Search results

The purpose of this paper is to empirically distinguish the influence of the individual factors (enjoyment in helping others and knowledge self-efficacy), organizational factors (top management support and organizational rewards) and technology factors (information and communication technology use) on knowledge-sharing processes.

Data were collected through a survey of 196 employees from large ICT companies in Croatia, and structural equation modeling was used to investigate the research model.

Results of the empirical research indicate that enjoyment in helping others as an individual factor, top management support as an organizational factor, and ICT use as a technology factor significantly influence knowledge-sharing processes. The results also suggest that the willingness of employee to donate and collect knowledge enables the firm to improve innovation capability. No influence of the individual factor knowledge self-efficacy on the employee knowledge-sharing behavior was found within this research.

Subjectivity of respondents, Likert scale – perception, and future research can include higher number of population and examine how personal traits (such as age, level of education, and working experience) and organizational characteristics (such as firm size) may moderate the relationships between knowledge enablers and processes.

From a practical perspective, the relationships among knowledge-sharing enablers, processes, and firm innovation capability may provide a guide regarding how firms can promote knowledge-sharing cultures to sustain their innovation performance.

This study identifies several factors essential to successful knowledge sharing, and discusses the implications of these factors for developing organizational strategies that encourage and foster knowledge sharing.

Hempseed oil is a valuable emerging food product with recognized health positivity due to its composition. The paper aims to propose a multi-methodological chemical profiling of nine organic hempseed oil samples (different brands and prices) from the retail market, followed by multivariate data analysis.

Fatty acids, tocopherols, squalene, total carotenoids content, phenolic compounds, total phenolic content and anti-radical activity (DPPH• and ABTS•+ assays) were determined through chromatographic and spectrophotometric techniques. Multivariate (explorative and discriminant) analyses were applied to the profiling results to classify samples according to information claimed on the label, i.e. geographical origin (EU vs. non-EU), extraction procedure (cold-pressed vs. not cold-pressed) and price (lower or higher than 10 € per 250 mL).

The chemical analysis confirmed the 3 to 1 ?-6:?-3 ratio and the excellent content in antioxidant species. However, no specific trend of results can be stressed. PCA (after variables selection) highlighted a natural grouping of samples, so three discriminant analyses were performed: kNN, Naïve Bayes and LDA. The best classification efficiency was reached for the extraction procedure verification (93–100% correct classification), followed by geographical origin (83–94%) and prices (81.6–90%).

The integrated approach of chemical profiling coupled with multivariate analyses allowed the assessment of label information of the analyzed organic hempseed oil samples, despite the wide heterogeneity of the selected samples.