DISCERN: Designing Decision Support Interfaces to Investigate the Complexities of Workplace Social Decision-Making With Line Managers

TL;DR

This work conducted iterative design research with line managers within a technology organization, investigating decision-making practices, and opportunities for technological support, and found they preferred tools for externalizing reasoning rather than tools that replace interpersonal interactions.

Abstract

Line managers form the first level of management in organizations, and must make complex decisions, while maintaining relationships with those impacted by their decisions. Amidst growing interest in technology-supported decision-making at work, their needs remain understudied. Further, most existing design knowledge for supporting social decision-making comes from domains where decision-makers are more socially detached from those they decide for. We conducted iterative design research with line managers within a technology organization, investigating decision-making practices, and opportunities for technological support. Through formative research, development of a decision-representation tool -- DISCERN -- and user enactments, we identify their communication and analysis needs that lack adequate support. We found they preferred tools for externalizing reasoning rather than tools that replace interpersonal interactions, and they wanted tools to support a range of intuitive and calculative decision-making. We discuss how design of social decision-making supports, especially in the workplace, can more explicitly support highly interactional social decision-making.

Figures (3)

• Figure 1: Concepts used in the interview to further understand managers' needs: Concept 1 is a tool that would automatically generate and administer short surveys to provide managers' near real-time feedback while preserving privacy. Concept 2 is a visual environment to author multi-attribute value representations as an environment to reflect and plan. Concept 3 is a tool that would simulate stakeholder opinions as a way to receive real-time proxies of team members' feedback when they may not be accessible.
• Figure 2: When a user wants to set up a decision within DISCERN, they are asked to provide their decision goal (Left), after which they are asked to provide a list of alternatives (Potential Responses (Right)) and a list of objectives they want to consider in making the decision (Potential Attributes (Right)). On clicking "launch environment," the user is presented with the interface shown in Fig. \ref{['systemfig']}.
• Figure 3: DISCERN's interface. Panel A shows an overview of the value tree, presented as an Icicle diagram. Users can click on attributes to navigate to them. Panel B allows users to edit the currently selected tree node. The node and its children are presented as a Sankey figure (B1). For a chosen node, users can add new children (B2), change a child's importance level (B3), and leave notes (B5). A list of suggestions reflects potential children that the selected node can be decomposed into (B4). Users can navigate to the selected node's children through the Sankey diagram (B3). Adding children to the selected node updates both the Sankey diagram as well as the value tree overview. Finally, DISCERN also creates spreadsheet tables for each non-primitive node (nodes with children), where users can store raw data (Panel C). These tables are generated and updated automatically when manipulations of the value tree are synced to the sheet (B6). For example, adding children to the "productivity impact" node and syncing the update creates a new corresponding table (C2).