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.
Use the enabler/challenge map to build adoption playbooks covering governance, change, skills, and integration—not just model performance.
The review synthesizes AI value-creation mechanisms and identifies enablers and challenges affecting organizational adoption and integration.
Pilot AI assistance with measurement of task completion, review rework, defects, security, and learning—not speed alone.
Examines how generative AI changes software-engineering work and developer productivity.
Provide machine-readable evidence, governance, and cross-functional consensus support in procurement workflows.
AI adoption is expected to increase reliance on AI-generated input, reduce organizational buying-center size, and increase coordination while reducing conflict.
Sell implementation as a readiness journey, with data/process enablement and business-technical translation—not only model capability.
Long-term AI success needs readiness in people, processes, data, and technology; innovative organizations should bridge technical and business functions.
Use risk- and urgency-aware review queues, with human override and calibration.
Study evaluates machine-intelligence approaches for prioritizing code-review requests.
Evaluate churn models with intervention profit, calibration, drift monitoring and explainability—not AUC alone.
Ensembles dominate; deep learning is growing; class imbalance, interpretability, drift and weak profit-oriented evaluation remain challenges.
Benchmark combined workflows against both solo baselines.
Combinations underperformed the best solo system on average; gains were stronger for content creation and when humans were better than AI.
Segment explanations and onboarding by user expertise; assess calibration.
Expertise changes trust and willingness to follow XAI; explanations do not have uniform effects.
Use selective friction or rationale prompts for consequential recommendations.
Cognitive forcing reduced overreliance and encouraged independent thinking, at a cost of effort.
Expose calibrated confidence and test appropriate acceptance/rejection.
Confidence displays and explanations affect accuracy and trust calibration; explanation alone is insufficient.
Optimize explanations for joint outcomes, not transparency alone.
Explanations can improve joint performance, depending on human ability and coordination.
Make onboarding interactive and boundary-case based.
Model-driven tutorials improved understanding of model behavior and decision support.
Audit user behavior and downstream outcomes, not model parity only.
Human interaction can reproduce or amplify disparities; fairness is socio-technical.
Design XAI around concrete workflow jobs.
Practitioners use explanations for debugging, validation, and communication but face usability challenges.
Explain purpose, process, and limits at the right level.
Transparency has a non-monotonic effect on trust; framing and amount matter.
Match automation positioning and controls to task subjectivity.
Algorithm aversion is stronger for subjective than objective tasks.
Use benchmarks and measure actual reliance, not stated preference.
People often preferred algorithmic advice, especially when algorithms were described as superior.
Sell readiness and integration support, not model access alone.
Digital and innovation capabilities strongly drive AI adoption; external support works best with internal capability.
Track data, talent, governance, and change routines as maturity dimensions.
AI capability combines tangible, human, and intangible resources and is associated with creativity/performance.
Provide skills and data-readiness services alongside product.
Readiness, data, skills, perceived benefits, and resources shape ML adoption.
Optimize both individual quality and portfolio diversity.
AI raised judged individual creativity but reduced diversity across outputs.
Use early signals to triage mentorship and follow-up, not as an automated gatekeeper for contributors.
Random forest performed best; repository and contributor metadata can predict long-time-contributor status, and the paper identifies important features for prediction.
Prioritize measurable usefulness and performance value, low effort, trust, and appropriate human contact in adoption experiments; do not treat stated intent as equivalent to sustained usage.
Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy positively predicted intention, willingness, and use across industries; in some cultural contexts human contact could not be replaced.
Use TOE and absorptive-capacity categories to map enterprise blockers, and make governance, privacy, transparency, and stakeholder legitimacy explicit product requirements rather than afterthoughts.
Technology–organization–environment variables and absorptive capacity influence adoption; relevant tensions include fairness, transparency, privacy, and human rights across the AI innovation lifecycle.
Instrument trust as a product outcome: provide transparency and reliability signals, design appropriate immediacy and representation, and validate trust in longitudinal customer workflows rather than one-off demos.
Tangibility, transparency, reliability, and immediacy behaviors support cognitive trust, while anthropomorphism is especially relevant to emotional trust; representation and machine intelligence are important antecedents.
Suggests testing reputation, platform/channel, history, and timing signals—not just feature lists—when building data-driven pricing models for software marketplaces or channel sales.
Platform choice and seller history are non-core signals that influence perceived customer value and pricing beyond core product attributes; machine learning can help SMEs predict cross-platform prices.
Use the five categories as a pre-sales or implementation-readiness checklist: align the AI use case to strategy, budget data and skills, build an adoption culture, and define measurable readiness gates.
Five readiness categories were identified-strategic alignment, resources, knowledge, culture, and data-with 18 factors and 58 illustrative indicators; readiness is purpose- and context-specific and should be assessed continuously.
Interview prospective customers about confidence, budget, data governance, and organizational scale before promising AI outcomes; tailor adoption playbooks by customer readiness.
The sector was a slow AI adopter; confidence in technology practices, financial resources, organization size, data management/protection, and COVID-19 effects shaped readiness.
Treat organizational change capacity and technical infrastructure as joint prerequisites; segment enterprise prospects by size and product complexity rather than assuming larger customers adopt faster.
Both technological and organizational readiness are needed for AI adoption, yet a readiness-adoption gap remains; size has a U-shaped relationship with adoption and product complexity matters.
Position AI as part of a complementary data stack; assess whether customers have IoT/data-analytics assets and avoid presenting the observational revenue relationship as a guaranteed causal ROI.
The study reports a positive association between AI adoption and revenue growth above 30%, with stronger growth associations when AI is implemented alongside IoT or big-data analytics.