Predictive Analysis and Causal Relationships in Business

This report delves into the critical concepts of predictive analysis and causal relationships, exploring their significance in the business context. It examines how predictive analysis, utilizing advanced statistical techniques, can forecast future events, while causal relationships identify connections between events. The report includes a comprehensive literature review, analyzing articles on topics such as topological data analysis in manufacturing, the relationship between HRM and firm performance, and the detection of causal relationships in complex systems. Each article is assessed for its relevance, strengths, and weaknesses, providing a nuanced understanding of the subject matter. The conclusion emphasizes the importance of predictive analysis in enhancing organizational performance and achieving business goals, highlighting the efficiency and accuracy of these methods. It also provides a future research direction for the improvement of the organization. The report underscores the practical implications of these analytical tools for entrepreneurs and businesses seeking to make data-driven decisions.

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Table of Contents
Introduction......................................................................................................................................2
Literature review..............................................................................................................................2
Conclusion.......................................................................................................................................4
References........................................................................................................................................5

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Introduction
The fundamental purpose of the paper is to discuss the aspects based on predictive analysis and
causal relationships. Predictive analysis is determined to be advanced analytics that is utilized to
make significant predictions regarding future events. It makes use of different statistics and
techniques to evaluate the existing data. While a causal relationship takes place between two
events that correlate with one another. Thus, the organizations are highly interested in finding a
relationship among the variables. Therefore, the paper will analyze the cause of predictive
analysis and causal relationships that creates an impact on the organization.
Literature review
The article “Identification of Key Features Using Topological Data Analysis for Accurate
Prediction of Manufacturing System Outputs” is based on evaluating the use of TDA, which is
widely utilized within the domain of manufacturing systems. The articles determine the process
of executing TDA using Mapper algorithm and demonstrate the rapid tracing casualty using
Mapper algorithm. It is relevant to the topic as TDA makes use of predictive models for
analyzing the influence of selected features. According to Guo & Banerjee, (2017), the model
causes a similar level of greater accuracy with the process variables. Its unique characteristics are
that it presents the use of significant features with causal relationships that serves a high level of
prediction accuracy. The significant strength of the article is that it discusses the aim of the
Mapper algorithm to minimize the need for measurement that can be widely used in the future.
Whereas, the weakness is that it only determines the use of TDA in the manufacturing systems
rather than other domains. The articles are required to make use of less number of processes that
impacts the manufacturing system and product quality. The article “Exploring the relationship
between HRM and Firm Performance: A Meta-Analysis of Longitudinal Studies" states the
positive relationship among firm’s performance as well as human HRM. It discussed the use of
meta-analysis for minimizing the impact of measurement and sampling error. The article is
relevant to the topic as it provides relevant knowledge of predictive research that possesses a
more extensive research design where causality is examined through longitudinal data. It states
that the relationship among HRM and firm performance can be measured with the help of
predictive analysis. According to Saridakis, Lai & Cooper, (2017), the unique characteristics of
the article is that it monitors the potential causal order among firm performance and HRM in

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small businesses. One of the major strengths of the article is that it has done wide research
related to direct and positive association among firm performance and HRM systems. While its
weakness is that it did not represent a clear and concise cause-order among performance and
HRM due to lack of longitudinal datasets and adequate research design (Sharma, Al-Badi,
Govindaluri & Al-Kharusi, 2016). The article “Spatial convergent cross mapping to detect causal
relationships from short time series” investigates causal relationship within complex systems.
The article combines the current techniques of dewdrop regression along with convergent cross-
mapping. As per Clark et al., (2015), it determines the efficient use of multispatial CCM that
helps in investigating causal relationships. The article is relevant to the topic as it discusses the
testing of predictive analysis and causal relationship. The unique characteristics of the article are
that it provides relevant knowledge regarding multispatial CCM that helps in detecting predictive
analysis and causal relationships. The strength of the article is that it uses various examples from
real-world ecological and simulated data, which helps in detecting the ability of multispatial
CCM to test causal relationships among processes (Tate, 2015). The weakness of the article us
that it did not use dynamic systems that would have helped to consider the general validity of
multispatial CCM.
The article “The relationship between perceived organizational support and proactive behaviour
directed towards the organization” monitors causal relationships among proactive behavior along
with perceived firm’s support towards the organizations. The article also investigated the two
potential mechanisms based on relationships like work involvement and obligation (Boulianne,
2015). This particular article is relevant to the topic as it tests and determines the underlying
mechanism of the of causality relationships. The unique characteristics of the article are that it
fosters employee’s proactivity and tries to create new ideas to help the organization. As
mentioned by Caesens, Marique, Hanin, & Stinglhamber, (2016), the article possesses various
strengths out of which its significant strength is that it demonstrates the relationship between felt
obligations and perceived organizational support that rely on the exchange ideology of the
employees. Secondly, the results are related to homogenous sampling that helps in replicating
different perspectives of people. The significant weakness is that the article uses self-reported
measures that cause issues of common method variance and social desirability. The article “Get
It Together! Synergistic Effects of Causal and Effectual Decision-Making Logics on Venture
Performance” states that venture performance is positively related to effectual action orientation

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and causal business planning that is effective for most of the entrepreneurs. It presents an
understanding of the advantages of causation to venture performance. The article is relevant to
the topic as it determines venture performance as controlling and predictive logics that is useful
for the entrepreneurs. The unique characteristics of the article are that it contributes to the
argument on commercial decision-making by discovering the relationship among effectuation
and causation. As stated by Smolka, Verheul, Burmeister–Lamp & Heugens, (2018), another
contribution is that it emphasizes the need to validate measurement scales as well as refine
existing. The strength of the article is the use of a mechanism for determining the relationship
between the creation and performance of precommitment. The weakness of the article is that
data taken for evaluating the results cannot come to a conclusion based on the order of events.
The article “Predicting Psychological and Subjective Well-Being from Personality: Incremental
Prediction from 30 Facets over the Big 5” investigates the significant relationship among the
facet levels and Big 5 leisurely factors and the scopes of both subjective along with
psychological well-being. As per Anglim & Grant, (2016), it emphasizes on the relationships of
factor-level, then facet-level and lastly on a broader methodological as well as theoretical issues.
The article is relevant to the topic as it explains the incremental prediction of personality facets
over a huge range of variance. The major contribution of the article is it analyzes the facet-level
for examining the relationship between well-being and personality factors. The strength of the
article is that it provides well explanations of both positive and negative effects of incremental
prediction on personality facets. The weakness of the article is that it needs more research to
gather information about the incremental prediction that relates to different kinds of facets and
personality tests (Guo, Yiu & González, 2016). On the other hand, the article made minimal use
of refining measures that help in minimizing the unnecessary aspects and use of irrelevant
measurement of values.
Conclusion
The paper demonstrated an understanding of the importance and use of predictive analysis and
causal relationships. Based on future research, it can be said that predictive analysis helps in
developing the organization's future if appropriate actions are taken and by detecting the causal
relationships. In the future, this will eventually help in enhancing the better performance of the
organization along with the employees. Therefore, the paper demonstrated the efficiency and

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accuracy of the predictive analysis that helps the entrepreneurs in achieving the goals. Thus, it
provided an analysis of the cause of predictive analytics within the organization.
References
Anglim, J., & Grant, S. (2016). Predicting psychological and subjective well-being from
personality: Incremental prediction from 30 facets over the Big 5. Journal of Happiness
studies, 17(1), 59-80.
Boulianne, S. (2015). Social media use and participation: A meta-analysis of current
research. Information, communication & society, 18(5), 524-538.
Caesens, G., Marique, G., Hanin, D., & Stinglhamber, F. (2016). The relationship between
perceived organizational support and proactive behaviour directed towards the
organization. European Journal of Work and Organizational Psychology, 25(3), 398-411.
Clark, A. T., Ye, H., Isbell, F., Deyle, E. R., Cowles, J., Tilman, G. D., & Sugihara, G. (2015).
Spatial convergent cross mapping to detect causal relationships from short time
series. Ecology, 96(5), 1174-1181.
Guo, B. H., Yiu, T. W., & González, V. A. (2016). Predicting safety behavior in the construction
industry: Development and test of an integrative model. Safety science, 84, 1-11.
Guo, W., & Banerjee, A. G. (2017). Identification of key features using topological data analysis
for accurate prediction of manufacturing system outputs. Journal of Manufacturing
Systems, 43, 225-234.
Saridakis, G., Lai, Y., & Cooper, C. L. (2017). Exploring the relationship between HRM and
firm performance: A meta-analysis of longitudinal studies. Human Resource
Management Review, 27(1), 87-96.
Sharma, S. K., Al-Badi, A. H., Govindaluri, S. M., & Al-Kharusi, M. H. (2016). Predicting
motivators of cloud computing adoption: A developing country perspective. Computers
in Human Behavior, 62, 61-69.

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Smolka, K. M., Verheul, I., Burmeister–Lamp, K., & Heugens, P. P. (2018). Get it together!
Synergistic effects of causal and effectual decision–making logics on venture
performance. Entrepreneurship Theory and Practice, 42(4), 571-604.
Tate, C. U. (2015). On the overuse and misuse of mediation analysis: It may be a matter of
timing. Basic and Applied Social Psychology, 37(4), 235-246.