Algorithmic Work: The Impact of Algorithms on Work with Social Media

Working with social media in professional settings is a complicated task. The operations of social media platforms are based on complex algorithms that are adjusted based on a constant stream of data created when users interacting with each other. Making sense of, and professionally acting on, algorithmically mediated data that influences the way the work tasks are performed, is what we call “algorithmic work.” The research questions addressed are: (i) What is algorithmic work? and (ii) How is algorithmic work with social media performed? Based on qualitative text analysis, in-depth interviews and participatory workshops from a longitudinal study with professional municipal communicators, we analyze Facebook interaction and how algorithms impact and influence the work with Facebook over time. The findings show how the social media algorithms push the communicators to rethink how they phrase their posts and how they approach the audience while constantly considering their professional role. The main contribution is the conceptualization of algorithmic work as socio-technical processes of: i) balancing openness and closeness of platform operations; ii) adjusting the work operations and performative interactions as the algorithms change; and iii) strategizing on how to understand and utilize advantages of algorithmic logic for the organizational and professional mission

Artificial and human aspects of Industry 4.0: an industrial work-integrated-learning research agenda

The manufacturing industry is currently under extreme pressure to transform their organizations and competencies to reap the benefits of industry 4.0. The main driver for industry 4.0 is digitalization with disruptive technologies such as artificial intelligence, machine learning, internet of things, digital platforms, etc. Industrial applications and research studies have shown promising results, but they rarely involve a human-centric perspective. Given this, we argue there is a lack of knowledge on how disruptive technologies take part in human decision-making and learning practices, and to what extent disruptive technologies may support both employees and organizations to “learn”. In recent research the importance and need of including a human-centric perspective in industry 4.0 is raised including a human learning and decision-making approach. Hence, disruptive technologies, by themselves, no longer consider to solve the actual problems. Considering the richness of this topic, we propose an industrial work-integrated-learning research agenda to illuminate a human-centric perspective in Industry 4.0. This work-in-progress literature review aims to provide a research agenda on what and how application areas are covered in earlier research. Furthermore, the review identifies obstacles and opportunities that may affect manufacturing to reap the benefits of Industry 4.0. As part of the research, several inter-disciplinary areas are identified, in which industrial work-integrated-learning should be considered to enhance the design, implementation, and use of Industry 4.0 technologies. In conclusion, this study proposes a research agenda aimed at furthering research on how industrial digitalization can approach human and artificial intelligence through industrial work-integrated-learning for a future digitalized manufacturing

Artificial and human aspects of Industry 4.0: an industrial work-integrated-learning research agenda

The manufacturing industry is currently under extreme pressure to transform their organizations and competencies to reap the benefits of industry 4.0. The main driver for industry 4.0 is digitalization with disruptive technologies such as artificial intelligence, machine learning, internet of things, digital platforms, etc. Industrial applications and research studies have shown promising results, but they rarely involve a human-centric perspective. Given this, we argue there is a lack of knowledge on how disruptive technologies take part in human decision-making and learning practices, and to what extent disruptive technologies may support both employees and organizations to “learn”. In recent research the importance and need of including a human-centric perspective in industry 4.0 is raised including a human learning and decision-making approach. Hence, disruptive technologies, by themselves, no longer consider to solve the actual problems. Considering the richness of this topic, we propose an industrial work-integrated-learning research agenda to illuminate a human-centric perspective in Industry 4.0. This work-in-progress literature review aims to provide a research agenda on what and how application areas are covered in earlier research. Furthermore, the review identifies obstacles and opportunities that may affect manufacturing to reap the benefits of Industry 4.0. As part of the research, several inter-disciplinary areas are identified, in which industrial work-integrated-learning should be considered to enhance the design, implementation, and use of Industry 4.0 technologies. In conclusion, this study proposes a research agenda aimed at furthering research on how industrial digitalization can approach human and artificial intelligence through industrial work-integrated-learning for a future digitalized manufacturing

Innovation through knowledge codification

Academics and business professionals are currently showing a significant interest in understanding the management of knowledge and the roles to be played therein by information and communication technology (ICT). This paper takes a closer look at one of the primary issues raised when supporting the management of knowledge: how to understand the role of knowledge classification and codification as means for further organizational learning and innovation. Two manufacturing cases are analysed using particular perspectives from current theories on classification, namely the management of knowledge and organizational innovation. It is concluded that a more complex understanding of the interplay between cognitive and community models for knowledge management as informed by research on the social processes of classification can inform our understanding of both the role of classification of knowledge for organizational innovation and the viability of providing ICT support based on codified knowledge