Search results

This article identifies and discusses the most common cognitive biases affecting reviewers and editors when they deal with papers, books or any kind of scientific research/project and how they can undermine intellectual capital (IC) in scientific contexts (SCs) as universities and research institutions.

As we posit that certain biases prevent from publishing original and relevant scientific works, literature research and semi-structured interviews with experts have been conducted to identify these biases undermining IC of SCs.

This contribution identifies biases undermining IC in SCs distinguishing the ones influencing editors only (representativeness heuristic, group polarization, country/language and affinity bias) and the ones influencing both editors and reviewers (framing and halo effects, overconfidence/overoptimism, confirmation, adjustment, status quo, bias bias and single-action biases). Also, it provides practical examples on how to overcome them.

This work is based on a limited number of interviews (and most of them to researchers of the economic field). Then, future quantitative researches are needed to increase the generalizability of the data. With regard to implications, the results of this study can be adopted by academies and their components in order to preserve their IC and in particular their knowledge-based resources of human capital.

As this piece of research provides practical examples and it concludes with tips that come from scholars’ experience, it is useful for a wide audience of scholars (in particular for less experienced researchers) pursuing scientific career upgrades and for reviewers looking for useful guidelines.

This study offers a more comprehensive analysis on the factors influencing IC of SCs both mixing literature findings with practical experts’ experience and including different areas of knowledge (e.g. behavioral theories on decision making) as scarcely done in previous studies.

The purpose of this paper is to examine the resistance behavior of smart factories in small and medium-sized enterprises (SMEs). Drawing upon dual factor perspective, this study examines how two types of quality and perceived usefulness impact user resistance as enabling factors and how switching cost, skepticism, habit and inertia contribute to user resistance as inhibiting factors. Additionally, multi-group analysis is employed to compare small and medium enterprises.

Purposive sampling technique was employed to collect 460 Korean SMEs employees, consisting of 235 small enterprises and 225 medium enterprises. Partial least squares structural equation modeling was used for data analysis.

The results reveal that all three inhibiting factors, switching cost, skepticism and habit, are key antecedents of inertia. In small enterprises, skepticism has a greater impact on inertia, which in turn strongly affects resistance. Additionally, system quality is more crucial for small enterprises, whereas information quality holds more importance for medium enterprises in mitigating resistance. Moreover, when the implementation level of a smart factory is high, the effect of perceived usefulness on user resistance diminishes.

This study has revealed the importance of considering both enabling and inhibiting factors for the adoption of smart factory systems in the context of SMEs. Additionally, it has provided evidence that as the level of the smart factory system increases, the effect of perceived usefulness on user resistance decreases, thus making the transition to smart factory systems more challenging.