The main purpose of this study is to assess the impact that food delivery mobile applications have on consumers' behaviour in the context of the changes generated by the COVID-19 pandemic. Thus, we aimed to bridge the gap in the literature and practice by studying intrinsic and extrinsic variables that affect 18–50+ years old consumers' decision process. The data set was analysed using the Structural Equation Modelling Part Least Square model because this model has no limitations to integrating more variables into a path model. From a managerial perspective, our results show that food delivery companies should implement customer loyalty strategies, as the users' perceived risk of changing the online food supplier is high. The high degree of visibility of the food delivery applications is positively reflected in the consumers' empathy level and loyalty. Consumer loyalty is also based on the pricing strategy and time saving associated with using this type of applications. The safety value and accessibility represent both consumers' and organisations’ priorities that underline the importance of the strategies of reducing the perceived risks during the COVID-19 pandemic. Our research offers to researchers and practitioners a starting point for their future activities. It can help them make decisions considering both periods (during a crisis as generated by pandemic crisis and post-crisis as new normality).

Generation Z spends much time on mobile Internet and less time accessing Internet services using desktop computers. These traits make them an important niche that can be targeted by retailers. The objective of the present study is to analyze the impact of online advertising, social influence, and usage motivation on the behavioral intention related to m-commerce use among Romanian young adults. The research methodology is based on applying partial least square equation modelling (PLS-SEM), using data collected through a questionnaire. The proposed structural model includes four constructs that are first order reflective. The results reveal that among the influencing factors that determine Generation Z to use m-commerce, the usage motivation factor is the most important one, followed by social influence and online advertising. The research is useful for marketing professionals and retailers in establishing marketing strategies in accordance with the factors that influence the buying decision of young adults, representing Generation Z.

The purpose of this study is to identify the factors influencing e-commerce and to evaluate the long and short-term impacts on the development of e-commerce activity. After establishing the hypotheses to verify, we use multiple panel regressions to test the influence of education level, consumer’s residence, consumer’s labour market status, internet banking, mobile and non-mobile users on the development of e-commerce. For this matter, in this paper, by adopting the fully modified ordinary least squares (FMOLS) method and a vector error correction model (VECM), we performed an empirical analysis of the nexus between education level, consumer’s residence, consumer’s labour market status, internet banking and mobile and non-mobile users and e-commerce, based on panel data for EU–27 countries from 2011 to 2020. The results of the study indicate that all the variables involved in the two econometric models and associated with education level, consumer’s residence, labour market status, internet banking, mobile and non-mobile users, all have significant impacts on the development of e-commerce. Most of the variables positively influence the development of e-commerce except for internet purchases by individuals un-employed and for internet purchases by non-mobile users that are negatively correlated with e-commerce activity.

Nature, landscape, relaxation, and outdoor activities are important motivations when choosing rural destinations for vacations. Therefore, when selecting a rural area as a vacation destination, we assume that climate features are important. We investigated the appropriateness of the holiday climate index: urban (HCI:urban) in quantitatively describing the relationship between climate and tourism fluxes in such destinations. We employed data from 94 urban and rural tourist destinations in Romania and correlated the monthly mean HCI:urban values with sectoral data (overnight tourists) for 2010–2018. The results show that weather and climate influenced tourism fluxes similarly in rural and urban destinations, supporting the hypothesis that HCI:urban may be used for rural areas as well. The information derived from HCI:urban may be useful for tourists when planning their vacations as well as for tourism investors in managing their businesses and reducing the weather and climate-related seasonality in tourism fluxes.

Blue Economy represents a new and interesting concept on a global level, both from the economic potential but also by the fact that it can be used to reduce environmental degradation. The main goal of this research is to identify the causality relations between the greenhouse gas emissions, the Blue Economy and economic growth based on a panel of annual data from the 28 countries that are members of the European Union (EU) over the 2009–2018 period. After applying stationarity and cointegration tests, the long term cointegration coefficients shall be determined with the help of the fully modified ordinary least squares (FMOLS) estimator. Granger causality estimation based on the vector error correction model (VECM) was applied to identify the causality relationship between the variables and to detect the direction of causality. Based on the identified causality relations, the Blue Economy has a significant influence on greenhouse gas emissions in the long run. Unidirectional causality relations were identified from the economic growth of greenhouse gas emissions in the long term, as well as from the greenhouse gas emissions on economic growth in the short term.