Analyzing Trends in the Global Business Environment Report

This report examines the application of algorithms in the global business environment, exploring their role in business predictions and customer preference analysis. It discusses the increasing use of algorithms and their potential risks, particularly when users lack understanding of the underlying processes. The report delves into the literature on the literal nature of algorithms, major issues associated with their use, the importance of accurate data input, and the limitations of algorithm applications. It highlights the need for proper data organization and the challenges in connecting formal algorithmic systems with the informal world. The study concludes by emphasizing the benefits and drawbacks of algorithm use in business, including the need for careful data input and the limitations posed by the 'black box' nature of algorithms.

Running head: GLOBAL BUSINESS
Trends in Global Business Environment
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Table of Contents
Introduction......................................................................................................................................3
Discussion of the literature..............................................................................................................3
Links between the four questions and the core article.....................................................................6
Conclusion.......................................................................................................................................7
References........................................................................................................................................9

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Introduction
Use of the algorithms has been increasing rapidly in the recent years to predict things
more accurately. However, the business organizations use an algorithm to strengthen their
business predictions (Luca et al. 2016). Application of computer algorithm is beneficial for the
business owners to identify the distribution channel and to understand the customer preferences.
However, use of algorithm often creates risk if the users do not understand the process of
computer algorithm. As for example, Netflix has invested million dollars to develop the
algorithm in order to understand the customer preferences but often the viewer’s preferences do
not match with algorithm predictions. Therefore, often the social media sites use an algorithm
that maximizes the rate of pay per click, which dissatisfies the customers (Kotthoff 2016). This
current study deals with the application algorithm, its limitations and accessing issues.
Discussion of the literature
Why are algorithms considered as literal?
Literature shows that humanizing algorithms makes human being more comfortable
while they are using the algorithm. As for example, when an individual is designing automated
call functions then humanizing algorithm is effective. The behavior of algorithm is different from
the human. It can be said that the human being treats the algorithm and the machines that are
involved in algorithm process also treat the employees and the supervisor (Kleinberg et al.
2017). Algorithm can be literal if humans are not able to use this algorithm carefully. However,
it can be said that algorithm is a computer programming in order to solve issues and it serves as
the blueprint. Hence, it can be said that the user needs to apply the algorithm by using proper.

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Any error during the application of algorithm may create issues as algorithm is a literal process.
If the human being ignores any consideration during the use of algorithm then they may face
trouble while accessing the algorithm process. The instruction should follow properly during the
application of algorithm. Algorithm may operate the task literally, which will create a big trouble
for the human being.
What are the major issues of algorithm use?
Computer algorithm includes various programming. This is considered as the effective
predictive tool. However, this system may create problem if they are not applied in a proper
manner (Zhang et al. 2018). Often technical issues arise while computing the program. The
source of data needs to access in a proper manner otherwise the system can be damaged. As for
example, the social media sites often affected due to the use of algorithm.
It has been seen that many sites prefer to use the algorithm to select the ads and news in
order to attract the users. On the other hand, many websites deploy algorithms to choose the links
and ads for the users. These algorithms are associated with the enhancement of user click-
throughs, as a result, the sites become choked with poor quality click bait. As a result, the rate
per click becomes increased, which hampers the customer satisfaction level. Hence, the missteps
occur in algorithm process that is a big issue of algorithm use to carry out business predictions.
Apart from this often the business organization uses a computer algorithm to predict the
customer preferences. However, often mismatch occurs between the customer preference and the
algorithm prediction (Kleinberg et al. 2017). Hence, it has been found from the studies that
operating process of the algorithm is crucial to reduce issues regarding it. On the other hand, data
should be accessed properly otherwise the issues can occur in algorithm system. Therefore, a

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computer algorithm is a time-consuming process and big tasks are difficult to put into the
computer algorithm process. Hence, it has been found that computer algorithm is beneficial for
business prediction if the process is carried out properly. Any misuse of this process may create a
big error in the system, which can hamper the customer satisfaction in the context of any
business.
What is the importance of right data input during the application of algorithm?
In order to use the algorithm right data input is necessary for an organization. Based on
the literature review it has been received that the healthcare organizations are trying to measures
the forborne disease in restaurants. For this purpose, the healthcare organization uses online
reviews to understand which restaurant is violating the local health code. They create an
algorithm to collect this data. Use of algorithm the healthcare organizations are able to review
the large amount data. Algorithm is associated with the computer system thus right data input is
necessary. If the data is not given properly then expected outcome cannot be got (Ding et al.
2015). Firstly right data needs to input in algorithm process then it should be processed and
finally the outcome is got. To apply the algorithm process for a business purpose proper data
resource need to choose for the organization. However, it is important for the organization to
organize the data in a proper way to get expected outcomes from the algorithm process.
Increased length of data is helpful to improve the prediction trough algorithm process.
What are the limitations of algorithm use?
Algorithm process is beneficial for business prediction, however; it has some limitations
that often hamper the effectiveness of this process. Based on the given literature it has been
found that algorithm generates predictions based on the existing data and it is not transferable to

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a new issue and assesses the problem. One of the major limitations of algorithm process is the
formal system. It is often difficult to make a connection with the informal world and the
computer world. On the other hand, algorithms are considered as the black box. Maximum
business platforms are owned by the private owners and they do not want to expose their internal
working process by computer algorithm to the end users. As a result, complexity occurs in the
system and the user faces difficulties to comprehend this system (Xue et al. 2017). Therefore,
machine learning algorithm is used in high dimensional space to access millions of parameters.
This leads human being to comprehend this system.
Links between the four questions and the core article
The first key question refers that algorithm process is considered as the literal process.
This fact is also found in core article based on which the question is generated. From the
supportive journal it has been received that proper accessing is mandatory during the application
of algorithm otherwise issues will be generated in this system. Human operates algorithm and
due to an error in this process leads algorithm to monitored human. The second key question
refers to the issues related to algorithm application. From the supportive journal it has been
received that technical issue is a major issue of algorithm and poor accessing may create
customer dissatisfaction through the algorithm process. This issue is also found in the core
journal. The third question is related to the necessity of right data input in algorithm process. It
has been received from the supportive journal proper input of data is necessary to get expected
outcome from the algorithm process, which is also supported by the core article. The last key
question focuses on the limitations of algorithm process. It has been received that algorithm is
considered as the black box which is a major limitation among the other limitations of algorithm
process. Due to complexity in comprehending algorithm process, it is considered as the black

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box that also found in the core article. Hence, it can be said that each article related to the four
key questions support the information of core article.
Conclusion
The above piece of work reveals the advantages and limitations of the algorithm process
in business predictions. It has been received that algorithm process needs proper input of data.
Any error during the data input may create complexity in the algorithm process. Due to the
complexity algorithm is considered as the black box, which is the major limitation of this
process. Often the prediction of the algorithm process does match with the customer perception.
It is considered as the linear process. Despite these limitations algorithm is beneficial for
business prediction as it helps the user to review the large data quickly.

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References
Ding, S., Zhao, H., Zhang, Y., Xu, X. and Nie, R., 2015. Extreme learning machine: algorithm,
theory and applications. Artificial Intelligence Review, 44(1), pp.103-115.
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and Mullainathan, S., 2017. Human
decisions and machine predictions. The Quarterly Journal of Economics, 133(1), pp.237-293.
Kotthoff, L., 2016. Algorithm selection for combinatorial search problems: A survey. In Data
Mining and Constraint Programming (pp. 149-190). Springer, Cham.
Luca, M., Kleinberg, J. and Mullainathan, S., 2016. Algorithms need managers, too. Harvard
business review, 94(1), p.20.
Xue, J., Wu, P., Kryger, M., Wang, W., Cheng, P., Das, I.J. and Hu, K.S., 2017. Limitations of
Optimization Algorithm With Volumetric Modulated Arc Technique for Head and Neck Cancer
Radiation Therapy. International Journal of Radiation Oncology• Biology• Physics, 99(2),
p.E741.
Zhang, Y., Ye, P., Wu, J. and Zhang, H., 2018. An optimal curvature-smooth transition
algorithm with axis jerk limitations along linear segments. The International Journal of
Advanced Manufacturing Technology, 95(1-4), pp.875-888.