Web-Based, Interactive, Interest-Based Negotiation Training for Managing Conflict in Isolated Environments: Opportunistic Study With an e-Survey
Bibliographic record
Abstract
BACKGROUND: Effective negotiation in relationships is critical for successful long-duration space missions; inadequate conflict resolution has shown serious consequences. Less desirable forms of negotiation, including positional bargaining (eg, negotiating prices), can exacerbate conflicts. Traditional positional bargaining may work for simple, low-stakes transactions but does not prioritize ongoing relationships. High-stakes situations warrant interest-based negotiation, where parties with competing interests or goals collaborate in a mutually beneficial agreement. This is learnable but must be practiced. Refresher training during conflicts is important to prevent out-of-practice crew members from using less effective negotiation techniques. Training should be self-directed and not involve others because, on a space mission, the only other people available may be part of the conflict. OBJECTIVE: We aimed to develop and test an interactive module teaching principles and skills of interest-based negotiation in a way that users find acceptable, valuable for learning, and enjoyable. METHODS: Using a web-based, interactive-media approach, we scripted, filmed, and programmed an interest-based negotiation interactive training module. In the module, the program mentor introduces users to "The Circle of Value" approach to negotiation and highlights its key concepts through interactive scenarios requiring users to make selections at specific decision points. Each selection prompts feedback designed to reinforce a teaching point or highlight a particular negotiation technique. To evaluate the module, we sought populations experiencing isolation and confinement (an opportunistic design). This included 9 participants in isolated, confined environments in the Australian Antarctic Program and the Hawai'i Space Exploration Analog and Simulation Mars simulation, as well as a subset of people who self-identified as being isolated and confined during the COVID-19 pandemic. Feedback was collected from participants (n=54) through free-response answers and questionnaires with numerical scaling (0=strongly disagree to 4=strongly agree) at the end of the module. RESULTS: In total, 51 of 54 (94%) participants found the activity valuable for learning about conflict management (identified by those who selected either "somewhat agree" or "strongly agree"), including 100% of participants in the isolated and confined environment subset (mode=3). In total, 79% (128/162) of participant responses indicated that the module was realistic (mode=3), including 85% (23/27) of responses from participants in isolated and confined environments (mode=3). Most participants felt that this would be particularly valuable for new team members in an isolated, confined environment (46/54, 85% of all participants, mode 4; 7/9, 78% of the isolated and confined environment subset, mode 3) as well as veterans. CONCLUSIONS: This module offers a self-directed, consistent approach to interest-based negotiation training, which is well received by users. Although the data are limited due to the opportunistic study design, the module could be useful for individuals in isolated and confined environments and for anyone involved in high-stakes negotiations where sustaining relationships is essential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".