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Abstract
This study aims to evaluate the effectiveness and interplay of key performance parameters—Scalability, Development Speed, Operational Cost Reduction, AI Performance, and Ease of Integration—across various digital technology implementations. By analyzing survey data using SPSS, the research investigates how these parameters influence decision-making in technology adoption, particularly in serverless platforms and AI-integrated applications. The study further explores how factors such as app type, team size, industry domain, and geographic region affect these metrics. Findings indicate a generally high level of agreement across most evaluation parameters, with notable correlations and some trade-offs identified between performance and integration ease. These insights can support better strategic planning in tech deployment and AI feature integration.
Research Significance: As digital transformation accelerates, organizations must prioritize not only innovation but also operational efficiency and adaptability. This research provides a structured evaluation of crucial performance metrics to help stakeholders understand where trade-offs may exist—such as between development speed and AI performance—and where synergies can be leveraged. The study’s focus on real-world variables like industry type, application context, and geographical distribution adds to its practical relevance, particularly for decision-makers in software engineering, cloud computing, and AI development.
Methodology: SPSS The analysis was conducted using SPSS, focusing on reliability, descriptive, correlation, regression, and ANOVA statistics. Data were collected on five evaluation parameters—Scalability, Development Speed, Operational Cost Reduction, AI Performance, and Ease of Integration—from a sample of technology stakeholders. Additional context variables such as App Type, AI Feature, Serverless Platform usage, Team Size, Industry, and Region were included as alternative dimensions. Cronbach’s Alpha was used to assess internal consistency, while correlation and regression analyses helped identify interdependencies and predictive power among variables. Alternatives Considered: To capture diverse implementation scenarios, the study examined variation across multiple contextual dimensions: App Type (e.g., Web, Mobile, Enterprise), AI Feature (e.g., Recommendation Engine, Natural Language Processing), Serverless Platform (e.g., AWS Lambda, Azure Functions), Team Size (e.g., Small, Medium, Large), Industry (e.g., Finance, Healthcare, Retail), Region (e.g., North America, Asia-Pacific, Europe) These alternatives provided a broader understanding of how technical and organizational contexts influence performance outcomes.
Evaluation Parameters: Five key evaluation parameters were assessed: Scalability – The system’s ability to handle growing workloads. Development Speed – The efficiency and time required to build and deploy. Operational Cost Reduction – Cost savings achieved through technology. AI Performance – Effectiveness and intelligence of AI components. Ease of Integration – The simplicity of incorporating the solution into existing systems. Each parameter was measured through participant ratings and analyzed for statistical significance and interrelation. Results: The results demonstrated high average ratings across all evaluation metrics, indicating favorable perceptions. Strong correlations were found between Operational Cost Reduction and Ease of Integration, as well as between Scalability and AI Performance. However, a trade-off was observed between Development Speed and AI Performance, suggesting that accelerating delivery may compromise some aspects of intelligent system behavior. Regression and ANOVA results were not statistically significant, likely due to sample size limitations, but the trends offer valuable direction for further research.
