Vol 1, No 1 (2022) |
|
1. Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology
Pages 1-13 || Authors: Dipakkumar Kanubhai Sachani, Sunil Kumar Reddy Anumandla, Sai Sirisha Maddula
ABSTRACT
The paper explores the complex interplay among customer loyalty, technology advancement, and human interaction in the retail industry. The study intends to clarify the effect of self-checkout technology on customer loyalty and propose solutions for businesses to navigate this changing landscape through a thorough investigation of theoretical frameworks and empirical research. The study's methodology synthesizes significant thoughts and conclusions through data analysis and reviewing previous research. Key findings highlight the significance of balancing human interaction and technical efficiency, prioritizing emotional engagement to encourage loyalty, and modifying retail methods to accommodate changing customer preferences. The policy implications highlight the necessity of legal frameworks that support the ethical integration of self-checkout technology while preserving the integrity of retail experiences that prioritize the needs of people. Ultimately, the study offers insightful advice and strategic imperatives to help merchants negotiate the future of retail with resilience and confidence, allowing them to maximize customer loyalty in the age of self-checkout technology.
Key words:
Human Touch, Retail Experience, Customer Loyalty, Self-Checkout Technology, Consumer Behavior, Personal Interaction, Emotional Engagement, Technological Disruption
The paper explores the complex interplay among customer loyalty, technology advancement, and human interaction in the retail industry. The study intends to clarify the effect of self-checkout technology on customer loyalty and propose solutions for businesses to navigate this changing landscape through a thorough investigation of theoretical frameworks and empirical research. The study's methodology synthesizes significant thoughts and conclusions through data analysis and reviewing previous research. Key findings highlight the significance of balancing human interaction and technical efficiency, prioritizing emotional engagement to encourage loyalty, and modifying retail methods to accommodate changing customer preferences. The policy implications highlight the necessity of legal frameworks that support the ethical integration of self-checkout technology while preserving the integrity of retail experiences that prioritize the needs of people. Ultimately, the study offers insightful advice and strategic imperatives to help merchants negotiate the future of retail with resilience and confidence, allowing them to maximize customer loyalty in the age of self-checkout technology.
Key words:
Human Touch, Retail Experience, Customer Loyalty, Self-Checkout Technology, Consumer Behavior, Personal Interaction, Emotional Engagement, Technological Disruption
Cite as:
Sachani, D. K., Anumandla, S. K. R., Maddula, S. S. (2022). Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology. Silicon Valley Tech Review, 1(1), 1-13.
Sachani, D. K., Anumandla, S. K. R., Maddula, S. S. (2022). Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology. Silicon Valley Tech Review, 1(1), 1-13.
2. Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions
Pages 14-34 || Authors: Takudzwa Fadziso, Rahimoddin Mohammed, Kanaka Rakesh Varma Kothapalli, Manzoor Anwar Mohammed, Raghunath Kashyap Karanam
ABSTRACT
This abstract offers a comprehensive summary of a study that explores deep-learning techniques for Signal and image processing. It covers the main goals, methodology, fundamental discoveries, and potential policy implications. The study examines the latest advancements and future directions in deep learning techniques for signal and image processing tasks. By analyzing various literature sources extensively, we delve into the latest advancements in model architectures, training techniques, and application domains. Notable discoveries highlight impressive progress in artificial intelligence, particularly deep neural network architectures, attention mechanisms, and generative adversarial networks. However, there are still obstacles to overcome, including scalability, efficiency, and the ability to interpret models. It is crucial to address data bias, privacy, resource inequality, and ethical guidelines to develop and deploy deep learning technologies responsibly. The policy implications highlight the significance of these issues. The study provides insights into the ever-changing field of deep learning for Signal and image processing, showcasing possibilities for creativity and positive effects on society.
Key words:
Deep Learning, Signal Processing, Image Processing, State-of-the-Art, Neural Networks, AI Techniques, Machine Learning
This abstract offers a comprehensive summary of a study that explores deep-learning techniques for Signal and image processing. It covers the main goals, methodology, fundamental discoveries, and potential policy implications. The study examines the latest advancements and future directions in deep learning techniques for signal and image processing tasks. By analyzing various literature sources extensively, we delve into the latest advancements in model architectures, training techniques, and application domains. Notable discoveries highlight impressive progress in artificial intelligence, particularly deep neural network architectures, attention mechanisms, and generative adversarial networks. However, there are still obstacles to overcome, including scalability, efficiency, and the ability to interpret models. It is crucial to address data bias, privacy, resource inequality, and ethical guidelines to develop and deploy deep learning technologies responsibly. The policy implications highlight the significance of these issues. The study provides insights into the ever-changing field of deep learning for Signal and image processing, showcasing possibilities for creativity and positive effects on society.
Key words:
Deep Learning, Signal Processing, Image Processing, State-of-the-Art, Neural Networks, AI Techniques, Machine Learning
Cite as: Fadziso, T., Mohammed, R., Kothapalli, K. R. V., Mohammed, M. A., Karanam, R. K. (2022). Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions. Silicon Valley Tech Review, 1(1), 14-34.
title 2