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The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites

Received: 7 September 2023    Accepted: 9 January 2024    Published: 20 February 2024
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Abstract

In the rapidly evolving digital marketplace, customer service has become a critical factor influencing consumer behaviour. With the advent of Artificial Intelligence (AI), particularly chatbots, customer service companies are increasingly leveraging technology to enhance user experience. This study explores the relationship between customer emotions, detected during interactions with e-commerce chatbots, and their subsequent purchase intentions. Emotion detection within Human-Computer Interaction (HCI) is a vital area of research, as specific emotions, such as joy or frustration, can significantly impact marketing effectiveness and consumer decision-making. This research aims to understand how emotional responses to chatbot interactions can predict customer's intention to purchase, thereby offering insights for businesses to optimize their AI-driven customer service strategies. The study analyzes four diverse datasets – EmotionLines, CARER, GoEmotion, and EmotionPush – to identify emotion-labelled sentences indicative of purchase intention. Our findings reveal that Neutral and Joyful emotions are predominant in influencing customers' purchase intentions, highlighting the importance of understanding these emotional states in e-commerce settings. While Neutral emotion is most influential, Joy consistently plays a significant role in positive customer engagement. This research underscores the need for e-commerce businesses to focus on emotional intelligence in chatbots, enhancing customer experience and potentially driving sales. Future research directions include examining real chatbot-customer interactions to further understand the impact of AI-driven customer service on consumer emotions and behaviours.

Published in International Journal on Data Science and Technology (Volume 10, Issue 1)
DOI 10.11648/j.ijdst.20241001.11
Page(s) 1-10
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Artificial Intelligence, Chatbots, Customer Engagement, Customer Service, E-commerce, Purchase Intentions, User Emotions

References
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[3] R. Kaiser and K. Oertel, “Emotions in HCI – An Affective E-Learning System,” Computer (Long. Beach. Calif)., pp. 105–106, 2006.
[4] A. Seyeditabari, U. N. C. Charlotte, and U. N. C. Charlotte, “Emotion Detection in Text : a Review,” 2018.
[5] K. Jiang, M. Qin, and S. Li, “Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese consumers?,” J. Consum. Behav., vol. 21, no. 4, pp. 756–772, Jul. 2022, https://doi.org/10.1002/CB.2034
[6] S. Siripipatthanakul, W. Nurittamont, B. Phayaphrom, and S. Nuanchaona, “Factors affecting consumer’s purchase intention of chatbot commerce in Thailand,” Int. J. Business, Mark. Commun., vol. 1, no. 3, pp. 1–13, 2021.
[7] L. Grigorios, S. Magrizos, I. Kostopoulos, D. Drossos, and D. Santos, “Overt and covert customer data collection in online personalized advertising: The role of user emotions,” J. Bus. Res., vol. 141, no. March, pp. 308–320, 2022, https://doi.org/10.1016/j.jbusres.2021.12.025
[8] A. Xu, Z. Liu, Y. Guo, V. Sinha, and R. Akkiraju, “A new chatbot for customer service on social media,” in Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, May 2017, pp. 3506–3510. https://doi.org/10.1145/3025453.3025496
[9] J. Zamora, “Rise of the Chatbots : Finding a Place for Artificial Intelligence in India and US,” roceedings 22nd Int. Conf. Intell. User Interfaces Companion, pp. 109–112, 2017, https://doi.org/10.1145/3030024.3040201
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[12] H. Lin et al., “Psychological stress detection from cross-media microblog data using Deep Sparse Neural Network,” Proc. - IEEE Int. Conf. Multimed. Expo, vol. 2014-Septe, no. Septmber, pp. 1–6, 2014, https://doi.org/10.1109/ICME.2014.6890213
[13] R. G. Pillai, M. Thelwall, and C. Orasan, “Detection of Stress and Relaxation Magnitudes for Tweets,” vol. 2, pp. 1677–1684, 2018.
[14] H. White and S. Sabarwal, “Quasi-Experimental Design and Methods,” no. 8, 2014.
[15] S. Y. Chen, C. C. Hsu, C. C. Kuo, T. H. K. Huang, and L. W. Ku, “Emotionlines: An emotion corpus of multi-party conversations,” Lr. 2018 - 11th Int. Conf. Lang. Resour. Eval., pp. 1597–1601, 2019.
[16] E. Saravia, H. C. Toby Liu, Y. H. Huang, J. Wu, and Y. S. Chen, “Carer: Contextualized affect representations for emotion recognition,” Proc. 2018 Conf. Empir. Methods Nat. Lang. Process. EMNLP 2018, pp. 3687–3697, 2018, https://doi.org/10.18653/v1/d18-1404
[17] D. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade, and S. Ravi, “GoEmotions: A Dataset of Fine-Grained Emotions,” pp. 4040–4054, 2020, https://doi.org/10.18653/v1/2020.acl-main.372
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Cite This Article
  • APA Style

    Matini, A., Lekata, S., Kabaso, B. (2024). The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites. International Journal on Data Science and Technology, 10(1), 1-10. https://doi.org/10.11648/j.ijdst.20241001.11

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    ACS Style

    Matini, A.; Lekata, S.; Kabaso, B. The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites. Int. J. Data Sci. Technol. 2024, 10(1), 1-10. doi: 10.11648/j.ijdst.20241001.11

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    AMA Style

    Matini A, Lekata S, Kabaso B. The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites. Int J Data Sci Technol. 2024;10(1):1-10. doi: 10.11648/j.ijdst.20241001.11

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  • @article{10.11648/j.ijdst.20241001.11,
      author = {Abed Matini and Stanley Lekata and Boniface Kabaso},
      title = {The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites},
      journal = {International Journal on Data Science and Technology},
      volume = {10},
      number = {1},
      pages = {1-10},
      doi = {10.11648/j.ijdst.20241001.11},
      url = {https://doi.org/10.11648/j.ijdst.20241001.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20241001.11},
      abstract = {In the rapidly evolving digital marketplace, customer service has become a critical factor influencing consumer behaviour. With the advent of Artificial Intelligence (AI), particularly chatbots, customer service companies are increasingly leveraging technology to enhance user experience. This study explores the relationship between customer emotions, detected during interactions with e-commerce chatbots, and their subsequent purchase intentions. Emotion detection within Human-Computer Interaction (HCI) is a vital area of research, as specific emotions, such as joy or frustration, can significantly impact marketing effectiveness and consumer decision-making. This research aims to understand how emotional responses to chatbot interactions can predict customer's intention to purchase, thereby offering insights for businesses to optimize their AI-driven customer service strategies. The study analyzes four diverse datasets – EmotionLines, CARER, GoEmotion, and EmotionPush – to identify emotion-labelled sentences indicative of purchase intention. Our findings reveal that Neutral and Joyful emotions are predominant in influencing customers' purchase intentions, highlighting the importance of understanding these emotional states in e-commerce settings. While Neutral emotion is most influential, Joy consistently plays a significant role in positive customer engagement. This research underscores the need for e-commerce businesses to focus on emotional intelligence in chatbots, enhancing customer experience and potentially driving sales. Future research directions include examining real chatbot-customer interactions to further understand the impact of AI-driven customer service on consumer emotions and behaviours.
    },
     year = {2024}
    }
    

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    AU  - Abed Matini
    AU  - Stanley Lekata
    AU  - Boniface Kabaso
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    T2  - International Journal on Data Science and Technology
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    JO  - International Journal on Data Science and Technology
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    PB  - Science Publishing Group
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    AB  - In the rapidly evolving digital marketplace, customer service has become a critical factor influencing consumer behaviour. With the advent of Artificial Intelligence (AI), particularly chatbots, customer service companies are increasingly leveraging technology to enhance user experience. This study explores the relationship between customer emotions, detected during interactions with e-commerce chatbots, and their subsequent purchase intentions. Emotion detection within Human-Computer Interaction (HCI) is a vital area of research, as specific emotions, such as joy or frustration, can significantly impact marketing effectiveness and consumer decision-making. This research aims to understand how emotional responses to chatbot interactions can predict customer's intention to purchase, thereby offering insights for businesses to optimize their AI-driven customer service strategies. The study analyzes four diverse datasets – EmotionLines, CARER, GoEmotion, and EmotionPush – to identify emotion-labelled sentences indicative of purchase intention. Our findings reveal that Neutral and Joyful emotions are predominant in influencing customers' purchase intentions, highlighting the importance of understanding these emotional states in e-commerce settings. While Neutral emotion is most influential, Joy consistently plays a significant role in positive customer engagement. This research underscores the need for e-commerce businesses to focus on emotional intelligence in chatbots, enhancing customer experience and potentially driving sales. Future research directions include examining real chatbot-customer interactions to further understand the impact of AI-driven customer service on consumer emotions and behaviours.
    
    VL  - 10
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Author Information
  • Faculty of Information Technology, Cape Peninsula University of Technology, Cape Town, South Africa

  • Faculty of Information Technology, Cape Peninsula University of Technology, Cape Town, South Africa

  • Faculty of Information Technology, Cape Peninsula University of Technology, Cape Town, South Africab

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