# Evaluating the Impact of Strategic Alignment on Performance Components of Iranian Pharmaceutical Companies Using Machine Learning Techniques

## TL;DR

This study uses machine learning to show that aligning HR, marketing, and IT strategies is strongly linked to better performance in Iranian pharmaceutical companies.

## Contribution

The novel contribution is applying machine learning to assess the joint impact of HR, marketing, and IT alignment on performance in Iran’s pharmaceutical sector.

## Key findings

- Strong alignment across HR, marketing, and IT is associated with overall performance (R2 = 0.76).
- Aggressive marketing and organizational commitment are linked to profitability (R2 = 0.66 and 0.59 respectively).
- The model generalizes well with high R2 (0.91) and consistent validation/test metrics.

## Abstract

Sustainable performance in the pharmaceutical industry hinges on the strategic alignment of human resources (HR), marketing, and information technology (IT). Prior studies often examined these domains separately; evidence on their joint influence in Iran’s pharmaceutical sector remains limited.

To assess how HR, marketing, and IT strategic alignment relate to profitability, liquidity, and revenue growth using machine-learning methods, and to document model generalization and measurement validity.

This applied, cross-sectional study surveyed 323 managers in Tehran Stock Exchange (TSE)-listed pharmaceutical firms (May to Nov, 2024). A validated questionnaire [CVI/CVR; EFA/ confirmatory factor analysis (CFA); reliability reported] was used only to construct composite indices of HR, marketing, and IT alignment; organizational performance outcomes, profitability, liquidity, and revenue growth (year-over-year) were computed from audited financial statements and then z-standardized. Inputs were min-max scaled to [0, 1]. A feed-forward artificial neural network (ANN; 3-15-1 per outcome; ReLU hidden, linear output) was trained with Levenberg-Marquardt, early stopping, and L2 regularization. Data were split 70/15/15 (train/validation/test) with 5 × 10 repeated cross-validation; bootstrap resampling (B = 1000) produced BCa 95% CIs. Model performance was assessed using mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), and R2.

Aggregate fit was strong (R2 = 0.91; RMSE = 0.134), with comparable validation/test metrics indicating good generalization. The triadic alignment factor showed the highest association with overall strategic alignment (R2 = 0.76; P < 0.001). At the subcomponent level, organizational commitment related to profitability (R2 = 0.59), and aggressive marketing to profitability (R2 = 0.66). Results are associative, not causal.

Machine-learning evidence suggests that coordinated alignment across HR, marketing, and IT is strongly associated with key performance components. The validated instrument, explicit splits, cross-validation, and bootstrap CIs enhance robustness and provide a practical, data-driven framework for managerial action in Iran’s pharmaceutical industry.