Short answer

When designing AI for decision support, prioritize features that explain the 'why' behind recommendations, as this builds greater user confidence and reliance than simple suggestions alone.

Field: Innovation & Design
Source: Academic Publication (2026)
Method: Experimental study
Sample: 397 participants
Evidence: Strong effect

Providing AI users with explanations and knowledge nudges, rather than just simple recommendations, significantly increases their trust and reliance on AI for selecting collaborative partners. This innovation & design research insight is drawn from a 2026 study published in Academic Publication. Using Experimental study with 397 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing AI for decision support, prioritize features that explain the 'why' behind recommendations, as this builds greater user confidence and reliance than simple suggestions alone.

Study

Innovation & DesignNew This WeekStrong effect

AI Explanations, Not Just Recommendations, Drive Trust in Collaborative Partner Selection

Providing AI users with explanations and knowledge nudges, rather than just simple recommendations, significantly increases their trust and reliance on AI for selecting collaborative partners.

Academic Publication · 2026

Key Findings

01Richer AI support (explanations/nudges) enhances perceived AI social and intellectual capabilities.
02Perceptions of AI intellectual capabilities, more than social capabilities, predict greater reliance on AI.
03AI capabilities mediate the effect of AI support type on user reliance.

Application

Design takeaway

When designing AI for decision support, prioritize features that explain the 'why' behind recommendations, as this builds greater user confidence and reliance than simple suggestions alone.

How to apply

When developing an AI tool for team formation, ensure the AI can not only suggest candidates but also explain the rationale based on skill alignment and past performance data.

Project actions

01When designing an AI assistant, consider how it will present information to the user.
02Think about how to make the AI's decision-making process transparent.

Method & Evidence

AimHow do different forms of AI support (recommendation, explanation, knowledge nudges) influence user perceptions of AI capabilities and their willingness to rely on AI for selecting collaborative partners for competency-based versus trustworthiness-based tasks?

MethodExperimental study

ProcedureParticipants designed ideal partners for two collaborative tasks (competency-based and trustworthiness-based) while receiving one of three AI support conditions: recommendation, explanation, or knowledge nudges. User perceptions of AI capabilities, autonomy, and reliance were measured.

Sample397 participants

ContextCollaborative partner selection for tasks

Variables

IVType of AI support (recommendation, explanation, knowledge nudges)

DVUser reliance on AI, perceived AI intellectual capabilities, perceived AI social capabilities, perceived user autonomy

CVCollaborative task type (competency-based, trustworthiness-based)

Strengths & Limitations

Strengths

+Large sample size provides statistical power.
+Experimental design allows for causal inference regarding AI support types.

Limitations

The tasks used might not fully represent real-world complexity. Participant motivations and prior AI experience could influence results.

Reliability & validity

The study's experimental design and quantitative measures likely contribute to good internal validity. Reliability would depend on the consistency of the AI support delivery and measurement scales.

Think critically

To what extent does the perceived 'intelligence' of an AI influence trust, and could this lead to over-reliance or a de-skilling of human judgment?

Design Principles

"Transparency in AI reasoning enhances user trust and adoption."

In design practice, the way AI interfaces and presents information directly impacts user adoption and effectiveness. Understanding that richer AI interactions foster greater trust is crucial for developing AI tools that genuinely assist users in complex decision-making processes, such as team formation or client selection.

What This Means for Your Design

If you want people to trust your AI when picking a team, don't just tell them who to pick. Explain why that person is a good choice, and they'll be more likely to listen.

How to use in your project

1.Use this research to justify the design of your AI's interface, particularly how it communicates recommendations.
2.Cite this study when discussing the importance of explainable AI (XAI) in your design process.

Add to My Project

Quick Cite

Paragraph starter

This study by Matej Hrkalović et al. (2026) highlights that the design of AI support significantly impacts user reliance in collaborative partner selection. By providing explanations and knowledge nudges rather than just recommendations, AI systems can foster greater trust by enhancing users' perceptions of the AI's intellectual capabilities. This suggests that for design projects involving AI decision support, prioritizing transparency and detailed rationale in the AI's output is crucial for user acceptance and effective collaboration.

Source

Academic Publication

User Reliance on AI Support for Collaborative Partner Selection

journal · 2026

View source

Questions About This Research

What does the research say about ai explanations, not just recommendations, drive trust in collaborative partner selection?: When designing AI for decision support, prioritize features that explain the 'why' behind recommendations, as this builds greater user confidence and reliance than simple suggestions alone. Evidence: Academic Publication (2026).
Why does "AI Explanations, Not Just Recommendations, Drive Trust in Collaborative Partner Selection" matter for design?: In design practice, the way AI interfaces and presents information directly impacts user adoption and effectiveness. Understanding that richer AI interactions foster greater trust is crucial for developing AI tools that genuinely assist users in complex decision-making processes, such as team formation or client selection.
How can designers apply this research?: When designing AI for decision support, prioritize features that explain the 'why' behind recommendations, as this builds greater user confidence and reliance than simple suggestions alone.
What were the main findings?: Richer AI support (explanations/nudges) enhances perceived AI social and intellectual capabilities.. Perceptions of AI intellectual capabilities, more than social capabilities, predict greater reliance on AI.. AI capabilities mediate the effect of AI support type on user reliance.
What research method was used?: Experimental study with 397 participants.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2026 journal from Academic Publication.
What should I do differently in my next project?: When developing an AI tool for team formation, ensure the AI can not only suggest candidates but also explain the rationale based on skill alignment and past performance data.
What are the limitations?: The study focused on specific task types (competency vs. trustworthiness) and may not generalize to all collaborative decision-making scenarios. The long-term effects of AI reliance were not assessed.