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We examine how social media plays the role of an attention driver for traditional media. Social media attracts and channels attention to a topic. This attention triggers people to seek further information that is reported professionally in traditional media. Specifically, the volume of social media posts about a stock influences the attention to this stock the next day, proxied by the viewership of news articles on the same stock published the next day. We test this hypothesis in the stock market context because social media is less likely than traditional media to diffuse fundamental information in the stock market. Analysing stock-related news articles and stock-related social media posts from Sina Finance and Sina Weibo, we find that the social media post volume of a stock at time t-1 is associated with the traditional media viewership of the same stock at time t. This effect is amplified when social media sentiment about the stock is more intense or positive, and with an increase in the volume of verified social media posts about the stock. Our results provide evidence that social media platforms act as attention drivers, which differ from the information channel functions discussed in prior literature.

The Metaverse represents the next generation of the Internet that, at this instant in time, is still a concept. It is envisioned to provide interconnected experiences that are immersive and varied. This vision challenges both designers and users to understand its possible coordination architecture and develop strategies for participation. One way to understand the concept and affect its instantiation is to take a design science approach that articulates design principles that might guide the exploration of different architectures. We apply concepts related to platforms and business ecosystems as well as ideas about facilitating technologies including choreography and orchestration as ways of blending together experiences, providing transitions between virtual locations. We derive three design principles from an analysis of Metaverse scenarios: narrative composability, social assortativity, and path discoverability. Thinking through these aspects of design leads to a discussion about the tradeoffs that will face designers of the Metaverse.

The Metaverse, a term coined in science fiction, is now being discussed seriously as a new form of infra-structure. The Metaverse is intended to make possible thematically interconnected immersive experiences. In this paper, we conceptualize the Metaverse as a meta design space. Within this space, designers create var-ious interconnected design spaces. We highlight how the key dimensions of human experience (time, space, actors, and artifacts) each introduce tensions for making decisions in those design spaces, and we highlight the transitions between design spaces. This conceptual language opens up this novel and emergent phenomenon both to those wishing to design new disruptive technologies and those seeking to improve existing platform strategies. We conclude by highlighting how the Metaverse will not only comprise immersive virtual experiences but also transitions between physical and virtual experiences.

As work changes, so does technology. The two coevolve as part of a work ecosystem. This paper suggests a way of plotting this coevolution by comparing the embeddings - high dimensional vector representations - of textual descriptions of tasks, occupations and technologies. Tight coupling between tasks and technologies - measured by the distances between vectors - are shown to be associated with high task importance. Moreover, tasks that are more prototypical in an occupation are more important. These conclusions were reached through an analysis of the 2020 data release of The Occupational Information Network (O*NET) from the U.S. Department of Labor on 967 occupations and 19,533 tasks. One occupation, journalism, is analyzed in depth, and conjectures are formed related to the ways technologies and tasks evolve through both design and exaptation.

Occupations, like many other social systems, are hierarchical. They evolve with other elements within the work ecosystem including technology and skills. This paper investigates the relationships among these elements using an approach that combines network theory and modular systems theory. A new method of using work related data to build occupation networks and theorize occupation evolution is proposed. Using this technique, structural properties of occupations are discovered by way of community detection on a knowledge network built from labor statistics, based on more than 900 occupations and 18,000 tasks. The occupation networks are compared across the work ecosystem as well as over time to understand the interdependencies between task components and the coevolution of occupation, tasks, technology, and skills. In addition, a set of conjectures are articulated based on the observations made from occupation structure comparison and change over time.