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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 purpose of this work is twofold. First, looks to identify the main homogenous groups of companies after environmental, social, economic and governance (ESEG) disclosures, non-financial statement and earnings per share (EPS), and second investigates the connection between variables.

Using financial and non-financial information from annual reports of private listed companies, the authors performed two-step cluster analysis (TSCA) in the first stage of the research, followed by parametric, nonparametric correlation analysis, as well as regression analysis based on panel data, in the second stage.

Results of TSCA revealed a cluster of companies with good financial and non-financial outcomes and a cluster of companies with poor performance. The performance dynamics showed a slight improvement during the period for few companies and composition analysis of clusters by industries through Kruskal–Wallis test highlighted differences between clusters, only for 2017. The main findings confirm a direct, although weak in intensity but statistically significant correlation between ESEG disclosure index, its sustainability component and financial performance (FP), valid for the entire period. Also, the results showed a direct link of low intensity to average, but statistically significant between the non-financial statement and EPS, valid only for 2017 and 2018.

The results indicate mixed findings which invites further in-depth research. Limits of the study can be found in selected indicators and the short period of time analyzed. However, the practical implications are worth considering from the perspective of finding new managerial tools that can better shape the relationship between ESEG disclosures and FP.

ESEG Dindx can be an instrument for managers that can optimize the link between the FP of companies and its sustainable development.

ESEG Dindx measures the disclosure degree of ESEG information by the companies listed on Bucharest Stock Exchange (BSE). The main findings of the work confirm a direct, although weak in intensity but statistically significant correlation between ESEG disclosure index, its sustainability component and FP, valid for the entire period.

This study adds value to the existing literature by the proposed research framework, design of ESEG Dindx and the way correlations between variables were investigated.

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.