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  1. Purpose

    Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

    Design/methodology/approach

    We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

    Findings

    Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

    Originality/value

    Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

     
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    Free, publicly-accessible full text available April 5, 2025
  2. In response to the COVID-19 crisis, many local television (TV) newsrooms decided to have employees work from home (WFH) or from the field rather than from the newsroom, creating a kind of hybrid work characterized by a mix of work locations. From a review of research on telework and WFH, we identified possible impacts of WFH on work and on workers, with a particular focus on news work and news workers. Data on the impacts of hybrid work are drawn from interviews with local television news directors and journalists in the United States and observations of WFH. We found that through the creative application of technology, WFH news workers could successfully create a newscast, albeit with some concerns about story quality. However, WFH did not seem to satisfy workers’ needs for socialization or learning individually or as a group and created some problems coordinating work. Lifted restrictions on gatherings have mitigated some of the experienced problems, but we expect to see continued challenges to news workers’ informal learning in hybrid work settings. 
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    Free, publicly-accessible full text available April 22, 2025
  3. We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems. 
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    Free, publicly-accessible full text available January 1, 2025