KALİTE ALGISININ TEKNOLOJİ RAHATSIZLIĞINA VE HİZMET TATMİNSİZLİĞİNE ETKİSİ: SESLİ ASİSTANLAR ÜZERİNDE BİR UYGULAMA

Abstract

Dijitalleşmenin etkisiyle yapay zeka destekli sesli asistanların artan popülaritesi ile teknoloji rahatsızlığı ve tatmin olmayan tüketicilerin bakış açıları dikkat çekmektedir. Bu çalışmanın amacı sesli asistanların bilgi, sistem, hizmet ve yerelleştirme kalitesinin teknoloji rahatsızlığına ve müşteri tatminsizliğine etkisini incelemektir. Çalışmada nicel yöntem kullanılmıştır. Çalışmada 278 katılımcıdan yüz yüze anket yöntemiyle veri toplanmıştır. Elde edilen demografik veriler SPSS 23 paket programıyla, hipotezler ise Smart-PLS paket programıyla test edilmiştir. Çalışma sonucunda bilgi, hizmet, yerelleştirme kalitesinin teknoloji rahatsızlığı üzerinde etkisi olduğu, teknoloji rahatsızlığının da hizmet tatminsizliğine etkilediği görülmüştür. Sistem kalitesinin ise teknoloji rahatsızlığı üzerinde etkisi olmadığı sonucuna ulaşılmıştır. Bu araştırma dijitalleşen çağda özellikle teknoloji rahatsızlığı nedeniyle tatmin olmayan tüketicilerin bakış açılarını anlayabilme amacıyla literatüre ve uygulayıcılara katkı sağlayabilecektir. Bu çalışmanın bulgularının, teknoloji rahatsızlığının tüketici hizmet memnuniyetsizliği üzerindeki öncüllerini ve etkilerini belirlemede uygulayıcılar ve literatür açısından yararlı olduğu düşünülmektedir.

Keywords

• Sesli Asistan
• Kalite
• Teknoloji Rahatsızlığı
• Hizmet Tatminsizliği

THE EFFECT OF QUALITY PERCEPTION ON TECHNOLOGY IRRITATION AND SERVICE DISSATISFACTION: AN APPLICATION ON VOICE ASSISTANTS

Abstract

The increasing popularity of voice assistants supported by artificial intelligence due to digitalization draws attention to technology irritation and the perspectives of dissatisfied consumers. This study examines the effect of voice assistants' information, system, service, and localization quality on technology irritation and customer dissatisfaction. A quantitative method was used in the study. The study collected data from 278 participants via the face-to-face survey method. The demographic data obtained were tested with the SPSS 23 package program, and the hypotheses were tested with the Smart-PLS package program. As a result of the study, it was seen that information, service, and localization quality affected technology irritation, and technology irritation also affected service dissatisfaction. It was concluded that system quality did not affect technology irritation. This research can contribute to the literature and practitioners to understand the perspectives of dissatisfied consumers, especially due to technology irritation in the digital age. The findings of this study are considered useful for practitioners and the literature in identifying the antecedents and impacts of technology discomfort on consumer service dissatisfaction.

Keywords

• Voice Assistants
• Quality
• Technology Irritation
• Service Dissatisfaction

References

1. Al-Natour, S., Benbasat, I., & Cenfetelli, R. (2011). The adoption of online shopping assistants: Perceived similarity as an antecedent to evaluative beliefs. Journal of the Association for Information Systems, 12(5), 2. 10.17705/1jais.00267.
2. Ayyildiz, T., Ayyildiz, A. Y., & Koc, E. (2024). Illusion of control in service failure situations: customer satisfaction/dissatisfaction, complaints, and behavioural intentions. Current Psychology, 43(1), 515-530.https://doi.org/10.1007/s12144-023-04292-y
3. Badghish, S., Shaik, A. S., Sahore, N., Srivastava, S., & Masood, A. (2024). Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective. Technological Forecasting and Social Change, 198, 122972. https://doi.org/10.1016/j.techfore.2023.122972
4. Balakrishnan, J., & Dwivedi, Y. K. (2024). Conversational commerce: entering the next stage of AI-powered digital assistants. Annals of Operations Research, 333(2), 653- 687. https://doi.org/10.1007/s10479-021-04049-5
5. Berdasco, A., López, G., Diaz, I., Quesada, L., & Guerrero, L. A. (2019). User experience comparison of intelligent personal assistants: Alexa, Google Assistant, Siri and Cortana. In Proceedings (31), 1, 51, MDPI. https://doi.org/10.3390/proceedings2019031051
6. Buhalis, D., & Moldavska, I. (2022). Voice assistants in hospitality: using artificial intelligence for customer service. Journal of Hospitality and Tourism Technology, 13(3), 386- 403. https://doi.org/10.1108/JHTT-03-2021-0104
7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
8. Dambanemuya, H. K., & Diakopoulos, N. (2021). Auditing the information quality of news-related queries on the Alexa voice assistan. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-21. https://doi.org/10.1145/3449157

9. Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17(1), 4-11. https://doi.org/10.1057/palgrave.ejis.3000726
10. Feng, H., Fawaz, K., & Shin, K. G. (2017). Continuous authentication for voice assistants. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking (343-355). https://doi.org/10.1145/3117811.311782
11. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. Germanos, G., Kavallieros, D., Kolokotronis, N., & Georgiou, N. (2020). Privacy issues in voice assistant ecosystems. In 2020 IEEE World Congress on Services (SERVICES), (205-212). IEEE. 10.1109/SERVICES48979.2020.00050
12. Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. https://doi.org/10.1504/IJMDA.2017.087624
13. Hair, J., Sarstedt, M., & Ringle, C. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10-2018-0665
14. Hair, J.F., Ringle, C.M. & Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152, https://doi.org/10.2753/MTP1069-6679190202
15. Hansen, K. (2020). Accent beliefs scale (ABS): Scale development and validation. Journal of Language and Social Psychology, 39(1), 148-171. https://doi.org/10.1177/0261927X1988390
16. Hasan, B. (2016). Perceived irritation in online shopping: The impact of website design characteristic, Computers in Human Behavior, 54, 224-230. https://doi.org/10.1016/j.chb.2015.07.056
17. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
18. Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International marketing review, 33(3), 405-431. https://doi.org/10.1108/IMR-09-2014-0304
19. https://review42.com/resources/voice-search-stats/ Erişim Tarihi: 28.09.2024
20. https://www.ranktracker.com/tr/blog/voice-search-revolution-statistics-for-2024-revealed/Erişim Tarihi: 28.09.2024.
21. https://www.statista.com/statistics/973815/worldwide-digital-voice-assistant-in-use/ Erişim Tarihi: 28.09.2024
22. Hu, P., Gong, Y., Lu, Y., & Ding, A. W. (2023). Speaking vs. listening? Balance conversation attributes of voice assistants for better voice marketing. International Journal of Research in Marketing, 40(1), 109-127. https://doi.org/10.1016/j.ijresmar.2022.04.006
23. Jain, S., Basu, S., Ray, A., & Das, R. (2023). Impact of irritation and negative emotions on the performance of voice assistants: Netting dissatisfied customers’ perspectives, International Journal of Information Management, 72, 102662. https://doi.org/10.1016/j.ijinfomgt.2023.102662
24. Karakahraman, Y., & Özsaatcı, F. G. B. (2021). Algılanan hizmet kalitesinin müşteri tatmini, müşteri tatmini ve müşteri sadakatine etkileri: Katılım bankası örneği. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(2), 432-452. https://doi.org/10.25287/ohuiibf.708800
25. Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
26. Knote, R., Janson, A., Söllner, M., & Leimeister, J. M. (2020, March 9). Value co-creation in smart services: A functional affordances perspective on smart personal assistants. SSRN. https://doi.org/10.2139/ssrn.392370
27. Kumar, A., Bala, P. K., Chakraborty, S., & Behera, R. K. (2024). Exploring antecedents impacting user satisfaction with voice assistant app: A text mining-based analysis on Alexa services, Journal of Retailing and Consumer Services, 76, 103586. https://doi.org/10.1016/j.jretconser.2023.103586
28. Lin, H. F. (2007). The impact of website quality dimensions on customer satisfaction in the B2C e-commerce context Total Quality Management and Business Excellence, 18(4), 363- 378. https://doi.org/10.1080/14783360701231302
29. Luo, M. M., & Remus, W. (2014). Uses and gratifications and acceptance of Web-based information services: An integrated model. Computers in Human Behavior, 38, 281- 295. https://doi.org/10.1016/j.chb.2014.05.042
30. Mari, A., Mandelli, A., & Algesheimer, R. (2024). Empathic voice assistants: Enhancing consumer responses in voice commerce. Journal of Business Research, 175, 114566. https://doi.org/10.1016/j.jbusres.2024.114566
31. McLean, G., & Osei-Frimpong, K. (2019). “Hey Alexa… examine the variables influencing the use of artificial intelligent in-home voice assistants”, Computers in Human Behavior, 99, 28-37. https://doi.org/10.1016/j.chb.2019.05.009
32. Melby, L., & Nair, R. D. (2024). We have no services for you… so you have to make the best out of it’: A qualitative study of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients’ dissatisfaction with healthcare services. Health Expectations, 27(1), e13900. https://doi.org/10.1111/hex.13900
33. Meyer-Waarden, L., & Cloarec, J. (2022). Baby, you can drive my car: Psychological antecedents that drive consumers’ adoption of AI-powered autonomous vehicles. Technovation, 109, 102348. https://doi.org/10.1016/j.technovation.2021.102348
34. Moriuchi, E. (2019). Okay, Google!: An empirical study on voice assistants on consumer engagement and loyalty. Psychology ve Marketing, 36(5), 489-501. https://doi.org/10.1002/mar.21192
35. Mugisa, G., Nalwebuga, J., & Masaba, C. E. (2024). Uncovering patient dissatisfaction with Healthcare Services at a tertiary hospital in Uganda: Patient perspective in a descriptive cross-sectional study.International Journal of Science and Research Archive, 13(01), 528–540. https://doi.org/10.30574/ijsra.2024.13.1.1625
36. Nasirian, F., Ahmadian, M., & Lee, O. K. D. (2017). AI-based voice assistant systems: Evaluating from the interaction and trust perspectives. Twenty-third Americas Conference on Information Systems, Boston, 1-11. https://aisel.aisnet.org/amcis2017/AdoptionIT/Presentations/27.
37. Oosterholt, R. (2021). My virtual assistant sounds like my neighbor: how human-like is human-like enough?. Master’s thesis, School of Humanities and Digital Sciences Tilburg University, Tilburg.
38. Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of service research, 7(3), 213- 233. https://doi.org/10.1177/1094670504271156
39. Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207. https://doi.org/10.1016/j.jretconser.2020.102207
40. Pitardi, V., & Marriott, H. R. (2021). Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence. Psychology & Marketing, 38(4), 626-642. https://doi.org/10.1002/mar.21457
41. Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: a measure of information systems effectiveness. MIS quarterly, 173-187. https://www.jstor.org/stable/249687
42. Purington, A., Taft, J. G., Sannon, S., Bazarova, N. N., & Taylor, S. H. (2017, May). Alexa is my new BFF" social roles, user satisfaction, and personification of the Amazon Echo. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems (2853-2859). https://doi.org/10.1145/3027063.305324
43. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research, The international journal of human resource management, 31(12), 1617-1643. https://doi.org/10.1080/09585192.2017.1416655
44. Sattarapu, P. K., Wadera, D., Nguyen, N. P., Kaur, J., Kaur, S., & Mogaji, E. (2024). Tomeito or Tomahto: Exploring consumer's accent and their engagement with artificially intelligent interactive voice assistants. Journal of Consumer Behaviour, 23(2), 278-298. https://doi.org/10.1002/cb.2195
45. Seaborn, K., Miyake, N. P., Pennefather, P., & Otake-Matsuura, M. (2021). Voice in human–agent interaction: A survey, ACM Computing Surveys (CSUR), 54(4), 1-43. https://doi.org/10.1145/3386867
46. Sharma, A., Dwivedi, R., Mariani, M. M., & Islam, T. (2022). Investigating the effect of advertising irritation on digital advertising effectiveness: A moderated mediation model, Technological Forecasting and Social Change, 180, 121731. https://doi.org/10.1016/j.techfore.2022.121731
47. Thota, S. C. (2012). A resolution model of consumer irritation consequences and company strategies: social networking and strategy implications. Journal of Applied Business and Economics, 13(4), 114-124.