Designing information systems for business administration through human and computer interaction
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.2.24Keywords:
Business administration, Human-computer interaction, Artificial intelligence, semantics, banking, customer serviceDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
AI is increasingly incorporated into business operations; it appears in every aspect of life. However, a strategy that can integrate human and machine interaction is required for long-term implementation. To identify characteristics that can enhance domain operations and interpersonal interactions. To elucidate these obstacles and underscore specific pivotal decisional considerations that necessitate resolution before the effective collaboration of cognitive machines and humans in delivering authentic financial services. This article utilizes the published framework to analyze a case study in retail banking to identify the necessary cognitive abilities, individually and collectively. Each of these capabilities provides usage examples and demonstrates how they comprise a unified deliberative architecture for human-robot interaction. Customer service is an area where this design could be advantageous. Experimental evidence indicates that explicit knowledge management at the geometric and symbolic levels facilitates the incorporation of human-level semantics into the deliberative system of the robot, thereby enhancing the quality and authenticity of human-robot interactions.Abstract
How to Cite
Downloads
Similar Articles
- Sharayu Mirasdar, Mangesh Bedekar, Knowledge graphs for NLP: A comprehensive analysis , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Thangatharani T, M. Subalakshmi, Development of an adaptive machine learning framework for real-time anomaly detection in cybersecurity , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- R. Saarumathi, Logistics Optimization Through Composite Payday Installment in Favor of Requisite Ultimatum Vacillating Carrying Cost and Gradual Degeneration Under Non-stocked and Continuous Circumstances Using Hexagonal Fuzzy Number , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Archana Dhamotharan, Kanthalakshmi Srinivasan, Analog Circuits Based Fault Diagnosis using ANN and SVM , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Amudavalli L, K. Muthuramalingam, Integrated energy-efficient routing and secure data management for location-aware wireless sensor networks with PFO leveraged improved fuzzy unequal clustering algorithm (IFUC) , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- P. Hepsibah Kenneth, E. George Dharma Prakash Raj, Priority based parallel processing multi user multi task scheduling algorithm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Ritu Nagila, Abhishek Kumar Mishra, Ashish Nagila, Role of big data in enhancing lung cancer prediction and treatment , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Ayesha Shakith, L. Arockiam, Enhancing classification accuracy on code-mixed and imbalanced data using an adaptive deep autoencoder and XGBoost , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Raja S, Nagarajan L., Hybridization of bio-inspired algorithms with machine learning models for predicting the risk of type 2 diabetes mellitus , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Yasodha V, V. Sinthu Janita, AI-driven IoT routing: A hybrid deep reinforcement learning and shrike optimization framework for energy-efficient communication , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
<< < 18 19 20 21 22 23 24 25 26 27 > >>
You may also start an advanced similarity search for this article.

