Online Measuring Feature for Batik Size Prediction using Mobile Device: A Potential Application for a Novelty Technology
Abstract
The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.
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IDN Financials, “Exports of batik industry exceeds USD533 million,” IDN Financials, Oct. 06, 2021. https://www.idnfinancials.com/news/40832/exports-batik-industry-exceeds-usd (accessed Aug. 22, 2023).
Agustin Windianingsih, Wawang Darmawan, and Akhmad Najih, “Exploring the Potential of Strengthtening for Batik Industry in Digital Era (Study of Handmade Batik Kulon Progo of Jogjakarta),” International Journal of Integrative Sciences, vol. 2, no. 5, pp. 725–734, May 2023, doi: 10.55927/ijis.v2i5.4302.
H. Kim and M. L. Damhorst, “The Relationship of Body-Related Self-Discrepancy to Body Dissatisfaction, Apparel Involvement, Concerns With Fit and Size of Garments, and Purchase Intentions in Online Apparel Shopping,” Clothing and Textiles Research Journal, vol. 28, no. 4, pp. 239–254, Sep. 2010, doi: 10.1177/0887302X10379266.
K. Liu, J. Wang, E. Kamalha, V. Li, and X. Zeng, “Construction of a prediction model for body dimensions used in garment pattern making based on anthropometric data learning,” The Journal of The Textile Institute, vol. 108, no. 12, pp. 2107–2114, Dec. 2017, doi: 10.1080/00405000.2017.1315794.
E. F. Stewart, V. Chattaraman, and K. P. Teel, “Men’s fit preferences in outdoor performance clothing: A qualitative investigation,” Critical Studies in Men’s Fashion, vol. 1, no. 3, pp. 255–274, Sep. 2014, doi: 10.1386/csmf.1.3.255_1.
M. A. Tongue, R. Otieno, and T. Diane Cassidy, “Evaluation of sizing provision among high street retailers and consumer buying practices of children’s clothing in the UK,” Journal of Fashion Marketing and Management: An International Journal, vol. 14, no. 3, pp. 429–450, Jan. 2010, doi: 10.1108/13612021011061870.
K. Liu et al., “An evaluation of garment fit to improve customer body fit of fashion design clothing,” The International Journal of Advanced Manufacturing Technology, vol. 120, no. 3, pp. 2685–2699, 2022, doi: 10.1007/s00170-022-08965-z.
A. Kantaros, T. Ganetsos, and F. I. T. Petrescu, “Three-Dimensional Printing and 3D Scanning: Emerging Technologies Exhibiting High Potential in the Field of Cultural Heritage,” Applied Sciences, vol. 13, no. 8, 2023, doi: 10.3390/app13084777.
A. Zizka, T. Joerger-Hickfang, S. Imhof, and L. Méndez, “LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost,” Taxon, vol. 72, no. 2, pp. 233–236, Apr. 2023, doi: https://doi.org/10.1002/tax.12861.
A. Nouman and S. Muneer, “A Systematic Literature Review On Heart Disease Prediction Using Blockchain And Machine Learning Techniques,” International Journal of Computational and Innovative Sciences, vol. 1, no. 4, pp. 1–6, Dec. 2022, [Online]. Available: https://ijcis.com/index.php/IJCIS/article/view/52
M. Nabipour, P. Nayyeri, H. Jabani, S. S, and A. Mosavi, “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis,” IEEE Access, vol. 8, pp. 150199–150212, 2020, doi: 10.1109/ACCESS.2020.3015966.
G. Ding and L. Qin, “Study on the prediction of stock price based on the associated network model of LSTM,” International Journal of Machine Learning and Cybernetics, vol. 11, no. 6, pp. 1307–1317, 2020, doi: 10.1007/s13042-019-01041-1.
T. K. Lee, J. H. Cho, D. S. Kwon, and S. Y. Sohn, “Global stock market investment strategies based on financial network indicators using machine learning techniques,” Expert Syst Appl, vol. 117, pp. 228–242, 2019, doi: https://doi.org/10.1016/j.eswa.2018.09.005.
L. Micol Policarpo et al., “Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review,” Comput Sci Rev, vol. 41, p. 100414, 2021, doi: https://doi.org/10.1016/j.cosrev.2021.100414.
A.-S. Sheikh et al., “A deep learning system for predicting size and fit in fashion e-commerce,” in Proceedings of the 13th ACM Conference on Recommender Systems, ACM , Sep. 2019. doi: 10.1145/3298689.3347006.
V. Jeleskovic, D. Behrens, and W. Härdle, A novel statistical framework for the analysis of the degree of technology adoption. 2023.
J. Namabira, “New E-Technologies and Practices Adoption Framework (Ne-TPAF) Dealing with the Crucial Times,” TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH, vol. 9, no. 2, pp. 72–81, Apr. 2022, doi: 10.21522/tijar.2014.09.02.art006.
A. Schmitz and J. Hamann, “The Nexus between Methods and Power in Sociological Research,” American Sociologist, vol. 53, no. 3, pp. 415–436, Sep. 2022, doi: 10.1007/s12108-022-09537-0.
V. Venkatesh, M. Morris, G. Davis, and F. Davis, “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly, vol. 27, pp. 425–478, Sep. 2003, doi: 10.2307/30036540.
T. Zhou, “Understanding online community user participation: a social influence perspective,” Internet Research, vol. 21, no. 1, pp. 67–81, Jan. 2011, doi: 10.1108/10662241111104884.
E.-M. Schomakers, C. Lidynia, L. S. Vervier, A. Calero Valdez, and M. Ziefle, “Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study,” JMIR Mhealth Uhealth, vol. 10, no. 1, p. e27095, 2022, doi: 10.2196/27095.
R. Pérez, C. Ruiz, and S. Blas, “Social network loyalty: Evaluating the role of attitude, perceived risk and satisfaction,” Online Information Review, vol. 37, Feb. 2013, doi: 10.1108/14684521311311630.
A. Bandura, “Human Agency in Social Cognitive Theory The Nature and Locus of Human Agency,” 1989.
A. Bandura, Self-efficacy: The exercise of control. New York, NY, US: W H Freeman/Times Books/ Henry Holt & Co, 1997.
A. Bandura, Social foundations of thought and action: A social cognitive theory. in Prentice-Hall series in social learning theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc, 1986.
D. R. Compeau and C. A. Higgins, “Computer Self-Efficacy: Development of a Measure and Initial Test,” MIS Quarterly, vol. 19, no. 2, pp. 189–211, 1995, doi: 10.2307/249688.
G. M. Marakas, M. Y. Yi, and R. D. Johnson, “The Multilevel and Multifaceted Character of Computer Self-Efficacy: Toward Clarification of the Construct and an Integrative Framework for Research,” Information Systems Research, vol. 9, no. 2, pp. 126–163, 1998, [Online]. Available: http://www.jstor.org/stable/23010924
G. Migliore, R. Wagner, F. S. Cechella, and F. Liébana-Cabanillas, “Antecedents to the Adoption of Mobile Payment in China and Italy: an Integration of UTAUT2 and Innovation Resistance Theory,” Information Systems Frontiers, vol. 24, no. 6, pp. 2099–2122, 2022, doi: 10.1007/s10796-021-10237-2.
B. M. Martinez and L. E. McAndrews, “Do you take...? The effect of mobile payment solutions on use intention: an application of UTAUT2,” Journal of Marketing Analytics, 2022, doi: 10.1057/s41270-022-00175-6.
F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989, doi: 10.2307/249008.
V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology,” MIS Quarterly, vol. 36, no. 1, pp. 157–178, 2012, doi: 10.2307/41410412.
A. Raman and Y. Don, “Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model,” International Education Studies, vol. 6, Jun. 2013, doi: 10.5539/ies.v6n7p157.
S. Nath, “ICT integration in Fiji schools: A case of in-service teachers,” Educ Inf Technol (Dordr), vol. 24, no. 2, pp. 963–972, 2019, doi: 10.1007/s10639-018-9803-6.
R. Nath, K. T. Bhal, and G. T. Kapoor, “Factors influencing IT Adoption by Bank Employees: An Extended TAM Approach,” Vikalpa, vol. 38, no. 4, pp. 83–96, Oct. 2013, doi: 10.1177/0256090920130406.
Y.-T. Jou, K. A. Mariñas, C. S. Saflor, and M. N. Young, “Investigating Accessibility of Social Security System (SSS) Mobile Application: A Structural Equation Modeling Approach,” Sustainability, vol. 14, no. 13, 2022, doi: 10.3390/su14137939.
Jacob Cohen, Statistical Power Analysis for the Behavioral Sciences. London: Academic Press, 1977.
J. Henseler, G. Hubona, and P. A. Ray, “Using PLS path modeling in new technology research: updated guidelines,” Industrial Management & Data Systems, vol. 116, no. 1, pp. 2–20, Jan. 2016, doi: 10.1108/IMDS-09-2015-0382.
Sekaran, Research Methods for Business: A Skill-Building Approach, 4th ed. New York: John Wiley & Sons, 2003.
D. Gefen, E. E. Rigdon, and D. Straub, “Editor’s Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research,” MIS Quarterly, vol. 35, no. 2, pp. iii–xiv, 2011, doi: 10.2307/23044042.
C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, 1981, doi: 10.2307/3151312.
J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” European Business Review, vol. 31, no. 1, pp. 2–24, Jan. 2019, doi: 10.1108/EBR-11-2018-0203.
K. Talsma, K. Robertson, C. Thomas, and K. Norris, “COVID-19 Beliefs, Self-Efficacy and Academic Performance in First-year University Students: Cohort Comparison and Mediation Analysis,” Front Psychol, vol. 12, 2021, [Online]. Available: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.643408
H. Gangwar, H. Date, and R. Ramaswamy, “Developing a Cloud-Computing Adoption Framework,” Global Business Review, vol. 16, no. 4, pp. 632–651, Jul. 2015, doi: 10.1177/0972150915581108.
M. Anil Job, “Measuring Technological, Organizational and Environmental Factors Influencing the Adoption Intentions of Public Cloud Computing Using a Proposed Integrated Model,” Academy and Industry Research Collaboration Center (AIRCC), May 2016, pp. 55–66. doi: 10.5121/csit.2016.60706.
J. Moonsun, K. C. Theodore, and B. M. Margaret, “지방정부의 저영향개발 적용에 영향을 주는 요인 연구,” 환경영향평가, vol. 24, no. 2, pp. 119–133, Apr. 2015, doi: 10.14249/EIA.2015.24.2.119.
DOI: https://doi.org/10.47738/jads.v4i3.121
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