Impact of digital inclusive finance on agribusiness innovation performance: Evidence from listed agribusinesses in China

This paper reassesses the relationship between working capital management (WCM) and firm performance in the Nigerian context. The study is motivated by the limited insights available on the impacts of WCM on firm performance in the country. To date, most studies from Nigeria have been largely descriptive and focused on a small sample size that is non-representative of the population. In addition, there are limited rigorous statistical analyses involved in such studies. This paper addresses the methodological limitations apparent in prior literature and provides a better understanding of the relationship between WCM and firm performance, revealing how firms can manage their operations more profitably. The paper adopts a panel data regression analysis on a sample of 75 non-financial firms listed on the Nigerian Stock Exchange from 2007 to 2015. The results of the analyses showed that WCM variables have an inconsistent relationship with the measures of performance adopted, which were return on assets and Tobin’s Q. Specifically, accounts receivable management and inventory management were negatively associated with the return on assets, while accounts payable management, cash conversion cycle and cash conversion efficiency were positively associated with return on assets. Additionally, accounts receivable management and inventory management were positively associated with Tobin’s Q, whereas accounts payable management, cash conversion cycle and cash conversion efficiency were negatively associated with Tobin’s Q. These results were found to be robust using quantile regression. The results of the quantile regression showed inconsistency across the various quantiles used (0.10, 0.25, 0.50 and 0.75). These findings have two important implications. The first is that WCM variables influence the performance of firms. The second is that the mixed findings partly indicate that firms and managers must understand and formulate WCM policies that reflect their peculiar conditions.

To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.

This study aims to investigate the interplay between employee engagement and innovation performance in shaping the success of manufacturing and service enterprises in Albania and Kosovo. The data collection involved the distribution of questionnaires to entrepreneurs, managers, and employees of the enterprises, with direct interviews conducted in some cases. The data collection period spanned from the end of 2022 to the beginning of 2023. SPSS was employed to create the database and analyze the collected data. The study encompasses 190 companies operating in the services and production sectors, with 100 located in Albania and 90 in Kosovo. The selection of these sectors is based on their significant contribution to the region’s economic landscape. Of the total enterprises, 105 (55%) are manufacturing enterprises, and 85 (45%) are service enterprises.
The findings reveal a robust and positive correlation between employee engagement, innovation performance, and business success. Employee engagement (r = 0.458**) and innovation performance (r = 0.396**) exhibit significant correlations (p < 0.01), with both factors demonstrating statistical significance (p = 0.000). The multivariate regression analysis confirms the positive influence of both employee engagement and innovation performance on business success, supported by a significance of ANOVA analysis (p = .000). This study contributes to the understanding of the specific dynamics of employee engagement and innovation performance in the context of manufacturing and service enterprises in Albania and Kosovo. It offers valuable insights for managers and entrepreneurs seeking to enhance the success of their organizations.