HRBUS84 Assignment 2 2025
Unique Number:
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 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
DISCLAIMER & TERMS OF USE
Educational Aid: These study notes are intended to be used as educational resources and should not be seen as a
replacement for individual research, critical analysis, or professional consultation. Students are encouraged to perform
their own research and seek advice from their instructors or academic advisors for specific assignment guidelines.
Personal Responsibility: While every effort has been made to ensure the accuracy and reliability of the information in
these study notes, the seller does not guarantee the completeness or correctness of all content. The buyer is
responsible for verifying the accuracy of the information and exercising their own judgment when applying it to their
assignments.
Academic Integrity: It is essential for students to maintain academic integrity and follow their institution's policies
regarding plagiarism, citation, and referencing. These study notes should be used as learning tools and sources of
inspiration. Any direct reproduction of the content without proper citation and acknowledgment may be considered
academic misconduct.
Limited Liability: The seller shall not be liable for any direct or indirect damages, losses, or consequences arising from
the use of these notes. This includes, but is not limited to, poor academic performance, penalties, or any other negative
consequences resulting from the application or misuse of the information provided.
, For additional support +27 81 278 3372
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.
2. MACHINE LEARNING PRACTICES INFLUENCING CONSUMER BEHAVIOUR
IN SOUTH AFRICA
Machine learning (ML) is increasingly used by businesses to shape consumer
behaviour. In South Africa, companies in retail, banking, telecommunications, and e-
Unique Number:
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 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
DISCLAIMER & TERMS OF USE
Educational Aid: These study notes are intended to be used as educational resources and should not be seen as a
replacement for individual research, critical analysis, or professional consultation. Students are encouraged to perform
their own research and seek advice from their instructors or academic advisors for specific assignment guidelines.
Personal Responsibility: While every effort has been made to ensure the accuracy and reliability of the information in
these study notes, the seller does not guarantee the completeness or correctness of all content. The buyer is
responsible for verifying the accuracy of the information and exercising their own judgment when applying it to their
assignments.
Academic Integrity: It is essential for students to maintain academic integrity and follow their institution's policies
regarding plagiarism, citation, and referencing. These study notes should be used as learning tools and sources of
inspiration. Any direct reproduction of the content without proper citation and acknowledgment may be considered
academic misconduct.
Limited Liability: The seller shall not be liable for any direct or indirect damages, losses, or consequences arising from
the use of these notes. This includes, but is not limited to, poor academic performance, penalties, or any other negative
consequences resulting from the application or misuse of the information provided.
, For additional support +27 81 278 3372
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.
2. MACHINE LEARNING PRACTICES INFLUENCING CONSUMER BEHAVIOUR
IN SOUTH AFRICA
Machine learning (ML) is increasingly used by businesses to shape consumer
behaviour. In South Africa, companies in retail, banking, telecommunications, and e-
