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Enterprise Search and Discovery Capability

The Factors and Generative Mechanisms for User Satisfaction​

Introduction​

This paper examines user satisfaction in enterprise search environments, where users often express dissatisfaction compared to public search engines like Google. Cleverley and Burnett analyze feedback from over 1,000 users within a multinational organization, revealing that the root causes of dissatisfaction are often linked to user knowledge and expectations rather than technology alone. The study provides a holistic view of the factors and underlying mechanisms driving satisfaction in enterprise search systems.

Target​

The findings are relevant to enterprise-level search system designers, IT management, and knowledge management teams looking to enhance information retrieval within large organizations.

Key Insights​

  • Human Factors Over Technology: 62% of dissatisfaction events were attributed to human factors, such as information literacy and unrealistic expectations influenced by the "Google Habitus."
  • Generative Mechanisms:
    • Cognitive Biases: Users bring biases, like assuming enterprise search should function like Google, which skews satisfaction levels.
    • Google Habitus: The widespread use of internet search engines has shaped user expectations, leading to frustration when enterprise systems don't provide instant, relevant results.
    • Complexity of Organizational Information: Internal documents often lack metadata or meaningful tags, making retrieval difficult.
  • Technology and Information Quality: While technology contributes to satisfaction, issues like poor indexing, irrelevant results, and system errors also impact the user experience.
  • Organizational Strategies: Emphasis on "systems thinking" and training in search literacy can improve user satisfaction and search success.

Supporting Data​

  • User Feedback Analysis: Over 79% of feedback indicated dissatisfaction, with main categories including:
    • Technology Issues (38%): Slow performance, poor ranking, or irrelevant search results.
    • Information Quality (36%): Missing or outdated information and poor tagging.
    • Information Literacy (26%): User difficulties in formulating effective queries and interpreting search results.
  • Feedback Volatility: Satisfaction fluctuated significantly depending on updates to the system and IT infrastructure quality.

Other Insights​

  • Importance of Metadata: Quality metadata and tagging were critical to improving search success, underscoring the importance of structured information management.
  • Iterative Improvements: Feedback loops, such as a Search Service Center of Excellence, were beneficial in identifying and rectifying issues but required dedicated resources.

Practical Applications​

  • Improve Information Literacy: Provide users with training on effective search practices within enterprise environments, addressing differences from public search engines.
  • Structured Metadata Practices: Encourage tagging and metadata practices within the organization to improve search relevance.
  • Design with User Bias in Mind: Design interfaces and feedback mechanisms that align more closely with user expectations shaped by internet search experiences.

Reference​

Cleverley, P.H., & Burnett, S. 2019. Enterprise Search and Discovery Capability: The Factors and Generative Mechanisms for User Satisfaction. Journal of Information Science, 45(1), 29-52.