author(s)
Simon Suwanzy Dzreke and Semefa Elikplim Dzreke

abstract
When multimillion-dollar B2B partnerships fail due to service issues, can algorithms effectively rebuild the trust that human relationships rely on? This innovative study examines the specific circumstances under which algorithmic atonement AI-driven efforts to remedy service lapses outperforms human empathy in restoring relational fairness. Through a rigorously planned 2x2 between-subjects experiment involving 224 B2B procurement professionals, the study compares AI and human recovery agents across relationship-threatening core failures and peripheral service gaps. An unanticipated valence reversal effect emerges: AI systems clearly outperform human agents in recovering transactional trust after peripheral failures, using speed and procedural consistency to increase relational equity by 38%. Human specialists, on the other hand, are vital for fundamental crises such as a pharmaceutical temperature-control breach that destroyed $2.3 million in therapies where genuine empathy rebuilds emotional relationships that algorithmic solutions cannot. Critically, the analysis reveals a key flaw: when AI overreacts with anthropomorphic empathy amid extreme failures, it causes Uncanny Valley aversion, which is viewed as manipulative "emotional puppetry," reducing trust by 31%. As the first study to incorporate Computers-Are-Social-Actors (CASA) theory, Uncanny Valley mechanics, and Justice Theory into B2B scenarios, it substantially reframes atonement as a specialized function rather than a human monopoly. The findings provide an actionable path. AI can be deployed as first responders for logistical recoveries (e.g., automated smart contracts resolving shipment delays), but human crisis specialists should be reserved for ethical breaches that require moral accountability (such as executive video apologies for safety-critical failures). The future of resilient B2B relationships lies in hybrid recovery systems that are carefully calibrated to respect the vital boundaries between algorithmic precision and irreplaceable human conscience.