Araştırma Makalesi
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Yıl 2021, Cilt: 19 Sayı: 2, 82 - 87, 25.10.2021

Ahmet Raşit Petekci

Öz

In this research, the answer of the question was searched about "In technology design; in what conditions originality, in what conditions should data-based design be preferred?" The subject of the study is related to the field of human computerınteraction, the usability branch. Qualitative research techniques have been used to solve the problem, and a literature review has been made. "data-driven design", "human computer interaction", "usability" keywords and English articles published between 2012-2020 were examined for literature review. Of the 122 studies examined, 50 (41%) were related to” usability“, 44 (36%) to” human computer interaction“, 28 (23%) to” data-driven design " keywords. Most of the studies on the word "data-driven design" from the keywords are in the USA, mostly in engineering subjects; The most studies on the word "human computer interaction" are in Germany, the most work areas are in medicine and dentistry; The most work on the word "usability" has been in China, the most widely in computer science.As a result of the research it has emerged that designers need to use the method of originality to create brand, trust, reputation and long-lasting goals in the product about making decisions between options, not being able to predict user behaviour and preferring the data-based design method in digital product designs.

Anahtar Kelimeler

Human computer interaction usability technology design data-driven design originality.

Kaynakça

  • Preece, J., Sharp, H., & Rogers, Y. (2015). Interaction design: beyond human-computer interaction. John Wiley & Sons.
  • Kim, G. (2015). Human-computer interaction. Auerbach Publications.
  • Qi, J., Jiang, G., Li, G., Sun, Y., & Tao, B. (2019). Intelligent human-computer interaction based on surface EMG gesture recognition. IEEE Access, 7, 61378-61387.
  • Bachmann, D., Weichert, F., & Rinkenauer, G. (2018). Review of three-dimensional human-computer interaction with focus on the leap motion controller. Sensors, 18(7), 2194.
  • Quitadamo, L. R., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S., & Saggio, G. (2017). Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review. Journal of neural engineering, 14(1), 011001.
  • Poole, E. S. (2013). HCI and mobile health interventions: how human–computer interaction can contribute to successful mobile health interventions. Translational behavioral medicine, 3(4), 402-405.
  • Klemmer, S. (2020, Eylül 26). Scott Klemmer. https://d.ucsd.edu/srk/
  • Kortum, P. T., & Bangor, A. (2013). Usability ratings for everyday products measured with the System Usability Scale. International Journal of Human-Computer Interaction, 29(2), 67-76.
  • Baharuddin, R., Singh, D., & Razali, R. (2013). Usability dimensions for mobile applications-a review. Res. J. Appl. Sci. Eng. Technol, 5(6), 2225-2231.
  • Tuch, A. N., Roth, S. P., HornbæK, K., Opwis, K., & Bargas-Avila, J. A. (2012). Is beautiful really usable? Toward understanding the relation between usability, aesthetics, and affect in HCI. Computers in Human Behavior, 28(5), 1596-1607.
  • Ding, S. X. (2014). Data-driven design of fault diagnosis and fault-tolerant control systems. Springer London.
  • Zhang, R. Z., Gucci, F., Zhu, H., Chen, K., & Reece, M. J. (2018). Data-driven design of ecofriendly thermoelectric high-entropy sulfides. Inorganic chemistry, 57(20), 13027-13033.
  • Chi, R., Hou, Z., Huang, B., & Jin, S. (2015). A unified data-driven design framework of optimality-based generalized iterative learning control. Computers & Chemical Engineering, 77, 10-23.
  • MIT Technology Review. (2020, Ağustos 25). How Analytics and Machine Learning Help Organizations Reap Competitive Advantage. Amazonaws. https://s3.amazonaws.com/files.technologyreview.com/whitepapers/Google-Analytics-Machine-Learning.pdf
  • Philips, M. (2020, Temmuz 3). The True ROI of UX: B2B Redesign Case Studies. Designers. https://www.toptal.com/designers/ux/roi-of-ux-redesign-case-studies
  • Stelzner, M. (2020, Haziran 15). 2018 Social Media Marketing Industry Report. Social Media Examiner. https://www.socialmediaexaminer.com/social-media-marketing-industry-report-2018/
  • Ding, S. X. (2012). Data-driven design of model-based fault diagnosis systems. IFAC Proceedings Volumes, 45(15), 840-847.
  • Agrawal, R., Golshan, B., & Papalexakis, E. (2016). Toward Data-Driven Design of Educational Courses: A Feasibility Study. Journal of Educational Data Mining, 8(1), 1-21.
  • Zhang, Y., Yang, Y., Ding, S. X., & Li, L. (2014). Data-driven design and optimization of feedback control systems for industrial applications. IEEE Transactions on Industrial Electronics, 61(11), 6409-6417.
  • Sari, A. H. A. (2014). Data-driven design of fault diagnosis systems: Nonlinear multimode processes. Springer Science & Business.
  • Du, X., & Zhu, F. (2018). A new data-driven design methodology for mechanical systems with high dimensional design variables. Advances in Engineering Software, 117, 18-28.
  • Bier, H., & Knight, T. (2014). Data-driven design to production and operation. Footprint, 1-8.
  • Kim, H., Liu, Y., Wang, Y., & Wang, C. (2016). Data-Driven Design (D3). Journal of Mechanical Design, 138(12).
  • Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.
  • Grossberg, K. A. (2016). The new marketing solutions that will drive strategy implementation. Strategy & Leadership.
  • Schümmer, T., Haake, J. M., & Stark, W. (2014, July). Beyond rational design patterns. In Proceedings of the 19th European Conference on Pattern Languages of Programs (pp. 1-13).
  • Muniz Jr, J., de Carvalho, C. P., & Ribeiro, V. B. (2018, December). Worker and Manager Judgements About Factors that Facilitate Knowledge Sharing: Insights from the Brazilian Glass Segment. In International Conference on Production and Operations Management Society (pp. 809-817). Springer, Cham.
  • Hoshi, K., & Waterworth, J. (2020). Why Primitive Interaction Design?. In Primitive Interaction Design (pp. 3-25). Springer, Cham.
  • Manoela, P., & Cecilia, C. L. (2013). Considerations Regarding The Size Of Innovative Organization In The Knowledge Economy. Romanian Economic and Business Review, 402.
  • Sekliuckiene, J. (2017). Factors leading to early internationalization in emerging Central and Eastern European economies. European Business Review.
  • Heller, S., & D'Onofrio, G. (2017). The Moderns: Midcentury American Graphic Design. Abrams.
  • Hughes, H., Wolf, R., & Foth, M. (2017). Informed digital learning through social living labs as participatory methodology. Information and Learning Science.
  • Britton, C. (2015). Designing the Requirements: Building Applications that the User Wants and Needs. Addison-Wesley Professional.
  • Schultz, T. (2019). Decolonising Design: Mapping Futures.
  • Dalí, S. (2013). The secret life of Salvador Dali. Courier Corporation.
  • Mackey, A., Wakkary, R., Wensveen, S., & Tomico, O. (2017). “Can I Wear This?” Blending Clothing and Digital Expression by Wearing Dynamic Fabric. International Journal of Design, 11(3), 51-65.
  • Sideris, D., Paraskeva, F., Alexiou, A., & Chatziiliou, A. (2014, October). Create a'Wonderful'Virtual World: The Case of Arigatou in Second Life. In European Conference on Games Based Learning (Vol. 2, p. 775). Academic Conferences International Limited.
  • Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9-12), 3563-3576.
  • Sierla, S., Kyrki, V., Aarnio, P., & Vyatkin, V. (2018). Automatic assembly planning based on digital product descriptions. Computers in Industry, 97, 34-46.
  • Lyytinen, K., Yoo, Y., & Boland Jr, R. J. (2016). Digital product innovation within four classes of innovation networks. Information Systems Journal, 26(1), 47-75.
  • Banfield, R., Lombardo, C. T., & Wax, T. (2015). Design sprint: A practical guidebook for building great digital products. " O'Reilly Media, Inc.".
  • Hoffman, M. T., & Phillips, C. J. (2013). U.S. Patent Application No. 13/668,168.
  • Nylén, D., & Holmström, J. (2015). Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Business Horizons, 58(1), 57-67.
  • Stef, I. D., Draghici, G., & Draghici, A. (2013). Product design process model in the digital factory context. Procedia technology, 9(1), 451-462.
  • Ashby, M. F., & Johnson, K. (2013). Materials and design: the art and science of material selection in product design. Butterworth-Heinemann.
  • Paritala, P. K., Manchikatla, S., & Yarlagadda, P. K. (2017). Digital manufacturing-applications past, current, and future trends. Procedia engineering, 174, 982-991.
  • Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2015). Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer-Aided Design, 59, 1-14.

Teknoloji Tasarımcılarının Paradoksu: Özgünlük mü? Veriye Dayalı Tasarım mı?

Yıl 2021, Cilt: 19 Sayı: 2, 82 - 87, 25.10.2021

Ahmet Raşit Petekci

Öz

Bu araştırmada, "Teknoloji tasarımında; hangi koşullarda özgünlük, hangi şartlarda veriye dayalı tasarım tercih edilmelidir?" sorusuna cevap aranmıştır. Çalışma konusu, İnsan Bilgisayar Etkileşimi alanı, Kullanılabilirlik dalı ile ilgilidir. Problemin çözümü için nitel araştırma teknikleri kullanılmış olup, literatür taraması yapılmıştır. Literatür taraması için "data-driven design", "human computer interaction", "usability" anahtar kelimeleri ile 2012-2020 arasında yayınlanmış İngilizce makaleler incelenmiştir. Araştırmanın sonucunda; seçenekler arasında karar verme, kullanıcı davranışlarını öngörememe ve dijital ürün tasarımlarında veriye dayalı tasarımı kullanmanın; marka, güven, itibar ve uzun süreli hedefler için ise tasarımcının özgünlüğünü öne çıkarmanın daha verimli olacağı ortaya çıkmıştır.

Anahtar Kelimeler

insan bilgisayar etkileşimi kullanılabilirlik teknoloji tasarım veriye dayalı tasarım özgünlük

Kaynakça

  • Preece, J., Sharp, H., & Rogers, Y. (2015). Interaction design: beyond human-computer interaction. John Wiley & Sons.
  • Kim, G. (2015). Human-computer interaction. Auerbach Publications.
  • Qi, J., Jiang, G., Li, G., Sun, Y., & Tao, B. (2019). Intelligent human-computer interaction based on surface EMG gesture recognition. IEEE Access, 7, 61378-61387.
  • Bachmann, D., Weichert, F., & Rinkenauer, G. (2018). Review of three-dimensional human-computer interaction with focus on the leap motion controller. Sensors, 18(7), 2194.
  • Quitadamo, L. R., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S., & Saggio, G. (2017). Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review. Journal of neural engineering, 14(1), 011001.
  • Poole, E. S. (2013). HCI and mobile health interventions: how human–computer interaction can contribute to successful mobile health interventions. Translational behavioral medicine, 3(4), 402-405.
  • Klemmer, S. (2020, Eylül 26). Scott Klemmer. https://d.ucsd.edu/srk/
  • Kortum, P. T., & Bangor, A. (2013). Usability ratings for everyday products measured with the System Usability Scale. International Journal of Human-Computer Interaction, 29(2), 67-76.
  • Baharuddin, R., Singh, D., & Razali, R. (2013). Usability dimensions for mobile applications-a review. Res. J. Appl. Sci. Eng. Technol, 5(6), 2225-2231.
  • Tuch, A. N., Roth, S. P., HornbæK, K., Opwis, K., & Bargas-Avila, J. A. (2012). Is beautiful really usable? Toward understanding the relation between usability, aesthetics, and affect in HCI. Computers in Human Behavior, 28(5), 1596-1607.
  • Ding, S. X. (2014). Data-driven design of fault diagnosis and fault-tolerant control systems. Springer London.
  • Zhang, R. Z., Gucci, F., Zhu, H., Chen, K., & Reece, M. J. (2018). Data-driven design of ecofriendly thermoelectric high-entropy sulfides. Inorganic chemistry, 57(20), 13027-13033.
  • Chi, R., Hou, Z., Huang, B., & Jin, S. (2015). A unified data-driven design framework of optimality-based generalized iterative learning control. Computers & Chemical Engineering, 77, 10-23.
  • MIT Technology Review. (2020, Ağustos 25). How Analytics and Machine Learning Help Organizations Reap Competitive Advantage. Amazonaws. https://s3.amazonaws.com/files.technologyreview.com/whitepapers/Google-Analytics-Machine-Learning.pdf
  • Philips, M. (2020, Temmuz 3). The True ROI of UX: B2B Redesign Case Studies. Designers. https://www.toptal.com/designers/ux/roi-of-ux-redesign-case-studies
  • Stelzner, M. (2020, Haziran 15). 2018 Social Media Marketing Industry Report. Social Media Examiner. https://www.socialmediaexaminer.com/social-media-marketing-industry-report-2018/
  • Ding, S. X. (2012). Data-driven design of model-based fault diagnosis systems. IFAC Proceedings Volumes, 45(15), 840-847.
  • Agrawal, R., Golshan, B., & Papalexakis, E. (2016). Toward Data-Driven Design of Educational Courses: A Feasibility Study. Journal of Educational Data Mining, 8(1), 1-21.
  • Zhang, Y., Yang, Y., Ding, S. X., & Li, L. (2014). Data-driven design and optimization of feedback control systems for industrial applications. IEEE Transactions on Industrial Electronics, 61(11), 6409-6417.
  • Sari, A. H. A. (2014). Data-driven design of fault diagnosis systems: Nonlinear multimode processes. Springer Science & Business.
  • Du, X., & Zhu, F. (2018). A new data-driven design methodology for mechanical systems with high dimensional design variables. Advances in Engineering Software, 117, 18-28.
  • Bier, H., & Knight, T. (2014). Data-driven design to production and operation. Footprint, 1-8.
  • Kim, H., Liu, Y., Wang, Y., & Wang, C. (2016). Data-Driven Design (D3). Journal of Mechanical Design, 138(12).
  • Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.
  • Grossberg, K. A. (2016). The new marketing solutions that will drive strategy implementation. Strategy & Leadership.
  • Schümmer, T., Haake, J. M., & Stark, W. (2014, July). Beyond rational design patterns. In Proceedings of the 19th European Conference on Pattern Languages of Programs (pp. 1-13).
  • Muniz Jr, J., de Carvalho, C. P., & Ribeiro, V. B. (2018, December). Worker and Manager Judgements About Factors that Facilitate Knowledge Sharing: Insights from the Brazilian Glass Segment. In International Conference on Production and Operations Management Society (pp. 809-817). Springer, Cham.
  • Hoshi, K., & Waterworth, J. (2020). Why Primitive Interaction Design?. In Primitive Interaction Design (pp. 3-25). Springer, Cham.
  • Manoela, P., & Cecilia, C. L. (2013). Considerations Regarding The Size Of Innovative Organization In The Knowledge Economy. Romanian Economic and Business Review, 402.
  • Sekliuckiene, J. (2017). Factors leading to early internationalization in emerging Central and Eastern European economies. European Business Review.
  • Heller, S., & D'Onofrio, G. (2017). The Moderns: Midcentury American Graphic Design. Abrams.
  • Hughes, H., Wolf, R., & Foth, M. (2017). Informed digital learning through social living labs as participatory methodology. Information and Learning Science.
  • Britton, C. (2015). Designing the Requirements: Building Applications that the User Wants and Needs. Addison-Wesley Professional.
  • Schultz, T. (2019). Decolonising Design: Mapping Futures.
  • Dalí, S. (2013). The secret life of Salvador Dali. Courier Corporation.
  • Mackey, A., Wakkary, R., Wensveen, S., & Tomico, O. (2017). “Can I Wear This?” Blending Clothing and Digital Expression by Wearing Dynamic Fabric. International Journal of Design, 11(3), 51-65.
  • Sideris, D., Paraskeva, F., Alexiou, A., & Chatziiliou, A. (2014, October). Create a'Wonderful'Virtual World: The Case of Arigatou in Second Life. In European Conference on Games Based Learning (Vol. 2, p. 775). Academic Conferences International Limited.
  • Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9-12), 3563-3576.
  • Sierla, S., Kyrki, V., Aarnio, P., & Vyatkin, V. (2018). Automatic assembly planning based on digital product descriptions. Computers in Industry, 97, 34-46.
  • Lyytinen, K., Yoo, Y., & Boland Jr, R. J. (2016). Digital product innovation within four classes of innovation networks. Information Systems Journal, 26(1), 47-75.
  • Banfield, R., Lombardo, C. T., & Wax, T. (2015). Design sprint: A practical guidebook for building great digital products. " O'Reilly Media, Inc.".
  • Hoffman, M. T., & Phillips, C. J. (2013). U.S. Patent Application No. 13/668,168.
  • Nylén, D., & Holmström, J. (2015). Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Business Horizons, 58(1), 57-67.
  • Stef, I. D., Draghici, G., & Draghici, A. (2013). Product design process model in the digital factory context. Procedia technology, 9(1), 451-462.
  • Ashby, M. F., & Johnson, K. (2013). Materials and design: the art and science of material selection in product design. Butterworth-Heinemann.
  • Paritala, P. K., Manchikatla, S., & Yarlagadda, P. K. (2017). Digital manufacturing-applications past, current, and future trends. Procedia engineering, 174, 982-991.
  • Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2015). Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer-Aided Design, 59, 1-14.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Makine Mühendisliği
Bölüm Araştırma, Geliştirme ve Uygulama Makaleleri
Yazarlar

Ahmet Raşit Petekci 0000-0003-4355-6845

Yayımlanma Tarihi 25 Ekim 2021
Gönderilme Tarihi 20 Kasım 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 19 Sayı: 2

Kaynak Göster

Vancouver Petekci AR. Teknoloji Tasarımcılarının Paradoksu: Özgünlük mü? Veriye Dayalı Tasarım mı?. MATİM. 2021;19(2):82-7.
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