speed ranks 4th worldwide and where the yearly increase in internet and social media users is approximately 10%. By using confirmatory factor analysis and structural equation modelling, the research validates the applicability of UTAUT2 in examining consumers’ behavioral intent of using m-commerce during a pandemic. Hedonic motivation is the strongest predictor of consumers’ behavioral intentions to continue using m-commerce. The findings contribute to understandings of consumers’ behavior with m-commerce in an emerging market, extending knowledge based on the adapted UTAUT2 model and allowing for comprehension of the key role of trust and social influences in affecting consumers’ perceptions of the COVID-19 outbreak in relation to shopping patterns.

The tourism industry has been gravely affected by the COVID-19 pandemic. In this context, the purpose of this study was to explore residents’ support for sustainable tourism development in an integrative model that considered well-established factors and the impact of the COVID-19 pandemic. Addressing various research gaps caused by the pandemic, this study aimed to examine a model based on eight hypotheses. Using the premises of a cross-sectional study, data was gathered from residents from Sibiu. Data analysis implied various steps to provide an accurate understanding of the hypotheses and the model was developed based on structural equation modeling. Considering the results of hypothesis testing, our study reconfirmed the applicability of social exchange theory in describing residents’ attitudes toward tourism development and positioned quality of life as an important predictor for this construct. Also, the outcomes highlighted a negative relationship between the host community’s perceptions of the coronavirus pandemic and their subsequent support for sustainable tourism development. Overall, the results focused on offering contributions for a better understanding of residents’ behavior and the influence of the COVID-19 outbreak on their support for sustainable tourism development. The conceptual and practical ramifications of the study were addressed in the article’s conclusion.

The purpose of the paper is to present a detailed application of the factor analysis technique within the domain of relationship marketing. A comprehensive literature review is undertaken regarding the concept of customer loyalty – an important variable of relationship marketing. Loyalty is defined by its two dimensions – behavioral and attitudinal; the second dimension is understood through the three components of the human psyche: affective, cognitive and conative. Conative Loyalty is measured through four items which are statistically and semantically reduced to two items without a heavy loss of information

In recent years, bank-related decision analysis has reflected a relevant research area due to key factors that affect the operating environment of banks. This study’s aim is to develop a model based on the linkages between the performance of banks and their operating context, determined by country risk. For this aim, we propose a multi-analytic methodology using fuzzy analytic network process (fuzzy-ANP) with principal component analysis (PCA) that extends existing mathematical methodologies and decision-making approaches. This method was examined in two studies. The first study focused on determining a model for country risk assessment based on the data extracted from 172 countries. Considering the first study’s scores, the second study established a bank performance model under the assumption of country risk, based on data from 496 banks. Our findings show the importance of country risk as a relevant bank performance dimension for decision makers in establishing efficient strategies with a positive impact on long-term performance. The study offers various contributions. From a mathematic methodology perspective, this research advances an original approach that integrates fuzzy-ANP with PCA, providing a consistent and unbiased framework that overcomes human judgement. From a business and economic analysis perspective, this research establishes novelty based on the performance evaluation of banks considering the operating country’s risk.