Journal of Management Information Systems
Mind and the Machine examines the relationship between Generative AI usage and developer coding performance.
Read publication ↗Research & Innovation
Evidence-based thinking for enterprise AI.
Research into how AI changes software engineering, organizational performance and the design of work—combined with applied innovation in enterprise technology.
23qualitative interviews with senior technology and business leaders
260qualified survey respondents
60.5%R² for the task-efficiency regression model
2.027Durbin-Watson diagnostic
Executive DBA research
Generative AI in Agile software engineering
Pawan’s doctoral research at Temple University’s Fox School of Business studied how integrating Generative AI into Agile software engineering affects task efficiency, sprint speed, rework, collaboration and organizational adoption.
The mixed-methods program combined 23 qualitative interviews with 260 qualified survey respondents, connecting lived practitioner experience with quantitative evidence.
Selected quantitative findings
Task Efficiency model R² = 0.605. Sprint Speed showed a positive association with task efficiency (β = 0.390, p < .001), as did Rework Reduction (β = 0.469, p < .001). Collaboration Impact showed a negative coefficient in the model (β = −0.220, p = .002), while Organizational AI Support was not statistically significant.
Adoption signals
Seventy-four percent of respondents rated AI outputs reliable or highly reliable. Most reported meaningful time reduction, while experience with AI was associated with differences in code-quality and defect outcomes.
U.S. Patent 12,475,154
AI-based field-service assistance for telecommunications operations using contextual and multimodal intelligence.
View patent ↗Innovation, governance and adoption
Research and executive practice focused on scaling AI with the organizational, ethical and governance disciplines required for durable value.
Temple AI & Ethics ↗