Rozpoznawanie i pomiar emocji w badaniach doświadczeń klienta

• Authors: Małgorzata Budzanowska-Drzewiecka , Adrian Lubowiecki-Vikuk

Title variants

EN

Recognition and Measurement of Emotions in Customer Experience Research

Abstracts

EN

The study of customer experience requires the development of methodologies which measure such experience and account for its complexity. One important component of customer experience is emotion, the recognition and measurement of which is still a challenge for researchers. The purpose of this article is to discuss methods and techniques used to recognise and measure emotions in customer experience research. Particular attention is paid to the use of techniques derived from consumer neuroscience, including the dilemmas associated with reaching for automatic analysis of facial expressions. The literature review is indicative of the ongoing discussion on the benefits and limitations of using the automatic analysis of facial expressions technique in measuring customer experience. Despite its limitations, such a technique can be an attractive complement to methods and techniques used to capture the emotional components of customer experience at different stages (before, during, and after purchase).

PL

Badanie doświadczeń klienta wymaga rozwijania metodyki ich pomiaru pozwalającej na uwzględnienie ich złożoności. Jedną z ważnych składowych doświadczeń są emocje, których rozpoznawanie i pomiar stanowi wciąż wyzwanie dla badaczy. Celem artykułu jest dyskusja na temat metod i technik wykorzystywanych do rozpoznawania i pomiaru emocji w badaniach doświadczeń klienta. Szczególną uwagę poświęcono wykorzystaniu technik wywodzących się z neuronauki konsumenckiej, w tym dylematom związanym z sięganiem po automatyczną analizę ekspresji mimicznej. Studia literaturowe pozwoliły na dyskusję dotyczącą korzyści i ograniczeń stosowania automatycznej analizy ekspresji mimicznej w pomiarze doświadczeń klientów. Mimo ograniczeń, mogą one być traktowane jako atrakcyjne uzupełnienie metod i technik pozwalających na uchwycenie emocjonalnych komponentów doświadczenia klienta na różnych etapach (przed zakupem, w jego czasie i po nim).

Keywords

PL

doświadczenie klienta; rozpoznawanie emocji; automatyczna analiza ekspresji twarzy; FaceReader; pomiar emocji

EN

customer experience; emotion recognition; automatic facial expression analysis; FaceReader; measuring emotions

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
• Year: 2023
• Volume: 67
• Issue: 5
• Pages: 67-77

Dates

published

2023

Contributors

author

Małgorzata Budzanowska-Drzewiecka

• Uniwersytet Jagielloński w Krakowie

• https://orcid.org/0000000166514037

author

Adrian Lubowiecki-Vikuk

• Szkoła Główna Handlowa w Warszawie

References

• Abbott, L. (1955). Quality and Competition: An Essay in Economic Theory. Columbia University Press, New York. https://doi.org/10.7312/abbo92492
• Alsharif, A. H., Salleh, N. O. R. Z., Baharun, R. i Yusoff, M. E. (2021). Consumer Behaviour through Neuromarketing Approach. Journal of Contemporary Issues in Business and Government, 27(03). https://doi.org/10.47750/cibg.2021.27.03.048
• Babin, B. J., Darden, W. R., University, L. S., Shreveport, L. i Babin, L. A. (1998). Negative Emotions in Marketing Research: Affect or Artifact? Journal of Business Research, 42(3), 271-285. https://doi.org/10.1016/S0148-2963(97)00124-0
• Bagozzi, R. P., Brady, M. K. i Huang, M. H. (2022). AI Service and Emotion. Journal of Service Research, 25(4), 499-504. https://doi.org/10.1177/10946705221118579
• Bagozzi, R. P., Gopinath, M. i Prashanth, U. N. (1999). The Role of Emotions in Marketing. Journal of the Academy of Marketing Science, 27(2), 184-206.
• Becker, L. i Jaakkola, E. (2020). Customer Experience: Fundamental Premises and Implications for Research. Journal of the Academy of Marketing Science, 48(4), 630-648. https://doi.org/10.1007/s11747-019-00718-x
• Bell, L., Vogt, J., Willemse, C., Routledge, T., Butler, L. T. i Sakaki, M. (2018). Beyond Self-Report: A Review of Physiological and Neuroscientific Methods to Investigate Consumer Behavior. Frontiers in Psychology, 9, 1-16. https://doi.org/10.3389/fpsyg.2018.01655
• Budzanowska-Drzewiecka, M. (2018a). Wkład neuromarketingu do wiedzy na temat reklamy – próba systematyzacji wątków badawczych. Studia Oeconomica Posnaniensia, 6(5), 19-32. https://doi.org/10.18559/soep.2018.5.2
• Budzanowska-Drzewiecka, M. (2018b). Zastosowanie neuroobrazowania w badaniach konsumentów – możliwości i ograniczenia. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 525, 197-210.
• Bustamante, J. C. (2017). Measuring Customer Experience in Physical Retail Environments. Journal of Service Management, 28(5), 884-913. https://doi.org/10.1108/JOSM-06-2016-0142
• Cambria, E., Das, D., Bandyopadhyay, S. i Feraco, A. (2017). Affective Computing and Sentiment Analysis. W: E. Cambria, D. Das, S. Bandyopadhyay, A. Feraco (red.), A Practical Guide to Sentiment Analysis (p. 196). Springer.
• Caridakis, G., Castellano, G., Kessous, L., Raouzaiou, A., Malatesta, L., Asteriadis, S. i Karpouzis, K. (2007). Multimodal Emotion Recognition from expressive Faces, Body Gestures and Speech. IFIP International Federation for Information Processing, 247, 375-388. https://doi.org/10.1007/978-0-387-74161-1_41
• Cherubino, P., Martinez-Levy, A. C., Caratù, M., Cartocci, G., Di Flumeri, G., Modica, E., Rossi, D., Mancini, M. i Trettel, A. (2019). Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends. Computational Intelligence and Neuroscience, 2019, 1-41. https://doi.org/10.1155/2019/1976847
• Clatworthy, S. (2022). Doświadczenia klientów. Od idei firmy do zachwytu konsumenta. ICAN Institute.
• Danner, L. i Duerrschmid, K. (2018). Automatic facial expressions analysis in consumer science. W: Methods in Consumer Research (p. 231-252). Woodhead Publishing. https://doi.org/10.1016/B978-0-08-101743-2.00010-8
• Danner, L., Sidorkina, L., Joechl, M., Duerrschmid, K. (2014). Make a Face! Implicit and Explicit Measurement of Facial Expressions Elicited by Orange Juices Using Face Reading Technology. Food Quality and Preference, 32, 167-172. https://doi.org/10.1016/j.foodqual.2013.01.004
• Duerden, M. D., Ward, P. J. i Freeman, P. A. (2015). Conceptualizing Structured Experiences: Seeking Interdisciplinary Integration. Journal of Leisure Research, 47(5), 601-620. https://doi.org/10.18666/jlr-2015-v47-i5-6096
• Ekman, P. i Rosenberg, E. L. (2005). What the Face RevealsBasic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195179644.001.0001
• Farnadi, G., Sitaraman, G., Rohani, M., Kosinski, M., Stillwell, D., Moens, M. F., Davalos, S. i De Cock, M. (2014). How Are You Doing? Emotions and Personality in Facebook. CEUR Workshop Proceedings, 1181, 45-56.
• Fasel, B. i Luettin, J. (2003). Automatic facial Expression Analysis: A Survey. Pattern Recognition, 36(1), 259-275. https://doi.org/10.1016/S0031-3203(02)00052-3
• Feldman-Barrett, L. (2022). Facial Expressions Do Not Reveal Emotions. Scientific American, https://www.scientificamerican.com/article/darwin-was-wrong-your-facial-expressions-do-not-reveal-your-emotions/
• Ferreira, L. B. C., Morais Fabrício, D., Chagas, Hortes, M. N. (2021). Are Facial Emotion Recognition Tasks Adequate for assessing social cognition in older people? A review of the literature. Archives of Gerontology and Geriatrics, 92, 104277. https://doi.org/10.1016/j.archger.2020.104277
• Gaur, S. S., Herjanto, H. i Makkar, M. (2014). Review of Emotions Research in Marketing, 2002-2013. Journal of Retailing and Consumer Services, 21(6), 917-923. https://doi.org/10.1016/j.jretconser.2014.08.009
• Godovykh, M. i Tasci, A. D. A. (2020). Customer Experience in Tourism: A Review of Definitions, Components, and Measurements. Tourism Management Perspectives, 35, 1-10. https://doi.org/10.1016/j.tmp.2020.100694
• Hadinejad, A., Moyle, B. D., Kralj, A. i Scott, N. (2019a). Physiological and Self-Report Methods to the Measurement of Emotion in Tourism. Tourism Recreation Research, 44(4), 466-478. https://doi.org/10.1080/02508281.2019.1604937
• Hadinejad, A., Moyle, B. D., Scott, N., Kralj, A. (2019b). Emotional responses to tourism advertisements: The application of FaceReader™. Tourism Recreation Research, 44(1), 131-135. https://doi.org/10.1080/02508281.2018.1505228
• Hadinejad, A., Scott, N., Kralj, A. i Moyle, B. (2021). Multi-methods in the Measurement of Emotion in Tourism Marketing. W: R. Nunkoo, V. Teeroovengadum, C. M. Ringle (red.), Handbook of Research Methods for Marketing Management (p. 283--291). Edward Elgar Publishing. https://doi.org/10.4337/9781788976954.00021
• Hamilton, L. i Allard, E. (2022). Investigating Mixed Emotion Elicitation Across the Life Span Via Intensity and Networks. Emotions, https://doi.org/10.1037/emo0001177
• Hirschman, E. C. i Holbrook, M. B. (1982). Hedonic Consumption: Emerging Concepts, Methods and Propositions. Journal of Marketing, 46(3), 92. https://doi.org/10.2307/1251707
• Huang, M. H. (2001). The Theory of Emotions in Marketing. Journal of Business and Psychology, 16(2), 239-247.
• Kelter, D., Oatley, K. i Jenkins, J. M. (2021). Zrozumieć emocje. Wydawnictwo Naukowe PWN.
• Khare, Khare, S. K., Blanes-Vidal, V., Nadimi, E. S. i Acharya, U. R. (2024). Emotion Recognition and Artificial Intelligence: A Systematic Review (2014-2023) and Research Recommendations. Information Fusion, 102, 102019. https://doi.org/10.1016/j.inffus.2023.102019
• Klaus, P. (2015). Measuring Customer Experience. Palgrave Macmillan UK. https://doi.org/10.1057/9781137375469
• Kosinski, M. (2021). Facial Recognition Technology Can Expose Political Orientation from Naturalistic Facial Images. Scientific Reports, 11(1), 1-7. https://doi.org/10.1038/s41598-020-79310-1
• Kosinski, M., Stillwell, D. i Graepel, T. (2013). Private Traits and Attributes Are Predictable from Digital Records of Human Behavior. Proceedings of the National Academy of Sciences of the United States of America, 110(15), 5802-5805. https://doi.org/10.1073/pnas.1218772110
• Kranzbühler, A. M., Kleijnen, M. H. P., Morgan, R. E. i Teerling, M. (2018). The Multilevel Nature of Customer Experience Research: An Integrative Review and Research Agenda. International Journal of Management Reviews, 20(2), 433-456. https://doi.org/10.1111/ijmr.12140
• Kumar, P., Hollebeek, L. D., Kar, A. K. i Kukk, J. (2022). Charting the intellectual structure of customer experience research. Marketing Intelligence and Planning, https://doi.org/10.1108/MIP-05-2022-0185
• Lemon, K. N. i Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
• Lewinski, P., Den Uyl, T. M. i Butler, C. (2014). Automated Facial Coding: Validation of Basic Emotions and FACS AUs in FaceReader. Journal of Neuroscience, Psychology, and Economics, 7(4), 227-236. https://doi.org/10.1037/npe0000028
• Manthiou, A., Hickman, E. i Klaus, P. (2020). Beyond good and bad: Challenging the suggested role of emotions in customer experience (CX) research. Journal of Retailing and Consumer Services, 57, 102218. https://doi.org/10.1016/j.jretconser.2020.102218
• Martinez, L., Falvello, V. B., Aviezer, H. i Todorov, A. (2016). Contributions of facial expressions and body language to the rapid perception of Dynamic Emotions. Cognition and Emotion, 30(5), 939-952. https://doi.org/10.1080/02699931.2015.1035229
• Maximiano-Barreto, M. A., Bomfim, A. J. de L., Borges, M. M., de Moura, A. B., Luchesi, B. M. i Chagas, M. H. N. (2022). Recognition of Facial Expressions of Emotion and Depressive Symptoms Among Caregivers with Different Levels of Empathy Recognition of Facial Expressions of Emotion and Depressive Symptoms Among. Clinical Gerontologist, 1-8. https://doi.org/10.1080/07317115.2021.1937426
• Pine, B. J. i Gilmore, J. H. (1998). Welcome to the Experience Economy. Harvard Business Review Press, 76(4), 97-105.
• Richins, M. L. (1997). Measuring emotions in the consumption experience. Journal of Consumer Research, 24(2), 127-146. https://doi.org/10.1086/209499
• Schmitt, B. H. (1999). Experiential marketing. Journal of Marketing Management, 15(1-3), 53-67.
• Skiendziel, T. i Ro, A. G. (2019). Assessing the Convergent Validity between the Automated Emotion Recognition Software Noldus FaceReader 7 and Facial Action Coding System Scoring. PLoS ONE, 14(10), e0223905. https://doi.org/10.1371/journal.pone.0223905
• Soodan, V. i Pandey, A. C. (2016). Influence of emotions on consumer buying behavior. Journal of Entrepreneurship, Business and Economics, 4(2), 163-181.
• Turek, T. (2017). Possibilities of Using Sentiment Analysis in Prosumption Processes. Ekonomiczne Problemy Usług, 126(1), 285-294. https://doi.org/10.18276/epu.2017.126/2-29
• Verhulst, N., De Keyser, A., Gustafsson, A., Shams, P. i Van Vaerenbergh, Y. (2019). Neuroscience in Service Research: An Overview and Discussion of Its Possibilities. Journal of Service Management, 30(5), 621-649. https://doi.org/10.1108/JOSM-05-2019-0135
• Verhulst, N., Vermeir, I., Slabbinck, H., Larivière, B., Mauri, M. i Russo, V. (2020). A Neurophysiological Exploration of the Dynamic Nature of Emotions During the Customer Experience. Journal of Retailing and Consumer Services, 57, 102217. https://doi.org/10.1016/j.jretconser.2020.102217
• Wedel, M., Bigné, E. i Zhang, J. (2020). Virtual and Augmented Reality: Advancing Research in Consumer Marketing. International Journal of Research in Marketing, 37(3), 443-465. https://doi.org/10.1016/j.ijresmar.2020.04.004
• Yaden, D. B., Eichstaedt, J. C., Kern, M. L., Smith, L. K., Buffone, A., Stillwell, D. J., Kosinski, M., Ungar, L. H., Seligman, M. E. P. i Schwartz, H. A. (2018). The Language of Religious Affiliation: Social, Emotional, and cognitive Differences. Social Psychological and Personality Science, 9(4), 444-452. https://doi.org/10.1177/1948550617711228
• Yu, C. Y. i Ko, C. H. (2017). Applying FaceReader to Recognize Consumer Emotions in Graphic Styles. Procedia CIRP, 60, 104-109. https://doi.org/10.1016/j.procir.2017.01.014

Identifiers

DOI

10.15611/pn.2023.5.06

Biblioteka Nauki

27839578