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The purpose of this article is to highlight the relevance of the factors that influence the satisfaction of e-commerce consumers in Romania and Moldova. The study aims to clearly define the main influence factors, so that the marketing managers of the online stores can adopt scientific well-founded decisions.

The paper opted for a study including a large sample of 399 respondents from two countries. For the analysis of the factors influencing the e-commerce satisfaction, multiple linear regression analysis was implemented, and their differentiation within the two countries was highlighted by multivariate analysis of variance.

The research conducted and the results obtained show that there is a differentiation of the factors that influence the level of satisfaction of e-commerce users in Romania and Moldova.

The research is still limited in terms of population studied to only two countries: Romania and Moldova. Although the study is intended to be exhaustive by analyzing no less than 11 factors influencing the satisfaction generated by e-commerce, it is still limited to this group of representative factors. Another limitation has to do with the limited number of demographic variables the authors have included.

Based on the results, the managerial implications for e-commerce companies that want to uniquely address consumers in Romania and Moldova are related to the decisions of marketing and sales managers who must allocate budgets and resources to improve the eight aspects highlighted in the paper. Also, the e-commerce companies should not allocate resources for the implementation of functionalities such as in-app after sales services, the possibility to cancel an order or the live consultant support feature, because these aspects do not influence the satisfaction of e-commerce consumers in Romania and Moldova.

This paper is the first in the scientific literature that implements a comparative study on the influence factors regarding the e-commerce satisfaction in Romania and Moldova. Also, it is important to mention that the present study is an exhaustive one because it includes many influence factors that were analyzed separately in the previous research papers from literature review.

The authors investigate the relationship between instant messenger (IM) use and work performance, mediated by interruptions and two key indicators of the stress associated with technology use: overload and complexity.

The authors validate this research model using partial least squares structural equation modelling (PLS-SEM) with data collected through a survey of 416 working professionals.

The data reveal that while IM use contributes minimally to work interruptions and to a greater extent to technological complexity, these two constructs fully mediate the direct influence of IM use at work on technology overload, and meanwhile significantly and directly contribute to work performance.

This research provides theoretical insights into the deployment of IM and its actual impacts in the workplace. To improve the generalisation of the findings, the authors call for more IM-related research in other countries, with more native theories and various methodologies in this domain.

The level of stress generated through IM use is moderate, considering IM is not a significant contributor to work interruptions. Thus, despite the potential negative effects of IM communication, the positive effects of using IM at work prevail. As a result, the technology can be promoted as long as employees, their managers and the organisation as a whole are well prepared. Employees can transfer skills and behaviour from the personal setting to their work environment and thus may find an intrinsic motivation to make better use of the IM technology at work.

The authors argue that this research model is novel for its perspective on evaluating the actual impacts of IM use at work instead of the reasons of using it. The authors conceptualise the process to explain how IM contributes to interruptions and other technostress indicators in the working context, and the impact on performance. Contrary to some prior research, the authors find that overall IM applications do not have a negative impact on work performance, and instead may enhance it.

This research investigates the antecedents of positive and negative electronic word-of-mouth (eWOM) propensity, as well as the impact of eWOM propensity on the intention to repurchase the product.

Two types of eWOM predictors were considered: product related variables and personal factors. The data were collected through an online survey conducted on a sample of 335 Romanian subjects, and the analysis method was Structural Equation Modelling.

Our findings show that personal factors – social media usage behaviour, marketing mavenism and need to evaluate – are the most important antecedents of the intention to write product reviews and comments online, either positive or negative. From the product related factors, only brand trust influences the propensity to provide eWOM. Furthermore, both positive and negative eWOM intentions are associated with the repurchase intention.

The major theoretical contribution of this study consists of revealing that personal traits are stronger eWOM propensity predictors than product related variables. In light of our results, most people will likely not share their positive or negative opinions about products and brands, no matter how good or bad their experience was, no matter how satisfied or dissatisfied they are. The high eWOM intention levels are associated with the presence of some inner characteristics that push customers to voice their feelings and thoughts in the social networks.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.