The impact of selected components of industry 4.0 on project management

Ecosystem orchestration practices for industrial firms: A qualitative meta-analysis, framework development and research agenda

“…In the context of a flourishing technological revolution, digital technology has become the primary arena for innovation, bringing about significant developments in business (Chen et al, 2022;Dana et al, 2022;Kanski & Pizon, 2023;Shang et al, 2023;Tu et al, 2023). With the rapid advancement of digital technologies, such as big data analytics, the Internet of things, artificial intelligence, blockchain, and machine learning, digitization has opened up new opportunities for ecosystems (Jovanovic, Sjödin, et al, 2022).…”

When your supporters become your opponents: Exploring the unintended effects of parodies on social media engagement

“…Second, the literal meaning of the comments may differ from the intended meaning. To overcome these issues, based on parody users' comments, we performed the most commonly used techniques and methods (Vermeer et al, 2019) and advanced algorithms (Kanski & Pizon, 2023) to detect the comment valence and target to determine which method is the most predictive. These include traditional methods such as sentiment analysis based on French Expanded Emotion Lexicon (FEEL), developed by Abdaoui et al (2017), dictionary‐based identification categories, and modern methods such as semi‐supervised machine‐learning techniques.…”

“…Second, the literal meaning of the comments may differ from the intended meaning. To overcome these issues, based on parody users' comments, we performed the most commonly used techniques and methods(Vermeer et al, 2019) and advanced algorithms(Kanski & Pizon, 2023) to detect the comment valence and target to determine which method is the most predictive. These include traditional methods such as sentiment analysis based on French Expanded Emotion Lexicon (FEEL), developed byAbdaoui et al (2017), dictionary-based identification categories, and modern methods such as semi-supervised machine-learning techniques.Regarding semi-supervised machine-learning (ML) techniques, we trained different ML models that are widely used in the data mining field(Kaiser & Bodendorf, 2012): multinomial Bayes (MNB), support vector machines (SVMs), random forests (RFs), artificial neural networks (NNETs), and gradient boosting (GBM).…”