Digital marketing in SMEs via data-driven strategies: Reviewing the current state of research

“Automatic” interpretation of multiple correspondence analysis (MCA) results for nonexpert users, using R programming

Purpose

The purpose of this paper is to create an automatic interpretation of the results of the method of multiple correspondence analysis (MCA) for categorical variables, so that the nonexpert user can immediately and safely interpret the results, which concern, as the authors know, the categories of variables that strongly interact and determine the trends of the subject under investigation.

Design/methodology/approach

This study is a novel theoretical approach to interpreting the results of the MCA method. The classical interpretation of MCA results is based on three indicators: the projection (F) of the category points of the variables in factorial axes, the point contribution to axis creation (CTR) and the correlation (COR) of a point with an axis. The synthetic use of the aforementioned indicators is arduous, particularly for nonexpert users, and frequently results in misinterpretations. The current study has achieved a synthesis of the aforementioned indicators, so that the interpretation of the results is based on a new indicator, as correspondingly on an index, the well-known method principal component analysis (PCA) for continuous variables is based.

Findings

Two (2) concepts were proposed in the new theoretical approach. The interpretative axis corresponding to the classical factorial axis and the interpretative plane corresponding to the factorial plane that as it will be seen offer clear and safe interpretative results in MCA.

Research limitations/implications

It is obvious that in the development of the proposed automatic interpretation of the MCA results, the authors do not have in the interpretative axes the actual projections of the points as is the case in the original factorial axes, but this is not of interest to the simple user who is only interested in being able to distinguish the categories of variables that determine the interpretation of the most pronounced trends of the phenomenon being examined.

Practical implications

The results of this research can have positive implications for the dissemination of MCA as a method and its use as an integrated exploratory data analysis approach.

Originality/value

Interpreting the MCA results presents difficulties for the nonexpert user and sometimes lead to misinterpretations. The interpretative difficulty persists in the MCA's other interpretative proposals. The proposed method of interpreting the MCA results clearly and accurately allows for the interpretation of its results and thus contributes to the dissemination of the MCA as an integrated method of categorical data analysis and exploration.

Technological Innovation and the expansion of Entrepreneurship Ecosystems

To date much of the entrepreneurial ecosystems literature treated ecosystems as confined geographic locations with definitive boards. However, in the past decades, technological innovation and developments in social relationships (e.g., online platforms, social media, and the sharing economy) have extended and blurred the boundaries of entrepreneurial ecosystems. Thus, current research on entrepreneurial ecosystems often underestimates the reach and impacts of a given ecosystem. To remedy this, we advocate the use of a more holistic approach in modern entrepreneurial ecosystems frameworks which includes social relationships and technology, thus extending beyond geographical barriers. We discuss how technology has dissolved locational barriers and connected elements of ecosystems, how social relationships maximize advantages through greater resource access, and how the entrepreneurship ecosystem now exists on a plane that is both physical and cyber.

Working in the 21st Century. The Coronavirus Crisis: A Driver of Digitalisation, Teleworking, and Innovation, with Unintended Social Consequences

(1) Background: This article seeks to shed a light on the innovation, digitalisation, and teleworking processes that have occurred because of the coronavirus crisis. (2) Methods: To this end, we analyse data from Eurostat (2020), the European Companies Survey (2013; 2019) and the Living, Working and COVID-19 Dataset (2020), the latter two gathered by Eurofound. (3) Results: Our main findings reveal that COVID-19 has accelerated a process of digitalisation that has produced relevant changes in labour relations and, consequently, in companies’ organisation. (4) Conclusions: In short, home confinement has had a profound impact on work and occupational risks.