Mental Models of Meeting Goals: Supporting Intentionality in Meeting Technologies

Title of the Paper

Mental Models of Meeting Goals: Supporting Intentionality in Meeting Technologies

Paper Information

• Subject Area: Human-Computer Interaction (HCI) and Collaborative Technologies
• Keywords: Meeting goals, agenda, goal-orientedness, calendar, team, workflow, video conferencing

Research Background and Issues

• Identified Problems or Challenges:

  • Excessive and goal-lacking meetings are a major cause of inefficiency in modern work environments.
  • Current calendar and meeting technologies lack tools to help users establish and articulate meeting goals.
  • Workers' attention is fragmented across multiple tasks and tools, undermining goal-orientedness.

• Significance:

  • Effective meetings can enhance team collaboration, reduce conflicts, and improve productivity.
  • Post-COVID-19, video conferencing fatigue highlights the importance of meeting management.
  • Supporting goal-orientedness helps better organize meetings and reduce content ambiguity.

• Research Motivation and Related Work:

  • Fatigue and ineffective meetings primarily stem from unclear meeting goals.
  • HCI research often focuses on the potential of technology to support collaboration but rarely explores the sociotechnical issues of meeting goal-setting in depth.
  • Literature indicates that meeting goals significantly impact team effectiveness, information sharing, and collaborative experiences.

Proposed Solution

• Proposed Approach by the Authors:

  • Investigate the mental models of meeting goals to understand workers' attitudes and practices regarding meeting purposes.
  • Promote goal-orientedness by designing and refining calendar and meeting technology interfaces.
  • Explore the integration of "meeting goal fields" and the use of generative AI to design intelligent meeting support tools.

• Innovative Contributions:

  • Proposed two mental models of meeting goals: meetings as a means to achieve work objectives and meetings as ends in themselves.
  • Identified barriers to goal-setting and suggested how technology can help clearly communicate meeting goals.
  • Explored embedding goal-orientedness throughout the meeting lifecycle, with technical support from conceptualization to execution.

• Implementation Steps and Key Technologies:

  • Interviewed 21 employees from a tech company to analyze their practices and barriers in setting meeting goals.
  • Proposed recommendations to enhance goal clarity through intuitive interaction designs (e.g., calendar goal fields and generative AI).
  • Designed a framework to support intentional reflection, including timeline analysis and adaptation to various interaction interfaces.

Research Outcomes

• Specific Findings:

  • Established two primary mental models of meeting goals and their impacts: one focusing on external work outcomes and the other emphasizing internal interaction and flexibility.
  • Highlighted issues in current meeting management, such as time pressure, technical limitations, and individual differences that hinder goal-setting.
  • Explored potential technological solutions, such as introducing goal fields in scheduling interfaces.

• Comparison with Existing Solutions and Advantages:

  • Compared to traditional meeting management methods (often lacking goal fields or with vague goal mentions), goal fields significantly improve meeting clarity.
  • More intelligent generative AI can dynamically adapt to team issues and provide customized solutions.
  • By promoting clear meeting goals, the negative impacts of excessive meetings are mitigated.

• Experimental or Evaluation Results:

  • Interview data indicated that explicit meeting goal fields enhance collaboration efficiency and reduce participant stress.
  • Generative AI was perceived as capable of linking information across platforms and fragmented tasks, improving meeting goal execution.

• Limitations and Future Directions:

  • Limitations:
    • The sample was limited to a single global tech company, whose culture and practices may restrict the study's generalizability.
    • Cross-cultural differences were not fully tested (e.g., differences in meeting styles between the U.S. and Japan).
    • Some participants self-selected into the study, potentially being more concerned about meeting issues than average employees.
  • Future Directions:
    • Further research on differences in meeting goal practices across multicultural teams.
    • Development of customized goal-oriented systems for various work domains.
    • Exploration of dynamic meeting interface designs based on generative AI to further enhance goal clarity and collaborative experiences.