Fathima Rustha S, Dr Jasmine A


The purpose of this study was to ascertain the purchasing habits of working women. The online form made with Google Forms was distributed to working women in Chennai who are above the age of eighteen and have purchasing power throughout the study's data gathering phase. The study comprised a total of 187 people who willingly agreed to participate and who thoroughly completed the questionnaire. The study employed a descriptive research paradigm, and basic random sampling was utilized to get the data. Using the SPSS 22 package program, a number of statistical tests were used to analyze the data, including the "Independent T test," "KMO test," "Factor Analysis," and "Reliability." In the aspects of employed women's purchasing style views, nine components surfaced. The factors that were collected were named based on the things that comprised each factor. "Confused," "Brand Loyal," "Careless," "Contented," "Fashion Conscious," "Pleasure Conscious," "Careful and Price Conscious," and "Perfectionist" are the variables, in that order. Furthermore, the study revealed statistically significant relationships between the work status of female customers and their purchasing patterns. This study suggests that housewives are more vulnerable in terms of careless and happy dimensions, whereas employed women are more sensitive in terms of brand conscious, pleasure conscious, price conscious and cautious, and confused.

Keyword : Purchasing style, employed women, consumer behaviour.

Published in Issue
March 15, 2024
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Akbıyık, F. (2020). Helal Gıda ve Tüketici Davranışları Helal Sertifika Algısının Satın Alma Tarzıyla İlişkisi. ÇizgiKitabevi. Akbıyık, F. (2021). A Research on Determining the Relationship Between Consumers’ Purchasing Styles andTheir Religious Orientations. Pazarlama ve Pazarlama Araştırmaları Dergisi, 14 (1), 31-59. Akter, K. (2018). Behaviour of Employed women on Convenience Food Buying: A Study from Bangladesh Perspectives. Business Management and Compliance, 1 (2), 43-57. Gupta, Y., & Shome, S. (2020). Social Media Advertisements and Buying Behaviour: A Study of Indian Employed women. Int. J. Online Mark, 10 (3), 48-61. Kantha, R. K., Rout, D., & Mishra, S. J. (2020). Shopping Behaviour -A Study of Urban Employed women in Bhubaneswar. Journal of Engineering Sciences, 11 (7), 1-10. Mohiuddin, Z. A. (2018). Effect of Lifestyle on Consumer Decision Making: A Study of Women Consumer of Pakistan, Journal of Accounting, Business and Finance Research. Scientific Publishing Institute, 2(1), 12- 15. Panicker, V. B. & Mohammad, K. A. (2017). The Shopping Behaviour of Urban Women Consumers in India for Certain Products and Services. International Journal of Research in Management, Economics and Commerce, 7 (12), 107-115. Prathaban, S. & Khurana, N., (2018). Impact of Brands on Urban and Rural Working and Non-Employed women Buying Behaviour with Special Reference to Jabalpur City. National Journal of Multidisciplinary Research and Development, 3 (1), 586-588. Ramprabha, K. (2017). Consumer Shopping Behaviour and The Role of Women in Shopping – A Literature Review. The International Journal of Research Publication’s, 7 (8), 50-63. Shreeraksha. S., & Maiya, U. (2020). Shopping Habits Among Women: A Study with Reference to Udupi District.Asia Pacific Journal of Research, I (105), 110-114. Şeker, A. (2016). Kadın Tüketiciler, Kadın Tüketicilerin Satın Alma Davranişlari ve Kadinlara Yönelik PazarlamaStratejieri. Uluslararası Sosyal Araştırmalar Dergisi, 9 (43), 2204-2214. DOI: 10.17719/jisr.20164317785. Menaga, D., & Revathi, S. (2018). Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding. IET Information Security, 12(4), 332-340. Menaga, D., & Saravanan, S. (2022). GA-PPARM: constraint-based objective function and genetic algorithm for privacy preserved association rule mining. Evolutionary Intelligence, 15(2), 1487-1498. Menaga, D., & Revathi, S. (2020). Deep learning: a recent computing platform for multimedia information retrieval. In Deep learning techniques and optimization strategies in big data analytics (pp. 124-141). IGI Global. Probabilistic principal component analysis (PPCA) based dimensionality reduction and deep learning for cancer classification D Menaga, S Revathi Intelligent Computing and Applications: Proceedings of ICICA 2019, 353-368 Menaga, D., & Revathi, S. (2021). Fractional-atom search algorithm-based deep recurrent neural network for cancer classification. Journal of Ambient Intelligence and Humanized Computing, 1-11. Menaga, D., & Revathi, D. S. (2018). Privacy preserving using bio inspired algorithms for data sanitization. In International Conference on Electrical, Electronics, Computers, Communication, Mechanical and Computing (EECCMC) (pp. 201-206). Menaga, D., & Saravanan, S. (2021). Application of artificial intelligence in the perspective of data mining. In Artificial Intelligence in Data Mining (pp. 133-154). Academic Press. Menaga, D., & Revathi, S. (2020). An empirical study of cancer classification techniques based on the neural networks. Biomedical Engineering: Applications, Basis and Communications, 32(02), 2050013. Menaga, D., & Begum, I. H. (2020). Bio-inspired algorithms for preserving the privacy of data. Journal of Computational and Theoretical Nanoscience, 17(11), 4971-4979. Menaga, D., Ambati, L. S., & Bojja, G. R. (2023). Optimal trained long short-term memory for opinion mining: a hybrid semantic knowledgebase approach. International Journal of Intelligent Robotics and Applications, 7(1), 119-133. Naeem, A. B., Senapati, B., Islam Sudman, M. S., Bashir, K., & Ahmed, A. E. (2023). Intelligent road management system for autonomous, non-autonomous, and VIP vehicles. World Electric Vehicle Journal, 14(9), 238. Naeem, A. B., Senapati, B., Mahadin, G. A., Ghulaxe, V., Almeida, F., Sudman, S. I., & Ghafoor, M. I. (2024). Determine the Prevalence of Hepatitis B and C During Pregnancy by Using Machine Learning Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 744-751. Yadav, S., Sudman, M. S. I., Dubey, P. K., Srinivas, R. V., Srisainath, R., & Devi, V. C. (2023, October). Development of an GA-RBF based Model for Penetration of Electric Vehicles and its Projections. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1-6). IEEE. Thingom, C., Tammina, M. R., Joshi, A., Agal, S., Sudman, M. S. I., & Byeon, H. (2023, August). Revolutionizing Data Capitalization: Harnessing Blockchain for IoT-Enabled Smart Contracts. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 490-496). IEEE. Sakthivel, M., Sudman, M. S. I., Ravishankar, K., Avinash, B., Kumar, A., & Ponnusamy, M. (2023, October). Medical Image Analysis of Multiple Myeloma Diagnosis Using CNN and KNN Based Approach. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 92-97). IEEE. Mohammed, A. H. (2021). Fish schooling and sorensen trust based wireless sensor network optimization. International Journal, 9, 6. Mohammed, A. H. DDoS Malicious Node Detection by Jaccard and Page Rank Algorithm in Cloud Environment. Mohammed, A. H. (2021). Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment. Faisal, L., Rama, V. S. B., Roy, S., & Nath, S. (2022). Modelling of electric vehicle using modified sepic converter configuration to enhance dc–dc converter performance using matlab. In Smart Energy and Advancement in Power Technologies: Select Proceedings of ICSEAPT 2021, Volume 2 (pp. 643-653). Singapore: Springer Nature Singapore. Faisal, L., Rama, V. S. B., Yang, J. M., Wajid, A., & Ghorui, S. K. (2022, May). Performance and simulation analysis of ipmsyncrm (internal permanent magnet synchronous reluctance motor) for advanced electric vehicle design. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE. Mohd, R., & Faisal, L. (2022). Smart Agricultural Practices using Machine Learning techniques For Rainfall Prediction: A case Study of Valkenburg station, Netherlands. Mathematical Statistician and Engineering Applications, 71(4), 8451-8462. Wani, A. A., & Faisal, L. (2022). Design & development of novel hybrid set of rules for detection and type of malignant or non-malignant tumor in human brain based on svm using artificial intelligence classifier. Mathematical Statistician and Engineering Applications, 71(4), 10253-10276. Choudhuri, S. S., Bowers, W., & Siddiqui, M. N. (2023). U.S. Patent No. 11,763,241. Washington, DC: U.S. Patent and Trademark Office. Zanzaney, A. U., Hegde, R., Jain, L., Choudhuri, S. S., & Sharma, C. K. (2023, September). Crop Disease Detection Using Deep Neural Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-5). IEEE.