Search for: All records

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.

Design artifacts in online innovation communities are increasingly becoming a primary source of innovation for organizations. A distinguishing feature of such communities is that they are organized around design artifacts, not around people. The search for novel innovations thus equates to a search for novel designs. This is not a trivial problem since the novelty of a design is a function of its relationship to other designs, and this relationship changes as each design is added. These relations between artifacts affect both consumption and production. Moreover, these relations form a landscape whose structure affects the emergence of novelty. We find evidence for our theorizing using an analysis of over 35,000 Thingiverse design artifacts. This work identifies the differential effects of different forms of novelty, visual and verbal, on subsequent innovation, and identifies the differential effects of different degrees of structure in the landscape on novelty.

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.

Autonomous, intelligent tools are reshaping all sorts of work practices, including innovative design work. These tools generate outcomes with little or no user intervention and produce designs of unprecedented complexity and originality, ushering profound changes to how organizations will design and innovate in future. In this paper, we formulate conceptual foundations to analyze the impact of autonomous design tools on design work. We proceed in two steps. First, we conceptualize autonomous design tools as ‘rational’ agents which will participate in the design process. We show that such agency can be realized through two separate approaches of information processing: symbolic and connectionist. Second, we adopt control theory to unpack the relationships between the autonomous design tools, human actors involved in the design, and the environment in which the tools operate. The proposed conceptual framework lays a foundation for studying the new kind of material agency of autonomous design tools in organizational contexts. We illustrate the analytical value of the proposed framework by drawing on two examples from the development of Ubisoft’s Ghost Recon Wildlands video game, which relied on such tools. We conclude this essay by constructing a tentative research agenda for the research into autonomous design tools and design work.

Metahuman systems are new, emergent, sociotechnical systems where machines that learn join human learning and create original systemic capabilities. Metahuman systems will change many facets of the way we think about organizations and work. They will push information systems research in new directions that may involve a revision of the field’s research goals, methods and theorizing. Information systems researchers can look beyond the capabilities and constraints of human learning toward hybrid human/machine learning systems that exhibit major differences in scale, scope and speed. We review how these changes influence organization design and goals. We identify four organizational level generic functions critical to organize metahuman systems properly: delegating, monitoring, cultivating, and reflecting. We show how each function raises new research questions for the field. We conclude by noting that improved understanding of metahuman systems will primarily come from learning-by-doing as information systems scholars try out new forms of hybrid learning in multiple settings to generate novel, generalizable, impactful designs. Such trials will result in improved understanding of metahuman systems. This need for large-scale experimentation will push many scholars out from their comfort zone, because it calls for the revitalization of action research programs that informed the first wave of socio-technical researchmore »at the dawn of automating work systems.« less

The collective intelligence of online communities often depends on implicit forms of coordination, given the fluidity of membership and the lack of traditional hierarchies and associated incentive structures. This coordination drives knowledge production. Studying temporal dynamics may help elucidate how coordination happens. Specifically, the rate of interaction with an artifact such as a Wikipedia page can function as a signal that affects future interactions. Many activities can be characterized as bursty, meaning activity is not evenly spread or random, but is instead concentrated. This study analyzes 3,260 Wikipedia articles and shows that the coordination pattern in the Wikipedia community is mostly bursty. More importantly, the extent of burstiness affects article quality. This work highlights the important role temporal dynamics can play in the coordination process in online communities, and how it can affect the quality of knowledge production.