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Library of Congress Cataloging‑in‑Publication Data
Names: Dhaya, R., 1983– editor. | Kanthavel, R., 1974– editor.
Title: Internet of behaviours (IoB) / [edited by] Dr. R. Dhaya, King Khalid University Abha,
Saudi Arabia, Dr. R. Kanthavel, King Khalid University Abha, Saudi Arabia.
Description: First edition. | Boca Raton : CRC Press, 2023. | Includes bibliographical
references and index.
Identifiers: LCCN 2022055185 (print) | LCCN 2022055186 (ebook) | ISBN 9781032304533
(hardback) | ISBN 9781032304540 (paperback) | ISBN 9781003305170 (ebook)
Subjects: LCSH: Internet of things—Psychological aspects. | Human-computer interaction—
Psychological aspects. | Internet users—Psychology. | Consumer behavior. | Computer
security—Psychological aspects.
Classification: LCC TK5105.8857 .I544 2023 (print) | LCC TK5105.8857 (ebook) |
DDC 004.67/8—dc23/eng/20230222
LC record available at https://lccn.loc.gov/2022055185
LC ebook record available at https://lccn.loc.gov/2022055186
ISBN: 978-1-032-30453-3 (hbk)
ISBN: 978-1-032-30454-0 (pbk)
ISBN: 978-1-003-30517-0 (ebk)
DOI: 10.1201/9781003305170
Typeset in Sabon
by Apex CoVantage, LLC](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-9-320.jpg)
![2 Internet of Behavior (IoB)
It also aids in cost cutting of manufacturing and goods delivery. IoT can be
considered the most prominent technology in our daily life in order to ease
our daily tasks and make our life smarter. It is gradually improving as more
companies recognize the potential applications of devices [1–3].
IoB is the process of analyzing user-regulated data from a behavioral
and psychological point of view, using the results of this analysis to inform
analysts about new perspectives to design user experience and sell end prod-
ucts and services offered by the venture. Therefore, implementing IoB in an
organization is technically simple but psychologically complex. For ethical
and legal reasons, we need to conduct statistical surveys that map daily
habits and behaviors without fully disclosing consumer information [4].
IoB aims to understand and analyze every human behavior, further utiliz-
ing data in the enhancement of digital experience and other technological
advancements. People’s behaviors are monitored and incentives or disincen-
tives are carried out to persuade them to act in the direction of a favored set
of operational parameters. IoB is not descriptive but proactive. IoB impacts
purchaser choice; however, it additionally redesigns the fee chain. While a
few customers are cautious about supplying their data, many others are
willing to do so as long as it provides a fee – a data-pushed fee. For compa-
nies, this indicates being capable of extending their image or marketplace
merchandise to their clients or enhancing the customer experience (CX) of
a product or service. Hypothetically, statistics may be accumulated for all
aspects of a user’s life, with the final aim of enhancing performance and
quality [5].
The IoB aims to discuss how data can be better understood and used to
build and promote new products from the perspective of human psychology.
IoB can be used in different ways by public or private institutions. This tech-
nology is becoming an attractive new marketing and sales platform for busi-
nesses and organizations around the world. The IoB platform enables the
development of a comprehensive customer understanding that every com-
pany needs. For example, IoB can connect all the phones in an app, check for
mistakes and get visual recommendations for enhancing swings and shots.
Device networking creates many new data points, including from the Inter-
net of Things. Ventures collect information from their customers by data
sharing between connected devices [6]. These devices are actually monitored
by a single computer. Aggregating usage and information from IoB devices
provides useful insights into consumer behavior, desires, and preferences,
which can be simply referred as computer network. This includes devices
ranging from phones to vehicles, training replenishments, and credit cards,
to those that are literally connected to the Internet.Therefore, the goal of IoB
is to record, analyze, understand and respond to all forms of human behav-
ior so that people can be tracked and understood by advances in technology
and machine learning algorithms.](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-17-320.jpg)
![Benefits and Pitfalls of Internet of Behavior 3
Studies show that IoB tends to be an advancing technology which has
huge potential in the future. However, it has several risk factors which may
or may not affect humans directly. IoB will take its crown in the near future,
providing smarter solutions to the world. Both customers and organizations
will benefit from IoB. This chapter discusses the benefits and challenges of
IoB with the help of online surveys and analysis of various research papers.
1.2 METHOD OF STUDY
IoB is an advancing technology which has huge future potential. Many stud-
ies have been performed for the applications and future scope of IoB. Fur-
thermore, researchers have shed light on various sectors where IoB plays a
major role. Our chapter mainly focuses on the benefits and challenges of IoB
in various scenarios [7, 8].
IoB is considered an expansion of the IoT since it has the capability to
reproduce various patterns in order to have an influence on human behav-
ior. Consequently, IoB can be referred to as a mixture of technology, data
analytics and behavioral psychology. Studies have already proved the poten-
tial of IoT in various scenarios. Therefore, one can imagine how powerful
the combination of IoB and IoT could be. IoB can easily tackle consumer
behavior and gather relevant data for the improvement of the consumer
experience. Subsequently, the area of digital marketing is also affected as
consumer interests become available via IoB. Digital marketing becomes
easier with personalized content interests. The IoB can very well be associ-
ated with social media platforms and the most-used search engine, Google
[9, 10]. The advertisements shown between surfing the Internet and scrolling
social media would be the ones the user was looking for. This happens with
periodic tracking of customer behavior and interests. However, IoT devices
fail to gather data easily. The data processing in the IoT is bit complex, and
hence many companies do not have access to the data from it, whereas IoB
is customer centric, and it is easier to track human behavior and share data
with the relevant companies. More often companies may prefer IoB because
of its customer-centric behavior. It has the potential to trigger customers in
favor of a product purchase. Through such approaches, companies get an
insight into product enhancement according to consumer interests. Thus,
sales can be increased. Similarly, various sectors have advantages through
IoB with relevant applications. Besides these benefits of IoB, the design of
the most efficient approaches to capturing human psychology is increasingly
important and needs to be focused on [11–13].
We have gathered information through the review of various studies per-
formed on IoB. The studies mainly focused on consumer experience and
product development. An online survey was conducted for the collection of](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-18-320.jpg)
![4 Internet of Behavior (IoB)
customer experience regarding the appearance of advertisements and data
sharing in various applications. This helped us calculate a periodic tracking
system of IoB [14, 15]. Service quality and the value chain can be improved
with the aid of such data gathered. The analysis of this data helped to elu-
cidate the benefits and risk factors or drawbacks of IoB in various sectors.
1.3 RESULTS AND ANALYSIS
IoB is applicable in various sectors. This section deals with the benefits and
pitfalls of IoB in various sectors. With the ability to decipher customer intent
as described in natural language statements, search engine optimization
focuses heavily on intent, as well as words and keywords that describe the
function and benefit of the product. Therefore, it is possible to take advan-
tage of much better insight into user psychology and a product outlook with
an understanding of the possible ways of choosing a product and services.
Being alert about the time and place of customer shopping can affect your
business positively, as notifications can be pushed regarding offers and dis-
counts. In addition, IoB allows personalization, which helps in the enhance-
ment of service quality and efficiency [16, 17].With the help of IoB, you have
the opportunity to improve the quality of the data collected and combine
data from different sources to gain your own insights from this information.
Companies need to be very cautious regarding privacy concerns and cyber
security of the customer data. If any kind of violation occurs, it may directly
affect the company’s reputation and service. As we know in the advanced
technological era, hackers and cyber criminals could misuse sensitive data.
Therefore, it is highly important to adopt measures against these concerns in
order to implement IoB in any application.
1.3.1 Benefits of IoB
Sales Industry: The sales industry greatly benefits from IoB. Products and
services can be offered to customers with knowledge on the personal inter-
ests of the customers. This provides a very good customer flow for demand-
ing products and services. Since the dawn of advertising, marketing and
psychology have been intertwined. Therefore, behavioral analytics and psy-
chology could provide new insights into the data generated by the Inter-
net of Things. For businesses and organizations around the world, IoB has
the potential to become a powerful new marketing and sales tool [18–20].
Organizations can use the concept of IoB to look at past performance and
predict the future. Enterprises organize their development, marketing and
sales activities based on the data received via the IoT. Several companies
and organizations have created health apps for cell phones that track food,](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-19-320.jpg)
![Benefits and Pitfalls of Internet of Behavior 5
sleep habits, heart rate and blood sugar levels in the medical field. The
software finds the issue with the health of the user and suggests behavioral
changes for the best outcome. To cite an example, let us consider the over-
the-top platform Netflix. Netflix gets user data to predict the likes and
dislikes of the viewers. It recommends movies and shows based on personal
interests and ratings. Another example: Uber and other taxi aggregators
use the IoT to track drivers and passengers. They provide a survey after
each trip to review the guest’s experience. However, you can observe driver
behavior, understand passenger reactions and automatically construct those
inputs via IoB, so you can collect historical data and avoid investigations.
The behavioral insights obtained can be used to transform a major prospect
to sales. With the help of behavioral psychology, IoB identifies and inter-
prets human behavior by analyzing how human humans react in each situ-
ation via various technologies. Some companies utilize facial recognition to
examine behavior patterns at a specific time. This helps in the improvement
of sales team efficiency by examining their behavior. IoB aids in alteration
of sales engagement with consumers in real time.
Marketing Sector: IoB plays a major role in the marketing sector and has
huge potential to trigger marketing, which further affects sales and services.
The behavioral data gathered with the help of IoB aids in effective campaign-
ing. It helps in the optimization of marketing campaigns and marketing to
consumers efficiently. IoB data can be collected from various platforms in
order to analyze customer buying habits. This includes the place and time
of shopping, which aids in the personalization of advertisements on various
platforms. Besides these, IoB initiates marketing notifications on the latest
offers and discounts depending on behavioral data. Furthermore, IoB data
helps in analyzing the needs of customers, and companies can provide the
required User Interface (UI) [21].
Marketing, or rather digital marketing services, will be one of the biggest
winners of IoB technology, as Internet connectivity is a prerequisite for IoB.
Digital marketing is an area where data is used primarily to sell products
and services to people around the world. With access to behavioral analytics
and interpretation tools, we are in a stronger position to provide better reach
to those at the end of the purchasing process. Digital marketing services are
booming all over the world. Digital marketing is being used more promi-
nently by brand promotion, lead generation, sales generation and people.
If you also need digital marketing help or advice, hire a true wireless stereo
digital marketing expert to meet your needs.
Customer Experience: The Internet of Behavior helps agencies learn
everything about consumer behavior so they can deliver exactly what their
clients want. If a customer encounters positive issues during the adventure,
the agency can deal with them and enhance overall customer happiness. This
allows them to stabilize (and study) consumer loyalty over time [22].](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-20-320.jpg)
![10 Internet of Behavior (IoB)
on personal data such as Facebook, Yahoo and LinkedIn. Ventures need to
develop IoB devices and systems that comply with the regulations of multiple
jurisdictions. On IoB systems, it may be necessary to disable some features
of the area that limits data collection. People want better products and ser-
vices, better customer experiences and improved lifestyles in exchange for
sharing personal information. The value provided to consumers must be
commensurate with the risk, as IoB data can contain highly sensitive infor-
mation. Like IoT devices, IoB technology is another potential attack vector
for criminals. IoB data can contain highly sensitive information, which can
increase the severity of the attack. Behavioral data can provide cybercrimi-
nals with access to information and leave people vulnerable to digital and
physical attacks. Personalization aids in the enhancement of products and
services affect our values. It can go to extremes to make products customers
love. Some studies show that when too many products were identified, it
resulted in pregnancy prediction. It was followed by customer complaints, as
they were not aware of it and their personal data was not safe [9–21]. Orga-
nizations should ensure that privacy laws are followed before implementing
any solution. In addition to complying with the law, we need to ensure that
the collection and utilization of data is ethical and follows moral standards
from the perspective of public and consumers. An organization may use IoB
data by in violating existing local privacy laws. These violations can result
in penalties and police cases and therefore loss of reputation. In such cases,
businesses will be at risk of being sued by an individual and facing govern-
ment fines. Organizations must agree with local data protection laws, which
can vary widely from region to region. They need to perform rigorous data
attribution to evaluate which data is stored for which client, client or user,
and they need to comply with the law regarding where and how that data is
stored. Only then can data be deleted at the request of the user and no fines
for violations imposed. The company may agree to the laws but continue
to behave unethically in front of both internal and external stakeholders.
The use of IoB data to monitor the staff in an organization may improve
the efficiency of work. But it can badly affect the relationship between the
employees and their trust of the management of the organization, as their
privacy is not considered.
According to McKinsey, there are more connected devices today than
people. From smart phones to smart watches, speakers, voice assistants and
surveillance cameras, more than 30 billion connected devices have been
added worldwide. With sensors and auto-launch capabilities, these devices
permeate every area and constantly send large amounts of sensitive data to
businesses for data analytics. After a few years, around 40% of the world’s
population will be digitally monitored for minute actions [22–24]. It will
hardly be possible to evade the tracking systems of devices. All our actions
are being monitored. In the present scenario, where we are being tracked by](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-25-320.jpg)
![Benefits and Pitfalls of Internet of Behavior 11
almost all devices and artificial intelligence provides faster and more accu-
rate data, IoB will have a huge impact on philosophical, ethical and legisla-
tive business and society in general. As Daryl Plummer, chief research officer
and Gartner Fellow, points out, the very existence of these devices and how
governments and businesses use the data need to be rethought in existing
frameworks. Therefore, in the long run, almost everyone in the society will
be influenced by the impacts of IoB [25, 26].
1.4 CONCLUSION
Internet of Behavior is a technology that uses the Internet of Things in order
to gather behavioral patterns of humans for the enhancement of digital expe-
riences, sales, marketing and many more sectors. This chapter focused on
the benefits and pitfalls of Internet of Behavior. Each sector was studied in
detail, and the benefits of IoB in those sectors were analyzed. IoB applica-
tions range from the sales and marketing sector to consumer experience. IoB
works based on behavioral psychology. Human behaviors are analyzed and
recorded for the enhancement of various platforms and applications in order
to improve production and services. For companies, it becomes easier with
IoB to attain a huge turnaround annually. Even marketing becomes easier, as
advertisements and campaigns can be personalized based on consumer inter-
ests and dislikes. From the research, it is clear that IoB has a huge potential
in the future. IoB can revolutionize the technological area in no time. Further
research is needed to learn about the applications of IoB in sensitive sectors.
IoB aids in providing business solutions to achieve more sales and keep cli-
ents highly satisfied. IoB can replace multiple customer surveys, which are
a complex task and time consuming for clients as well as the companies. It
helps gather sensitive information on customer interaction with the provided
products and services. Companies can get a good understanding about the
shopping habits of consumers. Companies can also track the period of time
in which a customer is purchasing or browsing for products. It can help in
initiating notification alerts at that particular time. This will provide more
efficiency in offering products and services.
As we are aware that IoB is in early stages, there are lot of challenges to be
tackled. The chapter also analyzed various risk factors and challenges of IoB
which can arise besides the huge benefits. The major factor to be considered
is the protection of data, or data privacy. As far as political laws regarding
cyber security and data privacy are exist, IoB faces challenges, especially
when gathering sensitive customer data. Data shared using IoB can be mis-
used by hackers and cyber criminals for monetary purposes. If this occurs,
companies might be at risk, which may affect their reputation and services.
IoB can negatively impact people, as it may gather sensitive information on](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-26-320.jpg)
![DOI: 10.1201/9781003305170-2 15
CONTENTS
2.1 Introduction......................................................................................15
2.2 Materials and Methods of IoB..........................................................16
2.3 Working Process of IoB.....................................................................17
2.4 Discussion: The Importance of IoB...................................................18
2.5 Conclusion: Prospects for the Future................................................21
References.................................................................................................23
2.1 INTRODUCTION
In the last few years, the use of Internet of Things (IoT) devices has more
than doubled. All the small appliances being used around us, whether it
is smart watch or washing machine, the modernity of these machines has
given only the feeling of interacting with the world. Data and data col-
lection by these modern machines provide important information about
the behavior and interests of the user, and thus a new process of Internet
of Behavior is born. It is one step ahead of the IoT [1]. We can say that
where the work of IoT ends, that is where IoB starts. The IoB pioneers the
Internet of Things system. With the help of which we analyze the collected
data completely. In today’s modern world, we are all surrounded by differ-
ent types of devices that are fully connected to the Internet, such as mobile
phones, smart watches, and smart TVs. Today our daily life has become
dependent on many such devices, whether it is online shopping or order-
ing food. Internet of Behavior is a part of the IoT. The Internet of Things
connects all types of devices to a network and collects all kinds of different
information. From there the work of Internet of Behavior starts. Internet
of Behavior analyses the collected data and better facilitates the user expe-
rience. The Internet of Behavior is able to interpret the future reaction of
any person according to the data used. The great researcher Gartner has
also said that in the year 2023, the use of Internet of Behavior will be at its
Chapter 2
Working of IoB
Manisha Verma](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-30-320.jpg)
![16 Internet of Behavior (IoB)
peak level. Any big company can see from the experiences of their custom-
ers what they like and what they do not.
Earlier, a study would be used to examine consumer behavior and
response to a product or service. Customer feedback and background may
be a response to a product or service. Initially sample analysis is completed;
then trust in the product or its service within the firm is discovered. IoB plat-
forms are designed to gather, combine, and analyze data generated from a
large number of sources, as well as digital devices, wearable computers, and
online human activity. The information is then analyzed in terms of activity
science to uncover patterns that may be employed by promotion and sales
groups to influence future shopper behavior.
Today the Internet of Behavior is being used in all kinds of small and big
companies. Wolfram Alpha is a type of computational knowledge engine
that was developed by Stephen Wolfram. It can provide information based
on the activities of any person on Facebook and is also able to discover their
thoughts and interests. The Internet of Behavior creates other opportunities
for companies to collect and analyze data, the purpose of which is to provide
better facilities to employees and customers.
2.2 MATERIALS AND METHODS OF IOB
We are surrounded by the Internet of Behavior; today the trends of devices
and technology are happening all around us. One of these is the Internet of
Behavior, so this section examines how its use affects our lives and in which
areas it is useful.
In the Field of Business
• Today, business is dominant, and people are trying to connect with
customers through online advertising through various businesses, pro-
viding all they want to any customer according to their own interests
[1, 2]. Google and Facebook both use the Internet of Behavior the
most to show ads on their sites. These companies interact with their
customers and observe their experiences through their own advertising
through the medium of ClearDro.
• In the same area, YouTube also knows the experiences of its users,
which allows it to show only those videos and topics in which the user
is interested.
During the COVID–19 Pandemic
• At the time of the pandemic, when the world was going through a
big crisis, the use of Internet of Behavior increased. Employers can
use sensors or tags to see if there are any discrepancies in compliance](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-31-320.jpg)
![Working of IoB 17
with security standards. For example, if we talk about any food deliv-
ery application, they also use protocol information to guide their
decisions [3–5].
• Take, for example, Zomato and Swiggy, both of which demonstrated
and promoted restaurant safety practices. They recorded and trans-
mitted delivery person’s temperature to assure the consumer that they
were safe.
For the Insurance Industry
• IoB has become very beneficial for the insurance industry. Driving
tracking tools are increasingly being used by insurance companies like
Allstate and State Farm to track drivers’ behavior and protect them
[6, 7]. Using the Internet of Behavior, it can be found out whether
an
accident that has happened is a certain event or it is a false belief
generated by it.
2.3 WORKING PROCESS OF IOB
The stages of operating flow method of comprehending the web of Behav-
ior for client necessities and creating those methods to feel happier with
their shopping for is solely mirrored. The standard enhancement jour-
ney starts with assembling data from all relevant sources. The informa-
tion primarily supported facts were then collection of varied sources then
analysis as needs data then sensing data and final compile all data and
serving to customer. Once plotting pro re nata data graphs, and last the
ultimate section is to implement the gathering and analysis data front for
the realization of IoB for customer satisfaction in any relevant services the
information may be obtained from many completely different shops. There
are various methodologies of IoB that provide client services. Industrial
shopper data to the welfare media and additional data is also accessible
to all. That mixes numerous technologies for visual recognition, location
monitoring, and intensive data that focuses directly on the person and
connects the data to behavioral events. IoB is regarding how data can be
higher understood and the way it can turn out and sell new merchandise
from human psychology. The IoB provides digital analysis from differ-
ent outlets and may be used to manipulate behavior. This has led to new
insights into the IoT through behavioral analysis and psychology. IoB can
become a tool for corporations and organizations worldwide for sales and
promotion [8–11]. Corporations effectively link all primary devices to the
web and keep them on their tracking list. All the information is currently
being employed for advertising business and non-commercial products.
Digital transformation methods will considerably impact the automotive
sector, and here the IoB is taking its position among IoT growth, artificial](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-32-320.jpg)
![18 Internet of Behavior (IoB)
intelligence, and artificial intelligence technologies utilized in manufac-
turing. In the industrial sector, the IoB is being used to track employer
operating capability [12]. With an outsized quantity of data, distribution
mechanisms for sales merchandise and services to interested people may be
strategically implemented. It will additionally be ready to meet individu-
als who are at the top of the acquisition chain who access instruments for
behavioral analysis and explanations. Digital marketing is being employed
more conspicuously by individuals for whole ads, lead generation, and
sales generation [13]. One can modify people’s lives by opening up new
technological fronts. it’s go with several excitations and brings power and
get to the technical environment. Business can be expedited to include new
solutions with the help of IoB and a huge quantity of user information
for client analysis, product analysis, observation research, experimental
research, and simulation results [14]. So as to develop careful strategies,
the research business will help contemplate the business demands. This
allows the tracking, monitoring, and operation of physical devices through
the Internet [15, 16]. IoB expands this by connecting these instruments and
therefore the information they gather to regulate personal conduct. The
processes are as follows:
• Dataset – collect from various sources – analysis of collected required
data – sensing data and its behavior – collect data in various terms –
serve data as required.
If we follow the India web platform, when someone interacts with any
other device from a user’s laptop, then IoB analyses clearly how the user is
interacting with the resource, for example, which button they are clicking.
Internet of Behavior is a process in which the data received by the user is ana-
lyzed in the context of psychology and the analysis obtained from the user
experience, search experience optimization, knowing the user’s interests, we
develop new concepts to promote the growth of the company makes.
We are currently taking full advantage of this by integrating the Internet of
Behavior with machine learning. In the self driving process, machine learn-
ing via artificial intelligence and Internet of Behavior is able to choose the
better option by remembering the data of past experiences. By analyzing the
experience gained by other human beings in a psychological way, it brings
efficiency to the work.
2.4 DISCUSSION:THE IMPORTANCE OF IOB
It is even easier to explain it with an example: we go to any shopping mall
and buy different types of things, then pay, and then, according to a survey,
he tries to know that your experience was cash. Here we use the IoT, but as](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-33-320.jpg)
![Working of IoB 19
we know, IoB works one step ahead of it. In contrast, IoB does not require a
survey. IoB also tracks the behavior of the employees of the company accord-
ing to it and according to them it understands the behavior of the customers
on its own. And then it works to improve all kinds of collected data to the
customers and the employees [17–19].
We know very well how important someone’s data is in today’s era. We
know any human being by their behavior; in the same way, IoB also explains
to the public and understands a user’s identity and activities in daily life
and its usefulness according to human data. Even an organization named
gather also made a prediction and said that in the year 2021–2022 Internet
of Behavior in the world will get the best place in the world and its use and
development will be very fast [19, 20].
With the help of the Internet of Behavior, all the companies and businesses
will put four moons in their progress, its use will improve human life by
making progress in every field from the pandemic to climate change [21].
IoB is completely new in today’s world, and on top of that, there are a lot of
applications being developed and work going on right now.
IoB, in its own way, collects all the activities done by humans on the Inter-
net, analyses them thoroughly. It enables a user to track the usage of any
device, provides additional touch-points per the user’s purchase decisions,
and thus provides better products to the customer. The Internet of Behavior
is considered a better source of getting complete information about the user.
The data received through all kinds of approaches promotes the quality of
service and the value chain and interest of the people towards it. With the
efficiency of understanding the intention of the users described by natural
language statements in search engines and playing a vital role in enhancing
the product features [22, 23].
The IoB will further improve the quality and efficiency of services. With
the help of Internet of Behavior, the goals of increasing the quality of all the
data being collected and keeping data secure become very important. In such
a situation, it also becomes very important for the user to be aware of their
security and privacy [24].
• Security: Both the customer and the company need to be aware of
security because there are many criminals who will constantly try to
steal user data. In this case, it is also the user’s responsibility to keep
their data safe. In which off behavior can play an important role or say
that it is playing. They need to be careful about this. As we know, the
Internet of Behavior is capable of identifying someone’s behavior from
the data received. If we imagine that a person searches for informal
data on a search engine, then with the help of Internet of Behavior, we
can analyze his behavior and identify him easily. We are able to trace
any kind of informal activities of such a user. We can take care of them
by getting the data of their equipment [25, 26].](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-34-320.jpg)
![20 Internet of Behavior (IoB)
• Health: The Internet of Behavior will prove to be a boon in the health
field. In which we can analyze them with different help and make them
more efficient by the many types of mistakes being made by the doctor.
Tracking patients in real time enables providing better treatment to
them by which they can meet the needs of their body by tracking daily
life through the device. With the help of Internet of Behavior, we are
able to predict their health by giving them the best possible treatment
and help them live much longer. Or as if we cannot allow humans to
get sick [27].
Since the beginning of advertising, marketing and science have coexisted.
In this way, the knowledge gathered through the Internet of Things has been
enhanced by activity analysis and psychology. IoB has the potential to be a
powerful new marketing and sales tool for companies and organizations all
around the world. Utilizing this tool can help you develop a thorough under-
standing of your clients, which is crucial for any organization.
Undoubtedly, corporations have used analytics, A/B testing, SWOT analy-
sis, and many other tactics to design their products and selling strategies
over the years in order to develop and market products that customers will
want to purchase. IoB is anticipated to make a significant impact in the sales
sector.
With this idea, businesses want to be able to look at past results and make
predictions about the future. Companies’ development, marketing, and sales
operations will be reorganized as a result of the knowledge amassed through
the Internet of Things. It basically consists of dynamic industries and deal-
ing techniques, as well as digital marketing. Modern society can no longer
ignore the effects of advanced technology and the IoB because they will have
an impact on consumer behavior and the success of established sales chan-
nels [28].
Therefore, it’s crucial to start integrating the Internet of Behavior into
your digital marketing plan in order to profit and quickly gather a significant
amount of the most satisfied customers. Access information from various
points of contact with the Internet of Things. You are able to examine the
entire client journey this way.
In other words, you’ll be able to identify the customer’s initial point of
interest in the product, their path toward purchase, and the goal of purchase,
indicating you may be able to create a significant number of touch points for
a positive engagement with customers. Additionally, you’ll be able to learn
new techniques for communicating with clients so that you can establish a
connection with them earlier than at the time of purchase [18, 29].
An investigation of consumer activity on social networks and other plat-
forms, as well as details about their everyday lives, are all part of the idea
of IoB. Information is gathered using the Internet of Things, which the
average consumer can use to access occasional makers, thermostats, home](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-35-320.jpg)
![Working of IoB 21
automation systems, and wearable devices. Since each of these devices is a
part of our daily lives, it is likely that they will be able to collect data on
lifestyle trends, which will help us understand how and when particular
products and services are used [30].
Because it feels more comfortable and natural when we lift devices before
entering queries into Google, SEO pros see huge potential in IoB. Google
Home, Apple Siri, and Amazon Alexa are examples of AI-enabled devices
that demonstrate how search engines can understand human intent, not just
recognize keywords. This suggests that information processing system pages
won’t be evaluated solely on the basis of their keyword content but also on
the language used in those pages. Therefore, we have a tendency to see that
it’s important to change the SEO content approach so that it can support
intent. Businesses, diverse non-profit groups that care for the community,
and government agencies that uphold the rule of law all benefit from IoB.
Let’s see how this concept is used in modern society [31].
• During the epidemic, groups using portable computer vision started
to employ IoB to determine whether a person was wearing a mask.
Additionally, thermal imagers were sometimes used to produce images
of humans with higher body temperatures.
• Because you can track someone’s whereabouts using a smart phone, the
system can tell whether you’ve visited a market or a beauty parlor as
well as how long you stayed there. Uber analyzes IoT data to better
understand its audience’s preferences and find new ways to reach out
to customers. Large brands today are familiar with almost everything
about consumers, from hobbies to the reasoning behind purchases [32].
• In China, the introduction of a social credit score system was made
feasible with the use of AI, particularly face recognition. Remember
that the advent of AI during the Chinese era was intended to verify
the protection of the organization of data that is loyalty to the present
regime.
• The Directorix Barista for residential homes successfully included a
biometric authentication system in 2018. Thus, sex, age, and mood
are determined by goods purchased. The system evaluates the findings
and provides the customer with the appropriate beverage. The same
technology might be used in shops for customized advertising [33].
2.5 CONCLUSION: PROSPECTS FOR THE FUTURE
The Internet of Behavior is based on assessment data and is geared toward
dynamic models and the modern era of behavior. Record analytics may be
employed for well-timed modeling and behavior modification due to its net-
worked nature. The resulting device complexity may be managed with the](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-36-320.jpg)
![22 Internet of Behavior (IoB)
aid of illustration and access capabilities. The proposed approach adopts a
science-based, improvement approach. It targets organizational systems that,
as a result of the choreographic conduct encapsulation of useful entities, may
be progressed to IoB transformation. The transformation technique starts
off by describing the behavior that is, in my opinion, thought to be role or
task specific as a part of mutual interplay styles that may be challenged with
a particular purpose. The identified conduct encapsulations and interplay
styles are subtly included into executable method models in a subsequent
phase. In this way, businesses can experiment with IoB device solutions and
tailor the development of analytical intelligence to their own requirements.
Many pieces of software that only link users to a whole community from a
single application continue to be made available without Google, Facebook,
or Amazon’s consent.
Additionally, there are significant criminal and safety risks to the right
to privacy that may be distinct among states globally. Future research may
reveal that customers enjoy the entire experience. This will control the cus-
tomer’s interest in the product as it develops, their shopping experience lead-
ing up to the point of purchase, and the creation of additional contact points
to promote positive customer engagement. Additionally, it might research
fresh ways to communicate with customers so they can interact with the
brand prior to making a purchase. The top-level view of human behavior
included in the idea of Internet behavior incorporates knowledge about how
people interact with social networks and other media.
Data from everyday users, such as from coffee makers, thermostats, home
control systems, wearable technology, and so on, is collected online. It is
employed for gathering information on lifestyle trends that, in turn, might
provide a focus on using such goods and services. IoB will supply enough
records for the market analysis. Additionally, it will be utilized to improve
the security of well-known public places. It might present more opportuni-
ties for people in the market. It might lower the price of corporate tracking.
This provides a better customized product and service focused on delivering
a higher-quality customer experience, and new initiatives and solutions are
emerging [29].
Deep behavioral patterns and consumer insights can be recognized and
turned into practical ways to influence human behavior. Since a large num-
ber of people are practically tethered to our computers, our technology is
the most readily available tool for businesses to analyze and extrapolate
consumer behavior.
IoB has quickly evolved into a global setting that defines human behavior
in just a few years. To connect people and computer systems for behavior
analysis, a milestone is needed. IoB uses behavioral data records and then
assesses their potential. Businesses have examined, tested, and put into prac-
tice a variety of techniques to expand their tactics for offering and promoting](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-37-320.jpg)
![Working of IoB 23
goods to consumers. The data can serve as a foundation for growth, promo-
tion, and revenue strategies for businesses. The industries may also investi-
gate a variety of new materials and records. Additionally, it helps consumers
feel more brand loyalty and make more money [34]. The IoB makes it easier
to examine the complete experience by using data gathered from several
touch points. More information is produced in this way, as are fresh methods
of communicating with customers. IoB is utilized for marketing and advertis-
ing purposes and could help business people improve their businesses. It will
boost sales of devices that connect to the Internet and acquire and transfer
data across wireless networks without human assistance. The IoB takes the
gathering of records and converts it into insight. It integrates behavioral
psychology and links people to their behaviors. IoB issues a warning if there
is a bad situation and offers suggestions for reversing the course of action.
It gathers behavioral and user data obtained through the use of Internet-
connected devices and gives users insights into their motivations, interests,
and behaviors.
All kinds of technology have its advantages as well as disadvantages. It is
true that the prediction made by Gartner became true today, in 2022, and
we can say that by 2025, 70% of the world’s population will participate in
the IoB [35]. But even with all the convenience of using IoB, this trend raises
many questions related to security. Companies will have to implement cyber
security education and awareness programs, after which Internet of Behavior
will play a role in writing a new story in its future.
REFERENCES
1.
C. Arnold, D. Kiel, K.I. Voigt, How the industrial Internet of Things changes
business models in different manufacturing industries, Int. J. Innov. Manag.
20(8), 1640015 (2016).
2.
M. Augl, C. Stary, Adjusting capabilities rather than deeds in computer-
supported daily workforce planning. In: Ackerman, M.S., Goggins, S.P.,
Herrmann, T., Prilla, M., Stary, C. (eds.) Designing Healthcare that Works.
A Sociotechnical Approach, pp. 175–188. Academic Press/Elsevier, Cambridge
(2017).
3.
S.M. Li, T.M. Chung, Internet function and Internet addictive behavior, Comput.
Hum. Behav. 22 (6) (2006 Nov 1) 1067–1071.
4.
W. Buente, A. Robbin, Trends in Internet information behavior, 2000–2004, J.
Am. Soc. Inf. Sci. Technol. 59 (11) (2008 Sep) 1743–1760.
5.
H.P. Shih, Extended technology acceptance model of Internet utilisation behav-
ior, Inf. Manag. 41 (6) (2004 Jul 1) 719–729.
6.
S.Y. Yousafzai, G.R. Foxall, J.G. Pallister, Explaining Internet banking
behavior: Theory of reasoned action, theory of planned behavior, or
technology acceptance model? J. Appl. Soc. Psychol. 40 (5) (2010 May)
1172–1202.](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-38-320.jpg)
![Working of IoB 25
24.
Y. Lu, S. Papagiannidis, E. Alamanos, Internet of Things: A systematic review
of the business literature from the user and organisational perspectives, Tech‑
nol. Forecast. Soc. Change. 136 (2018 Nov 1) 285–297.
25.
D.N. Smith, K. Sivakumar, Flow and Internet shopping behavior: A con‑
ceptual model and research propositions, J. Bus. Res. 57 (10) (2004 Oct 1)
1199–1208.
26.
A. Tsitsika, M. Janikian, T.M. Schoenmakers, E.C. Tzavela, K. Olafsson, S.
Wojcik, G.F. Macarie, C. Tzavara, EU NET ADB Consortium, C. Richardson,
Internet addictive behavior in adolescence: A cross-sectional study in seven
European countries, Cyberpsychol. Behav. Soc. Netw. 17 (8) (2014 Aug 1)
528–535.
27.
D. Langford, Ethics and the Internet: Appropriate behavior in electronic com-
munication, Ethics Behav. 6 (2) (1996 Jun 1) 91–106.
28.
A.H. Crespo, I.R. Del Bosque, M.G. de los Salmones Sanchez, The influence
of perceived risk on Internet shopping behavior: A multidimensional perspective,
J. Risk Res. 12 (2) (2009 Mar 1) 259–277.
29.
M. Javaid, et al., Sensors Int. 2 (2021) 100122 6 [8] T.K. Yu, G.S. Wu, Deter-
minants of Internet shopping behavior: An application of reasoned behaviour
theory, Int. J. Manag. 24 (4) (2007 Dec 1) 744.
30.
L.A. Ho, T.H. Kuo, B. Lin, The mediating effect of website quality on Internet
searching behavior, Comput. Hum. Behav. 28 (3) (2012 May 1) 840–848.
31.
G. Schuh, V. Zeller, J. Hicking, A. Bernardy, Introducing a methodology for
smartification of products in manufacturing industry. Procedia CIRP. 81
(2019) 228–233.
32.
A. Sodero, Y.H. Jin, M. Barratt, The social process of big data and predic-
tive analytics use for logistics and supply chain management. Int. J. Phys.
Distrib. Logistics Manag. 49 (7) (2019) 706–726. https://doi.org/10.1108/
IJPDLM-01-2018-0041.
33.
L. Andrews, C. Bianchi, Consumer Internet purchasing behavior in Chile, J.
Bus. Res. 66 (10) (2013 Oct 1) 1791–1799.
34.
C. Perera, A. Zaslavsky, P. Christen, D. Georgakopoulos, Context-aware com-
puting for the Internet of Things: A survey, IEEE Commun. Surv. Tutorials.
16 (1) (2013 May 3) 414–454.
35.
S. Rab, S. Yadav, N. Garg, S. Rajput, D.K. Aswal, Evolution of measurement
system and SI units in India, MAPAN (2020) 1–16.](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-40-320.jpg)
![DOI: 10.1201/9781003305170-3 27
CONTENTS
3.1 Introduction......................................................................................27
3.2 Pattern of Shopping Using Customer Purchase Behavior...................29
3.2.1 A Priori Algorithm.................................................................29
3.3 Analysis of Sentiment on Social Media.............................................29
3.4 Decision Making Based on Wearable Dataset...................................34
3.5 The Internet of Things in the Insurance Industry...............................35
3.6 Discussion of Various Applications...................................................36
3.7 Challenges........................................................................................37
3.8 Conclusion........................................................................................38
References.................................................................................................38
Websites............................................................................................39
3.1 INTRODUCTION
The Internet of Behavior (IoB) [1] helps to analyze how information is bet-
ter utilized to develop new products from the outlook of human psychol-
ogy. The concept of IoB can be utilized in different ways by the various
sectors. The IoB is defined as the technology that collects and processes
information based on the behavior of users to provide quality of life for
users. The IoB [2] may be a network of interconnected physical objects
that collect and exchange information and data over the Internet, called
the Internet of Things (IoT). Within the Internet of Things, the range of
complexity is consistently expanding and evolving. The way devices are
connected and the calculations which will be performed autonomously
by those objects and therefore the data stored in the cloud are constantly
evolving in increasingly complex ways [3]. It’s called the Internet of Behav-
ior because it provides useful information about customer behavior, inter-
ests, and preferences collected through various data collection methods
(business intelligence, big data, customer data platform, etc.). The Internet
Chapter 3
A Detailed Study to Increase
Business Value
P. Devisivasankari, R.Vijayakumar,
and Vijayalakshmi V](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-42-320.jpg)
![28 Internet of Behavior (IoB)
of Things aims to analyze data from consumers’ online activities in terms
of behavior. It seeks to answer all the questions on the way to interpret the
data and how this understanding can be applied to the development and
commercialization of new products, all from the attitude of human psy-
chology and cognitive science. The term “Internet of Things” [2] describes
a way to look at user-controlled data from a behavioral psychological point
of view. Supported by the results of this survey, companies can develop new
user experiences (UX), search experience optimization (SXO), and ways
to promote their final products and services [4]. As a result, implementing
IoB within a corporation is technically simple but psychologically difficult.
Thanks to ethical and legal considerations, it’s necessary to conduct statis-
tical surveys that map daily habits and activities without fully disclosing
the personal information of consumers. Aside from that, the Internet of
Things integrates existing personally targeted technologies with big data,
like facial recognition and location tracking. This area consists of three
areas: technology, data analysis, and behavioral psychology [5]. Because of
big data, you’ll get information from various contacts. This enables you to
explore the customer experience from start to finish and understand where
the customer’s interest in the product begins, how the customer makes a
buying decision, and the way to complete the transaction. This provides
you the opportunity to generate more touch points with your consumers
and increases your chances of active involvement. The power to personal-
ize a service is critical to the efficiency of the service [6]. Users can still
operate the service and, if the service is efficient, change behavior sup-
ported by that efficiency. Human behavior is often captured, analyzed, and
understood using new innovations and developments in machine learn-
ing algorithms [7–10]. The Internet of Things helps us track and interpret
people’s behavior. A survey by IBM has come up with the prediction that
by 2030, each person might own ten devices. The IoT is a trendy topic that
connects devices for the exchange of information. Now the Internet of
Behavior is an emerging topic with the new concept where companies use
the huge amounts of data collected through the IoT to analyze customer
behavior [11, 12]. The result of the analysis is used to predict the buying
pattern of the customers, which in turn is used to give them suggestions
and recommendations [13, 14]. This concept will have a great influence on
the marketing side of the product. This chapter discusses:
1) Pattern of Shopping Using Customer Purchase Behavior
2) Analysis of Sentiment on Social Media
3) Decision Making Based on Wearable Dataset
4) IoB in the Insurance Industry](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-43-320.jpg)
![A Detailed Study to Increase Business Value 29
3.2
PATTERN OF SHOPPING USING CUSTOMER PURCHASE
BEHAVIOR
Customer buying behavior refers to the typical way in which customers will
purchase goods and benefit from services. This depends on the quantity,
duration, frequency and timing. This buying pattern can be used to predict
how customers will buy goods and services [15]. But this is sensitive data and
is highly expected to change.
3.2.1 A Priori Algorithm
This algorithm is used for mining frequently used item sets and their cor-
responding association rules. The two major steps of this algorithm are join
and prune. This refers to the occurrence frequency and the conditional prob-
ability. This algorithm uses prior knowledge of frequently occurring item set
properties.
# importing dataset
data(“Groc”)
# using the apri function
rules - apriori(Groc, param = list(sup = 0.02, conf = 0.3))
# using the function inspect ()
inspect(rules[1:10])
# using the function itemFrequencyPlot()
arules::itemFrequencyPlot(Groc, topN = 30, col = brewer.
pal(9, ‘Pastel2’), main = ‘Relative Item Frequency Plot’,
type = “relative”, ylab = “Item Frequency (Relative)”)
Figure 3.1 shows the relative frequency plot of grocery items. The graph
shows that people will have a buying pattern and follow it every time with
slight variation. The customer who buys rolls or buns is expected to buy
pastry. Similarly customers buying diapers are expected to buy baby soap
and shampoo.
3.3 ANALYSIS OF SENTIMENT ON SOCIAL MEDIA
The Twitter dataset for Apple is taken for implementation. This dataset con-
tains feedback from users about Apple and was collected in 2017.
This dataset contains 16 attributes and around 1000 records. An image
of the dataset is shown in Figure 3.2. The first attribute, ‘Text’, contains](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-44-320.jpg)
![A Detailed Study to Increase Business Value 31
feedback from the customers. Sentiment analysis is done for the dataset
using the R package.
apple - read.csv(file.choose(), header = T)
str(apple)
The dataset is read using the previous command. The STR function in R
is used to show the internal structure of the object.
# Clean text
corpus - tm_map(corpus, tolower)
corpus - tm_map(corpus, removePunctuation)
inspect(corpus[1:5])
The tolower function is used to convert all the text in the dataset to low-
ercase and, removePunctuation is used to remove the punctuation marks in
the dataset. So tolower and removePunctuation are used to clean the dataset
and prepare it for processing. The result of these commands on the data is
shown in Figure 3.3.
Functions from the tm package are used for cleaning the dataset in R.
1. Remove words
2. Stopwords
3. Remove URL
4. Cleanset
5. stripWhitespace
# Term document matrix
tdm - TermDocumentMatrix(cleanset)
tdm
Figure 3.3 Result of cleaning the dataset.
SimpleCorpus
Metadata: corpus specific: 1, document level (indexed): 0
Content: documents: 5
[1]rt options nipper aapl beat on both eps and revenues sees
4q rev 49b52b est 491b httpstcohfhxqj0iob
[2]rt options nipper aapl beat on both eps and revenues sees
4q rev 49b52b est 491b httpstcohfhxqj0iob
[3]lets see this break all timers aapl 15689
[4]NA
[5]aapl wow this was supposed to be a throwaway quarter and
aapl beats by over 500 million in revenue trillion dollar
company by 2018](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-46-320.jpg)
![32 Internet of Behavior (IoB)
Docs
Terms 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
beat 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
eps l 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0
est 1 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
options nipperl 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
rev 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
revenues 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sees 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
break 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
lets 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
see 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Figure 3.4 Matrix format.
tdm - as.matrix(tdm)
tdm[1:10, 1:20]
The raw text is converted into matrix format, as shown in Figure 3.4,
using the function matrix().
This matrix represents the frequency of all words appearing in the dataset.
The frequency count represents the number of times the word has been used
in the dataset. The resulting matrix is shown in Figure 3.5, which shows that
the word ‘earnings’ appeared 250 times in the dataset.
The function word cloud is used to generate a word cloud with the words
present in the dataset. The required package for generating the word cloud is
word cloud, and it has to be installed along with the cleaned file. The word
cloud is used to analyze the data in the view. The word cloud shows the
importance of the words given their frequency or the number of times they
are repeated in the dataset.
The Syuzhet package is used to extract sentiments from the words used in
the dataset. barplot represents the result graphically.
barplot(colSums(s),
las = 2,
col = rainbow(10),
ylab = ‘Count’,
Figure 3.7 shows that people have a positive attitude towards Apple.
From the figure, it is clear that the behavior of people and their sentiments
are taken into account and decide the buying patterns for the item.](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-47-320.jpg)
![34 Internet of Behavior (IoB)
Figure 3.7 Sentiment scores for Apple Tweets.
3.4 DECISION MAKING BASED ON WEARABLE DATASETS
Today, many customers are using various wearables like smart watches and
smart goggles. Smart devices used to give samples like heart rate, glucose
level and regular blood pressure (BP) level. If a person wants to consume
a drink, these wearable dataset values are cross-checked to see how much
sugar can be added, or, if it is soup, how much salt can be added. All these
decisions are purely based on the IoB [12, 16], since all the values will be
tracked and regularly monitored by the cloud database. Any changes in BP
levels or variations in heart rate can immediately be brought to the attention
of a medical practitioner to give first aid. Mostly the IoB can be effectively
used to track elderly people [17, 18]. The Internet of Behavior can be seen
as a fusion of the three domains of technology, data analytics, and behav-
ioral science, all working together. In terms of technology [19], behavioral
sciences are often divided into four categories. Choice, emotions, expan-
sion, and proficiency are all part of the game. For instance, the health app
on your smart phone can facilitate monitoring your sleep rhythm, blood
glucose, pulse, and more. This app can provide you with a warning about](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-49-320.jpg)
![A Detailed Study to Increase Business Value 35
potentially dangerous health conditions and recommend behavioral changes
[20, 21] that lead to positive and desirable results. In 2021, there are many
IoB applications that are often personalized and applied.A variety of compa-
nies use Internet advertising to reach their customers in today’s information
technology era. They will use the Internet of Behavior to discover and tar-
get specific individuals or groups that may benefit from their products and
services. For instance, Google and Facebook are social media sites that both
use behavioral data to serve ads to users who are already using the platform
and are susceptible to behavioral changes through it. This enables market-
ers to use ‘click-through’ statistics to interact with the ideal consumer and
track their behavior in response to ads. YouTube uses behavioral analytics to
enhance the viewer’s experience by recommending or promoting only videos
and topics that are relevant to the viewer, supported by the viewer’s past
viewing history and activity. The epidemic has raised awareness of the pre-
cautionary measures that need to be taken during this time. Employers can
use sensors or radio frequency identification tags to assess whether employ-
ees are inconsistent in how they follow safety rules. In fact, restaurants and
food delivery apps used their knowledge of the protocol to continue working
after receiving the protocol. In a number of more imaginative use cases, a
driver’s temperature was tracked and showed to reassure the customer of
safety [13].
3.5
THE INTERNET OF THINGS IN THE INSURANCE
INDUSTRY
The majority well-known insurance companies are currently utilizing user
monitoring solutions in order to investigate and collect information regard-
ing user behavior and performance. Here, with the assistance of IoB, they
will investigate behavior and most likely come to a conclusion as to whether
a particular occurrence was the result of an accident or individual error.
Accidents that are caused by drivers who are under the influence of alcohol
or drugs, drivers who are under the effect of prescribed medication, drivers
who are under the age of 18, or drivers who are retired can be avoided if this
is done. I hope that the preceding example was clear enough for everyone to
understand how IoB [22] would be utilized in 2023. If not, feel free to devise
your own use case that addresses your specific needs.
The Internet of Thing is a network of connected devices that collects, anal-
yses, and interprets data in order to recognize patterns in user behavior and
then make use of that information to produce the required action depending
on the behavior [23]. These initiatives have resulted in improved outcomes
for large businesses, such as increased sales, which have been accomplished
by targeting the appropriate viewers and successfully appealing to them.
Because it offers customization to an unprecedented degree, this technology](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-50-320.jpg)
![36 Internet of Behavior (IoB)
that will shape the future of product and service design, as well as market-
ing [22]. How can one bring about the appropriate feelings that will direct
a person to make the decisions that they want? The solutions can be found
through data mining and behavioral science [24]. The idea of the Internet of
Things gave rise to the concept of the Internet of Business as a natural pro-
gression of that idea: if there is too much data, you want to know how to use
it to your benefit [22]. The Internet of Things has a promising future in the
longer term because people want their belongings to be‘smart’.This includes
smart houses, smart cities, and eventually smart lives, in which everything in
our environment is secure. You can be productive while still having plenty
of time for your hobbies and creative endeavors. Although IoB is still a rela-
tively young technology, it has already demonstrated its potential as a potent
new tool for online and digitally based businesses.The practice of conducting
market research and user profiling can be challenging for many businesses
unless those businesses are Facebook or Google. The more gadgets that are
connected to the web, the more in-depth the behavioral insights can be. And
as a consequence of this, the businesses that will most likely give financial
incentives to customers will open the door to their own universe, which may
include their routines, lives, hobbies, and even dreams. The message may say
that we will utilize this information about you to better your life in some
way, such as by delivering the most relevant financial products, providing
individualized health advice, helping you reach your goals, and so on. Learn-
ing how to put one’s faith in other people is an extremely important skill. At
the end of the day, it’s supported by numbers and research, not guesswork.
There is no requirement to construct the perfect user persona when utilizing
IoB. The use of big data makes it possible to conduct multi-faceted analyses
of potential clients. You will generate a map of your client journey that is
exceedingly thorough, take an approach that is highly tailored, and increase
your overall engagement score. Using this strategy will make it much simpler
to sell specialized items aimed at smaller markets. Voice-enabled gadgets
will become more common in people’s daily lives, which will shift the focus
of search engine optimization to natural language and intent-based que-
ries. Behavioral insights will assist in optimizing the content of the website
accordingly. Geographical locations, which may be retrieved through the
user’s smart phone, are going to be increasingly essential. Ratings of restau-
rants and beauty salons will also depend on the number of times a user has
visited and how long they have stayed there.
3.6 DISCUSSION OF VARIOUS APPLICATIONS
IoB is mainly used to analyze human activities [25], and it is used in cul-
ture change like scanning people’s accounts from social media and contacts
for better forecast of behaviors regarding market products and to analyze](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-51-320.jpg)
![A Detailed Study to Increase Business Value 37
customer habits through Google search, Facebook posts or Amazon order-
ing. This leads to phishing attacks by cyber criminals. Nowadays corporate
firms are also implementing principles and guidelines for IoB. This project
mainly focuses on enhancing skills using various apps on a smart phone and
to monitor wearable devices, as well as to learn new techniques [25]. IoB is
mainly used to track and archive trends that are characteristic of customers’
decisions in purchasing items. In our day-to-day activities and jobs, more
awareness is provided by the Internet.
It incorporates existing technology, focusing mainly on the customer/
user to fetch data, such as monitoring the location where the user is, and
combining the data to relate behavioral activities like money transactions
or smart phone usage. The approach is straightforward for organizations
to influence all of human behavior. For example, the system can check
whether the workforce follows protocols by using IoB using computer
vision methods. IoB links all the data related to those individual behaviors,
like purchasing an item or a particular brand. In IoB, values are mainly
used to create the optimal state of activities for behavioral events. IoB is
mainly used to review businesses results from the past and forecast future
activities. Currently it’s used to track employees and to plan for various
products, services, and ways to market them. A few companies choose this
approach for a cyber protection policy to protect all responsive data. IoB
understand and relates the data obtained by the IoT to a person’s behav-
iors like selection of a brand. This collects data; then, though analytics, it
retrieves information from the collected data. This knowledge is subse-
quently used to investigate and influence human behavior. Sensors are used
to collect all behavioral data and suggest insight into consumers’ activities,
requirements, and expectations.
3.7 CHALLENGES
IoB data are vulnerable to cyber attacks; the data can easily get into attack-
ers’ hands and expose the behavior patterns of customers. In the long run,
all these sensitive data can be collected and sold to other parties. It’s quite
dangerous to the people who are using IoT technology. IoB may create more
cyber criminals to generate sophisticated scams. IoB can help cyber crimi-
nals generate scams customized to the behavior of individual users and thus
increase the probability of users getting scammed. This advanced technology
has certainly been quite helpful for businesses; it allows users or customers
to optimize their relations with other users based on the data collected based
on their lifestyle and living patterns. Finally, we can come to the conclusion
that IoB is definitely a modern technique used to transform information into
authentic perceptions.](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-52-320.jpg)
![A Detailed Study to Increase Business Value 39
11.
McCole P, Elaine R, John W. Trust considerations on attitudes towards online
purchasing: The moderating effect of privacy and security concerns. J Bus
Res. 2010;63(9–10):1018–1024.
12.
Nasco SA, Elizabeth GT, Peter PM. Predicting electronic commerce adoption
in Chilean SMEs. J Bus Res. 2008;61(6):697–705.
13.
Son M, Kyesook H. Beyond the technology adoption: Technology readiness
effects on post-adoption behavior. J Bus Res. 2011;64(11):1178–1182.
14.
Wang X, Chunling Y, Yujie W. Social media peer communication and impacts
on purchase intentions: A consumer socialization framework. J Interact Mark.
2012;26(4):198–208.
15.
Oliver RL, William OB. Crossover effects in the theory of reasoned action:
A moderating influence attempt. J Consum Res. 1985;12(3):324–340.
16.
Rohm AJ, Vanitha S. A typology of online shoppers based on shopping moti‑
vations. J Bus Res. 2004;57(7):748–757.
17.
Davis RA. A cognitive-behavioral model of pathological Internet use. Comput
Hum Commun. 2001;17:187–195. [Google Scholar]
18.
Alavi SS, Maracy MR, Jannatifard F, Eslami M, Haghighi M. A survey rela-
tionship between psychiatric symptoms and Internet addiction disorder
in students of Isfahan universities. Sci J Hamadan Univ Medi Sci Health
Serv. 2010;17:57–65. [Google Scholar]
19.
Chebb PI, Koong KS, Liu L, Prasanna C, et al. Some observations on Inter-
net addiction disorder research. J Info Syst Educ. 2000;11:97–104. [Google
Scholar]
20.
Young KS, Rogers RC. The relationship between depression and Internet
addiction. Cyberpsychol Behav. 1998;1:25–28. [Google Scholar]
21.
Noble EP, Zhang X, Ritchie T, Lawford BR, Grosser SC, Young RM, et al.
D2dopamine receptor and GABA-A receptor beta3 subunit genes and alco‑
holism. Psychiatr Res. 1998;81:133–147. [PubMed] [Google Scholar]
22.
Kulviwat S, Gordon CB, Obaid A-S. The role of social influence on adoption
of high tech innovations: The moderating effect of public/private consump‑
tion. J Bus Res. 2009;62(7):706–712.
23.
Holden C. “Behavioral” addictions: Do they exist? Science. 2001;294:980–982.
[PubMed] [Google Scholar]
24.
Potenza MN. Should addictive disorders include non-substance-related con‑
ditions? Addiction. 2006;101:142–151. [PubMed] [Google Scholar]
25.
Mohd Javaid, Abid Haleem, Ravi Pratap Singh, Shanay Rab, Rajiv Suman
Internet of Behaviors (IoB) and its role in customer services. https://doi.
org/10.1016/j.sintl.2021.100122.
Websites
1. www.analyticssteps.com/blogs/introduction-Internet-behaviour-iob
2.
www.financialexpress.com/industry/technology/Internet-of-behaviours-more-
power-to-digital-marketing/2371247/
3.
www.techfunnel.com/information-technology/Internet-of-behaviors/
4.
www.vectoritcgroup.com/en/tech-magazine-en/user-experience-En/what-is-
the-Internet-of-behaviour-iob-and-why-is-it-the-future/](https://image.slidesharecdn.com/24112563-250511185650-b2094ed1/85/Internet-Of-Behavior-Iob-R-Dhaya-R-Kanthavel-54-320.jpg)
Internet Of Behavior Iob R Dhaya R Kanthavel
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- 6. Internet of Behavior (IoB) This book is intended to survey the Internet of Behavior (IoB). The book begins with the benefits and potential pitfalls of IoB. Today, IoB has huge potential in every sector of the world. There are numerous applications for IoB which benefit users as well as the business market in order to enhance the user experience. In this book, the benefits of IoB and its negative constraints are discussed in detail. It is a high time that IoB is to take its crown and ruled the world. The work of IoB is critical in keeping our data secure because it can currently identify all humans who attempt to steal someone’s data. Moreover, the business uses of IoB are in high demand. By leveraging promising technical improvements and advances in machine learning algorithms, IoB enables capture, analysis, comprehension, and response for all types of human behavior in a technique that enables the tracking and interpretation of the behavior. IoB can be very useful wherever the behavior, preferences, interests, and location of people need to be examined. On the other hand, an analytical study on consumers’ social and behavioral psychology and their influence on online purchasing is much needed. With the help of visualization tools such as Tableau and detailed reporting on selection patterns, the impact of social media on decision making and the relationship between personality and purchasing power in various age groups is found. The presented study lists major decision-making psychometric factors and highlights critical factors affecting online purchases. The role of IoB is to shape customer service through the use of artificial intelligence, cloud computing, data, smart analytics, machine learning, and other volatile technologies. The attractive components of this book are discussions of dynamic routing mechanisms to reduce energy consumption in software-defined networks; deep insight into Internet of Things (IoT) and IoB security and privacy concerns – applications and future challenges; sentiment analysis and feature reduction using an arboreal monkey compression algorithm with a deep modified neural network classifier; cybersecurity concerns for IoB; and identification of nutrients and microbial contamination in fruits and vegetables using a technology using the Internet of Behavior. There is no doubt that this book covers numerous interesting themes and details on the Internet of Behavior.
- 7. ABOUT THE EDITORS Dr. R. Dhaya is presently working in the Department of Computer Science at King Khalid University, Abha, Kingdom of Saudi Arabia. She has teaching and research experience of over 16 years. Her research areas include wireless communication, advanced embedded systems, AI, ML, grid and cloud data communication, image classification, big data, and computing techniques. She has published more than 100 research articles in reputed journals and international conferences, which include SCI, Scopus, and IEEE/Springer/ Elsevier/ESI conferences. She received the Institute of Engineers, Kolkata’s Outstanding Young Women Engineer award as well as the Young Women Scientist award. Dr. R. Kanthavel is presently working in the Department of Computer Engineering at King Khalid University, Abha, Kingdom of Saudi Arabia. He has 23 years of teaching and research experience. He has been doing continuous research works in the field such as wireless communication, AI, machine learning, cooperative communication, image classification, and computing techniques. He published more than 150 research articles in refereed journals and peer reviewed international conferences.
- 8. Internet of Behavior (IoB) Edited By Dr. R. Dhaya and Dr. R. Kanthavel
- 9. Cover Image: Pixabay First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487–2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2023 selection and editorial matter, Dr. R. Dhaya and Dr. R. Kanthavel; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Dhaya, R., 1983– editor. | Kanthavel, R., 1974– editor. Title: Internet of behaviours (IoB) / [edited by] Dr. R. Dhaya, King Khalid University Abha, Saudi Arabia, Dr. R. Kanthavel, King Khalid University Abha, Saudi Arabia. Description: First edition. | Boca Raton : CRC Press, 2023. | Includes bibliographical references and index. Identifiers: LCCN 2022055185 (print) | LCCN 2022055186 (ebook) | ISBN 9781032304533 (hardback) | ISBN 9781032304540 (paperback) | ISBN 9781003305170 (ebook) Subjects: LCSH: Internet of things—Psychological aspects. | Human-computer interaction— Psychological aspects. | Internet users—Psychology. | Consumer behavior. | Computer security—Psychological aspects. Classification: LCC TK5105.8857 .I544 2023 (print) | LCC TK5105.8857 (ebook) | DDC 004.67/8—dc23/eng/20230222 LC record available at https://lccn.loc.gov/2022055185 LC ebook record available at https://lccn.loc.gov/2022055186 ISBN: 978-1-032-30453-3 (hbk) ISBN: 978-1-032-30454-0 (pbk) ISBN: 978-1-003-30517-0 (ebk) DOI: 10.1201/9781003305170 Typeset in Sabon by Apex CoVantage, LLC
- 10. v List of Contributors vii 1 Benefits and Pitfalls of Internet of Behavior 1 BOBIN CHANDRA B, MSR MARIYAPPAN, AND AISWARYA SANAL 2 Working of IoB 15 MANISHA VERMA 3 A Detailed Study to Increase Business Value 27 P. DEVISIVASANKARI, R. VIJAYAKUMAR, AND VIJAYALAKSHMI V 4 Analytical Study on Consumer Social and Behavioral Psychology and Its Influence on Online Purchasing 41 PARTH RAINCHWAR, RISHIKESH MATE, SOHAM WATTAMWAR, ADITYA GAISAMUDRE, PRATYUSH JHA, AND SHILPA SONAWANI 5 Role of Internet of Behavior in Shaping Customer Service61 JASPREET KAUR AND HARPREET SINGH 6 Dynamic Routing Mechanism to Reduce Energy Consumption in a Software-Defined Network 85 VIJI FLORANCE G 7 A Deep Insight into IoT and IoB Security and Privacy Concerns – Applications and Future Challenges 101 HINA BANSAL, VUSALA SRI SAI PRAVALLIKA, SHRAVANI M. PHATAK, AND VERONICA KUMAR Contents
- 11. vi Contents 8 Sentiment Analysis and Feature Reduction Using Arboreal Monkey Compression Algorithm with Deep Modified Neural Network Classifier 125 RAJALAXMI HEGDE, SANDEEP KUMAR HEGDE, MONICA R. MUNDADA, AND SEEMA S 9 Cyber Security Concerns for IoB 141 SAINATH PATIL, ASHISH VANMALI, AND RAJESH BANSODE 10 Identification of Nutrients and Microbial Contamination in Fruits and Vegetables – Technology Using Internet of Behavior 157 K. SUJATHA, N.P.G. BHAVANI, G. VICTO SUDHA GEORGE, D. KIRUBAKARAN, M. SUJITHA, AND V. SRIVIDHYA 11 Plagiarism Detection for Afghan National Languages (Pashto and Dari) 171 NIAZ M. DOOSTYAR AND B. SUJATHA 12 COVID-19 Vaccine Acceptance and Hesitancy in India Scenario 187 VIREN MODI, KARAN AJIT SHAH, SPRIHA SHEKHAR, AAYUSH GUPTA, AND SHILPA SONAWANI 13 Applications of Internet of Behavior 213 M. MAHALAKSHMI, B. VENKATESHWAR RAO, A. RAHUL RAJ, AND ANUSHA VASAMSETTI 14 Smart Facilities and 5G-Supported Systems in Social IoB 225 J. BANUMATHI AND S.K.B. SANGEETHA Index 243
- 12. vii Bobin Chandra B Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Tamil Nadu, India Hina Bansal Amity University Uttar Pradesh, India Rajesh Bansode Thakur College of Engineering and Technology Mumbai, India J. Banumathi University College of Engineering Nagercoil, Tamil Nadu, India N.P.G. Bhavani Saveetha School of Engineering SIMATS Chennai, Tamil Nadu, India P. Devisivasankari CMRIT Bangalore, India Niaz M. Doostyar Osmania University Telangana, India Viji Florance G New Horizon College of Engineering Bangalore, India Aditya Gaisamudre Aditya Gaisamudre MIT World Peace University Pune, Maharashtra, India G. Victo Sudha George MGR Educational and Research Institute Chennai, India Aayush Gupta MIT World Peace University Pune, India Rajalaxmi Hegde NMAM Institute of Technology Karnataka, India Sandeep Kumar Hegde NMAM Institute of Technology Karnataka, India Contributors
- 13. viii Contributors Pratyush Jha Dr. Vishwanath Karad’s MIT World Peace University Pune, Maharashtra, India Jaspreet Kaur Hans Raj Mahila Maha Vidyalaya Jalandhar, India D. Kirubakaran St. Joseph’s Institute of Technology Chennai, India Veronica Kumar Amity University Uttar Pradesh, India M. Mahalakshmi CMR College of Engineering Technology Telangana, India MSR Mariyappan Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Tamil Nadu, India Rishikesh Mate Rishikesh Mate MIT World Peace University Pune, Maharashtra, India Viren Modi MIT World Peace University Pune, Maharashtra, India Monica R. Mundada M S Ramaiah Institute of Technology Karnataka, India Sainath Patil Vidyavardhini’s College of Engineering Technology Mumbai, India Shravani M. Phatak Amity University Uttar Pradesh, India Vusala Sri Sai Pravallika Amity University Uttar Pradesh, India Parth Rainchwar Dr. Vishwanath Karad’s MIT World Peace University Pune, Maharashtra, India A. Rahul Raj CMR College of Engineering Technology Telangana, India B. Venkateshwar Rao CMR College of Engineering Technology Telangana, India Seema S M S Ramaiah Institute of Technology Karnataka, India Aiswarya Sanal Systems Engineer Infosys Private Limited Kerala, India S.K.B. Sangeetha SRM Institute of Science and Technology Chennai, India
- 14. Contributors ix Karan Ajit Shah MIT World Peace University Pune, Maharashtra, India Spriha Shekhar MIT World Peace University Pune, Maharashtra, India Harpreet Singh Hans Raj Mahila Maha Vidyalaya Jalandhar, India Shilpa Sonawani MIT World Peace University Pune, Maharashtra, India V. Srividhya Meenakshi College of Engineering Tamil Nadu, India B. Sujatha Osmania University Telangana, India K. Sujatha MGR Educational and Research Institute Chennai, India M. Sujitha MGR Educational and Research Institute Chennai, India Vijayalakshmi V CMRIT Bangalore, India Ashish Vanmali Vidyavardhini’s College of Engi- neering Technology Mumbai, India Anusha Vasamsetti CMR College of Engineering Technology Telangana, India Manisha Verma ITM University, Gwalior Madhya Pradesh, India R. Vijayakumar Jain University Bangalore, India Soham Wattamwar Dr. Vishwanath Karad’s MIT World Peace University Pune, Maharashtra, India
- 16. DOI: 10.1201/9781003305170-1 1 CONTENTS 1.1 Introduction........................................................................................1 1.2 Method of Study.................................................................................3 1.3 Results and Analysis...........................................................................4 1.3.1 Benefits of IoB..........................................................................4 1.3.2 Pitfalls of IoB...........................................................................7 1.4 Conclusion........................................................................................11 References.................................................................................................12 1.1 INTRODUCTION The Internet of Behavior (IoB) cannot be discussed without mentioning the Internet of Things (IoT). The IoT can be defined as the interconnected net- work of devices which collects data and further shares it online. Technologi- cal advancements are happening day by day. Moreover, this is reflected in the IoT. Today, the IoT involves more complexities than before. It has evolved to a level where calculations can be performed on its own. The IoT comprises network-enabled devices which gather, transfer and process the data cap- tured with the help of embedded systems. They provide sensor data collected via connecting IoT gateways. Furthermore, the data is passed to the cloud server for detailed analysis or analyzed locally. These devices have the proper communication abilities to share valuable information. These devices work without human aid.However,humans can have interaction with these devices for device setup and to assign tasks and access data. The IoT helps upgrade the lifestyle of people with smarter work and gadgets. It helps people have control over their lives. Besides producing smart devices for the automation of houses, IoT devices are inevitable in the business sector.They provide real- time insights into ventures inclusive of supply chain, logistics and machine performance. This helps in the reduction of labor costs and other expenses. Moreover, it enhances service delivery and initiates transparent transactions. Chapter 1 Benefits and Pitfalls of Internet of Behavior Bobin Chandra B, MSR Mariyappan, and Aiswarya Sanal
- 17. 2 Internet of Behavior (IoB) It also aids in cost cutting of manufacturing and goods delivery. IoT can be considered the most prominent technology in our daily life in order to ease our daily tasks and make our life smarter. It is gradually improving as more companies recognize the potential applications of devices [1–3]. IoB is the process of analyzing user-regulated data from a behavioral and psychological point of view, using the results of this analysis to inform analysts about new perspectives to design user experience and sell end prod- ucts and services offered by the venture. Therefore, implementing IoB in an organization is technically simple but psychologically complex. For ethical and legal reasons, we need to conduct statistical surveys that map daily habits and behaviors without fully disclosing consumer information [4]. IoB aims to understand and analyze every human behavior, further utiliz- ing data in the enhancement of digital experience and other technological advancements. People’s behaviors are monitored and incentives or disincen- tives are carried out to persuade them to act in the direction of a favored set of operational parameters. IoB is not descriptive but proactive. IoB impacts purchaser choice; however, it additionally redesigns the fee chain. While a few customers are cautious about supplying their data, many others are willing to do so as long as it provides a fee – a data-pushed fee. For compa- nies, this indicates being capable of extending their image or marketplace merchandise to their clients or enhancing the customer experience (CX) of a product or service. Hypothetically, statistics may be accumulated for all aspects of a user’s life, with the final aim of enhancing performance and quality [5]. The IoB aims to discuss how data can be better understood and used to build and promote new products from the perspective of human psychology. IoB can be used in different ways by public or private institutions. This tech- nology is becoming an attractive new marketing and sales platform for busi- nesses and organizations around the world. The IoB platform enables the development of a comprehensive customer understanding that every com- pany needs. For example, IoB can connect all the phones in an app, check for mistakes and get visual recommendations for enhancing swings and shots. Device networking creates many new data points, including from the Inter- net of Things. Ventures collect information from their customers by data sharing between connected devices [6]. These devices are actually monitored by a single computer. Aggregating usage and information from IoB devices provides useful insights into consumer behavior, desires, and preferences, which can be simply referred as computer network. This includes devices ranging from phones to vehicles, training replenishments, and credit cards, to those that are literally connected to the Internet.Therefore, the goal of IoB is to record, analyze, understand and respond to all forms of human behav- ior so that people can be tracked and understood by advances in technology and machine learning algorithms.
- 18. Benefits and Pitfalls of Internet of Behavior 3 Studies show that IoB tends to be an advancing technology which has huge potential in the future. However, it has several risk factors which may or may not affect humans directly. IoB will take its crown in the near future, providing smarter solutions to the world. Both customers and organizations will benefit from IoB. This chapter discusses the benefits and challenges of IoB with the help of online surveys and analysis of various research papers. 1.2 METHOD OF STUDY IoB is an advancing technology which has huge future potential. Many stud- ies have been performed for the applications and future scope of IoB. Fur- thermore, researchers have shed light on various sectors where IoB plays a major role. Our chapter mainly focuses on the benefits and challenges of IoB in various scenarios [7, 8]. IoB is considered an expansion of the IoT since it has the capability to reproduce various patterns in order to have an influence on human behav- ior. Consequently, IoB can be referred to as a mixture of technology, data analytics and behavioral psychology. Studies have already proved the poten- tial of IoT in various scenarios. Therefore, one can imagine how powerful the combination of IoB and IoT could be. IoB can easily tackle consumer behavior and gather relevant data for the improvement of the consumer experience. Subsequently, the area of digital marketing is also affected as consumer interests become available via IoB. Digital marketing becomes easier with personalized content interests. The IoB can very well be associ- ated with social media platforms and the most-used search engine, Google [9, 10]. The advertisements shown between surfing the Internet and scrolling social media would be the ones the user was looking for. This happens with periodic tracking of customer behavior and interests. However, IoT devices fail to gather data easily. The data processing in the IoT is bit complex, and hence many companies do not have access to the data from it, whereas IoB is customer centric, and it is easier to track human behavior and share data with the relevant companies. More often companies may prefer IoB because of its customer-centric behavior. It has the potential to trigger customers in favor of a product purchase. Through such approaches, companies get an insight into product enhancement according to consumer interests. Thus, sales can be increased. Similarly, various sectors have advantages through IoB with relevant applications. Besides these benefits of IoB, the design of the most efficient approaches to capturing human psychology is increasingly important and needs to be focused on [11–13]. We have gathered information through the review of various studies per- formed on IoB. The studies mainly focused on consumer experience and product development. An online survey was conducted for the collection of
- 19. 4 Internet of Behavior (IoB) customer experience regarding the appearance of advertisements and data sharing in various applications. This helped us calculate a periodic tracking system of IoB [14, 15]. Service quality and the value chain can be improved with the aid of such data gathered. The analysis of this data helped to elu- cidate the benefits and risk factors or drawbacks of IoB in various sectors. 1.3 RESULTS AND ANALYSIS IoB is applicable in various sectors. This section deals with the benefits and pitfalls of IoB in various sectors. With the ability to decipher customer intent as described in natural language statements, search engine optimization focuses heavily on intent, as well as words and keywords that describe the function and benefit of the product. Therefore, it is possible to take advan- tage of much better insight into user psychology and a product outlook with an understanding of the possible ways of choosing a product and services. Being alert about the time and place of customer shopping can affect your business positively, as notifications can be pushed regarding offers and dis- counts. In addition, IoB allows personalization, which helps in the enhance- ment of service quality and efficiency [16, 17].With the help of IoB, you have the opportunity to improve the quality of the data collected and combine data from different sources to gain your own insights from this information. Companies need to be very cautious regarding privacy concerns and cyber security of the customer data. If any kind of violation occurs, it may directly affect the company’s reputation and service. As we know in the advanced technological era, hackers and cyber criminals could misuse sensitive data. Therefore, it is highly important to adopt measures against these concerns in order to implement IoB in any application. 1.3.1 Benefits of IoB Sales Industry: The sales industry greatly benefits from IoB. Products and services can be offered to customers with knowledge on the personal inter- ests of the customers. This provides a very good customer flow for demand- ing products and services. Since the dawn of advertising, marketing and psychology have been intertwined. Therefore, behavioral analytics and psy- chology could provide new insights into the data generated by the Inter- net of Things. For businesses and organizations around the world, IoB has the potential to become a powerful new marketing and sales tool [18–20]. Organizations can use the concept of IoB to look at past performance and predict the future. Enterprises organize their development, marketing and sales activities based on the data received via the IoT. Several companies and organizations have created health apps for cell phones that track food,
- 20. Benefits and Pitfalls of Internet of Behavior 5 sleep habits, heart rate and blood sugar levels in the medical field. The software finds the issue with the health of the user and suggests behavioral changes for the best outcome. To cite an example, let us consider the over- the-top platform Netflix. Netflix gets user data to predict the likes and dislikes of the viewers. It recommends movies and shows based on personal interests and ratings. Another example: Uber and other taxi aggregators use the IoT to track drivers and passengers. They provide a survey after each trip to review the guest’s experience. However, you can observe driver behavior, understand passenger reactions and automatically construct those inputs via IoB, so you can collect historical data and avoid investigations. The behavioral insights obtained can be used to transform a major prospect to sales. With the help of behavioral psychology, IoB identifies and inter- prets human behavior by analyzing how human humans react in each situ- ation via various technologies. Some companies utilize facial recognition to examine behavior patterns at a specific time. This helps in the improvement of sales team efficiency by examining their behavior. IoB aids in alteration of sales engagement with consumers in real time. Marketing Sector: IoB plays a major role in the marketing sector and has huge potential to trigger marketing, which further affects sales and services. The behavioral data gathered with the help of IoB aids in effective campaign- ing. It helps in the optimization of marketing campaigns and marketing to consumers efficiently. IoB data can be collected from various platforms in order to analyze customer buying habits. This includes the place and time of shopping, which aids in the personalization of advertisements on various platforms. Besides these, IoB initiates marketing notifications on the latest offers and discounts depending on behavioral data. Furthermore, IoB data helps in analyzing the needs of customers, and companies can provide the required User Interface (UI) [21]. Marketing, or rather digital marketing services, will be one of the biggest winners of IoB technology, as Internet connectivity is a prerequisite for IoB. Digital marketing is an area where data is used primarily to sell products and services to people around the world. With access to behavioral analytics and interpretation tools, we are in a stronger position to provide better reach to those at the end of the purchasing process. Digital marketing services are booming all over the world. Digital marketing is being used more promi- nently by brand promotion, lead generation, sales generation and people. If you also need digital marketing help or advice, hire a true wireless stereo digital marketing expert to meet your needs. Customer Experience: The Internet of Behavior helps agencies learn everything about consumer behavior so they can deliver exactly what their clients want. If a customer encounters positive issues during the adventure, the agency can deal with them and enhance overall customer happiness. This allows them to stabilize (and study) consumer loyalty over time [22].
- 21. 6 Internet of Behavior (IoB) Companies use the Internet to collect various facts about consumer behav- ior. The Internet of Behavior transforms these personal facts into valuable records. This helps companies improve their business performance by know- ing everything about their customers. In addition, the Internet of Action makes it possible to collect facts about some contacts. Therefore, the agency can oversee the entire consumer adventure from the beginning to the end. This is how the Internet of Behavior helps businesses follow in the footsteps of their customers. IoB helps to tailor every need of the customer based on their likes and dislikes.This helps both the company and the customer in sav- ing time and effort to choose the best product and best customer experience. Research and Manufacturing Industry: IoB can benefit not only customer experience and sales but also can be utilized in the collection of data for research purposes. Research includes the testing and analyzing of various applications with the gathered human behavioral data.The research industry is supported by customer research, product research, observational research, experimental research and simulation data, bringing innovative technology to the market. The research industry will better understand market needs to derive profound solutions. Manufacturing is the largest area strongly influenced by digital transfor- mation technology. The Internet of Things is slowly playing a role in the misleading debate surrounding IoT development, artificial intelligence and robotics used in the manufacturing industry. The Internet of Behavior is used in the manufacturing industry to monitor employee and worker behavior. It increases productivity, reduces idle time, reduces unnecessary activity and is monitored to work on time. Miscellaneous: All new technologies affect some areas of the business world. What is the real reason behind all these possibilities for the internet downpour of standing (IoB) behavior IoB in the home? To answer this, there are many behavioral Internet offerings for the general household. Individu- als gain safety, comfort and efficiency in their daily lives. With the help of IoB, you will be able to make better decisions. To cite an example of Internet behavior, IoB was used during Covid-19 to determine if a person wore a face mask and washed their hands. People have always been reminded to follow guidelines to protect themselves. Shopping giants such as Wal-Mart, Ama- zon, Costco, Kroger, and Home Depot use the Internet of Behavior promi- nently to monitor store activities. All places where large numbers of people gather, not just shopping places, are monitored by IoB to promote specific etiquette. By 2025, physical checkout process staff will be mostly eliminated, and automatic checkout systems will be installed in most shopping locations. All this is made possible by the Internet of Action. Big data is used to improve people’s lives. The Internet of Behavior is a revenue-generating tool for most people, businesses and organizations. IoB certainly brings technical know- how to all sectors of the population. IoB provides enough data for market
- 22. Benefits and Pitfalls of Internet of Behavior 7 research. IoB can also be used to improve the security of public places with facial recognition. IoB advocates a personalized approach to all users. It will bring more business opportunities to people. This reduces industry monitor- ing costs and delivers a better customer experience through personalized targeting of products and services. IoB will usher in a new era of being in the digital world. Behavioral data can by analyzed to determine product demand and adjust production accordingly; collect previously unavailable data about how prospects and customers interact with a business, products and ser- vices; analyze staff behavior to improve the quality and efficiency of produc- tion and service departments; monitor customer and employee behavior to improve public health and safety; convert IoT data to IoB insights; and test the effectiveness of commercial and non-profit campaigns. Governments can adapt support programs and content to new legislation. In the healthcare field, healthcare providers can assess a patient’s condition and treatment efforts and obtain more lifestyle data. 1.3.2 Pitfalls of IoB Internet of Behavior protection is an important concern to be addressed. No technology can be considered fully secure, as it may have risks in the long run. There are enormous probabilities of data robbery and leakage of sensitive data that could adversely affect each individual or user. The abun- dance of information and perception can be a big undertaking to control and secure. There can be an extra desire for cyber security to prevent crimes. The Internet of Behavior continues to be in its early days and therefore numer- ous drawbacks may also pop out along with it. The blessings of Internet of Behavior structures are plausible for agencies that overcome the demanding situations inherent to deploying and keeping an IoB system. The toughest roadblock to achievement will be the gathering of highly secure information from clients and workers. People are already conversant with surrendering some non-public information for comfort and different benefits. The IoB calls for even more non-public information, together with intimate bodily information that measures outward appearances and inner functions. Con- vincing humans to give up non-public information on incredibly sensitive data won’t be easy. Companies will have to deal with customers’ privacy concern with contractual or monetary agreements. Subsequently, data will become more secure, preventing leakage and misuse. The Internet of Behavior is user-identifiable information using the Internet of Things and wearable technologies such as smart watches. It is a concept that collects and identifies user behavior. Through IoB, this data is combined and processed to generate information that can create new approaches to user experience development, search experience optimization and enterprise product and service promotion. The more data you get from the IoT, the
- 23. 8 Internet of Behavior (IoB) deeper your data insights will be. In general, the data collected by IoT comes from a variety of sources, including customer data, social media, public data, face recognition, location tracking and civilian data processed by govern- ment agencies. Inevitably, as the life of the IoT became easier, people were unaware that they were able to disclose their personal information and allow certain organizations and businesses to share data without asking for per- mission. This led to Io’s ethical issues, as there was no clear line between maximizing user needs and tampering. Cyber Security: The primary issue with the Internet of Behavior is the way information is amassed and stored. The Internet of Behavior is a huge database for cybercriminals to benefit from. Exclusive information from cus- tomers also can be compromised if it falls into the hands of unethical users. The integration of behavioral data with the IoT gives cybercriminals access to sensitive data that reveals user behavioral patterns. Leaked information makes users more vulnerable to cybercriminal activities such as ransomware, fraud, money laundering and theft of personal information. However, many companies and groups have all begun to deal with this issue. New cyber security protocols may come into existence that makes the use of the Internet of Behavior safer. Nonetheless, the Internet of Behavior is a modern concept predicted to affect marketing and advertising to an awesome extent. In summary, IoB risk is an issue whenever organizations that use sensitive data are unaware of their responsibility to protect their personal privacy. Therefore, organizations that decide to adopt the IoB approach must ensure that they implement a robust cyber security strategy to protect all sensitive data. Data Privacy: When an agency collects a vast amount of information about a customer’s mood, behavior and likes and dislikes, the customer has the opportunity to claim their right to privacy. A lot of information is col- lected through various sources. This is good in the first place, as people benefit from the facility. However, the biggest concern is the collection, navigation and usage of information, especially on a large scale. Behavioral data may provide cybercriminals with access to sensitive data that reveals consumer behavioral patterns. Cybercriminals may collect hacked property access codes, shipping routes and even bank access codes and sell them to other criminals. The possibilities are endless. However, impersonating an individual for fraudulent or other purposes is likely to take phishing to a new level. The rapid expansion of networks of IoT devices means that new cybersecurity protocols are being developed and enterprises need to be more vigilant and proactive than ever before. IoT gathers information from various companies. For instance, bike insur- ance may check your history for ensuring you are a good rider. As a society, we have decided that this is fair. However, insurance companies can also look at your social media profiles and interactions to predict if you are a safe
- 24. Benefits and Pitfalls of Internet of Behavior 9 rider. This is a suspicious and illegal move. It’s not difficult for businesses to link their smart phones to laptops, home voice assistants, home and car cameras and perhaps cell phone records. And it’s not just the device itself. Behind the scenes, many companies exchange data between corporate lines or with other affiliates. Google, Facebook and Amazon continue to acquire software that has the potential to bring users of a single app into the entire online ecosystem. In many cases, they do not have permission.This poses sig- nificant safety and legal risks, and there is little legal protection against these concerns. The outlook for IoB is still in its infancy, but as more and more new data and analytics become available, companies need to make sure they are aware of consumer behavior and trends. Adopting a strong data security regime followed by data stewardship best practices, cyber security training and awareness programs helps organizations stay ahead of the curve. In fact, IoB is expected to become widespread soon, and by the end of 2025, more than half of the world’s population will be covered by at least one IoB program, whether commercial or governmental. Data privacy is the basic need for each customer and has to be valued and respected by companies. It should not be misused for monetary purposes. Data abundance and visibility are difficult to manage and protect, leading to major cybercrime.After all, IoB is currently in the growth stage and can be life-changing for people. IoB will bring a lot of excitement to achieve innova- tion and a lot of change to the world of technology. As a pyramid, the IoT transforms data into information, while IoB transforms knowledge into true wisdom. Therefore, while IoB provides contributions, especially in business and health care, its risks should not be ignored. Also, IoB is a personalized one-on-one marketing holy grail. It can combine online and offline data with behavioral science and use detailed data profiles to influence behavior. For example, coffee chains use facial recognition software associated with surveillance cameras to track customers across locations and match faces to transactions and use data to geofence relevant marketing offers. Organiza- tions can force employees to comply with standards, for instance, to wear a mask, by using facial recognition to identify nonconformities. Governments can use this data to track citizens and monitor IoB data for signs of unwanted behavior such as terrorism or the organization of political protests. The IoT itself is not a problem in nature. Many people prefer to synchronize their devices to benefit from this setting. IoB raises issues regarding data owner- ship, privacy and data security. These datasets can be combined between intermediaries, and third parties buy and sell aggregated data to provide businesses with more detailed insights into individual consumers. There are also ethical concerns associated with tracking individuals and aggregating their data into a viable format to influence their behavior. Miscellaneous: Privacy has turned out to be a political concern in many jurisdictions, especially after massive data breaches by platforms that rely
- 25. 10 Internet of Behavior (IoB) on personal data such as Facebook, Yahoo and LinkedIn. Ventures need to develop IoB devices and systems that comply with the regulations of multiple jurisdictions. On IoB systems, it may be necessary to disable some features of the area that limits data collection. People want better products and ser- vices, better customer experiences and improved lifestyles in exchange for sharing personal information. The value provided to consumers must be commensurate with the risk, as IoB data can contain highly sensitive infor- mation. Like IoT devices, IoB technology is another potential attack vector for criminals. IoB data can contain highly sensitive information, which can increase the severity of the attack. Behavioral data can provide cybercrimi- nals with access to information and leave people vulnerable to digital and physical attacks. Personalization aids in the enhancement of products and services affect our values. It can go to extremes to make products customers love. Some studies show that when too many products were identified, it resulted in pregnancy prediction. It was followed by customer complaints, as they were not aware of it and their personal data was not safe [9–21]. Orga- nizations should ensure that privacy laws are followed before implementing any solution. In addition to complying with the law, we need to ensure that the collection and utilization of data is ethical and follows moral standards from the perspective of public and consumers. An organization may use IoB data by in violating existing local privacy laws. These violations can result in penalties and police cases and therefore loss of reputation. In such cases, businesses will be at risk of being sued by an individual and facing govern- ment fines. Organizations must agree with local data protection laws, which can vary widely from region to region. They need to perform rigorous data attribution to evaluate which data is stored for which client, client or user, and they need to comply with the law regarding where and how that data is stored. Only then can data be deleted at the request of the user and no fines for violations imposed. The company may agree to the laws but continue to behave unethically in front of both internal and external stakeholders. The use of IoB data to monitor the staff in an organization may improve the efficiency of work. But it can badly affect the relationship between the employees and their trust of the management of the organization, as their privacy is not considered. According to McKinsey, there are more connected devices today than people. From smart phones to smart watches, speakers, voice assistants and surveillance cameras, more than 30 billion connected devices have been added worldwide. With sensors and auto-launch capabilities, these devices permeate every area and constantly send large amounts of sensitive data to businesses for data analytics. After a few years, around 40% of the world’s population will be digitally monitored for minute actions [22–24]. It will hardly be possible to evade the tracking systems of devices. All our actions are being monitored. In the present scenario, where we are being tracked by
- 26. Benefits and Pitfalls of Internet of Behavior 11 almost all devices and artificial intelligence provides faster and more accu- rate data, IoB will have a huge impact on philosophical, ethical and legisla- tive business and society in general. As Daryl Plummer, chief research officer and Gartner Fellow, points out, the very existence of these devices and how governments and businesses use the data need to be rethought in existing frameworks. Therefore, in the long run, almost everyone in the society will be influenced by the impacts of IoB [25, 26]. 1.4 CONCLUSION Internet of Behavior is a technology that uses the Internet of Things in order to gather behavioral patterns of humans for the enhancement of digital expe- riences, sales, marketing and many more sectors. This chapter focused on the benefits and pitfalls of Internet of Behavior. Each sector was studied in detail, and the benefits of IoB in those sectors were analyzed. IoB applica- tions range from the sales and marketing sector to consumer experience. IoB works based on behavioral psychology. Human behaviors are analyzed and recorded for the enhancement of various platforms and applications in order to improve production and services. For companies, it becomes easier with IoB to attain a huge turnaround annually. Even marketing becomes easier, as advertisements and campaigns can be personalized based on consumer inter- ests and dislikes. From the research, it is clear that IoB has a huge potential in the future. IoB can revolutionize the technological area in no time. Further research is needed to learn about the applications of IoB in sensitive sectors. IoB aids in providing business solutions to achieve more sales and keep cli- ents highly satisfied. IoB can replace multiple customer surveys, which are a complex task and time consuming for clients as well as the companies. It helps gather sensitive information on customer interaction with the provided products and services. Companies can get a good understanding about the shopping habits of consumers. Companies can also track the period of time in which a customer is purchasing or browsing for products. It can help in initiating notification alerts at that particular time. This will provide more efficiency in offering products and services. As we are aware that IoB is in early stages, there are lot of challenges to be tackled. The chapter also analyzed various risk factors and challenges of IoB which can arise besides the huge benefits. The major factor to be considered is the protection of data, or data privacy. As far as political laws regarding cyber security and data privacy are exist, IoB faces challenges, especially when gathering sensitive customer data. Data shared using IoB can be mis- used by hackers and cyber criminals for monetary purposes. If this occurs, companies might be at risk, which may affect their reputation and services. IoB can negatively impact people, as it may gather sensitive information on
- 27. 12 Internet of Behavior (IoB) people, which is against privacy protection laws. It can even trigger unneces- sary conflicts in political bodies. Therefore, IoB should be implemented with utmost awareness regarding the laws and concerns of the public. Compa- nies must ensure data privacy protection of users. IoB can revolutionize the tech era with smarter solutions and smarter implementations using human behavior analysis. IoB can be a life saver if properly implemented and uti- lized. This chapter covered major areas where IoB is beneficial and the risk factors it entails. Therefore, one can easily study the potential risks associ- ated with IoB along with the enormous possibilities offered by IoB. REFERENCES 1. Javaid, M., Haleem,A., Singh, R., Rab, S., Suman, R. Internet of Behaviours (IoB) and its role in customer services, Sensors Int. (2021). https://doi.org/10.1016/j. sintl.2021.100122. 2. Stary, C. The Internet-of-Behavior as organizational transformation space with choreographic Intelligence. International Conference on Subject- Oriented Business Process Management. The Digital Workplace – Nucleus of Trans‑ formation. S-BPM ONE 2020. Communications in Computer and Infor‑ mation Science, vol. 1278. Springer, Cham (2020). https://doi.org/10.1007/ 978-3-030-64351-5_8. 3. Holland, J. H. Complex adaptive systems, Daedalus. 121 (17–30) (1992). 4. Goto, S., Yoshie, O., Fujimura, S., Tamaki, K. Preliminary study on workshop facilitation for IoT innovation as industry-university collaboration PLM pro- gram for small and medium sized enterprises. In: Ríos, J., Bernard, A., Bouras, A., Foufou, S. (eds.) PLM 2017. IAICT, vol. 517, pp. 285–296. Springer, Cham (2017). 5. Gilchrist, A. Industry 4.0: The Industrial Internet of Things. Apress, New York (2016). 6. He, J. S., Lo, D. C., Xie, Y., Lartigue, J. W. (2016). Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. 2016 IEEE Frontiers in Educa‑ tion Conference (FIE), Erie, PA, USA, pp. 1–9. 7. Fleischmann, A., Schmidt, W., Stary, C., Obermeier, S., Börger, E. Subject-Ori‑ ented Business Process Management, ISBN 978-3-642-32392-8. Springer, Hei- delberg, https://doi.org/10.1007/978-3-642-32392-8. 8. Guo, B., Zhang, D., Wang, Z. Living with Internet of Things: The emer- gence of embedded intelligence. 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Com‑ puting, pp. 297–304. (2011). 9. Fleischmann, A., Schmidt, W., Stary, C. (eds.). S-BPM in the Wild: Practical Value Creation. Springer, Cham (2015). 10. Lingling Gao Xuesong Bai. A unified perspective on the factors influencing con- sumer acceptance of Internet of Things technology, Asia Pac. J. Mark. Logist. 26 (2) (2014) 211–231.
- 28. Benefits and Pitfalls of Internet of Behavior 13 11. Hevner, A. R. A three cycle view of design science research, Scand. J. Inf. Syst. 19 (2) (2007) 4. 12. Li, S., Chung, T. Internet function and Internet addictive behavior, Comput. Hum. Behav. 22 (2006) 1067–1071. 13. Gross, T., Stary, C., Totter, A. User-centered awareness in computer-supported cooperative work-systems: Structured embedding of findings from social sci- ences, Int. J. Hum. Comput. Interact. 18 (2005) 323–360. 14. Shih, H. Extended technology acceptance model of Internet utilization behav- ior, Inf. Manag. 41 (2004) 719–729. 15. Smith, Donnavieve N., Sivakumar, K. Flow and Internet shopping behavior: A conceptual model and research propositions, J. Bus. Res. 57 (10) (2004, Oct) 1199–1208. 16. Patrick, Y., Chau, K., Hu, Paul J. Examining a model of information technol- ogy acceptance by individual professionals: An exploratory study, J. Manag. Inf. Syst. 18 (4) (2002) 191–229. https://doi.org/10.1080/07421222.2002. 11045699. 17. Chau, P. Y., Hu, P. J. Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories, Inf. Manag. 39 (2002) 297–311. 18. Chang, M. K., Cheung, W. Determinants of the intention to use Internet/WWW at work: a confirmatory study, Inf. Manag. 39 (2001) 1–14. 19. Adams, D. A., Nelson, R. R., Todd, P. A. Perceived usefulness, ease of use, and usage of information technology: A replication, MIS Q. 16 (1992) 227–247. 20. Ajzen, I. The theory of planned behavior, Organ. Behav. Hum. Dec. Proc. 50 (1991) 179–211. 21. Tsitsika, A., Janikian, M., Schoenmakers, T. M., Tzavela, E. C., Olafsson, K., Wojcik, S., Macarie, G. F., Tzavara, C., EU NET ADB Consortium, Richard- son, C. Internet addictive behavior in adolescence: A cross-sectional study in seven European countries, Cyberpsychol. Behav. Soc. Netw. 17 (8) (2014 Aug 1) 528–535. 22. Fleischmann, A., Schmidt, W., Stary, C., Strecker, F. Nondeterministic events in business processes. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 364–377. Springer, Heidelberg (2013). 23. Guo, B., Zhang, D., Wang, Z., Yu, Z., Zhou, X. Opportunistic IoT: Explor- ing the harmonious interaction between human and the Internet of Things, J. Netw. Comput. Appl. 36 (6) (2013) 1531–1539. 24. Andrews, L., Bianchi, C. Consumer Internet purchasing behavior in Chile, J. Bus. Res. 66 (10) (2013 Oct 1) 1791–1799. 25. Crespo, A. H., Del Bosque, I. R., de los Salmones Sanchez, M. G. The influence of perceived risk on Internet shopping behavior: A multidimensional perspective, J. Risk Res. 12 (2) (2009 Mar 1) 259–277. 26. Buente, W., Robbin, A. Trends in Internet information behavior, 2000–2004, J. Am. Soc. Inf. Sci. Technol. 59 (11) (2008 Sep) 1743–1760. 27. Kidd, C. What Is the Internet-of-Behavior. IoB Explained, The Business of IT Blog, BMC (2019).
- 30. DOI: 10.1201/9781003305170-2 15 CONTENTS 2.1 Introduction......................................................................................15 2.2 Materials and Methods of IoB..........................................................16 2.3 Working Process of IoB.....................................................................17 2.4 Discussion: The Importance of IoB...................................................18 2.5 Conclusion: Prospects for the Future................................................21 References.................................................................................................23 2.1 INTRODUCTION In the last few years, the use of Internet of Things (IoT) devices has more than doubled. All the small appliances being used around us, whether it is smart watch or washing machine, the modernity of these machines has given only the feeling of interacting with the world. Data and data col- lection by these modern machines provide important information about the behavior and interests of the user, and thus a new process of Internet of Behavior is born. It is one step ahead of the IoT [1]. We can say that where the work of IoT ends, that is where IoB starts. The IoB pioneers the Internet of Things system. With the help of which we analyze the collected data completely. In today’s modern world, we are all surrounded by differ- ent types of devices that are fully connected to the Internet, such as mobile phones, smart watches, and smart TVs. Today our daily life has become dependent on many such devices, whether it is online shopping or order- ing food. Internet of Behavior is a part of the IoT. The Internet of Things connects all types of devices to a network and collects all kinds of different information. From there the work of Internet of Behavior starts. Internet of Behavior analyses the collected data and better facilitates the user expe- rience. The Internet of Behavior is able to interpret the future reaction of any person according to the data used. The great researcher Gartner has also said that in the year 2023, the use of Internet of Behavior will be at its Chapter 2 Working of IoB Manisha Verma
- 31. 16 Internet of Behavior (IoB) peak level. Any big company can see from the experiences of their custom- ers what they like and what they do not. Earlier, a study would be used to examine consumer behavior and response to a product or service. Customer feedback and background may be a response to a product or service. Initially sample analysis is completed; then trust in the product or its service within the firm is discovered. IoB plat- forms are designed to gather, combine, and analyze data generated from a large number of sources, as well as digital devices, wearable computers, and online human activity. The information is then analyzed in terms of activity science to uncover patterns that may be employed by promotion and sales groups to influence future shopper behavior. Today the Internet of Behavior is being used in all kinds of small and big companies. Wolfram Alpha is a type of computational knowledge engine that was developed by Stephen Wolfram. It can provide information based on the activities of any person on Facebook and is also able to discover their thoughts and interests. The Internet of Behavior creates other opportunities for companies to collect and analyze data, the purpose of which is to provide better facilities to employees and customers. 2.2 MATERIALS AND METHODS OF IOB We are surrounded by the Internet of Behavior; today the trends of devices and technology are happening all around us. One of these is the Internet of Behavior, so this section examines how its use affects our lives and in which areas it is useful. In the Field of Business • Today, business is dominant, and people are trying to connect with customers through online advertising through various businesses, pro- viding all they want to any customer according to their own interests [1, 2]. Google and Facebook both use the Internet of Behavior the most to show ads on their sites. These companies interact with their customers and observe their experiences through their own advertising through the medium of ClearDro. • In the same area, YouTube also knows the experiences of its users, which allows it to show only those videos and topics in which the user is interested. During the COVID–19 Pandemic • At the time of the pandemic, when the world was going through a big crisis, the use of Internet of Behavior increased. Employers can use sensors or tags to see if there are any discrepancies in compliance
- 32. Working of IoB 17 with security standards. For example, if we talk about any food deliv- ery application, they also use protocol information to guide their decisions [3–5]. • Take, for example, Zomato and Swiggy, both of which demonstrated and promoted restaurant safety practices. They recorded and trans- mitted delivery person’s temperature to assure the consumer that they were safe. For the Insurance Industry • IoB has become very beneficial for the insurance industry. Driving tracking tools are increasingly being used by insurance companies like Allstate and State Farm to track drivers’ behavior and protect them [6, 7]. Using the Internet of Behavior, it can be found out whether an accident that has happened is a certain event or it is a false belief generated by it. 2.3 WORKING PROCESS OF IOB The stages of operating flow method of comprehending the web of Behav- ior for client necessities and creating those methods to feel happier with their shopping for is solely mirrored. The standard enhancement jour- ney starts with assembling data from all relevant sources. The informa- tion primarily supported facts were then collection of varied sources then analysis as needs data then sensing data and final compile all data and serving to customer. Once plotting pro re nata data graphs, and last the ultimate section is to implement the gathering and analysis data front for the realization of IoB for customer satisfaction in any relevant services the information may be obtained from many completely different shops. There are various methodologies of IoB that provide client services. Industrial shopper data to the welfare media and additional data is also accessible to all. That mixes numerous technologies for visual recognition, location monitoring, and intensive data that focuses directly on the person and connects the data to behavioral events. IoB is regarding how data can be higher understood and the way it can turn out and sell new merchandise from human psychology. The IoB provides digital analysis from differ- ent outlets and may be used to manipulate behavior. This has led to new insights into the IoT through behavioral analysis and psychology. IoB can become a tool for corporations and organizations worldwide for sales and promotion [8–11]. Corporations effectively link all primary devices to the web and keep them on their tracking list. All the information is currently being employed for advertising business and non-commercial products. Digital transformation methods will considerably impact the automotive sector, and here the IoB is taking its position among IoT growth, artificial
- 33. 18 Internet of Behavior (IoB) intelligence, and artificial intelligence technologies utilized in manufac- turing. In the industrial sector, the IoB is being used to track employer operating capability [12]. With an outsized quantity of data, distribution mechanisms for sales merchandise and services to interested people may be strategically implemented. It will additionally be ready to meet individu- als who are at the top of the acquisition chain who access instruments for behavioral analysis and explanations. Digital marketing is being employed more conspicuously by individuals for whole ads, lead generation, and sales generation [13]. One can modify people’s lives by opening up new technological fronts. it’s go with several excitations and brings power and get to the technical environment. Business can be expedited to include new solutions with the help of IoB and a huge quantity of user information for client analysis, product analysis, observation research, experimental research, and simulation results [14]. So as to develop careful strategies, the research business will help contemplate the business demands. This allows the tracking, monitoring, and operation of physical devices through the Internet [15, 16]. IoB expands this by connecting these instruments and therefore the information they gather to regulate personal conduct. The processes are as follows: • Dataset – collect from various sources – analysis of collected required data – sensing data and its behavior – collect data in various terms – serve data as required. If we follow the India web platform, when someone interacts with any other device from a user’s laptop, then IoB analyses clearly how the user is interacting with the resource, for example, which button they are clicking. Internet of Behavior is a process in which the data received by the user is ana- lyzed in the context of psychology and the analysis obtained from the user experience, search experience optimization, knowing the user’s interests, we develop new concepts to promote the growth of the company makes. We are currently taking full advantage of this by integrating the Internet of Behavior with machine learning. In the self driving process, machine learn- ing via artificial intelligence and Internet of Behavior is able to choose the better option by remembering the data of past experiences. By analyzing the experience gained by other human beings in a psychological way, it brings efficiency to the work. 2.4 DISCUSSION:THE IMPORTANCE OF IOB It is even easier to explain it with an example: we go to any shopping mall and buy different types of things, then pay, and then, according to a survey, he tries to know that your experience was cash. Here we use the IoT, but as
- 34. Working of IoB 19 we know, IoB works one step ahead of it. In contrast, IoB does not require a survey. IoB also tracks the behavior of the employees of the company accord- ing to it and according to them it understands the behavior of the customers on its own. And then it works to improve all kinds of collected data to the customers and the employees [17–19]. We know very well how important someone’s data is in today’s era. We know any human being by their behavior; in the same way, IoB also explains to the public and understands a user’s identity and activities in daily life and its usefulness according to human data. Even an organization named gather also made a prediction and said that in the year 2021–2022 Internet of Behavior in the world will get the best place in the world and its use and development will be very fast [19, 20]. With the help of the Internet of Behavior, all the companies and businesses will put four moons in their progress, its use will improve human life by making progress in every field from the pandemic to climate change [21]. IoB is completely new in today’s world, and on top of that, there are a lot of applications being developed and work going on right now. IoB, in its own way, collects all the activities done by humans on the Inter- net, analyses them thoroughly. It enables a user to track the usage of any device, provides additional touch-points per the user’s purchase decisions, and thus provides better products to the customer. The Internet of Behavior is considered a better source of getting complete information about the user. The data received through all kinds of approaches promotes the quality of service and the value chain and interest of the people towards it. With the efficiency of understanding the intention of the users described by natural language statements in search engines and playing a vital role in enhancing the product features [22, 23]. The IoB will further improve the quality and efficiency of services. With the help of Internet of Behavior, the goals of increasing the quality of all the data being collected and keeping data secure become very important. In such a situation, it also becomes very important for the user to be aware of their security and privacy [24]. • Security: Both the customer and the company need to be aware of security because there are many criminals who will constantly try to steal user data. In this case, it is also the user’s responsibility to keep their data safe. In which off behavior can play an important role or say that it is playing. They need to be careful about this. As we know, the Internet of Behavior is capable of identifying someone’s behavior from the data received. If we imagine that a person searches for informal data on a search engine, then with the help of Internet of Behavior, we can analyze his behavior and identify him easily. We are able to trace any kind of informal activities of such a user. We can take care of them by getting the data of their equipment [25, 26].
- 35. 20 Internet of Behavior (IoB) • Health: The Internet of Behavior will prove to be a boon in the health field. In which we can analyze them with different help and make them more efficient by the many types of mistakes being made by the doctor. Tracking patients in real time enables providing better treatment to them by which they can meet the needs of their body by tracking daily life through the device. With the help of Internet of Behavior, we are able to predict their health by giving them the best possible treatment and help them live much longer. Or as if we cannot allow humans to get sick [27]. Since the beginning of advertising, marketing and science have coexisted. In this way, the knowledge gathered through the Internet of Things has been enhanced by activity analysis and psychology. IoB has the potential to be a powerful new marketing and sales tool for companies and organizations all around the world. Utilizing this tool can help you develop a thorough under- standing of your clients, which is crucial for any organization. Undoubtedly, corporations have used analytics, A/B testing, SWOT analy- sis, and many other tactics to design their products and selling strategies over the years in order to develop and market products that customers will want to purchase. IoB is anticipated to make a significant impact in the sales sector. With this idea, businesses want to be able to look at past results and make predictions about the future. Companies’ development, marketing, and sales operations will be reorganized as a result of the knowledge amassed through the Internet of Things. It basically consists of dynamic industries and deal- ing techniques, as well as digital marketing. Modern society can no longer ignore the effects of advanced technology and the IoB because they will have an impact on consumer behavior and the success of established sales chan- nels [28]. Therefore, it’s crucial to start integrating the Internet of Behavior into your digital marketing plan in order to profit and quickly gather a significant amount of the most satisfied customers. Access information from various points of contact with the Internet of Things. You are able to examine the entire client journey this way. In other words, you’ll be able to identify the customer’s initial point of interest in the product, their path toward purchase, and the goal of purchase, indicating you may be able to create a significant number of touch points for a positive engagement with customers. Additionally, you’ll be able to learn new techniques for communicating with clients so that you can establish a connection with them earlier than at the time of purchase [18, 29]. An investigation of consumer activity on social networks and other plat- forms, as well as details about their everyday lives, are all part of the idea of IoB. Information is gathered using the Internet of Things, which the average consumer can use to access occasional makers, thermostats, home
- 36. Working of IoB 21 automation systems, and wearable devices. Since each of these devices is a part of our daily lives, it is likely that they will be able to collect data on lifestyle trends, which will help us understand how and when particular products and services are used [30]. Because it feels more comfortable and natural when we lift devices before entering queries into Google, SEO pros see huge potential in IoB. Google Home, Apple Siri, and Amazon Alexa are examples of AI-enabled devices that demonstrate how search engines can understand human intent, not just recognize keywords. This suggests that information processing system pages won’t be evaluated solely on the basis of their keyword content but also on the language used in those pages. Therefore, we have a tendency to see that it’s important to change the SEO content approach so that it can support intent. Businesses, diverse non-profit groups that care for the community, and government agencies that uphold the rule of law all benefit from IoB. Let’s see how this concept is used in modern society [31]. • During the epidemic, groups using portable computer vision started to employ IoB to determine whether a person was wearing a mask. Additionally, thermal imagers were sometimes used to produce images of humans with higher body temperatures. • Because you can track someone’s whereabouts using a smart phone, the system can tell whether you’ve visited a market or a beauty parlor as well as how long you stayed there. Uber analyzes IoT data to better understand its audience’s preferences and find new ways to reach out to customers. Large brands today are familiar with almost everything about consumers, from hobbies to the reasoning behind purchases [32]. • In China, the introduction of a social credit score system was made feasible with the use of AI, particularly face recognition. Remember that the advent of AI during the Chinese era was intended to verify the protection of the organization of data that is loyalty to the present regime. • The Directorix Barista for residential homes successfully included a biometric authentication system in 2018. Thus, sex, age, and mood are determined by goods purchased. The system evaluates the findings and provides the customer with the appropriate beverage. The same technology might be used in shops for customized advertising [33]. 2.5 CONCLUSION: PROSPECTS FOR THE FUTURE The Internet of Behavior is based on assessment data and is geared toward dynamic models and the modern era of behavior. Record analytics may be employed for well-timed modeling and behavior modification due to its net- worked nature. The resulting device complexity may be managed with the
- 37. 22 Internet of Behavior (IoB) aid of illustration and access capabilities. The proposed approach adopts a science-based, improvement approach. It targets organizational systems that, as a result of the choreographic conduct encapsulation of useful entities, may be progressed to IoB transformation. The transformation technique starts off by describing the behavior that is, in my opinion, thought to be role or task specific as a part of mutual interplay styles that may be challenged with a particular purpose. The identified conduct encapsulations and interplay styles are subtly included into executable method models in a subsequent phase. In this way, businesses can experiment with IoB device solutions and tailor the development of analytical intelligence to their own requirements. Many pieces of software that only link users to a whole community from a single application continue to be made available without Google, Facebook, or Amazon’s consent. Additionally, there are significant criminal and safety risks to the right to privacy that may be distinct among states globally. Future research may reveal that customers enjoy the entire experience. This will control the cus- tomer’s interest in the product as it develops, their shopping experience lead- ing up to the point of purchase, and the creation of additional contact points to promote positive customer engagement. Additionally, it might research fresh ways to communicate with customers so they can interact with the brand prior to making a purchase. The top-level view of human behavior included in the idea of Internet behavior incorporates knowledge about how people interact with social networks and other media. Data from everyday users, such as from coffee makers, thermostats, home control systems, wearable technology, and so on, is collected online. It is employed for gathering information on lifestyle trends that, in turn, might provide a focus on using such goods and services. IoB will supply enough records for the market analysis. Additionally, it will be utilized to improve the security of well-known public places. It might present more opportuni- ties for people in the market. It might lower the price of corporate tracking. This provides a better customized product and service focused on delivering a higher-quality customer experience, and new initiatives and solutions are emerging [29]. Deep behavioral patterns and consumer insights can be recognized and turned into practical ways to influence human behavior. Since a large num- ber of people are practically tethered to our computers, our technology is the most readily available tool for businesses to analyze and extrapolate consumer behavior. IoB has quickly evolved into a global setting that defines human behavior in just a few years. To connect people and computer systems for behavior analysis, a milestone is needed. IoB uses behavioral data records and then assesses their potential. Businesses have examined, tested, and put into prac- tice a variety of techniques to expand their tactics for offering and promoting
- 38. Working of IoB 23 goods to consumers. The data can serve as a foundation for growth, promo- tion, and revenue strategies for businesses. The industries may also investi- gate a variety of new materials and records. Additionally, it helps consumers feel more brand loyalty and make more money [34]. The IoB makes it easier to examine the complete experience by using data gathered from several touch points. More information is produced in this way, as are fresh methods of communicating with customers. IoB is utilized for marketing and advertis- ing purposes and could help business people improve their businesses. It will boost sales of devices that connect to the Internet and acquire and transfer data across wireless networks without human assistance. The IoB takes the gathering of records and converts it into insight. It integrates behavioral psychology and links people to their behaviors. IoB issues a warning if there is a bad situation and offers suggestions for reversing the course of action. It gathers behavioral and user data obtained through the use of Internet- connected devices and gives users insights into their motivations, interests, and behaviors. All kinds of technology have its advantages as well as disadvantages. It is true that the prediction made by Gartner became true today, in 2022, and we can say that by 2025, 70% of the world’s population will participate in the IoB [35]. But even with all the convenience of using IoB, this trend raises many questions related to security. Companies will have to implement cyber security education and awareness programs, after which Internet of Behavior will play a role in writing a new story in its future. REFERENCES 1. C. Arnold, D. Kiel, K.I. Voigt, How the industrial Internet of Things changes business models in different manufacturing industries, Int. J. Innov. Manag. 20(8), 1640015 (2016). 2. M. Augl, C. Stary, Adjusting capabilities rather than deeds in computer- supported daily workforce planning. In: Ackerman, M.S., Goggins, S.P., Herrmann, T., Prilla, M., Stary, C. (eds.) Designing Healthcare that Works. A Sociotechnical Approach, pp. 175–188. Academic Press/Elsevier, Cambridge (2017). 3. S.M. Li, T.M. Chung, Internet function and Internet addictive behavior, Comput. Hum. Behav. 22 (6) (2006 Nov 1) 1067–1071. 4. W. Buente, A. Robbin, Trends in Internet information behavior, 2000–2004, J. Am. Soc. Inf. Sci. Technol. 59 (11) (2008 Sep) 1743–1760. 5. H.P. Shih, Extended technology acceptance model of Internet utilisation behav- ior, Inf. Manag. 41 (6) (2004 Jul 1) 719–729. 6. S.Y. Yousafzai, G.R. Foxall, J.G. Pallister, Explaining Internet banking behavior: Theory of reasoned action, theory of planned behavior, or technology acceptance model? J. Appl. Soc. Psychol. 40 (5) (2010 May) 1172–1202.
- 39. 24 Internet of Behavior (IoB) 7. G. Guzman, Internet search behavior as an economic forecasting tool: The case of inflation expectations, J. Econ. Soc. Meas. 36 (3) (2011 Jan 1) 119–167. 8. T. De Vass, H. Shee, S.J. Miah, The effect of “Internet of Things” on supply chain integration and performance: An organisational capability perspective, Austr. J. Informat. Syst. (2018 Jun 27) 22. 9. C. Perera, C.H. Liu, S. Jayawardena, M. Chen, A survey on Internet of Things from industrial market perspective, IEEE Access. 2 (2014) 1660–1679. 10. S. Wachter, Normative challenges of identification in the Internet of Things: Privacy, profiling, discrimination, and the GDPR, Comput. Law Secur. Rep. 34 (3) (2018 Jun 1) 436–449. 11. J. Chin, V. Callaghan, Educational living labs: A novel Internet-of-Things based approach to teaching and research. In: 2013 9th International Con‑ ference on Intelligent Environments, pp. 92–99. IEEE (2013 Jul 16). doi: 10.1109/IE.2013.48. 12. C. Kidd, What Is the Internet-of-Behavior. IoB Explained, The Business of IT Blog, BMC (2019). www.bmc.com/blogs/iob-internet-of-behavior/. Accessed 10 Feb 2020. 13. V. Lala, V. Arnold, S.G. Sutton, L. Guan, The impact of relative information quality of e-commerce assurance seals on Internet purchasing behavior, Int. J. Account. Inf. Syst. 3 (4) (2002 Dec 1) 237–253. 14. C. Stary, Non-disruptive knowledge and business processing in knowledge life cycles – aligning value network analysis to process management, J. Knowl. Manag. 18 (4) (2014) 651–686. 15. D. Jani, J.H. Jang, Y.H. Hwang, Big five factors of personality and tour‑ ists’ Internet search behavior, Asia Pac. J. Tourism Res. 19 (5) (2014 May 4) 600–615. 16. A.H. Turan, Internet shopping behavior of Turkish customers: Comparison of two competing models, J.Theoret.Appl. Electr. Comm. Res. 7 (1) (2012 Apr) 77–93. 17. S. Rab, S. Yadav, S.K. Jaiswal, A. Haleem, D.K. Aswal, Quality Infrastructure of National Metrology Institutes: A Comparative Study (2021). 18. Y. Do, J.Y. Kim, M. Lineman, D.K. Kim, G.J. Joo, Using Internet search behav- ior to assess public awareness of protected wetlands, Conserv. Biol. 29 (1) (2015 Feb) 271–279. 19. Z. Pan, W. Yan, G. Jing, J. Zheng, Exploring structured inequality in Internet use behavior, Asian J. Commun. 21 (2) (2011 Apr 1) 116–132. 20. L. Lancieri, N. Durand, Internet user behavior: Compared study of the access traces and application to the discovery of communities, IEEE Trans. Syst. Man Cybern. Syst. Hum. 36 (1) (2005 Dec 19) 208–219. 21. C.L. Hsu, K.C. Chang, M.C. Chen, Flow experience and Internet shopping behavior: Investigating the moderating effect of consumer characteristics, Syst. Res. Behav. Sci. 29 (3) (2012 May) 317–332. 22. H.P. Breivold, K. Sandstrom, Internet of Things for industrial automation € –challenges and technical solutions. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 532–539. IEEE (2015 Dec 11). 23. C. Metallo, R. Agrifoglio, F. Schiavone, J. Mueller, Understanding business model in the Internet of Things industry, Technol. Forecast. Soc. Change. 136 (2018 Nov 1) 298–306.
- 40. Working of IoB 25 24. Y. Lu, S. Papagiannidis, E. Alamanos, Internet of Things: A systematic review of the business literature from the user and organisational perspectives, Tech‑ nol. Forecast. Soc. Change. 136 (2018 Nov 1) 285–297. 25. D.N. Smith, K. Sivakumar, Flow and Internet shopping behavior: A con‑ ceptual model and research propositions, J. Bus. Res. 57 (10) (2004 Oct 1) 1199–1208. 26. A. Tsitsika, M. Janikian, T.M. Schoenmakers, E.C. Tzavela, K. Olafsson, S. Wojcik, G.F. Macarie, C. Tzavara, EU NET ADB Consortium, C. Richardson, Internet addictive behavior in adolescence: A cross-sectional study in seven European countries, Cyberpsychol. Behav. Soc. Netw. 17 (8) (2014 Aug 1) 528–535. 27. D. Langford, Ethics and the Internet: Appropriate behavior in electronic com- munication, Ethics Behav. 6 (2) (1996 Jun 1) 91–106. 28. A.H. Crespo, I.R. Del Bosque, M.G. de los Salmones Sanchez, The influence of perceived risk on Internet shopping behavior: A multidimensional perspective, J. Risk Res. 12 (2) (2009 Mar 1) 259–277. 29. M. Javaid, et al., Sensors Int. 2 (2021) 100122 6 [8] T.K. Yu, G.S. Wu, Deter- minants of Internet shopping behavior: An application of reasoned behaviour theory, Int. J. Manag. 24 (4) (2007 Dec 1) 744. 30. L.A. Ho, T.H. Kuo, B. Lin, The mediating effect of website quality on Internet searching behavior, Comput. Hum. Behav. 28 (3) (2012 May 1) 840–848. 31. G. Schuh, V. Zeller, J. Hicking, A. Bernardy, Introducing a methodology for smartification of products in manufacturing industry. Procedia CIRP. 81 (2019) 228–233. 32. A. Sodero, Y.H. Jin, M. Barratt, The social process of big data and predic- tive analytics use for logistics and supply chain management. Int. J. Phys. Distrib. Logistics Manag. 49 (7) (2019) 706–726. https://doi.org/10.1108/ IJPDLM-01-2018-0041. 33. L. Andrews, C. Bianchi, Consumer Internet purchasing behavior in Chile, J. Bus. Res. 66 (10) (2013 Oct 1) 1791–1799. 34. C. Perera, A. Zaslavsky, P. Christen, D. Georgakopoulos, Context-aware com- puting for the Internet of Things: A survey, IEEE Commun. Surv. Tutorials. 16 (1) (2013 May 3) 414–454. 35. S. Rab, S. Yadav, N. Garg, S. Rajput, D.K. Aswal, Evolution of measurement system and SI units in India, MAPAN (2020) 1–16.
- 42. DOI: 10.1201/9781003305170-3 27 CONTENTS 3.1 Introduction......................................................................................27 3.2 Pattern of Shopping Using Customer Purchase Behavior...................29 3.2.1 A Priori Algorithm.................................................................29 3.3 Analysis of Sentiment on Social Media.............................................29 3.4 Decision Making Based on Wearable Dataset...................................34 3.5 The Internet of Things in the Insurance Industry...............................35 3.6 Discussion of Various Applications...................................................36 3.7 Challenges........................................................................................37 3.8 Conclusion........................................................................................38 References.................................................................................................38 Websites............................................................................................39 3.1 INTRODUCTION The Internet of Behavior (IoB) [1] helps to analyze how information is bet- ter utilized to develop new products from the outlook of human psychol- ogy. The concept of IoB can be utilized in different ways by the various sectors. The IoB is defined as the technology that collects and processes information based on the behavior of users to provide quality of life for users. The IoB [2] may be a network of interconnected physical objects that collect and exchange information and data over the Internet, called the Internet of Things (IoT). Within the Internet of Things, the range of complexity is consistently expanding and evolving. The way devices are connected and the calculations which will be performed autonomously by those objects and therefore the data stored in the cloud are constantly evolving in increasingly complex ways [3]. It’s called the Internet of Behav- ior because it provides useful information about customer behavior, inter- ests, and preferences collected through various data collection methods (business intelligence, big data, customer data platform, etc.). The Internet Chapter 3 A Detailed Study to Increase Business Value P. Devisivasankari, R.Vijayakumar, and Vijayalakshmi V
- 43. 28 Internet of Behavior (IoB) of Things aims to analyze data from consumers’ online activities in terms of behavior. It seeks to answer all the questions on the way to interpret the data and how this understanding can be applied to the development and commercialization of new products, all from the attitude of human psy- chology and cognitive science. The term “Internet of Things” [2] describes a way to look at user-controlled data from a behavioral psychological point of view. Supported by the results of this survey, companies can develop new user experiences (UX), search experience optimization (SXO), and ways to promote their final products and services [4]. As a result, implementing IoB within a corporation is technically simple but psychologically difficult. Thanks to ethical and legal considerations, it’s necessary to conduct statis- tical surveys that map daily habits and activities without fully disclosing the personal information of consumers. Aside from that, the Internet of Things integrates existing personally targeted technologies with big data, like facial recognition and location tracking. This area consists of three areas: technology, data analysis, and behavioral psychology [5]. Because of big data, you’ll get information from various contacts. This enables you to explore the customer experience from start to finish and understand where the customer’s interest in the product begins, how the customer makes a buying decision, and the way to complete the transaction. This provides you the opportunity to generate more touch points with your consumers and increases your chances of active involvement. The power to personal- ize a service is critical to the efficiency of the service [6]. Users can still operate the service and, if the service is efficient, change behavior sup- ported by that efficiency. Human behavior is often captured, analyzed, and understood using new innovations and developments in machine learn- ing algorithms [7–10]. The Internet of Things helps us track and interpret people’s behavior. A survey by IBM has come up with the prediction that by 2030, each person might own ten devices. The IoT is a trendy topic that connects devices for the exchange of information. Now the Internet of Behavior is an emerging topic with the new concept where companies use the huge amounts of data collected through the IoT to analyze customer behavior [11, 12]. The result of the analysis is used to predict the buying pattern of the customers, which in turn is used to give them suggestions and recommendations [13, 14]. This concept will have a great influence on the marketing side of the product. This chapter discusses: 1) Pattern of Shopping Using Customer Purchase Behavior 2) Analysis of Sentiment on Social Media 3) Decision Making Based on Wearable Dataset 4) IoB in the Insurance Industry
- 44. A Detailed Study to Increase Business Value 29 3.2 PATTERN OF SHOPPING USING CUSTOMER PURCHASE BEHAVIOR Customer buying behavior refers to the typical way in which customers will purchase goods and benefit from services. This depends on the quantity, duration, frequency and timing. This buying pattern can be used to predict how customers will buy goods and services [15]. But this is sensitive data and is highly expected to change. 3.2.1 A Priori Algorithm This algorithm is used for mining frequently used item sets and their cor- responding association rules. The two major steps of this algorithm are join and prune. This refers to the occurrence frequency and the conditional prob- ability. This algorithm uses prior knowledge of frequently occurring item set properties. # importing dataset data(“Groc”) # using the apri function rules - apriori(Groc, param = list(sup = 0.02, conf = 0.3)) # using the function inspect () inspect(rules[1:10]) # using the function itemFrequencyPlot() arules::itemFrequencyPlot(Groc, topN = 30, col = brewer. pal(9, ‘Pastel2’), main = ‘Relative Item Frequency Plot’, type = “relative”, ylab = “Item Frequency (Relative)”) Figure 3.1 shows the relative frequency plot of grocery items. The graph shows that people will have a buying pattern and follow it every time with slight variation. The customer who buys rolls or buns is expected to buy pastry. Similarly customers buying diapers are expected to buy baby soap and shampoo. 3.3 ANALYSIS OF SENTIMENT ON SOCIAL MEDIA The Twitter dataset for Apple is taken for implementation. This dataset con- tains feedback from users about Apple and was collected in 2017. This dataset contains 16 attributes and around 1000 records. An image of the dataset is shown in Figure 3.2. The first attribute, ‘Text’, contains
- 45. 30 Internet of Behavior (IoB) Figure 3.1 Relative frequency plot. Figure 3.2 Dataset of the company.
- 46. A Detailed Study to Increase Business Value 31 feedback from the customers. Sentiment analysis is done for the dataset using the R package. apple - read.csv(file.choose(), header = T) str(apple) The dataset is read using the previous command. The STR function in R is used to show the internal structure of the object. # Clean text corpus - tm_map(corpus, tolower) corpus - tm_map(corpus, removePunctuation) inspect(corpus[1:5]) The tolower function is used to convert all the text in the dataset to low- ercase and, removePunctuation is used to remove the punctuation marks in the dataset. So tolower and removePunctuation are used to clean the dataset and prepare it for processing. The result of these commands on the data is shown in Figure 3.3. Functions from the tm package are used for cleaning the dataset in R. 1. Remove words 2. Stopwords 3. Remove URL 4. Cleanset 5. stripWhitespace # Term document matrix tdm - TermDocumentMatrix(cleanset) tdm Figure 3.3 Result of cleaning the dataset. SimpleCorpus Metadata: corpus specific: 1, document level (indexed): 0 Content: documents: 5 [1]rt options nipper aapl beat on both eps and revenues sees 4q rev 49b52b est 491b httpstcohfhxqj0iob [2]rt options nipper aapl beat on both eps and revenues sees 4q rev 49b52b est 491b httpstcohfhxqj0iob [3]lets see this break all timers aapl 15689 [4]NA [5]aapl wow this was supposed to be a throwaway quarter and aapl beats by over 500 million in revenue trillion dollar company by 2018
- 47. 32 Internet of Behavior (IoB) Docs Terms 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 beat 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 eps l 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 est 1 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 options nipperl 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 rev 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 revenues 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sees 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 break 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 lets 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 see 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 3.4 Matrix format. tdm - as.matrix(tdm) tdm[1:10, 1:20] The raw text is converted into matrix format, as shown in Figure 3.4, using the function matrix(). This matrix represents the frequency of all words appearing in the dataset. The frequency count represents the number of times the word has been used in the dataset. The resulting matrix is shown in Figure 3.5, which shows that the word ‘earnings’ appeared 250 times in the dataset. The function word cloud is used to generate a word cloud with the words present in the dataset. The required package for generating the word cloud is word cloud, and it has to be installed along with the cleaned file. The word cloud is used to analyze the data in the view. The word cloud shows the importance of the words given their frequency or the number of times they are repeated in the dataset. The Syuzhet package is used to extract sentiments from the words used in the dataset. barplot represents the result graphically. barplot(colSums(s), las = 2, col = rainbow(10), ylab = ‘Count’, Figure 3.7 shows that people have a positive attitude towards Apple. From the figure, it is clear that the behavior of people and their sentiments are taken into account and decide the buying patterns for the item.
- 48. A Detailed Study to Increase Business Value 33 Figure 3.5 Word frequency bar chart. Figure 3.6 Wordcloud.
- 49. 34 Internet of Behavior (IoB) Figure 3.7 Sentiment scores for Apple Tweets. 3.4 DECISION MAKING BASED ON WEARABLE DATASETS Today, many customers are using various wearables like smart watches and smart goggles. Smart devices used to give samples like heart rate, glucose level and regular blood pressure (BP) level. If a person wants to consume a drink, these wearable dataset values are cross-checked to see how much sugar can be added, or, if it is soup, how much salt can be added. All these decisions are purely based on the IoB [12, 16], since all the values will be tracked and regularly monitored by the cloud database. Any changes in BP levels or variations in heart rate can immediately be brought to the attention of a medical practitioner to give first aid. Mostly the IoB can be effectively used to track elderly people [17, 18]. The Internet of Behavior can be seen as a fusion of the three domains of technology, data analytics, and behav- ioral science, all working together. In terms of technology [19], behavioral sciences are often divided into four categories. Choice, emotions, expan- sion, and proficiency are all part of the game. For instance, the health app on your smart phone can facilitate monitoring your sleep rhythm, blood glucose, pulse, and more. This app can provide you with a warning about
- 50. A Detailed Study to Increase Business Value 35 potentially dangerous health conditions and recommend behavioral changes [20, 21] that lead to positive and desirable results. In 2021, there are many IoB applications that are often personalized and applied.A variety of compa- nies use Internet advertising to reach their customers in today’s information technology era. They will use the Internet of Behavior to discover and tar- get specific individuals or groups that may benefit from their products and services. For instance, Google and Facebook are social media sites that both use behavioral data to serve ads to users who are already using the platform and are susceptible to behavioral changes through it. This enables market- ers to use ‘click-through’ statistics to interact with the ideal consumer and track their behavior in response to ads. YouTube uses behavioral analytics to enhance the viewer’s experience by recommending or promoting only videos and topics that are relevant to the viewer, supported by the viewer’s past viewing history and activity. The epidemic has raised awareness of the pre- cautionary measures that need to be taken during this time. Employers can use sensors or radio frequency identification tags to assess whether employ- ees are inconsistent in how they follow safety rules. In fact, restaurants and food delivery apps used their knowledge of the protocol to continue working after receiving the protocol. In a number of more imaginative use cases, a driver’s temperature was tracked and showed to reassure the customer of safety [13]. 3.5 THE INTERNET OF THINGS IN THE INSURANCE INDUSTRY The majority well-known insurance companies are currently utilizing user monitoring solutions in order to investigate and collect information regard- ing user behavior and performance. Here, with the assistance of IoB, they will investigate behavior and most likely come to a conclusion as to whether a particular occurrence was the result of an accident or individual error. Accidents that are caused by drivers who are under the influence of alcohol or drugs, drivers who are under the effect of prescribed medication, drivers who are under the age of 18, or drivers who are retired can be avoided if this is done. I hope that the preceding example was clear enough for everyone to understand how IoB [22] would be utilized in 2023. If not, feel free to devise your own use case that addresses your specific needs. The Internet of Thing is a network of connected devices that collects, anal- yses, and interprets data in order to recognize patterns in user behavior and then make use of that information to produce the required action depending on the behavior [23]. These initiatives have resulted in improved outcomes for large businesses, such as increased sales, which have been accomplished by targeting the appropriate viewers and successfully appealing to them. Because it offers customization to an unprecedented degree, this technology
- 51. 36 Internet of Behavior (IoB) that will shape the future of product and service design, as well as market- ing [22]. How can one bring about the appropriate feelings that will direct a person to make the decisions that they want? The solutions can be found through data mining and behavioral science [24]. The idea of the Internet of Things gave rise to the concept of the Internet of Business as a natural pro- gression of that idea: if there is too much data, you want to know how to use it to your benefit [22]. The Internet of Things has a promising future in the longer term because people want their belongings to be‘smart’.This includes smart houses, smart cities, and eventually smart lives, in which everything in our environment is secure. You can be productive while still having plenty of time for your hobbies and creative endeavors. Although IoB is still a rela- tively young technology, it has already demonstrated its potential as a potent new tool for online and digitally based businesses.The practice of conducting market research and user profiling can be challenging for many businesses unless those businesses are Facebook or Google. The more gadgets that are connected to the web, the more in-depth the behavioral insights can be. And as a consequence of this, the businesses that will most likely give financial incentives to customers will open the door to their own universe, which may include their routines, lives, hobbies, and even dreams. The message may say that we will utilize this information about you to better your life in some way, such as by delivering the most relevant financial products, providing individualized health advice, helping you reach your goals, and so on. Learn- ing how to put one’s faith in other people is an extremely important skill. At the end of the day, it’s supported by numbers and research, not guesswork. There is no requirement to construct the perfect user persona when utilizing IoB. The use of big data makes it possible to conduct multi-faceted analyses of potential clients. You will generate a map of your client journey that is exceedingly thorough, take an approach that is highly tailored, and increase your overall engagement score. Using this strategy will make it much simpler to sell specialized items aimed at smaller markets. Voice-enabled gadgets will become more common in people’s daily lives, which will shift the focus of search engine optimization to natural language and intent-based que- ries. Behavioral insights will assist in optimizing the content of the website accordingly. Geographical locations, which may be retrieved through the user’s smart phone, are going to be increasingly essential. Ratings of restau- rants and beauty salons will also depend on the number of times a user has visited and how long they have stayed there. 3.6 DISCUSSION OF VARIOUS APPLICATIONS IoB is mainly used to analyze human activities [25], and it is used in cul- ture change like scanning people’s accounts from social media and contacts for better forecast of behaviors regarding market products and to analyze
- 52. A Detailed Study to Increase Business Value 37 customer habits through Google search, Facebook posts or Amazon order- ing. This leads to phishing attacks by cyber criminals. Nowadays corporate firms are also implementing principles and guidelines for IoB. This project mainly focuses on enhancing skills using various apps on a smart phone and to monitor wearable devices, as well as to learn new techniques [25]. IoB is mainly used to track and archive trends that are characteristic of customers’ decisions in purchasing items. In our day-to-day activities and jobs, more awareness is provided by the Internet. It incorporates existing technology, focusing mainly on the customer/ user to fetch data, such as monitoring the location where the user is, and combining the data to relate behavioral activities like money transactions or smart phone usage. The approach is straightforward for organizations to influence all of human behavior. For example, the system can check whether the workforce follows protocols by using IoB using computer vision methods. IoB links all the data related to those individual behaviors, like purchasing an item or a particular brand. In IoB, values are mainly used to create the optimal state of activities for behavioral events. IoB is mainly used to review businesses results from the past and forecast future activities. Currently it’s used to track employees and to plan for various products, services, and ways to market them. A few companies choose this approach for a cyber protection policy to protect all responsive data. IoB understand and relates the data obtained by the IoT to a person’s behav- iors like selection of a brand. This collects data; then, though analytics, it retrieves information from the collected data. This knowledge is subse- quently used to investigate and influence human behavior. Sensors are used to collect all behavioral data and suggest insight into consumers’ activities, requirements, and expectations. 3.7 CHALLENGES IoB data are vulnerable to cyber attacks; the data can easily get into attack- ers’ hands and expose the behavior patterns of customers. In the long run, all these sensitive data can be collected and sold to other parties. It’s quite dangerous to the people who are using IoT technology. IoB may create more cyber criminals to generate sophisticated scams. IoB can help cyber crimi- nals generate scams customized to the behavior of individual users and thus increase the probability of users getting scammed. This advanced technology has certainly been quite helpful for businesses; it allows users or customers to optimize their relations with other users based on the data collected based on their lifestyle and living patterns. Finally, we can come to the conclusion that IoB is definitely a modern technique used to transform information into authentic perceptions.
- 53. 38 Internet of Behavior (IoB) 3.8 CONCLUSION Within few years, the IoB trend has changed the worldwide environment that defines a person’s behavior. It’s a milestone in analyzing the behavior of com- puters with humans. IoB mainly uses behavioral data and then evaluates its prospects. Various companies have analyzed, experienced, and applied this approach to sell goods to customers by developing techniques to produce new trends. One way it increases income is by connecting objects via the Internet. This connects various behaviors with individuals and uses behav- ioral psychology. This can instruct us about undesirable scenarios and offer guidelines to alter actions for scenario change. IoB collects behavioral and user information which can be obtained from smart devices and provides insights about their needs, interests, and behaviors. The Internet of Behav- ior provides companies with innovative strategies for promoting their prod- ucts and services as well as influencing the behavior of their customers and employees. This technology is extremely beneficial to businesses because it enables them to improve the quality of their consumer connections in response to the information that is obtained. REFERENCES 1. Li SM, Chung TM. Internet function and Internet addictive behavior. Comput Hum Behav. 2006 Nov 1;22(6):1067–1071. 2. Hernández B, Julio J, Martín MJ. Customer behavior in electronic commerce: The moderating effect of e-purchasing experience. J Bus Res. 2010;63(9–10): 964–971. 3. Andrews L, Bianchi C. Consumer Internet purchasing behavior in Chile. J Bus Res. 2013 Oct 1;66(10):1791–1799. 4. Wayne Buente, Alice Robbin, Trends in Internet information behavior, 2000–2004. J Am Soc Inf Sci Technol. 2008 Sep;59(11):1743–1760. 5. Lu, Y, Papagiannidis, S, Alamanos, E. Internet of Things: A systematic review of the business literature from the user and organisational perspectives. Techno‑ logical Forecasting and Social Change 2018;136(C):285–297. 6. Breivold HP, Sandström K. Internet of Things for industrial automation – chal- lenges and technical solutions. 2015 IEEE International Conference on Data Sci- ence and Data Intensive Systems, IEEE (2015 Dec 11), pp. 532–539. 7. Perera C, Zaslavsky A, Christen P, Georgakopoulos D. Context-aware comput- ing for the Internet of Things: A survey IEEE. Commun Surv Tutorials. 2013 May 3;16(1):414–454. 8. Choi H, Youngchan K, Jinwoo K. Driving factors of post adoption behavior in mobile data services. J Bus Res. 2011;64(11):1212–1217. 9. Farrell AM. Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). J Bus Res. 2010;63(3):324–327. 10. Garbarino E, Michal S. Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. J Bus Res. 2004;57(7): 768–775.
- 54. A Detailed Study to Increase Business Value 39 11. McCole P, Elaine R, John W. Trust considerations on attitudes towards online purchasing: The moderating effect of privacy and security concerns. J Bus Res. 2010;63(9–10):1018–1024. 12. Nasco SA, Elizabeth GT, Peter PM. Predicting electronic commerce adoption in Chilean SMEs. J Bus Res. 2008;61(6):697–705. 13. Son M, Kyesook H. Beyond the technology adoption: Technology readiness effects on post-adoption behavior. J Bus Res. 2011;64(11):1178–1182. 14. Wang X, Chunling Y, Yujie W. Social media peer communication and impacts on purchase intentions: A consumer socialization framework. J Interact Mark. 2012;26(4):198–208. 15. Oliver RL, William OB. Crossover effects in the theory of reasoned action: A moderating influence attempt. J Consum Res. 1985;12(3):324–340. 16. Rohm AJ, Vanitha S. A typology of online shoppers based on shopping moti‑ vations. J Bus Res. 2004;57(7):748–757. 17. Davis RA. A cognitive-behavioral model of pathological Internet use. Comput Hum Commun. 2001;17:187–195. [Google Scholar] 18. Alavi SS, Maracy MR, Jannatifard F, Eslami M, Haghighi M. A survey rela- tionship between psychiatric symptoms and Internet addiction disorder in students of Isfahan universities. Sci J Hamadan Univ Medi Sci Health Serv. 2010;17:57–65. [Google Scholar] 19. Chebb PI, Koong KS, Liu L, Prasanna C, et al. Some observations on Inter- net addiction disorder research. J Info Syst Educ. 2000;11:97–104. [Google Scholar] 20. Young KS, Rogers RC. The relationship between depression and Internet addiction. Cyberpsychol Behav. 1998;1:25–28. [Google Scholar] 21. Noble EP, Zhang X, Ritchie T, Lawford BR, Grosser SC, Young RM, et al. D2dopamine receptor and GABA-A receptor beta3 subunit genes and alco‑ holism. Psychiatr Res. 1998;81:133–147. [PubMed] [Google Scholar] 22. Kulviwat S, Gordon CB, Obaid A-S. The role of social influence on adoption of high tech innovations: The moderating effect of public/private consump‑ tion. J Bus Res. 2009;62(7):706–712. 23. Holden C. “Behavioral” addictions: Do they exist? Science. 2001;294:980–982. [PubMed] [Google Scholar] 24. Potenza MN. Should addictive disorders include non-substance-related con‑ ditions? Addiction. 2006;101:142–151. [PubMed] [Google Scholar] 25. Mohd Javaid, Abid Haleem, Ravi Pratap Singh, Shanay Rab, Rajiv Suman Internet of Behaviors (IoB) and its role in customer services. https://doi. org/10.1016/j.sintl.2021.100122. Websites 1. www.analyticssteps.com/blogs/introduction-Internet-behaviour-iob 2. www.financialexpress.com/industry/technology/Internet-of-behaviours-more- power-to-digital-marketing/2371247/ 3. www.techfunnel.com/information-technology/Internet-of-behaviors/ 4. www.vectoritcgroup.com/en/tech-magazine-en/user-experience-En/what-is- the-Internet-of-behaviour-iob-and-why-is-it-the-future/
- 55. Random documents with unrelated content Scribd suggests to you:
- 56. his Wanderings in the Great Forests of Borneo, speaks of “the facile dissemination of the various species of Ficus through the agency of birds,” and he arrives at certain important conclusions which are discussed in Chapter XXXIII. I have before alluded to the absence of Ficus from Hawaii. This group possesses the Honey-Eaters (Meliphagidæ), birds well suited for dispersing species of Ficus over Polynesia; but this family of birds is only represented by peculiar genera in Hawaii, and therein lies the explanation. At the time when the Honey-Eaters roamed over Polynesia, the genus Ficus had not arrived from Malaya. The connection between the bird and the plant is well shown on Fernando Noronha, which possesses a peculiar species of Ficus and a peculiar species of dove, the only fruit-eating bird in the island (Ridley). The Absentees from Tahiti Generally speaking, all the “difficult” genera which puzzle the student of plant-dispersal in Fiji and Hawaii are absent from the Tahitian region. Those with stone-fruits and with large seeds, where the stone or seed is an inch in size and over, are absent from Tahiti. Thus the genera Canarium, Dracontomelon, Myristica, Sterculia, and others, of which the three first-named are known to be dispersed by fruit-pigeons, have not advanced into the Pacific eastward of the Fijian region. We miss in the Tahitian islands the large-fruited palms of Fiji, such as the Veitchias with fruits two to two and a half inches (5 to 6 cm.) long, and we find in their place a Ptychosperma, evidently very rare, and the widely spread Pritchardia pacifica, that may have been introduced by man, both with drupes not far exceeding half an inch (1·2 cm.) in size. The islands of the Tahitian region also lack the Coniferæ; and genera like Dammara, Dacrydium, and Podocarpus that give such a character to the Fijian forests are not to be found. In this region we do not find many of the large- seeded Leguminous genera, such as Cynometra, Storckiella, and
- 57. Afzelia, that occur in Fiji, the only large-seeded genera that it possesses being such as are brought by the currents, namely, Mucuna, Strongylodon, Cæsalpinia. The difficulties presented by the occurrence of the inland species of Canavalia and Mezoneuron in Hawaii do not offer themselves in Tahiti (see Chapter XV). Tahiti also lacks, as often before observed, the mangroves and most of the plants of the mangrove-formation. As above remarked, the Fijian trees with large “stones” and heavy seeds an inch in size are not to be reckoned amongst the indigenous Tahitian plants, “size” being an important determining factor in the exclusion. The occurrence of Elæocarpus in Rarotonga presents no real difficulty, as I have explained in Chapter XXVI. An apparent exception is presented by the existence in Tahiti of Calophyllum spectabile, where the stones are about an inch across; but since its fruits can float in sea-water for nearly a month, and on account of the value placed on its timber by the Polynesians, we cannot altogether exclude the agencies of man and the currents. One seeming exception is also offered by the presence of Serianthes myriadenia, a tree which in Fiji grows both in the forests and on the banks of the tidal estuaries. Its seeds, which are six to seven-tenths of an inch (15 to 18 mm.) in length, have no buoyancy, and the pods float only two or three weeks. The case of Lepinia tahitensis is alluded to elsewhere, but it may be added that these and other difficulties await further investigation. A great many Fijian plants are not found in the Tahitian region, such as Micromelum, those of the order Meliaceæ, the Melastomaceous genus Medinilla, Myrmecodia, Ophiorrhiza, c., which are often quite as well fitted for over-sea transport as are several of the plants already established there. But it should be remembered that crowding out would often come into play in such a contracted region. The area, however, has been very generously dealt with as regards plant genera. Though the total land-surface cannot be more than one-fourth or one-third that of Fiji or Hawaii, it possesses more than half the number of genera found in Fiji, and four-fifths of the number found in Hawaii.
- 58. Fiji The Fijian Genera not found in either the Tahitian or Hawaiian Regions We have already in some degree dealt with Fiji in so far as the partial dispersal of genera over the Pacific islands is concerned. We have seen that it possesses very few genera (not a score in all) in common with Hawaii that are not found in the Tahitian region, and it is assumed that in most cases such genera reached Hawaii independently and not through the South Pacific. On the other hand, excluding the grasses, sedges, and vascular cryptogams, Fiji owns in common with Tahiti between sixty and seventy genera that do not occur in Hawaii. This shows unmistakably the trend of plant migration in the Pacific islands. Several interesting features in plant- distribution have been already brought out, and notably the fact that Indo-Malayan genera with large seeds or “stones” an inch in size have been arrested in the Fijian region in their passage into the South Pacific. Thus Canarium, Dracontomelon, Myristica, and Sterculia have not extended eastward of the Fijian area. Yet a very large proportion of the Fijian genera, quite half of the total number, are not represented either in the Tahitian or in the Hawaiian region; and of many of them it is obvious that they are as well fitted to be carried over the Pacific as are those that have actually reached Tahiti and Hawaii. Take, for instance, Begonia, which has not extended east of Fiji, though Hillebrandia, a genus of the order, is peculiar to Hawaii. Nor can we explain why with three genera like Geissois, Dolicholobium, and Alstonia, possessing seeds dispersed by the winds, only the last-named has passed beyond Fiji. However, as before remarked, it is probable that lack of opportunity rather than capacity for dispersal has determined the matter, and we must, therefore, assume that many of the genera have halted in the Fijian region because they entered the Pacific after the age of active general dispersal over that ocean.
- 59. Occasionally we notice in this region that which we have observed in the case of Cyrtandra in different Pacific groups, namely, a sudden development of what Hillebrand terms “formative energy” in a genus, such as we find in the case of Elatostema in Samoa, and in that of Psychotria in Fiji and Samoa. The principle of polymorphism in the development of species is also illustrated by Micromelum and by Limnanthemum. In the last case we possess a typical polymorphous species in Limnanthemum indicum that has played in this respect the rôle of Naias marina in the warm waters of the globe. With several genera that like Gnetum, Myristica, and Sterculia occur both in the Old and the New World, it is evident that in explaining their distribution we are dealing with something more than questions of means of dispersal. With these genera, and with others like Lindenia, it seems almost futile to talk of means of dispersal, when to all appearance their existing distribution is but the remnant of an age of general dispersion over the greater part of the warm regions of the world. These genera, with others, might be cited in favour of the continental hypothesis relating to the islands of the Western Pacific. Trees with stone-fruits, such as Canarium, Couthovia, Dracontomelon, and Veitchia, where the stones are an inch and more in length, might be also adduced by some in evidence of this theory. But in these cases the lesson of Elæocarpus (Chapter XXVI) should always be remembered, since the “stones” of drupes may vary greatly in size amongst the different species of a genus, and species seemingly “impossible” from the standpoint of dispersal in one group may be represented in other groups by species where the size of the “stone” presents no difficulty in attributing the dispersal of the genus to frugivorous birds. Sterculia The problem connected with the presence of this genus in Fiji is but a part of the still more difficult problem connected with the
- 60. dispersal of the genus over the tropics. The riddle presented by the Fijian species seems, indeed, difficult enough; but it merely presents in miniature the great mystery surrounding the whole genus. According to the Index Kewensis no other species have been found in oceanic islands except those occurring in the Western Pacific, as in Fiji, the New Hebrides, and New Caledonia, and most of these seem to be confined to those islands. We have here a genus that repeats the Dammara difficulty of the Western Pacific. The trees are common in places in the Vanua Levu forests, where the large, woody, open follicles may be seen lying in numbers on the ground, empty and in all stages of decay. The seeds of one species, near Sterculia vitiensis, were nearly an inch long and sank like stones. The unopened follicles will float for weeks; but it is evident that Nature does not disperse the genus in this fashion, since the fruits before dehiscence remain on the tree. It is also noteworthy that Gaudichaud, when describing the floating drift of the Molucca seas, refers to the open follicles of two or three species of Sterculia (Bot. Chall. Exped., iii, 279). The fruits never came under my notice in the drift of Fiji. The seeds of a Fijian species examined by me were four-fifths of an inch (2 cm.) long. They had a thin, brittle, outer skin and crustaceous inner test, and, being edible, might attract birds; but such birds would be ground feeders, like the Megapod, and the Goura pigeon of New Guinea, and the Nicobar pigeon, birds of this habit being rare in Fiji. I should doubt whether the seeds are sufficiently protected to be preserved from injury in a bird’s stomach during a long sea-passage; and they may thus be placed in the same category with the seeds of Myristica, a genus that has also failed to reach Tahiti and Hawaii. But the distribution of Sterculia raises other more important questions than that connected with its occurrence in Fiji, which involves an over-sea passage of only 500 or 600 miles. As in Podocarpus amongst the Coniferæ, which has a similar distribution in the Western Pacific, we have to explain the existence of the genus in the three great continental masses of Africa, Asia, and America, now separated by oceans several thousands of miles across. Here
- 61. also we must look far back into the ages for a common centre of diffusion in the extreme north, such as is in a sense suggested by the occurrence of the order in the Eocene beds of Europe. As showing unmistakably that Fiji received its species from the Old World, it may be observed that one of its trees, Sterculia vitiensis, is very closely allied to S. fœtida, widely spread in tropical Asia, in Malaya, and Australia, as well as in Africa. Trichospermum (Sterculiaceæ) There are only two species of this tree recorded in the Index Kewensis, one in Java, and one in Fiji as well as in Samoa. The fruit is a capsule with small, flat seeds, margined by long hairs, that might possibly attach themselves to a bird’s feathers. Micromelum (Rutaceæ) This small genus of tropical Asia, Malaya, tropical Australia and the islands of the Western Pacific, has one species, Micromelum pubescens, possessing the range of the genus with other species that are restricted to different localities. We thus have apparently another illustration of the part played by a wide-ranging polymorphous plant in providing new species. The red berries would easily attract frugivorous birds; but the seed-tests seem too delicate to allow the seeds to remain more than a few hours in a bird’s stomach without injury. Cananga odorata (Anonaceæ) This tree, which is cultivated in many places in tropical Asia and Malaya, but is certainly indigenous, according to the authors of the
- 62. Flora Indica, in Ava and Tenasserim, has apparently extended into the Pacific by cultivation. But though much valued by the natives on account of its fragrant flowers, and in consequence often planted by them near their villages, it grows in some localities in Fiji and Samoa as an indigenous plant. The berries are especially suited for dispersal by frugivorous birds, their flat seeds, 8 mm. in length, possessing hard crustaceous tests that would enable them to pass unharmed in a bird’s droppings. According to Reinecke the fruits are sought after by pigeons, and particularly by Didunculus strigirostris, the Samoan Tooth-Billed Pigeon. The tree has not travelled eastward of Tonga and Samoa, with the exception of its occurrence in Rarotonga; and according to Mr. Cheeseman the Rarotongans received it from Samoa several years ago. Geissois (Saxifragaceæ) This genus of seven or eight known species is found in Australia, New Caledonia, the New Hebrides, and Fiji. Since New Caledonia possesses four species, it may be considered the home of the genus. To the Fijian endemic species, G. ternata, I paid special attention. The capsules dehisce on the tree and allow the small seeds to escape. These seeds, which are very light, 150 to 200 going to a grain, are 3 to 4 mm. long and are winged at one end. They could no doubt be carried some distance by strong winds; but they possess no buoyancy. Large bats probably aid in their dispersal. The Fijians assert that these animals are in the habit of visiting the trees for the sake of the honey furnished by the conspicuous red flowers. When they see a bat flying towards these trees, they are wont to remark that it is going to drink the “se ni vota,” that is, to suck the flowers of the Vota tree. It is very likely that seeds would sometimes be carried in their fur for considerable distances. Begonia
- 63. Before the discovery of Hillebrandia, a new genus of the Begoniaceæ, in Hawaii, the order was not known from Polynesia. However, in 1878 Mr. Horne collected a species of Begonia in Fiji, and it was probably this species that frequently came under my notice in the rain-forests of the Vanua Levu mountains. In 1883 I collected a Begonia in the Solomon Islands, which I gave to Baron F. von Mueller, who informed me that it was the first record of the genus east of New Guinea, the description of Mr. Horne’s Fijian plant apparently not having been published (see Guppy’s Solomon Islands, p. 288). It is not easy to explain why a genus with such minute seeds, which are apparently as well fitted for dispersal as those of the orchids, should have such a limited distribution in the Pacific. Dolicholobium (Rubiaceæ) In the Index Kewensis this genus, containing five species, is restricted to Fiji. It must, however, be more generally distributed in the Western Pacific, since the genus was identified at Kew among my Solomon Island collections, and it is recorded in the list given in my book on that group (pages 283, 288, 297). The showy, large, white, fragrant flowers of these small trees recall those of Lindenia, with which Dolicholobium is often associated in Fiji by the sides of streams and rivers. As Horne observes, the Fijian Dolicholobiums range from the sea-shores and the heads of the estuaries to the tops of the highest mountains. As noticed by me in the Solomon Islands they affected the same station, being especially common on the banks of streams. The genus has a long, narrow capsule six inches or more in length. The linear seeds, though very light, are an inch or more long, the coats being drawn out into a long tail at either end, and thus differing greatly from those of Lindenia, the other Rubiaceous genus, with which these plants are so frequently associated at the river-side. I can only suppose that the seeds are transported by the winds. The history of the genus is suggested in my remarks on Lindenia.
- 64. Lindenia (Rubiaceæ) Respecting its distribution in the Pacific, this genus of showy river- side shrubs takes the same place amongst the plants that Galaxias takes among the fishes. It is full of mystery. Of the four species known, two grow on the river-banks of Central America and two in similar stations in the islands of the Western Pacific. Of the last- named both occur in New Caledonia, one of them being endemic, whilst the other, Lindenia vitiensis, is found also in Fiji and Samoa. Reinecke seemingly records no Samoan species, but in the list of additions at the end of his Flora Vitiensis, Seemann refers to the Fijian species as having been found in Samoa by Dr. Graeffe. Lindenia vitiensis, as Horne aptly remarks, adorns the rocky banks of many Fijian streams with its cream-coloured flowers, which impregnate the air with their sweet odour. I found it in Vanua Levu, both at the heads of the estuaries and beside the stream and the torrent in the heart of the mountains. It was often associated with a species of Dolicholobium, which it resembled strangely in its large, showy, scented flowers and in the form of the leaf. Seemann says it is also accompanied at the river-side in Viti Levu by Ficus bambusæfolia and Acalypha rivularis. It is noteworthy that all the four plants here mentioned as being associated river-side plants in Fiji possess the long, narrow leaves of the willow type, a subject that is discussed in note 79. The capsules of Lindenia vitiensis contain numbers of small, angular seeds about 1·5 mm. across, some 400 of them when well dried going to a grain. The seeds float buoyantly by reason of their outer covering of crisp, air-bearing, cellular tissue. When this outer covering is stripped off, the minute nucleus, or seed proper, which is barely a millimetre across and is but slightly protected, sinks at once. As the seeds float on the surface of a stream they might readily get on the plumage of an aquatic bird; but they have no special means of attachment; though, if they dried on the feathers they might adhere to some extent. That they could be carried in mud adhering to a bird across an ocean’s breadth I think most unlikely; and it
- 65. should be remembered in this connection that only the dead or sickly seeds would be found at the bottom of a stream. The most reasonable explanation of the extraordinary distribution of Lindenia is that it was in a past age found over the tropical regions of both America and the Old World, and that it has died out over the greater part of its original area. To study the means of dispersal of plants with such a distribution seems almost futile. I am inclined to think that the limited range of Dolicholobium, so frequently its station-companion in Fiji, may be similarly explained. Limnanthemum (Gentianaceæ) This interesting genus of aquatic plants is dispersed over the tropical and temperate regions of the globe, but with the exception of Fiji and the New Hebrides it is not found in oceanic groups, though it occurs in large continental islands like New Caledonia and Cuba. About twenty species are enumerated in the Index Kewensis, but it is stated in the Genera Plantarum that they can probably be reduced to ten, the reduction being chiefly applicable to the tropical species, nearly all of which are reducible to varieties of L. indicum, the temperate species being often very distinct. It would thus appear that although dispersal is still active in the tropics, it is in part suspended in the temperate zone, and we seem to possess in L. indicum a typical polymorphous species that has played the rôle of Naias marina in the warm, fresh waters of the globe (see page 368). Although some of the temperate species, like Limnanthemum nymphæoides in Europe and Northern Asia, have a wide range, it is probable that this is connected not so much with means of dispersal, as with its relation to present and past drainage-areas. Rivers in the lapse of ages change their courses and carry their aquatic floras with them, leaving, however, a few of their plants around the springs and in the lakes which serve still as centres of dispersal. Rivers may even exchange their plants in flood-time in extensive level districts. Nor is the occurrence of the genus in the Old and New Worlds in the
- 66. northern hemisphere to be connected with questions of dispersal across an ocean. Except in the case of small-seeded plants, like Nasturtium and Lythrum, where the dispersal could be carried on by water-fowl, the plant-species being often identical on both sides of the Atlantic, it is probable that most of the large-seeded river-side genera common to Europe and North America, such as Iris and Acorus, had in past ages their home in the extreme north, whence the plants spread as from a focus into the continents of America and Eurasia. It is also to be doubted whether even in the tropics there has been much over-sea dispersal of Limnanthemum without the aid of man, and reasons will be given for the belief that probably in Fiji, in the New Hebrides, and in New Caledonia the seeds of the first plants were unintentionally introduced by the aborigines. Following Bentham we may regard the species of the Western Pacific Islands as a form of the wide-ranging Limnanthemum indicum. These plants in Fiji do not play the part in river-vegetation that they do in the temperate regions, as for instance in the Upper Thames. They are not common except in places, and seem to be chiefly confined to Viti Levu, particularly to ponds in the Rewa delta, where their rôle is that of an Indian tank plant. In the Rewa delta they may be sometimes seen thriving in brackish water having a density of 1·005. Looking at the mode of dispersal to which the Limnanthemums owe their existence in the Western Pacific, we cannot disregard, especially in Fiji, the possibility of the seeds having been unintentionally transported by the natives when they carried in their migrations their edible tubers, such as Colocasia antiquorum, Alocasia indica, and Cyrtosperma edulis, that are cultivated in wet places. It is in the ponds around which these plants grow that the Limnanthemums thrive. The Chinese, with their peculiar methods of cultivation, are now carrying with them strange water-plants over the warmer regions of the globe; and it would be surprising if the Pacific islanders in their migrations did not do the same. If such an introduction, however, took place, it must have happened before the time of Captain Cook, when the plant was found in New Caledonia.
- 67. (It may be remarked in this connection that the seeds of the genus will germinate after being kept dry for years. Seeds of the British species which I had kept dry for two and a half years germinated healthily when placed in water.) Some years ago I ascertained that the seeds of the British plants were enabled, by means of their fringe of hairs, to attach themselves firmly to the downy plumage of a bird’s breast. This could not happen with the Fijian plant as the seeds are naked, and the same may be said of some species described by Gray and Chapman as widely spread over the United States. The seeds of the genus appear quite unsuited for safe transport inside the body of a bird. The Fijians give the plants a variety of names, nearly all of which are associated with the word for a duck, and none of them bear an ancient impress. Thus we find such names as “Ndambe-ndambe-ni- nga” and “Vothe-vothe-ni-nga,” meaning respectively “the duck’s seat” and “the duck’s paddle.” Ceratophyllum demersum This wonderful aquatic has been dispersed over most of the globe; but I will only mention its occurrence in oceanic islands, such as Fiji, Samoa, the Bermudas, and the Azores, to indicate the necessity of attributing its distribution in islands to birds. Several years ago I made a careful study in England of the habits and mode of germination of this plant, the results of which are given in Science Gossip for November, 1894; but reference can only be made here to such points as bear on the occurrence of the plant in the Pacific islands. It is well known that in our English ponds and rivers the plant propagates itself, as a rule, by budding; and that it is only in unusually hot and dry summers, such as that of 1893, when many ponds became very low and were excessively heated, that the fruits mature in any quantity. My observations clearly showed that a higher temperature is required for the completion of maturation than for
- 68. the early stage of the fruiting process and for the flowering. After a comparison of my river and pond temperatures, I formed the conclusion that whilst in water 12 to 18 inches deep this plant requires for a week or more an average daily maximum water temperature of 70° F. to produce its flowers, a warmth of 80° and over is necessary to mature its fruit, a condition to be found in England only in shallow ponds, where the plants may fruit abundantly, but not in rivers, where they flower and rarely mature the fruit (see also for the thermometric conditions my paper in Proc. Roy. Phys. Soc. Edin., xii, 296). Since a yet lower temperature (an average maximum water temperature of 66° for a week or more) is sufficient for germination, it follows that the thermal conditions of our English climate will allow Ceratophyllum to germinate and to flower, though but rarely to mature the fruit. Even in Fiji we can notice the distinction between the cooler river and the superheated ponds and swamps of the Rewa delta as regards the maturation of the fruit. In 1897 I found Ceratophyllum thriving in the main channel of the Lower Rewa where the water was quite fresh; whilst lower down where the water was often brackish its place was taken by Ruppia maritima. In the main river, where the water unmixed with sea-water rarely acquires a temperature of 80° F., the reading being usually 78° to 79°, I never found the plants in fruit, and it is only in the superheated shallow waters of the swamps and back-waters that they mature their fruits. Since Ceratophyllum even in tropical climates would probably only mature its fruits in the superheated waters of shallow ponds, tanks, and ditches, it follows that its dispersal by birds is confined to warm regions. In the cold waters of the Siberian lakes and rivers it would never mature its seeds, and could only be propagated by budding. If it existed in the head-springs of the sources of a river in these latitudes, it would be distributed by means of its floating shoots and fragments along the length of the river basin, and in the times of flood it might pass in the lower plains from one river system to another. When rivers changed their courses it would be left behind in the lakes and ponds and springs, and would also be carried away to
- 69. the new region. In this manner it would in the course of ages be distributed over a continent without the aid of seed, propagating itself in a vegetative fashion. In the case of oceanic islands, however, we have to appeal to the seed. Since the fruits sink in sea-water even after prolonged drying, and since a few days’ immersion in sea-water, as I found, kills the floating plant, we are driven to the agency of birds. The fruits, which without appendages are a quarter of an inch (6 mm.) in length, are too large and heavy to be carried in dry mud adhering to birds. The chances of their becoming entangled in a bird’s feathers by means of their basal spines and terminal style seem small, since they would be lying usually on the mud under the water. They are quite fitted for safe transport in the stomach and intestines of birds, such as is established in Chapter XXXIII for Potamogeton and Sparganium in the case of ducks. As my experiments show, drying for a period of three months does not injure the germinating capacity of the seeds. Dracontomelon (Anacardiaceæ) This is a genus accredited in the Index Kewensis with eight species, of which three belong to Borneo, one to Sumatra, one to Java, one to the Philippines, and two to Fiji, all the species being restricted in their range. My observations were confined to D. vitiense, Engler (D. sylvestre in Seemann’s work), the Tarawau of the Fijians, who regard it as a tree that is planted by the dead in Naithombothombo, the place of departed spirits, according to the legend given by Hazlewood in his Fijian Dictionary. Its method of dissemination in the Fijian forests is, however, far more prosaic. Pigs and fruit-pigeons assist in the dispersal of the seeds in these islands. Pigs are often found in the vicinity of a Tarawau tree; and evidently they much appreciate the fallen fleshy fruits, which are about 11 ⁄3 inch (3·3 cm.) across and inclose a large stone 7 ⁄8 inch (2·2 cm.) in diameter. The entire fruit and the detached stone sink in sea-water,
- 70. the last floating only a few hours, even after drying for four years. Mr. Hemsley regards the genus as probably dispersed by the currents, since a stone was found amongst the floating drift collected by the Challenger Expedition off the coast of New Guinea. The stone, however, is described as seedless, which may explain its buoyancy. It is, however, to the fruit-pigeon that we must look for the dispersal of this genus. In the crop of one of these birds shot in Fiji I found the entire fruit of a Tarawau tree. Canarium (Burseraceæ) This genus of trees, to which nearly a hundred species are referred in the Index Kewensis, belongs mainly to tropical Asia and Malaya, a few species occurring in tropical Africa, Madagascar, the Mascarene Islands, and Polynesia. Its great home is in Malaya, to which two-thirds of the species are confined; but its distribution in the oceanic islands of the Indian and Pacific Oceans is especially interesting, Mauritius, Bourbon, Fiji, Tonga, and Samoa (Horne) each possessing a species. The large drupes of the genus, as I found in Fiji, have no capacity for dispersal by currents; and we are, therefore, compelled to appeal to the agency of the frugivorous bird. Yet to a person unaccustomed to the ways of fruit-pigeons the transportation across a broad tract of ocean of large heavy “stones,” an inch and more in size, would seem impossible; and even to a student of dispersal improbable. Unless, however, we prefer to accept the Lemurian theory for the Indian Ocean and the theory of a Melanesian continent for the Pacific we are compelled to appeal to these birds; and it can scarcely be said that our appeal is without some justification. Both in the Solomon Islands and in the Fijis I was familiar with the dispersal of the stones of these trees by fruit-pigeons; and Wallace, amongst other writers, observed the same long ago in the Malayan Islands (Malay Archipelago). Stones obtained from the crops of Fijian pigeons measured 12 ⁄10 × 1 inch (3 × 2·5 cm.). In the Solomon
- 71. Islands these birds stock the interior of the coral islets with trees of the genus, and the ground below the trees is often strewn with the disgorged stones (Bot. Chall. Exped., iv, 310; Guppy’s Solomon Islands, p. 85). Although the difficulty concerned with the transport of the seeds across a broad tract of ocean seems very great, it is quite possible that further investigation will enable us to overcome this objection, just as we have done in Chapter XXVI when explaining how the genus Elæocarpus may have reached Hawaii. It is, indeed, not unlikely that, as with Elæocarpus, the stones of the drupes may in some species be much smaller and far more fitted for being carried in a bird’s body over several hundred miles of ocean. Couthovia (Loganiaceæ) Reference is here made to this genus because its mode of dispersal is known, and because I was familiar with it in Fiji. Seemann gives two species for Fiji, C. corynocarpa and C. seemanni, and the few other species known seem to be confined to the Western Pacific. Solereder gives a third species, C. densiflora, for Kaiser-Wilhelmsland in New Guinea (Engler’s Pflanz. Fam. teil 4, abth. 2); and a Solomon Island species, nearly allied to, if not a variety of, the Fijian species, C. seemanni, is referred to in the list of plants from that group given in my book on those islands. I found C. corynocarpa not infrequently growing on the banks of small rivers in the heart of Vanua Levu. Its drupes, which float for a few days in sea-water, are, according to Seemann, eaten by fruit-pigeons. The “stone” varies from 2 to 4 centimetres (3 ⁄4-11 ⁄2 inch) in length; and from the standpoint of dispersal the genus ranks with Canarium and Dracontomelon. Seemann describes and figures this species, which was constituted by Gray, in his Flora Vitiensis; but, apparently through an error, it is in the Index Kewensis accredited to Hawaii. Hillebrand makes no reference to the genus in his book on the Hawaiian flora.
- 72. Veitchia (Palmaceæ) This genus of palms is closely allied to Ptychosperma, a Malayan genus also represented in Fiji. The Index Kewensis names four species, one New Hebridean, and three Fijian. The fruits of two of the last-named species tested by me had no floating power. The seed is about an inch long, and the genus would be likely to be spread by fruit-pigeons. From the standpoint of dispersal the genus would be placed with Canarium and Couthovia; but possibly its presence in the Pacific may be indicative of an ancient Western Pacific continent. Hibbertia (Dilleniaceæ) This genus of some eighty known species is almost entirely Australian, with the exception of a few species found in New Caledonia, Tasmania, and apparently also in the Mascarene Islands. Horne was the first to record a species from Fiji, where it grows commonly in the “talasinga” plains on the lee sides of the islands, and also on the scantily vegetated mountain summits. In Vanua Levu I often found these plants growing on the rocky peaks of the highest mountains of the island, as on Mbatini, 3,500 feet, and on Mariko, 2,900 feet. Their presence on these isolated peaks can only be attributed to birds. The carpels contain one or two seeds, which have a membranous aril; but in the plains the seeds are usually destroyed by grubs. Myrmecodia and Hydnophytum (Rubiaceæ) These two genera of epiphytes, distributed over Malaya and extending to the islands of the Western Pacific, possess tuber-like stems, which are extensively chambered by ants that find a home in
- 73. the interior. They were familiar to me in the Solomon Islands, where they frequently grow on the mangroves and on other littoral trees. They do not form such a feature in the shore vegetation of Fiji, and judging from the observations of Dr. Seemann and myself they occur most often on the wooded mountain-peaks. The berries of these plants would attract frugivorous birds; and their pyrenes, which in a Fijian Myrmecodia I found to be 4 millimetres long, appear quite suitable for dispersal through this agency. It would seem that germination may occur in the berry on the plant. A specimen of Myrmecodia in fruit, that had been lying overlooked for a fortnight between newspapers during one of my mountain journeys, displayed on examination the pyrenes in a germinating condition, the process being subsequently completed. The reader will find these interesting plants described and illustrated in the English edition of Schimper’s work on Plant-Geography, pp. 149, 150. Myristica The Nutmeg trees, though principally at home in Indo-Malaya, are found also in the warm regions of Africa and America, as well as in the islands of the Western Pacific from the Solomon group eastward to Fiji, Tonga, and Samoa. The Tongan and Samoan groups possess two species in common, whilst Fiji seems to possess its own species, four or five in number. The seeds of this genus have long been known to be dispersed by fruit-pigeons. Mr. Moseley, in his Notes of a Naturalist, and in the Journal of the Linnean Society (vol. xv), tells us how at one time these birds in their dissemination of the seeds in the Banda Islands were active opponents of the policy of the Dutch Government in preserving their monopoly of the cultivation of the nutmeg of commerce. He found numbers of wild nutmegs in the crops of these birds in the Admiralty Islands, some of which were partially digested and others seemingly sound; and Mr. Hemsley includes the genus as amongst those dispersed in the Western Pacific by birds (Bot. Chall.
- 74. Exped., Introd. 46; iv, 229, 308). In my book on the Solomon Islands I refer to the occurrence of these seeds in the crops of fruit- pigeons; and I found that the seeds were similarly dispersed by these birds in Fiji. It is likely that the absence of the genus from Eastern Polynesia is to be partially connected with the insufficient protection of the seeds against injury during such a long ocean passage in a bird’s body. Gaudichaud, as quoted by Hemsley, refers to the occurrence of the fruits of three or four species of Myristica in the drift floating in the Molucca Sea. When in the Solomon Islands I noticed that the unopened fruits of a species floated in sea-water. In later years in Fiji I tested this point, and found that whilst the fruits just before dehiscing will float between three and seven days in sea-water, the seeds sink. As I have pointed out in the chapter on Drift, rivers carry down to the sea an abundance of seeds and fruits that can float a few days but do not imply dispersal by currents. Although, as I have above remarked, the localised range of the genus in Polynesia may be in part connected with the insufficient protection of the seed, it is apparent that in the case of a genus found in Asia, Africa, and America we are brought into contact with questions other than those of means of dispersal. No one would pretend that Myristica seeds could be carried by birds uninjured across the Pacific Ocean; and to explain the present distribution of the genus we must recall cases of a similar kind, such as Podocarpus, where the genus in past ages had a home in the north, from which, as from a focus of dispersion, it extended into the continents of the Old and the New World (see p. 302). Rhaphidophora (Araceæ) This genus of climbing aroids, which gives a character to the forests of Indo-Malaya as well as to those of the Western Pacific, is represented in the New Hebrides, Fiji, Tonga, and Rarotonga by a variety of the widely spread R. pertusa that ranges over Indo-Malaya
- 75. and Eastern Australia. The ripe berries would readily attract birds; and the seeds, 4·5 millimetres long in the case of a Fijian plant, appear hard enough to pass unharmed through a bird’s digestive canal. We seem here to have evidence of a somewhat recent connection between Indo-Malaya and Polynesia through the agency of frugivorous birds. That the genus has been long established in Polynesia is, however, indicated by the occurrence there of a species seemingly peculiar to Fiji. We are disappointed that in Engler’s recent contribution to the Pflanzenreich (in his volume on the Araceæ- Pothoideæ) he has not been able to include this genus in the field of his studies. Gnetum (Gnetaceæ) This Gymnospermous genus, which is found in the warm regions both of the Old and the New World, is represented in Fiji by a Malayan species, Gnetum gnemon, which exists also in the Solomon group with other species of the genus (Guppy’s Solomon Islands, pp. 288, 301). I was familiar with this species in both Fiji and the Solomon group; but in the first-named locality it is seemingly restricted to the borders of Wainunu Bay on the south side of Vanua Levu, where Dr. Harvey first found it. It grows there abundantly in young wood. It seems almost idle to discuss the mode of dispersal of a genus that is placed in a class apart with the African Welwitschia and the European Ephedra, possessing with them a history of which we know nothing. Yet it is ranked by Mr. Hemsley amongst those genera that are dispersed in Polynesia by birds, and he produces better evidence in support of this view than we possess for many other plants. Thus a fruit of a species of Gnetum, perhaps G. gnemon, has been found in a New Guinea fruit-pigeon; and the fruits of two species of the genus were found in the crops of fruit-pigeons shot by Mr. Moseley in the Admiralty Islands (Bot. Chall. Exped., Introd. 46; iv, 308). The red drupes of Gnetum gnemon of Fiji would readily
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