TEJAS Journal of Technologies and Humanitarian Science

Decision Support System by Means of Virtual Assistant

Nishi Srivastava
Dept. of CSE, Goel Institute of technology & Management, Lucknow, India

Author

Yogendra Pratap Singh
Dept. of CSE, Goel Institute of technology & Management, Lucknow, India

Author

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Abstract:

Virtual assistants are very useful to humans these days. It makes people's lives easier, just like operating a PC or laptop with just voice commands. The use of a virtual assistant is basically to save time. We can also perform other tasks and save time by using virtual assistants. Most popular of the time, virtual assistants are cloud-based application which needs an internet- connected device. Virtual assistants have freedom to pay for the services provided which they can actually require. By speaking, users may ask their assistant questions, manage all the basic activities like emails and to-do lists, open or dismiss any app, message anyone on WhatsApp, and more. Voice commands are now the sole way to operate home automation devices and media playing. Intelligent personal assistants, automated personal assistants, virtual digital assistants, and chatbots are some other varieties of voice assistants. In this paper, we have created a voice assistant that can successfully accomplish out other task, including changing user commands. There some features added in the support which is helpful to it, the fact that it just listens to the user’s voice and isn't get triggered by the noise of background. This project is more adaptable in nature and easy to grasp because of its modular structure. Without affecting functionality, we can increase functionality of the programs. This code is written using the VS Code integrated development environment (IDE), which has all the necessary loaded packages which are used for the Python programming language. The project's Python version is 3.x, and the data for several noises are attained from the virtual environment.

References

[1] H. J. Watson, “Preparing for the cognitive generation of decision support,” MIS Quarterly Executive, vol. 16, no. 3, 2017.

[2] C. Bartl, M. H. Gouthier, and M. Lenker, “Delighting consumers click by click: Antecedents and effects of delight online,” Journal of Service Research, vol. 16, no. 3, pp. 386–399, 2013.

[3] R. K. Behera, P. K. Bala, and A. Ray, “Cognitive chatbot for personalized contextual customer service: Behind the scene and beyond the hype,” Information Systems Frontiers, pp. 1–21, 2021.

[4] I. Bruder, “A social mission is not enough: Reflecting the normative foundations of social entrepreneurship,” Journal of Business Ethics, vol. 174, no. 3, pp. 487–505, 2021.

[5] J. V. Chen, H. T. Le, and S. T. T. Tran, “Understanding automated conversational agent as a decision aid: Matching agent's conversation with customer's shopping task,” Internet Research, 2021.

[6] X. Cheng, Y. Bao, A. Zarifis, W. Gong, and J. Mou, “Exploring consumers' response to text-based chatbots in e-commerce: The moderating role of task complexity and chatbot disclosure,” Internet Research, 2021.

[7] E. Cho, M. D. Molina, and J. Wang, “The effects of modality, device, and task differences on perceived human likeness of voice-activated virtual assistants,” Cyberpsychology, Behavior, and Social Networking, vol. 22, no. 8, pp. 515–520, 2019.

[8] S. Diederich, A. B. Brendel, S. Morana, and L. Kolbe, “On the design of and interaction with conversational agents: An organizing and assessing review of human-computer interaction research,” Journal of the Association for Information Systems, vol. 23, no. 1, pp. 96–138, 2022.

[9] “Impact of response failure on user perception of anthropomorphic conversational service agents,” in Proc. 28th European Conf. Information Systems (ECIS), 2020.

[10] M. Dubiel, M. Halvey, L. Azzopardi, D. Anderson, and S. Daronnat, “Conversational strategies: Impact on search performance in a goal-oriented task,” in Proc. 3rd Int. Workshop Conversational Approaches to Information Retrieval, Vancouver, Canada, 2020.

[11] M. Dubiel, M. Halvey, L. Azzopardi, and S. Daronnat, “Investigating how conversational search agents affect user behavior, performance and search experience,” in Proc. 2nd Int. Workshop Conversational Approaches to Information Retrieval, Ann Arbor, USA, 2018.

[12] I. M. Enholm, E. Papagiannidis, P. Mikalef, and J. Krogstie, “Artificial intelligence and business value: A literature review,” Information Systems Frontiers, 2021, doi: 10.1007/s10796-021-10186-w.

[13] M. Foley, G. Casiez, and D. Vogel, “Comparing smartphone speech recognition and touchscreen typing for composition and transcription,” in Proc. CHI Conf. Human Factors in Computing Systems, New York, USA, 2020.

[14] T. Reddy, “Chatbots for customer service will help businesses save $8 billion per year,” IBM Watson Blog, 2017. [Online]. Available: https://www.ibm.com/blogs/watson/2017/05/chatbots-customer-service-will-help-businesses-save-8-billion-per-year/

[15] T. Reddy, “How chatbots can help reduce customer service costs by 30%,” IBM Watson Blog, 2017. [Online]. Available: https://www.ibm.com/blogs/watson/2017/10/how-chatbots-reduce-customer-service-costs-by-30-percent/

[16] A. Shevat, Designing Bots: Creating Conversational Experiences. UK: O’Reilly Media, 2017.