Presenting a Model for the Development of Cosmetic Products Based on the Combined ISM-DEMATEL Method and Artificial Intelligence

Subject Areas : improving the performance of the public sector; Taking advantage of the opportunities available to fulfill the needs of citizens

BEHZAD BALAZADEH ¹ , hossein Bodaghi khajeh Nobar ² , morteza Mahmoodzadeh ³

1 - Ph.D. student in Business Administration, Marketing major, Tabriz Azad University
2 - Assistant Professor of Management Department, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
3 - Assistant Professor, Department of Business Administration, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Received: 2024-08-27 Accepted : 2024-10-19 Published : 2025-04-21

Keywords: Product development, cosmetics, artificial intelligence,

Abstract :

Product development, in general, is one of the main ways for companies to survive in competitive and dynamic markets. Considering the high failure rate of new products in most companies and in order to better respond to the changing needs and preferences of customers, increase satisfaction and strengthen their loyalty, expand the market, strengthen the brand, create a competitive advantage, and ultimately increase profitability, product development is of great importance. The aim of this study was to present a model for the development of cosmetic products based on the combined ISM-DEMATEL method. This research is applied in nature because, in addition to its informative and scientific aspect, it will also have practical applications for related companies. Given its aim and nature, this research is a mixed-methods study (qualitative-quantitative). The qualitative section was approached through grounded theory, and subsequently, using the ISM-DEMATEL method, variables were ranked and relationships between them were determined. The results showed that the model for developing cosmetic products with an artificial intelligence approach includes 85 open codes, 18 categorical codes, and 6 final axial codes. The main categories included advertising, packaging, appearance, quality, price, and distribution channels. Additionally, the findings indicate that quality and advertising are linkage factors, price is an influencing factor, and packaging, appearance, and distribution channels are dependent factors.

References:

حیدری، مهدی و امیری، حمیدرضا. (1401). بررسی قدرت مدل های مبتنی بر هوش مصنوعی در پیش بینی روند قیمت سهام بورس اوراق بهادار تهران. تحقیقات مالی، شماره 4، 602-623.#
عظیمی، محمد حسن؛ محمدی، زینب؛ رفیعی نسب، فاطمه. (1400). بررسی آگاهی و میزان استفاده کتابداران دانشگاهی از فناوری هوش مصنوعی: مطالعه موردی (کتابداران دانشگاه‌های شهید چمران اهواز و علوم پزشکی جندی شاپور)، کتابخانه مرکزی آستان قدس رضوی، کتابداری و اطلاع رسانی، دوره 24، شماره 4، صص 177-15.#
مرادی، گلمراد. (1401). استفاده دانشجویان دختر از لوازم آرایشی و عوامل جامعه شناختی مؤثر بر آن، زن در توسعه و سیاست، دوره 1، شماره 2، صفحات 106-87.#
Beata Piątkowska. (2022). Value Co-Creation on Public Social Media at Different Stages of the New Product Development Process. A Case Study of a Polish Clothes Manufacturer. Journal of Marketing and Consumer Behaviour in Emerging Markets 1(14)2022.
Belk,W., Belanche, R., & Flavian, C. (2023): Key concepts in artificial intelligence and technologies 4.0 in services. Service Business (2023) 17:1–9.
Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence– challenges and opportunities for international HRM: a review and research agenda. The InTernaTIonal Journal of human resource managemenT, 33(6), 1065-1097.
Chatterjee, S., (2020). AI strategy of India: policy framework, adoption challenges and actions for government. Transforming Government: People, Process and Policy, 14(5), pp. 757-775. https://doi. org/ 10. 1108/TG-05-2019-0031.
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899.
Foltynek, T., Bjelobaba, S., Glendinning, I., Khan, Z. R., Santos, R., Pavletic, P., & Kravjar, J. (2023). ENAI Recommendations on the ethical use of Artificial Intelligence in Education. International Journal for Educational Integrity, 19(1), 12.
Li, M., Yin, D., Qiu, H., & Bai, B. (2021). A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management, 95, Article 102930. https://doi.org/10.1016/j. ijhm.2021.102930.
McCabe, M., T. de Waal Malefyt, and A. Fabri, Women, makeup, and authenticity: Negotiating embodiment and discourses of beauty. Journal of Consumer Culture, 2020. 20(4): p. 656-677.
Mirbabaie, M., Brünker, F., Möllmann Frick, N. R., & Stieglitz, S. (2021). The rise of artificial intelligence–understanding the AI identity threat at the workplace. Journal of Electronic Markets, 32, 73-99.
Mustak, M. , Salminen, J. , Ple, L. , Wirtz, J. (2020). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research. 389404. https://doi. org/10. 1016/j. jbusres. 2020. 10. 044.
NSTC (2020), Preparing for the Future of Artificial Intelligence. National science and Technology Council.
Pantano, E. & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services. 55-102096. https://doi. org/10. 1016/j. jretconser. 2020. 102096.
Yadav, S. P., Mahato, D. P., Linh, N. T. D. (Eds. ). (2020). Distributed artificial intelligence: A modern approach. CRC Press. https://www. amazon. com/Distributed-Artificial-Intelligence-Approach-Everything/dp/0367466651.
Yuping Liu Thompkins, Shintaro Okazaki & Hairong Li. (2022). Artifcial empathy in marketing interactions: Bridging the human AI gap in afective and social customer experience. Journal of the Academy of Marketing Science (2022) 50:1198–1218 https://doi.org/10.1007/s11747-022-00892-
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