HRBUS84
Assignment 2 2025
Consumer Fatigue from Hyperconnectivity and
Machine Learning
Due Date: 31 July 2025
LITERATURE REVIEW: CONSUMER FATIGUE FROM HYPERCONNECTIVITY AND
MACHINE LEARNING
1. INTRODUCTION
In today’s digital world, consumers interact constantly with smart devices, apps, and
websites. These interactions are shaped by machine learning (ML), which helps
businesses offer personalised content, product recommendations, and targeted
advertisements. While these technologies make life more convenient, they can also
overwhelm users. This experience of exhaustion and emotional burnout caused by
digital exposure is referred to as ―consumer fatigue.‖
Machine learning plays a central role in this problem. By collecting and analysing user
data, it delivers non-stop content such as ads, suggestions, and notifications. As Abbas
Terms of use
et al. (2022) explain, the Stressor-Strain Theory helps us understand how such systems
By making use of this document you agree to:
become digital stressors that Use negatively affect
this document as ausers.
guide forThe result
learning, is not only
comparison emotional
and reference purpose,
Terms of use
Not to duplicate, reproduce and/or misrepresent the
and mental fatigue but also dissatisfaction with digital platforms. contents of this document as your own work,
By making use of this document you agree to:
document
Use this
Fully accept the consequences
solely as a guide forshould you plagiarise
learning, reference,or and
misuse this document.
comparison purposes,
Ensure originality of your own work, and fully accept the consequences should you plagiarise or misuse this document.
Comply with all relevant standards, guidelines, regulations, and legislation governing academic and written work.
Disclaimer
Great care has been taken in the preparation of this document; however, the contents are provided "as is" without any express or
implied representations or warranties. The author accepts no responsibility or liability for any actions taken based on the
information contained within this document. This document is intended solely for comparison, research, and reference purposes.
Reproduction, resale, or transmission of any part of this document, in any form or by any means, is strictly prohibited.
, +27 67 171 1739
LITERATURE REVIEW: CONSUMER FATIGUE FROM HYPERCONNECTIVITY
AND MACHINE LEARNING
1. INTRODUCTION
In today’s digital world, consumers interact constantly with smart devices, apps, and
websites. These interactions are shaped by machine learning (ML), which helps
businesses offer personalised content, product recommendations, and targeted
advertisements. While these technologies make life more convenient, they can also
overwhelm users. This experience of exhaustion and emotional burnout caused by
digital exposure is referred to as ―consumer fatigue.‖
Machine learning plays a central role in this problem. By collecting and analysing
user data, it delivers non-stop content such as ads, suggestions, and notifications.
As Abbas et al. (2022) explain, the Stressor-Strain Theory helps us understand how
such systems become digital stressors that negatively affect users. The result is not
only emotional and mental fatigue but also dissatisfaction with digital platforms.
In South Africa, mobile connectivity continues to rise, especially among the youth,
with more people gaining access to the internet and smart devices each year
(Brubaker, 2022). At the same time, consumers are being exposed to more machine
learning-powered content through shopping apps, streaming services, and social
media platforms. Despite this trend, limited research exists on how this exposure
affects the well-being of South African consumers.
This literature review explores the connection between machine learning and
consumer fatigue. It begins by reviewing recent studies, identifying key knowledge
gaps, and examining how machine learning affects consumer behaviour. It also
looks at the effects of hyperconnectivity and information overload and compares
theories relevant to this topic.
Disclaimer
Great care has been taken in the preparation of this document; however, the contents are provided "as is"
without any express or implied representations or warranties. The author accepts no responsibility or
liability for any actions taken based on the information contained within this document. This document is
intended solely for comparison, research, and reference purposes. Reproduction, resale, or transmission
of any part of this document, in any form or by any means, is strictly prohibited.
Assignment 2 2025
Consumer Fatigue from Hyperconnectivity and
Machine Learning
Due Date: 31 July 2025
LITERATURE REVIEW: CONSUMER FATIGUE FROM HYPERCONNECTIVITY AND
MACHINE LEARNING
1. INTRODUCTION
In today’s digital world, consumers interact constantly with smart devices, apps, and
websites. These interactions are shaped by machine learning (ML), which helps
businesses offer personalised content, product recommendations, and targeted
advertisements. While these technologies make life more convenient, they can also
overwhelm users. This experience of exhaustion and emotional burnout caused by
digital exposure is referred to as ―consumer fatigue.‖
Machine learning plays a central role in this problem. By collecting and analysing user
data, it delivers non-stop content such as ads, suggestions, and notifications. As Abbas
Terms of use
et al. (2022) explain, the Stressor-Strain Theory helps us understand how such systems
By making use of this document you agree to:
become digital stressors that Use negatively affect
this document as ausers.
guide forThe result
learning, is not only
comparison emotional
and reference purpose,
Terms of use
Not to duplicate, reproduce and/or misrepresent the
and mental fatigue but also dissatisfaction with digital platforms. contents of this document as your own work,
By making use of this document you agree to:
document
Use this
Fully accept the consequences
solely as a guide forshould you plagiarise
learning, reference,or and
misuse this document.
comparison purposes,
Ensure originality of your own work, and fully accept the consequences should you plagiarise or misuse this document.
Comply with all relevant standards, guidelines, regulations, and legislation governing academic and written work.
Disclaimer
Great care has been taken in the preparation of this document; however, the contents are provided "as is" without any express or
implied representations or warranties. The author accepts no responsibility or liability for any actions taken based on the
information contained within this document. This document is intended solely for comparison, research, and reference purposes.
Reproduction, resale, or transmission of any part of this document, in any form or by any means, is strictly prohibited.
, +27 67 171 1739
LITERATURE REVIEW: CONSUMER FATIGUE FROM HYPERCONNECTIVITY
AND MACHINE LEARNING
1. INTRODUCTION
In today’s digital world, consumers interact constantly with smart devices, apps, and
websites. These interactions are shaped by machine learning (ML), which helps
businesses offer personalised content, product recommendations, and targeted
advertisements. While these technologies make life more convenient, they can also
overwhelm users. This experience of exhaustion and emotional burnout caused by
digital exposure is referred to as ―consumer fatigue.‖
Machine learning plays a central role in this problem. By collecting and analysing
user data, it delivers non-stop content such as ads, suggestions, and notifications.
As Abbas et al. (2022) explain, the Stressor-Strain Theory helps us understand how
such systems become digital stressors that negatively affect users. The result is not
only emotional and mental fatigue but also dissatisfaction with digital platforms.
In South Africa, mobile connectivity continues to rise, especially among the youth,
with more people gaining access to the internet and smart devices each year
(Brubaker, 2022). At the same time, consumers are being exposed to more machine
learning-powered content through shopping apps, streaming services, and social
media platforms. Despite this trend, limited research exists on how this exposure
affects the well-being of South African consumers.
This literature review explores the connection between machine learning and
consumer fatigue. It begins by reviewing recent studies, identifying key knowledge
gaps, and examining how machine learning affects consumer behaviour. It also
looks at the effects of hyperconnectivity and information overload and compares
theories relevant to this topic.
Disclaimer
Great care has been taken in the preparation of this document; however, the contents are provided "as is"
without any express or implied representations or warranties. The author accepts no responsibility or
liability for any actions taken based on the information contained within this document. This document is
intended solely for comparison, research, and reference purposes. Reproduction, resale, or transmission
of any part of this document, in any form or by any means, is strictly prohibited.
