PESTLE analysis and the relationship with organisations’ financial performance

7. 7. 2020
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Introduction In the new age of extreme uncertainty, organizations endeavors to conceive innovative approaches to stay ahead of competitors and achieve sustainable growth. According to the Australian Securities and Investments Commissions (ASIC), approximately 8,500 (8,5%) businesses in Australia were under administration in 2018 (ASIC, 2019). The constant pressure caused by the external environment has called for a new paradigm shift that requires a deep understanding of the cause-effect relationship and creating a precise market depiction to thrive in such conditions. Yet, many businesses still operate in a vacuum entirely detached from the market ecosystem unwillingly adapting. The article aims to explore PESTLEmethodology as one of the most popular strategic tools deployed by organizations to identify and analyze external factors in order to minimize risks and maximize opportunities. The tool’s acronyms characterize Political, Economic, Social, Technical, Legal, and Environmental factors that are relevant to the context and scope of an organization’s activities. The paper is divided into theoretical and practical parts. The objective of the theoretical content is to critically evaluate the PESTLE framework, which includes implementation and, discusses its advantages and limitations. The practical part is dedicated to research, as the author assumes that using PESTLE could have a positive impact on the organizations’ financial performance. The objective of the study is to investigate whether a positive relationship exists between organizations utilizing PESTLE and their profitability. The overall goal of the article is to provide evidence that those organizations, including screening of the external environment in their strategic planning processes, achieve better financial performance over institutions that have taken a reactive approach instead.

 

PESTLE is a Strategic Framework

This chapter will analyze the PESTLE framework from a few different perspectives. The content will cover the evolution of the strategic tool and its various branches that have been created to suit diverse industries and scenarios. The following dialogue is dedicated to the applicability of the analysis as well as some of the most significant challenges and benefits arising from adopting PESTLE in strategic management. 

The official acknowledgment of the importance of macro-environmental factors referred to 1967 when Francis Aguilar identified four key sectors Political, Economic, Social, and Technical factors (PEST) are built around the capital, scarcities exploring organizations’ existential questions. The technological sector is looking at how organizations can achieve competitive advantage or address identified gaps. Legal factors refer to intellectual property rights and compliance, and Political aspects attempt to answer questions related to political situations, programs, and potential investments and regulations (Singh and Srivastava, 2019).

The mnemonic that symbolizes the author’s taxonomy of the environment has become imperative due to the flux in the market conditions (Aguilar, 1967). Consequently, with the increasing need for organizations’ awareness in hand with the framework simplicity, PESTLE has become a widely applied apparatus that later was further modified by including additional factors (e.g. C – Competitors, E – Ethical) to amplify the environmental picture. Hence, it could be concluded that the more elements are covered by the framework, the more comprehensive image of the outwards conditions could be crafted.

Although PESTLE provides simple guidance through macro-economic areas, the outcome of the analysis relies entirely on human expertise. Thus, the limitation of one’s knowledge could result in incomplete or inaccurate results. For instance, when identifying risks and opportunities in a legal area, the lack of local legal knowledge might provide inadequate information for forming a strategy for growth. Economic factors could present even more significant challenges for businesses relying on generic data from agencies, or limited internal knowledge as it is not common for small to medium-size organizations to pose a subject matter expert within the company. Apart from knowledge incapability, another significant obstacle could lie in the bias and authority through position ranking as it is not uncommon for high positioned managers to utilize their titles to achieve the desired outcome.

Analysis of the Relationship Between PESTLE and Organisations’ Profitability

The objective of the research was to identify whether the application of the PESTLE framework is positively associated with organizations’ profitability. The analysis was conducted on a sample of twenty organizations (n=20) from Australia (see Table 1). The companies were selected from a client portfolio, and the outcome of the analysis is discussed later in this chapter. 

The sample n=20 includes two types of variables:

  • (x1-20) dependent variables - PESTLE
  1. organizations using PESTLE
  2. organizations not using PESTLE 
  • (y1-20) independent variables – Profit Margin in percentage [%]

The organizations’ profitability was calculated as a Gross Profit Margin recorded at the end of the financial year 2018 in comparison to the previous 2017. The difference in their margins are recorded as either positive or negative percentages (see Table 1).

The regression analysis was performed in order to determine the significance of the relationship between the application of the PESTLE framework and organizations’ profitability. The study was performed in Excel, and the outcome was recorded in the regression statistics and ANOVA (see Table 2). To better understand the significance and the type of relationship a scatter plot (see Graph 1) was developed as a part of the regression analysis. 

Hypothesis

The hypotheses have been developed to investigate whether using PESTLE could have a positive impact on the organizations’ profitability as it is believed that companies with better awareness of the external environment would achieve better results. 

H0: There is a strong relationship between organizations using PESTLE and their increased profitability. 

H1: There is weak or no relationship between organizations using PESTLE and their increased profitability. 

P<0.05; Confidence level (CL) = 95%

Company

PESTLE 

Gross Profit [%]

Organization 1

Yes (1)

7,3

Organization 2

Yes (1)

16,9

Organization 3

No (0)

3,1

Organization 4

No (0)

8,3

Organization 5

Yes (1)

0.7

Organization 6

Yes (1)

2,1

Organization 7

No (0)

-0,9

Organization 8

No (0)

1,1

Organization 9

Yes (1)

11,5

Organization 10

Yes (1)

-2,9

Organization 11

No (0)

-6,3

Organization 12

Yes (1)

4,3

Organization 13

Yes (1)

0,1

Organization 14

Yes (1)

2,2

Organization 15

Yes (1)

6,1

Organization 16

No (0)

2,0

Organization 17

No (0)

3,4

Organization 18

No (0) 

9,0

Organization 19

No (0)

2,3

Organization 20

No (0)

0,4

Mean

3,535

Standard Deviation 

5,218

Minimum

-6,3

Maximum

16,9

 

Table 1: (Teply, 2020). Sample of twenty selected organizations.

Discussion

The regression analysis was conducted in order to determine whether a positive linear relationship between variables x (independent variable – PESTLE in use) and variable y (dependent variable – gross profit) exists. During the process of collecting data, it was noted that leaders utilizing PESTLE emphasized the importance of the analysis in the strategic process and that the tool allows for a better understanding of the external conditions. Moreover, it was discussed that PESTLE improves a company's risk-based approach and overall environmental awareness. Hence, the assumption was expected to fail to reject the H0. 

The outcome of the regression analysis, however, suggests that there is a weak positive linear relationship (correlation coefficient [r] = 0.254630966) between the variables x (PESTLE in use) and y (gross profit). The analysis, on the other side, does not explain whether the two variables are a non-linear relationship. Moreover, is it evident from table (x) that the p-value for the correlation is 0.278628754 which is higher than the significance level (alpha) of 0.05, so there is inconclusive evidence about the interrelationship between the variable x and y. For the reasons above the author has rejected the H0 and concluded that there is a weak or no relationship between organizations using PESTLE and their increased profitability.

Graph 1: (Teply, 2020) Scatter plot of the twenty randomly selected organizations and their linear relationship in excel.

Regression Statistics

Multiple R

0.254630966

R Square

0.064836929

Adjusted R Square

0.012883425

Standard Error

5.184190712

Observations

20

ANOVA

 

df

SS

MS

F

Significance F

Regression

1

33.5405

33.5405

1.247979908

0.278628754

Residual

18

483.765

26.8758333

   

Total

19

517.3055

     

 

Table2: (Teply, 2020) Regression statistics of twenty randomly selected organizations in excel.

Limitations

This research has been simplified due to the complexity and the author is aware of the following research limitations:

  1. Firstly, the sample of the organization could be considered as biased as the sample selection was convenient, and the organizations have not been randomly selected. 
  2. Secondly, the dynamics of different industries vary, and the gross profit margin could be impacted by external conditions. For instance, a decline in residential buildings in the construction industry as opposed to government investments in the infrastructure area.
  3. Thirdly, the regression analysis evaluates only a linear relationship; hence a non-linear correlation was not considered.

Conclusion

The rapidly growing complexity and uncertainty of the markets place a higher demand for organizations to develop a better understanding of external factors and monitor changes. To depict a macro-economic landscape, the PESTLE framework allows organizations to identify external positive and negative forces systematically. Despite the fact that PESTLE is a relatively simple tool, limited macro-economic knowledge and biases have been identified as the most significant challenges that could impair the results. 

On the other side, effective monitoring of outward conditions allows organizations to adapt to quickly changing environments and become more resilient. This led to the author’s assumption that organizations using PESTLE would achieve better financial results. To verify whether the statement is accurate, a regression analysis has been conducted to investigate the relationship between organizations using PESTLE and their financial performance. The results provided the conclusion that there is insufficient evidence sighted to be confident that using PESTLE would result in better financial performance. 

Although the null hypothesis was rejected, the author concludes that using the PESTLE element/feature or other similar tools is critical for organizations to build knowledge and monitor quickly changing risks and opportunities. Only then, organizations could become agile and make the right decision at the right time.

Bibliography (standard format of citations according to international standards):

 

Aguilar, F., 1967. Scanning the Business Environment. New York, NY: Macmillan Co., 239p.

Kiyak, D. and Pranckeviciute, L., 2016. Determining the Relation Between the Business Environment and Companies Solvency Factors in The Post–Crisis Period. Ekonomika, 95(3), Pp. 64-80.

Singh, S. and Srivastava, S., 2019. External Factors Affecting Indian Handloom Industry: A Paradigm Shift. International Journal of Business Insights and Transformation, 12(1), pp. 33-41.

Summary Analysis of Insolvency Statistics, ASIC – Australian Securities and Investments Commission. [ONLINE] Available at https://asic.gov.au/regulatory-resources/find-a-document/statistics/insolvency-statistics/summary-analysis-of-insolvency-statistics/. [Accessed 13 January 2020].

Teply, M., 2020. PESTLE analysis and the relationship with organizations’ financial performance, LIGS University.

 

Author: Marek Teply, a student at LIGS University, under the supervision of Zdeno Matta

 

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