AN INVESTIGATION OF CONSUMERS' BEHAVIORAL INTENTIONS TOWARDS FREELANCING PORTAL-BASED GIG ECONOMY SERVICE APPLICATIONS

Yıl 2024, Sayı: 69, 217 - 225

Ayşe Ersoy Yıldırım

https://doi.org/10.18070/erciyesiibd.1530894

Öz

The gig economy is a concept that first emerged in 2009, showing rapid and scalable growth in terms of both revenue and source of revenue. Developments in the gig economy are also reflected in consumer behavior, with an increasing demand for gig economy for temporary and short-term jobs offered by service providers to their customers through service applications. In addition to all the standard factors that lead to consumers' actual behavior, the factors that are important in the preference of gig apps are also an issue that needs to be examined. In this context, this study attempts to examine the factors affecting consumers' behavioral intentions. This study focuses on understanding the growing interest in the gig economy as a fast-growing business model in services, consumer attitudes towards service apps on digital platforms, and consumers' behavioral intentions. The study focuses on the gig economy, consumers' behavioral intentions and consumer trends. In the study with 419 participants, the factors that lead consumers to Gig service platforms are examined with the help of the technology acceptance model and consumer perspectives are examined with the help of the behavioral intention scale. In the study, it was found that hedonic motivation, facilitating conditions, habit and effort expectancy dimensions have a positive increasing effect on the level of behavioral intention. It was found that performance expectancy, social impact and price value levels did not significantly affect the level of behavioral intention. It was found that the most important factor affecting behavioral intention was hedonic motivation, followed by facilitating conditions, habit and effort expectancy dimensions.

Anahtar Kelimeler

Gig-based Service Platforms, Service Applications, Technology Acceptance Model, Behavioral Intention, Consumer Behavior.

SERBEST ÇALIŞMA PORTALI TABANLI GİG EKONOMİSİ HİZMET APLİKASYONLARINA YÖNELİK TÜKETİCİLERİN DAVRANIŞSAL NİYETLERİNİN İNCELENMESİ

Öz

Gig ekonomisi, ilk olarak 2009 yılında ortaya çıkan hem gelir hem de gelirin kaynağı açısından hızlı ve ölçeklenebilir bir büyüme gösteren bir kavramdır. Hizmet aplikasyonları üzerinden hizmet sağlayıcıların, müşterilerine sunduğu geçici ve kısa süreli işler için gig ekonomisine olan talebin artışıyla gig hizmet ekonomisindeki gelişmeler tüketici davranışlarına da yansımaktadır. Tüketicilerin fiili davranışlarına yol açan tüm standart faktörler yanında gig aplikasyonlarının tercih edilmesinde önem arz eden faktörler de incelenmesi gereken bir konudur. Bu bağlamda tüketicilerin davranışsal niyetlerine etki eden unsurlar bu çalışma ile incelenmeye çalışılmıştır. Bu çalışma, hizmetlerde hızla büyüyen bir iş modeli olarak gig ekonomisine olan artan ilginin, dijital platformlarda hizmet aplikasyonlarına yönelik tüketici tutumlarının ve tüketicilerin davranışsal niyetlerinin anlaşılmasına odaklanmaktadır. Çalışmada gig hizmet ekonomisi ile tüketicilerin davranışsal niyetleri ve tüketici eğilimlerine yer verilmiştir. 419 katılımcının yer aldığı çalışmada, tüketicileri Gig hizmet platformlarına yönlendiren faktörler teknoloji kabul modeli yardımıyla, tüketici bakış açıları ise davranışsal niyet ölçeği yardımıyla incelenmiştir. Çalışmada hedonik motivasyon, kolaylaştırıcı koşullar, alışkanlık ve çaba beklentisi boyutlarının davranış niyet düzeyini pozitif yönde artırıcı etkiye sahip olduğu yönünde bulgular elde edilmiştir. Performans beklentisi, sosyal etki ve fiyat değeri düzeylerinin davranışsal niyet düzeyini anlamlı şekilde etkilemediği bulgusuna ulaşılmıştır. Davranışsal niyete etki eden önemli faktörün hedonik motivasyon olduğu bunu sırası ile kolaylaştırıcı koşullar, alışkanlık ve çaba beklentisi boyutlarının takip ettiği tespit edilmiştir.

Anahtar Kelimeler

Gig Tabanlı Hizmet Platformları, Hizmet Aplikasyonları, Teknoloji Kabul Modeli, Davranışsal Niyet, Tüketici Davranışları

Etik Beyan

Bu çalışmanın verileri toplanmadan önce ilgili üniversiteden Etik Kurul izni alınmıştır. Bu makale etik hususlar dikkate alınarak yazılmıştır.

Kaynakça

• Alaiad, A., ve Zhou, L. (2014). The determinants of home healthcare robots adoption: An empirical investigation. International Journal of Medical Informatics, 83(11), 825-840.
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior And Human Decision Processes, 50(2), 179-211.
• Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology and Health, 26 (9), 1113-1127.
• Arman, A.A. ve Hartati, S. (2015). Development of user acceptance model for Electronic Medical Record system. International Conference on Information Technology Systems and Innovation, Bandung: Indonesia.
• Baabdullah, A. M., Alalwanb, A., A., Ranac, N., P., Kizginc, H. ve Patilc, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44, 38–52.
• Bennani, A. ve Oumlil, R. (2013). Factors fostering IT acceptance by nurses in Morocco. International Conference on Research Challenges in Information Science, Paris: France.
• Brown, S.A. ve Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29, 399.
• Brown, S., Venkatesh, V. ve Bala, H. (2006). Household technology use: Integrating household life cycle and the model of adoption of technology in households. The Information Society, 22 (4), 205-218.
• Collier, R. B., Dubal, V. B. ve Carter, C. (2017). Labour platforms and gig work: The failure to regulate. Institute for Research on Labour and Employment: Working Paper, 106–117.
• Chang, I., Hwang, H., Hung, W. ve Li, Y. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296-303.
• Chauhan, S. ve Jaiswal, M. (2016). Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. The International Journal of Management Education, 14 (3), 248-262.
• Chopdar, P., Korfiatis, N., Sivakumar, V. J. ve Lytras, M. D. (2018). Mobile shopping apps adoption ve perceived risks: A cross-country perspective utilizing the unified theory of acceptance and use of technology. Computers in Human Behavior, 1-62.
• Davis, F.D., Bagozzi, R.P. ve Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22 (14), 1111-1132.
• De Stefano, V. (2015). The rise of the “just-in-time workforce”: On-demand work, crowdwork, and labor protection in the “gig-economy”. Comparative Labor Law and Policy Journal, 37, 471-503. doi: 10.2139/SSRN.2682602.
• Devolder, P., Pynoo, B., Sijnave, B., Voet, T. ve Duyck, P. (2012). Framework for user acceptance: Clustering for fine-grained results. Information & Management, 49(5), 233-239.
• Dey, C., Ture, R. S., ve Ravi, S. (2022). Emerging world of gig economy: Promises and challenges in the Indian context. NHRD Network Journal, 15 (1), 71-82.
• Duggan J, Sherman U, Carbery R, McDonnell A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Managament Journal, 30, 114–132. https://doi.org/10.1111/1748-8583.12258.
• Fornell, C., ve Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
• Gandini, A. (2018). Labour process theory and the gig economy. Human Relations, 1-18.
• Gupta, B., Dasgupta, S. & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. The Journal of Strategic Information Systems, 17 (2), 140-154.
• Gramano, E. (2019). Digitalisation and work: Challenges from the platform economy. Contemporary Social Science, 15 (4), 476-488. doi: 10.1080/21582041.2019.1572919.
• Healy, J., Nicholson, D. ve Pekarek, A. (2017). Should we take the gig economy seriously?. Labour and Industry: A Journal Of The Social and Economic Relations Of Work, 27 (3), 232-248. doi: 10.1080/10301763.2017.1377048.
• Hu, L ve P.M. Bentler (2009). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55.
• Huws, U., Spencer, N.H., ve Joyce, S. (2016). The size and characteristics of the on demand economy in the UK and Europe. http://researchprofiles.herts.ac.uk. Erişim tarihi 10.08.2023.
• Kalleberg, A. L. ve Dunn, M. (2016). Good jobs, bad jobs in the gig economy. Perspectives on Work 20(2), 10–14.
• Kim, S.S., Malhotra, N.K. ve Narasimhan, S. (2005). Research note-two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16 (4), 418-432.
• Kuhn, K.M. (2016), “The rise of the ‘gig economy’ and implications for understanding work and workers”, Industrial and Organizational Psychology, Vol. 9 No. 1, pp. 157-162.
• Kuhn, K. M. ve Galloway, T. L. (2019). Expanding perspectives on gig work and gig workers, Journal of Managerial Psychology, 34(4), 186-191.
• Lee, J.M., Lee, B., Rha, J.Y. (2019). Determinants of mobile payment usage and the moderating effeect of gender: Extending the UTAUT model with privacy risk. International Journal of Electronic Commerce Studies, 10, 43–64.
• Limayem, Hirt, ve Cheung (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31 (4), 705.
• Meijerink, J. ve Keegan, A. (2019). Conceptualizing human resource management in the gig economy, Journal of Managerial Psychology, 34(4), 214-232.
• Mukhopadhyay, B. and Mukhopadhyay, B.K. (2020). What is the Gig Economy?, Tripura Times, Post-Editorial, 12th April.
• Nikou, S.A ve Economides, A.A. (2017). Mobile-based assessment: investigating the factors that influence behavioral intention to use. Computer Education, 109, 56-73.
• Phichitchaisopa, N. ve Naenna, T. (2013). Factors affecting the adoption of healthcare information technology. EXCLI Journal, 12, 413-436.
• Taşçıoğlu, M., Eastman, J. K., Bock, D., Shepherd C. D. (2019). The impact of retailers’ sustainability and price on consumers’ responses in different cultural contexts. The International Review of Retail, Distribution and Consumer Research, 29(2):1-26.
• Taylor, M., Marsh G, Nicol, D., Broadbent, P. (2017). Good Work: The Taylor Review on Modern Day Working Practices. https://www.gov.uk/government/publications/good-work-the-taylor-review-of-modern-working-practices. Erişim tarihi 22.05.2023.
• Thomas, S. M. ve Baddipudi, V. (2022). Changing nature of work and employment in the gig economy: The role of culture building and leadership in sustaining commitment and job satisfaction. NHRD Network Journal, 15 (1), 100-113. doi: 10.1177/26314541211064735.
• Thunnissen, M. (2016). Talent management: For what, how and how well? An empirical exploration of talent management in practice. Employee Relations, 38, 57–72. doi: 10.1108/ER-08-2015-0159.
• Upwork Community (2016). Addressing accounts that don’t show work activity. https://community.upwork.com/t5/Announcements/Addressing-accounts-that-don-t-show-work activity/td-p/274042. Erişim tarihi 20.05.2023.
• Venkatesh, V. ve Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46 (2), 186-204.
• Venkatesh, V. ve Speier, C. (1999). Computer Technology Training in the Workplace: A Longitudinal Investigation of the Effect of Mood. Organizational Behavior and Human Decision Processes, 79 (1), 1-28.
• Venkatesh, V., Morris, M.G., Davis, G.B. ve Davis, F.D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27/3, 425-478.
• Venkatesh, V., Thong, J. Y. ve Xu, T. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1) 157–178.
• Zhou, T., Lu, Y. ve Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26 (4), 760-767.