Zoekresultaten

Re-organise: Game-Based Learning of Circular Business Model Innovation

This study furthers game-based learning for circular business model innovation (CBMI), the complex, dynamic process of designing business models according to the circular economy principles. The study explores how game-play in an educational setting affects learning progress on the level of business model elements and from the perspective of six learning categories. We experimented with two student groups using our game education package Re-Organise. All students first studied a reader and a game role description and then filled out a circular business model canvas and a learning reflection. The first group, i.e., the game group, updated the canvas and the reflection in an interactive tutorial after gameplay. The control group submitted their updated canvas and reflection directly after the interactive tutorial without playing the game. The results were analyzed using text-mining and qualitative methods such as word co-occurrence and sentiment polarity. The game group created richer business models (using more waste processing technologies) and reflections with stronger sentiments toward the learning experience. Our detailed study results (i.e., per business model element and learning category) enhance understanding of game-based learning for circular business model innovation while providing directions for improving serious games and accompanying educational packages.

Decision Support System for corporate reputation based social media listening using a cross-source sentiment analysis engine

This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.

Exploring Corporate Reputation based on Sentiment Polarities That Are Related to Opinions in Dutch Online Reviews

This research demonstrates the power and robustness of the vocabulary method by Hernández-Rubio et al. (2019) for aspect extraction from online review data. We showcase that this algorithm not only works on the English language based on the CoreNLP toolkit, but also extend it on the Dutch language, specifically with aid of the Frog toolkit. Results on sampled datasets for three different retailers show that it can be used to extract fine-grained aspects that are relevant to acquire corporate reputation insights.

Withered mainstream areas and polarization

In this article, it is argued that In polarization, we fixate on two poles, like true and false, news versus fake news, real versusfalse, or in a social context, rich versus poor, and good versus bad. This is an instance of singular thinking, because a third factor is overlooked, being the area between the two poles, where the polarized elements come from. The gap, divide or rift, is in fact an area. With growth at the poles, polarization, instead of fixating on the poles, one should also look at the intermediate area, the mainstream area.

Pinot Gris: productief en gemotiveerd houden van oudere medewerkers

Het programma “Pinot Gris” helpt medewerkers van 55 jaar en ouder om bewust om te gaan met het resterende deel van hun loopbaan. De gedachte is dat oudere medewerkers die zich beter bewust zijn van hun kwaliteiten en drijfveren en op basis daarvan beslissingen nemen over hun loopbaan, productiever en gemotiveerder blijven. Het programma is opgezet en uitgevoerd binnen Hogeschool Inholland en heeft inmiddels drie maal plaatsgevonden. Dit artikel doet verslag van een evaluatieonderzoek naar de effectiviteit van het programma.

Trends in distribution centres and their locations