A curated scientific evidence directory
Explore peer-reviewed field experiments and meta-analyses on SaaS trials, email, paid search, referrals, content sharing and choice design. Compare methods, context and limits.
A detailed example of measuring message components against both engagement and purchase outcomes. Test words, offer type, and audience interactions; do not optimize opens as a proxy for sales.
Content and framing had heterogeneous effects along the funnel.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Humans generally outperform chatbots for symbolic loss, where emotional support matters, while the difference diminishes for utilitarian loss. Recovery-agent effects depend on failure severity: for low-severity failures human recovery is more effective, whereas the paper reports different comparative effects under high severity. The results caution against treating chatbot recovery as universally superior. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Direct effects of anthropomorphism and perceived intelligence on UX were nonsignificant in the abstract, while the full paper reports anthropomorphism improving UX through perceived empathy and trust and an interaction in which higher perceived intelligence helps when anthropomorphism is high but can reduce UX when anthropomorphism is low. The results emphasize emotional engagement rather than intelligence alone. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific b2b growth and sales question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
The study links AI exploitation to three managerial capabilities: data-based human capital, social capital for a knowledge-sharing culture, and a transformative AI-positive mindset. These foster AI-integrated knowledge processes at individual, team, and organizational levels. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Accuracy, computing power/capacity, and personalization significantly influence time-saving and cost reduction; automation and real-time experience do not significantly influence that outcome. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Perceived safety, empathy and anthropomorphism affect different components of chatbot trustworthiness; trustworthiness increases willingness to forgive after failure and reduces negative word of mouth. The paper highlights privacy concerns as a countervailing issue and finds chatbot traits work through trust-related mechanisms rather than as undifferentiated design features. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific b2b growth and sales question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Technology readiness positively relates to ICT and AI-CRM capability; ICT capability positively relates to AI-CRM capability; relationship performance positively relates to social-sustainability performance. ICT capability mediates the technology-readiness/AI-CRM link, and industry dynamism moderates the ICT-capability/AI-CRM link. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific b2b growth and sales question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
US providers benefited directly from both exploration and exploitation and indirectly through website interactivity; Asia-Pacific providers benefited solely through the indirect website-interactivity path. Higher technological turbulence intensified the positive mediation effect. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific b2b growth and sales question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Buyer engagement depends on supplier-selection criteria, buyer role/readiness and project-specific factors; positive prior attitudes, perceived value and willingness predict co-creation intention. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
The review addresses fragmented evidence and compares tools, implementation and outcomes across firm sizes; detailed synthesized results require the full paper. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Company size positively influenced AI adoption and use; digitized environments increased adoption likelihood through network effects; competition intensity had a nonlinear effect that initially stimulated adoption. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Information quality and service quality significantly enhanced perceived business-process performance; system quality did not have a meaningful direct effect; perceived AI usefulness amplified the effects of system and information quality. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Synthesizes five main AI effects on HR activities and maps opportunities/challenges across the HR triad; highlights automation, decision support, bias/ethics, changing roles and need for multilevel research. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Perceptions of AI’s effects on talent acquisition, training/development, performance assessment and pay/rewards predicted adoption intention; AI-tech trust positively mediated the tested relationships. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Five themes organize adoption factors: organizational/strategic, technological/operational, human-centric, challenges/opportunities, and environmental/economic; ethical and legal considerations were especially prominent. For a founder, use this as a question to test in your own context, not a universal playbook.
Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.
Three of five hypotheses were supported: AI adoption positively related to sustainable employee performance; HRM practices and digital culture also positively related to performance; AI did not moderate the HRM–performance link and AI adoption was not associated with digital culture. For a founder, use this as a question to test in your own context, not a universal playbook.
Evaluate AI features with randomized task-level tests that include quality and time, then measure sustained adoption rather than assuming a novelty effect equals retention.
In this study, ChatGPT reduced average time by 40% and raised output quality by 18%; inequality fell, and exposed workers were 2 times as likely to report real-job use two weeks later and 1.6 times as likely two months later. Founder use: Evaluate AI features with randomized task-level tests that include quality and time, then measure sustained adoption rather than assuming a novelty effect equals retention.
Prioritize AI copilot deployment where onboarding and experience gaps are largest; track quality, customer sentiment, escalation, and retention together.
In this study, Less-experienced and lower-skilled workers improved speed and quality more than experienced/high-skilled workers; AI assistance also improved customer sentiment and reduced requests to speak with a manager. The publisher search abstract reports a 15% average productivity increase. Founder use: Prioritize AI copilot deployment where onboarding and experience gaps are largest; track quality, customer sentiment, escalation, and retention together.
Design AI product analytics around task-level success and verification, not aggregate usage; expose where the model helps and require review where it can degrade decisions.
In this study, Within the AI capability frontier, AI users completed 12.2% more tasks, 25.1% faster, with higher quality; on an out-of-frontier managerial task they were 19% less likely to be correct. Founder use: Design AI product analytics around task-level success and verification, not aggregate usage; expose where the model helps and require review where it can degrade decisions.
For developer-tool founders, measure perceived and behavioral productivity together, segment by user, and make provenance, ownership, responsibility, and output-quality workflows explicit. Avoid assuming that aggregate positive sentiment means every team or task benefits.
The assistant generally produced net perceived productivity increases, but benefits were not experienced equally; the study also surfaced motivations, expectations about speed/quality, and questions about ownership and responsibility for generated code. For a SaaS founder: For developer-tool founders, measure perceived and behavioral productivity together, segment by user, and make provenance, ownership, responsibility, and output-quality workflows explicit. Avoid assuming that aggregate positive sentiment means every team or task benefits.
Strong evidence for testing AI-assisted engineering productivity, not a promise of proportional headcount or SaaS growth; run a controlled rollout with quality, security, rework, and customer outcomes.
Pooling the experiments, AI access increased completed tasks by 26.08% (SE 10.3%); less-experienced developers adopted more and had larger gains, while individual experiments were noisy and heterogeneous. Founder-use interpretation: Strong evidence for testing AI-assisted engineering productivity, not a promise of proportional headcount or SaaS growth; run a controlled rollout with quality, security, rework, and customer outcomes.