Designing a Dynamic Model for Predicting the Sales of New Electronic Products in the Iranian Market

Seyed Mahamoddin Tabatabaei; Vahidreza  Mirabi

Authors

Seyed Mahamoddin Tabatabaei Ph.D. Student, Department of Management, Qe.C, Islamic Azad University, Qeshm, Iran https://orcid.org/0000-0002-9476-0144
Vahidreza Mirabi Department of Management, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0003-2861-7494

Keywords:

Sales forecasting, new electronic products, system dynamics, purchase intention, electronic word of mouth, perceived risk, Iranian market

Abstract

This study aimed to design and validate a system dynamics model for predicting the sales volume of newly introduced electronic products in the Iranian market by integrating consumer perceptions, economic constraints, marketing influences, social feedback, and market-saturation mechanisms. This applied, quantitative study employed a cross-sectional field-survey design and system dynamics modeling. Data were collected from 447 consumers aged 15–29 years in electronic-product stores and shopping centers in Tehran. A researcher-developed questionnaire measured technological innovativeness, product awareness, perceived usefulness, ease of use, product quality, relative advantage, compatibility, brand trust, affordability, price sensitivity, perceived risk, social influence, promotional exposure, electronic word of mouth, purchase intention, and expected early adoption. Construct validity was assessed through exploratory and confirmatory factor analyses, while reliability was evaluated using Cronbach’s alpha and composite reliability. Structural equation modeling was used to estimate direct and indirect relationships among the variables. Statistically supported relationships were translated into stocks, flows, delays, and reinforcing and balancing feedback loops. The model was validated through sensitivity analysis, scenario simulation, and comparison of predicted and validation-sample sales outcomes. The structural model demonstrated satisfactory fit and explained 68.4% of the variance in purchase intention and 57.9% of the variance in expected early adoption. Perceived usefulness, electronic word of mouth, affordability, product quality, social influence, and brand trust significantly increased purchase intention, whereas perceived risk and price sensitivity significantly reduced it. Purchase intention was the strongest predictor of early adoption. The dynamic model explained 91.2% of sales-volume variation and produced a mean absolute percentage error of 7.84%. Baseline simulations predicted 28,746 sales in the first year, 61,382 cumulative sales by the second year, and 78,964 sales after 36 months. Combining price reduction with stronger electronic word of mouth generated the greatest sales increase, whereas adverse economic conditions and negative early-user reviews produced the largest declines. The developed system dynamics model provided an accurate and interpretable framework for forecasting new electronic-product sales and demonstrated that affordability, perceived value, consumer trust, online word of mouth, and perceived risk jointly shape the speed and volume of market adoption.

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