Revista Cientíca de la Escuela

Superior de Guerra del Ejército

Volúmen V, Número I, Julio 2026

ISNN: 2520 - 7628 (Impreso), 2789-2514 (En Línea)

https://doi.org/10.60029/rcesge

ESGE

REVISTA CIENTÍFICA

Jherson Manuel Jesús Apaza Gallardo*

https://orcid.org/0000-0002-2346-732X

ESIC University, Madrid, Spain

Enviado: 20 de Febrero 2026 • Evaluado: 10 de Abril 2026 • Aprobado: 1 de Mayo 2026

Citar como:

Apaza Gallardo, J. M. J. (2026). DOI–Performance Relationship in Government-to-Government Con-

tracting: A Case Study of Three UK Consultancies in Peru (2021–2023). Revista Cientíca de la Escue-

la Superior de Guerra del Ejército, 5(1), 133-168. https://doi.org/10.60029/v5n1art8

Resumen

Este estudio examina las implicaciones de la metodología del Grado de Internacionalización

(DOI) y la Relación de Desempeño en empresas que participan en contratos Gobierno a Gobierno

(G2G), centrándose en el proceso de internacionalización de tres consultoras británicas: Mace,

Arup y Gleeds, liales en Perú. El objetivo de este estudio es determinar las implicaciones del

DOI y la Relación de Desempeño en empresas que participan en contratos G2G con respecto a su

proceso de internacionalización.

Especícamente, investiga la relación entre el DOI, medido por Ventas en el Extranjero sobre

Ventas Totales (FSTS), Activos en el Extranjero sobre Activos Totales (FATA) y Filiales en el

Extranjero sobre Activos Totales (FATA2), y el desempeño, determinado por el Retorno sobre

Activos (ROA). Mediante análisis descriptivo y de correlación/regresión, el estudio analiza datos

de los primeros tres años del contrato G2G entre el Gobierno Peruano y el Gobierno del Reino

Unido (2021-2023). Los resultados indican una asociación positiva entre un mayor DOI y una

estrategia de internacionalización fructífera en el corto plazo, aunque con baja previsibilidad. Las

empresas participantes también realizaron campañas de autopromoción y promovieron capital

más allá de las fronteras del país de destino, independientemente de su grado de participación en

el contrato G2G. El estudio concluye que los contratos G2G ofrecen una oportunidad única para la

inmersión estratégica en los mercados objetivo y regionales. Las limitaciones incluyen el pequeño

tamaño de la muestra y la dependencia de datos estimados.

Palabras clave: Perú–Reino Unido, liales, Gobierno a Gobierno, compras públicas, provisión

de infraestructura, grado de internacionalización, relación de desempeño, internacionalización.

Relación DOI–desempeño en la contratación de gobierno a gobierno:

estudio de caso de tres consultorías británicas en el Perú (2021–2023)

* PhD Candidate, Doctoral Program in Business Administration and Management, ESIC

University, Madrid, Spain

E-mail: 607635@students.esic.university

Revista Cientíca de la Escuela

Superior de Guerra del Ejército

Volúmen V, Número I, Julio 2026

ISNN: 2520 - 7628 (Impreso), 2789-2514 (En Línea)

https://doi.org/10.60029/rcesge

ESGE

REVISTA CIENTÍFICA

Jherson Manuel Jesús Apaza Gallardo*

https://orcid.org/0000-0002-2346-732X

ESIC University, Madrid, Spain

Enviado: 20 February 2026 • Evaluado: 10 April 2026 • Aprobado: 1 May 2026

Cite as:

Apaza Gallardo, J. M. J. (2026). DOI–Performance Relationship in Government-to-Government Con-

tracting: A Case Study of Three UK Consultancies in Peru (2021–2023). Revista Cientíca de la Escue-

la Superior de Guerra del Ejército, 5(1), 133-168. https://doi.org/10.60029/v5n1art8

Abstract

This study examines the implications of the Degree of Internationalization (DOI) methodology

and Performance Relationship in companies participating in Government-to-Government (G2G)

contracts, focusing on the internationalization process of three British consultancies - Mace, Arup,

and Gleeds - subsidiaries in Peru. The objective of this study is to determine the implications

of DOI and Performance Relationship in companies participating in G2G contracts regarding

its internationalization process. Specically, it investigates the relationship between DOI, as

measured by Foreign Sales over Total Sales (FSTS), Foreign Assets over Total Assets (FATA),

and Foreign Aliates over Total Assets (FATA2), and performance, as determined by Return

on Assets (ROA). Using descriptive and correlation/regression analysis, the study analyzes

data from the rst three years of the G2G contract between the Peruvian Government and the

Government of the UK (2021-2023). Results indicate a positive association between a higher

DOI and fruitful internationalization strategy in the short term, albeit with low predictability.

Participating companies also engaged in self-promotion campaigns and promoted capital beyond

the destination country’s borders, regardless of their degree of participation in the G2G contract.

The study concludes that G2G contracts oer a unique opportunity for strategic immersion in

target and regional markets. Limitations include a small sample size and reliance on estimated

data.

Keywords: Peru–UK, subsidiaries, Government-to-Government, public procurement, infrastructure

delivery, degree of internationalization, performance relationship, internationalization.

DOI–Performance Relationship in Government-to-Government Contracting:

A Case Study of ree UK Consultancies in Peru (2021–2023)

* PhD Candidate, Doctoral Program in Business Administration and Management, ESIC

University, Madrid, Spain

E-mail: 607635@students.esic.university

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Introduction

The internationalization of rms is a complex process fraught with uncertainty, and

understanding the mechanisms that facilitate successful expansion into new markets is a pressing

concern for scholars and practitioners alike. While existing research has extensively explored the

role of institutional factors in shaping rms’ internationalization strategies (Cuervo et al., 2018),

there remains a notable gap in our understanding of how Government-to-Government (G2G)

contracting, as an institutional mechanism, inuences rm internationalization and performance.

G2G contracts have been touted as a means to promote transparency, reduce corruption, and enhance

the eciency of public procurement processes (Mchopa et al., 2024), but their implications for

rms’ internationalization trajectories remain underexplored. As governments increasingly rely

on G2G agreements to facilitate strategic partnerships and drive economic growth, understanding

the impact of these contracts on rms’ internationalization outcomes is crucial for informing

policy and practice in public sector contracting, integrity, and strategic delivery capacity. By

examining the intersection of G2G contracting and rm internationalization, this study aims to

shed light on the ways in which institutional mechanisms can be leveraged to promote sustainable

and responsible internationalization practices.

The gradual liberalization of markets has led to increased internationalization, with

programs and public incentives aimed at attracting and exporting products and services (Acevedo

et al. 2020). G2G agreements have become more signicant in this context, facilitating strategic

management, operational coordination, and evidence-based decision making (Chen, 2018).

However, G2G treaties also present challenges, such as maintaining political will and clarifying

roles and responsibilities among members (Chen et al. 2018). This study explores the impact

of internationalization on performance in companies participating in Peru-UK G2G contracts,

addressing the following research questions: How does Degree of Internationalization (DOI)

inuence Return on Assets (ROA) in Peruvian consultancies operating in the UK via G2G

contracts? What is the nature of the relationship between DOI and ROA (linear, diminishing

returns, or non-linear)? We test the hypothesis that DOI has a positive but diminishing returns

relationship with ROA, reecting increasing coordination costs and complexity as companies

expand internationally.

Moreover, Enjuto et al. (2021), who investigates the effects of the G2G contracting

in the state-society relations, mention that despite the indisputable advantages that a

company with pre-established connections with the government may have, that is, the

existence of inequality between the actors of the G2G contract, it is also indisputable that

the participation of private companies in this type of contracting increases the public sector

efficiency, since it strengthens the market principle of competition. From this perspective,

when the situation for multinational companies entails the question of whether a greater

DOI should be reflected in greater economic performance at the business level, it must be

observed from the level of the returns offered by the analysis of the DOI and performance

(Loncan and Meucci 2010).

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Objectives

The general objective of this empirical study is to examine the implications of The DOI

methodology and the Performance Relationship in companies participating in G2G contracts on

their internationalization process. Specically, this research aims to:

• Determine the relation between the Foreign Sales to Total Sales ratio and the companies’

performance relationship in G2G contracts.

• Determine the relation between the Foreign Assets to Total Assets ratio and the companies’

performance relationship in G2G contracts.

• Determine the relation between the Foreign Aliates to Total Assets ratio and the

companies’ performance relationship in G2G contracts.

Literature Review

The Internationalization Process

Throughout its research on the reasons for de-internationalization, which covers the

issues of export withdrawal, divestment of subsidiaries and back-shoring or reshoring, Da Fonseca

and Da Rocha (2023), warn that once a rm has begun the internationalization process, there are

not always guarantees that these activities abroad will continue, due to manifestations such as

de-internationalization, exit decision, foreign divestment, international market exit, export market

withdrawal, reverse internationalization or back-shoring, which are part of a bunch of possibilities

originated because of drastic switching in operation modes. However, the authors distinguish

from several trends that inuence the process of exploring new markets, the fact that an incursion

in a foreign market It is often due to the transfer of a subsidiary from a country to a neighboring

country, a decision that also mean the reduction of a company’s internationalization scope and a

dent in the internationalization strategy.

Another interesting aspect is what was pointed out by Domínguez et al. (2023), who

rethinks the existing internalization process in order to eliminate the paradigms that current

models and theories have set within the internationalization process, the move away from the step-

by-step internationalization model, to put in another way, the coexistence between this model and

the non-linear internationalization process, the step-by-step approach is still commonly perceived

as a safe internationalization process. Along these lines, the step-by-step process, or also called

the linear process of internationalization, assumes that the rst step abroad involves entering the

closest countries. For this reason, the culture of the country in question has become an important

factor when making decisions about the internalization strategy to implement.

On the contrary Breuillot et al. (2022), who investigate the factors that inuence early

internationalizing companies, mention that dierent levels of inuence are reected when it

comes to the internationalization process of a beginning company in the exploration of new

markets. Firstly, at the individual level, there are factors that inuence the members of the

work teams in charge of the internationalization process, such as previous experience in

Jherson Manuel Jesús Apaza Gallardo

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similar practices, the type of network they manage, their cognitive characteristics and personal

characteristics. In second order, there are other factors that inuence at the organizational level,

such as human capital, relationship capital, organizational capital, physical capital, and the

nancial capital that is available at the time of starting the internationalization process. Finally,

the authors mention that the characteristics of the home market, industrial factors or even the

evolution of new technologies are the factors that can aect the internationalization process at

an environmental level.

Degree of Internationalization

The DOI methodology, as described in the research carried out by Nguyen (2017),

is based on the index components, the measurement of the FS/TS, FA/TA, OS/TS ratios, the

international senior management experience (TMIE), and the psychic dispersion of international

operation (PDIO), which is also mentioned as a cultural phenomenon variable. In accordance

with the authors, The DOI index tends to be computed as follows: DOI = FSTS + FATA + TMIE

+ PDIO, the last two once comprises the management’s international experience and the psychic

dispersion of international operations, respectively. However, for a holistic understanding of the

application of this methodology, it is necessary to observe its application in related projects.

According to Sun and Lee (2013), who use the DOI methodology utilizing rm’s nancial

performance, in terms of degree of franchising, in order to provide a strategy for companies in

its decision-making process to internationalize as well as to determine the factors that inuence

a medium-sized enterprises’ expansion into international markets. In this sense, the authors focus

on the division of the number of properties that operate in foreign markets by the total number of

subsidiaries of the company, in order to determine if the relationship between the rm’s relative

performance and degree of internationalization is inverted U_x005F_xfe_shape. In other words,

the methodology helps us determine to what degree the relationship between both factors is

curvilinear, an essential factor for the subsequent analysis of a research.

In the same order, Bhandari et al. (2023), who study how digitalization and

internationalization aect the company performance, propose that in order to utilize in a very

eective way the DOI construct, it is best to start from the most solid data, at analyzing the degree

to which total company sales are derived from foreign sales, at dividing foreign sales by Total

sales in (%), or at analyzing the (FATA) Foreign assets/Total assets. In this sense, the authors

remarks that DOI would act as a moderator containing positive eects in other index such as the

digitalization performance relationship or the Foreign Direct Investment index (FDI), whether it

comes from the specic industry or the country where the study is carried out. In this way, the

authors point out that this is reected in the relatively steeper positive slope when the selected

factor is greater than 0.75.

Performance Relationship

The relationship between Degree of Internationalization (DOI) and performance in

companies participating in G2G contracts is complex and inuenced by various factors. López

and Gómez (2014) identify ve general models that explain this relationship:

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• Positive And Linear Model: DOI positively impacts performance, leading to increased

economic benets. This model assumes that as companies internationalize more, they

reap more benets, such as increased market share, revenue growth, and improved brand

recognition.

• Positive but Diminishing Returns Model: DOI initially boosts performance, but benets

decrease over time. This model suggests that while internationalization brings initial gains,

these benets gradually decrease as companies face increasing costs, competition, and

complexity in foreign markets.

• U-Shaped Relationship Model: Initial negative performance gives way to positive

relationship over time. Companies may struggle initially due to liability of foreignness, but

as they gain experience and adapt to local markets, performance improves.

• Inverted U-Shaped Relationship Model: Benets of DOI peak and then decline due to

increasing coordination costs. As companies expand internationally, they face growing

coordination costs, cultural and language barriers, and complexity, which eventually

outweigh the benets of internationalization.

• Sigmoid Relationship Model: Three-stage process: initial costs, then eciency gains,

followed by declining benets. Companies face initial costs of entry, then leverage

internationalization for eciency gains, but ultimately face declining benets as they over-

expand or face increased competition.

Given the context of Peru-UK G2G contracts, we expect a positive but diminishing

returns relationship between DOI and performance. Consultancies may initially benet from

internationalization, but face increasing coordination costs and complexity as they expand. This

study explores this relationship, examining how DOI inuences performance in this specic

context.

Method

To gain insight into the relationship between Degree of Internationalization (DOI)

and performance of companies participating in the Peru-UK G2G project, we examine the

evolution of specic nancial ratios over time. The study focuses on three British consultancies

and their Peruvian subsidiaries involved in the 2020 G2G project: Arup Group Limited/

Arup Peru Limited, Mace Limited/Mace Consultancy Peru, and Gleeds Advisory Limited/

Gleeds Peru Holding Limited. Given the small sample size, this exploratory study aims to

illustrate quantitative patterns and provide a comparative case analysis of how DOI relates

to performance in these companies, rather than seeking to make broad inferential claims. The

selected companies have limited experience in the Peruvian market, making them suitable for

examining the research objectives.

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Figure 1

G2G Agreement and Operative Contract Scheme

Note. Framework based on the interaction between the UKDT and the ARCC, own elaboration.

To ensure accurate and consistent measurement with the nature of the research, the

dimensions of the DOI index have been adjusted to the specic context of the Government-

to-Government agreement between Peru and the United Kingdom, redening the operational

parameters in the following nancial aspects: (1) External Sales, Sales specically recorded in the

Americas region (Peru) based on the rms’ ocial annual reports. (2) External Assets, Consulting

revenue generated in the Americas region (Peru) based on the annual consolidated nancial

statements. (3) External Subsidiaries, Number of projects executed under the G2G agreement

based on the history of participation in the agreement.

In this sense, a series of methodological principles are established, oriented towards the

nancial reality of the participating subsidiary companies through their nancial statements of

their rst three years of operations in Peruvian territory 2021-2023, taking into account the most

appropriate nancial ratios.

Firstly, It is intended that in terms of the constructs that make up the DOI methodology,

they are adapted to the Peruvian-British G2G case and not to the general internationalization

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activities applied by the rms. For this purpose, the following modications to the scope of each

of them are proposed to its constructs: (1) the ratio of Foreign Sales to Total Sales, to this extent the

“Foreign Sales” will be considered as the sales in the Americas region (Peru), whose origin come

from the annual report published on each of the ocial sites of the three companies mentioned,

(2) Foreign Assets to Total Assets, in this degree the “Foreign Assets” will be considered as the

consultancy revenue in the Americas region (Peru), whose main source come from the annual

consolidated statement published on each of the ocial sites of the three companies mentioned

(3) the ratios of Foreign Aliates to Total Assets, concerning the “Foreign aliates” there will

be considered the quantity of projects in which the company has participated during the G2G

agreement.

Secondly, given everything stated in the introduction of the Performance Relationship

variable, there is a homogeneous opinion when choosing the most appropriate type of Performance

Relationship for measurement during the research, it is the Financial Performance Relationship,

and within it, this research will take the ROA, which will be applied to each of the ocial

statements of the three British companies, in order to present solid data for the rigor of the analysis.

Figure 2

Relationship Between DOI Constructs and Performance Relationship

Note. Graphic representing the methodology applied, own elaboration.

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Analytical method

The Degree of Internationalization (DOI) was originally conceptualized by Sullivan

(1994) as a composite index comprising the ratio of foreign sales to total sales (FSTS), the ratio

of foreign assets to total assets (FATA), top management international experience (TMIE), and

the psychic dispersion of international operations (PDIO). Subsequent studies, including Nguyen

(2017), operationalized DOI using similar components, incorporating FSTS, FATA, TMIE, overseas

subsidiaries to total subsidiaries (OSTS), and PDIO as a cultural dispersion variable. In the present

study, TMIE and PDIO are not available in the dataset and therefore cannot be operationalized.

Consequently, rather than constructing a composite DOI index, this research adopts a proxy-based

approach to internationalization. Specically, three DOI proxies are employed: FSTS, FATA,

and Foreign Aliates to Total Aliates (FATA2), the latter replacing OSTS to better reect the

structure of the available data. Firm performance is measured using Return on Assets (ROA). The

empirical design examines the association between each DOI proxy (FSTS, FATA, and FATA2)

and ROA individually, rather than estimating a unied DOI construct. Due to the very limited

sample size (n = 3), the analysis is inherently descriptive and exploratory. As such, the results

are not intended for formal hypothesis testing or statistical inference, but instead aim to provide

preliminary insights into potential relationships between internationalization dimensions and rm

performance.

Performance relationships are commonly evaluated using accounting-based indicators

such as ROA, ROE, ROCE, ROS, Net Operating Income, Net Income, and the Zmijewski Score

(Galant and Cadez, 2017). In professional services rms engaged in Government-to-Government

(G2G) contracting, ROA is particularly appropriate because project execution depends primarily

on human capital, managerial expertise, and knowledge-based assets rather than physical capital.

ROA thus captures how eectively consulting rms transform their total asset base into operating

returns within complex public-sector projects.

Alshehhi et al. (2018) identify ROA as the most frequently employed protability

measure in prior studies. Consequently, ROA is adopted as the performance indicator in this

research. The ratio is calculated using consolidated nancial statements of the parent companies to

reect overall organizational performance and incorporate results from Peruvian subsidiaries. This

consolidated approach improves comparability across rms and aligns performance measurement

with the multinational structure of G2G delivery models.

Financial Statements

To acquire information on the productive activities of the Arup company in its

internationalization process through its participation in the G2G agreement between the UK and

Peru, for the FA/TA, FS/TS, FA/TA2 and ROA variables, the following Full Accounts have been

consulted in the Company Information Service of the GOV.UK.

The Financial Statements of the ARUP LIMITED 2021-2023, Company number

01312454, Nature of Business (SIC) 71122-Engineering related scientic and technical consulting

activities, where the second part of the FA/TA variable will be identied and collected.

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Table 1

ARUP LIMITED Balance Sheet 2021-2023

NOTE 2021

1.717.100.000

33.900.000

291.900.000

353.400.000

4.900.000

62.100.000

18.500.000

100.000

900.000

190.600.000

304.600.000

0.0

781.000.000

1.512.800.000

285.800.000

731.800.000Total

Total

Revenue

Other income

Assets

Non-current assets

Property, plant and equipment

Right-of-use assets

Intangible assets

Investments accounted for using

the equity method

Deferred income tax assets

Financial assets at fair value

through prot or loss

Net investments in subleases

Fullment contract assets

Other non-current assets

2022

1.893.800.000

22.300.000

299.000.000

324.100.000

5.200.000

1.200.000

21.800.000

200.000

800.000

196.000.000

339.100.000

0.0

842.500.000

1.800.000

1.558.900.000

307.100.000

2.400.000

714.600.000

59.900.000

2023

2.163.600.000

3.300.000

301.600.000

317.300.000

8.900.000

3.300.000

16.600.000

100.000

700.000

204.700.000

382.700.000

0.0

822.500.000

2.800.000

1.564.200.000

235.100.000

2.200.000

738.900.000

88.200.000

Current Assets

Contract assets

Trade and other receivables

Derivative nancial instruments

Cash and cash equivalents

Assets classied as held for sale

Total assets

Note. Arup Limited nancial statement audited in accordance with ISAs UK and applicable law.

Registration number 01312454. (GOV.UK, 03 January 2023).

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The Financial Statements of the ARUP PERU LIMITED 2021-2023, Company number

12686337, Nature of Business (SIC) 71122-Engineering related scientic and technical consulting

activities, where the rst part of the FA/TA variable will be identied and collected.

Table 2

ARUP PERU LIMITED Balance Sheet 2021-2023

NOTE 2021

1.607.000

1.658.000

1.000

52.000

199.000

251.000

4.000

302.000

306.000

1.122.000

353.000

1.623.000

1.929.000

148.000

51.000

Total

Revenue by destination (Americas)

Employee benet expense

Changes from sub-consultants

Depreciation

Communications

Net impairment losses on

nancial and contract assets

2022

10.192.000

4.526.000613.000

1.224.00057.000

329.00066.000

2.547.00922.000

8.626.000

1.566.000

1.531.000

1.998.000

467.000

86.000

2.627.000

319.000

5.244.000

5.330.000

2.298.000

0.0

35.000

2023

6.612.000

3.444.000

1.147.000

129.000

2.221.000

117.000

7.058.000

446.000

569.000

816.000

1.385.000

44.000

1.459.000

973.000

5.422.000

5.466.000

2.990.000

9.000

132.000

Assets

Non-current assets

Right-of-use assets

Deferred income tax assets

Current assets

Contract assets

Trade and other receivables

Cash and cash equivalents

Total assets

Operating (loss) / prot

Finance income

Finance cost

Income tax charge

Loss for the nancial year

(Loss) / prot before income tax

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Note. Part of the Arup Peru Limited, subsidiary of Arup Group Limited, audited nancial

statement in accordance with international Standards on Auditing (ISAs UK) and applicable law.

Registration number 12686337. (GOV.UK, 05 January 2023).

The Financial Statements of the MACE LIMITED 2021-2023, Company number

02410626, Nature of Business (SIC) 70229-Management consultancy activities other than nancial

management, where the second part of the FA/TA variable will be identied and collected.

Table 3

MACE LIMITED Balance Sheet 2021-2023

NOTE 2021

1.933.017.000

22.956.000

51.419.000

6.908.000

1.341.000

- -

1.199.000

96.821.000

80.947.000

12.543.000

164.888.000

237.000

858.477.000

5.578.000

12.996.000

623.947.000

Total

Revenue

Non-current assets

Property, plant and equipment

Intangible assets

Deferred income tax assets

Investments in joint ventures &

associates

Other investments

Restricted cash

Trade and other receivables

Deferred consideration

2022

1.892.583.000

26.993.000

51.949.000

7.903.000

944.000

11.314.000

600.000

99.947.000

44.430.000

7.494.000

932.000

159.974.000

26.281.000

876.536.000

1.353.000

637.004.000

244.000

2023

2.356.792.000

33.405.000

43.426.000

4.631.000

251.000

6.675.000

4.337.000

92.977.000

33.700.000

7.382.000

172.953.000

953.441.000

2.143.000

10.842.000

726.421.000

252.000

Current Assets

Trade and other receivables

Development loan to join venture

Development work in progress

Asset of a disposal group

classied as held for sale

Current tax assets

Restricted cash

Cash at bank

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Note. Mace Consultancy Peru report in accordance with (ISAs (UK)) and applicable law.

Registration number 02410626. (GOV.UK, 24 July 2023).

The Financial Statements of the MACE CONSULTANCY THE AMERICAS (PERU)

LIMITED 2021-2023, Company number 10874751, Nature of Business (SIC) 70229-Management

consultancy activities other than nancial management, where the rst part of the FA/TA variable

will be identied and collected.

Table 4

MACE CONSULTANCY THE AMERICAS (PERU) Balance Sheet 2021-2023

NOTE 2021

2.000

125.000

127.000

(25.000)

102.000

102.0000

102.000

42.000

(60.000)

Revenue by destination (The

Americas)

Cost of sales

Gross (loss)/prot

Administrative expensive

Exceptional items

Administrative expensive

Operating (loss) / prot

Other Comprehensive income

Current assets

Prot before tax

Prot for the year

Tax on prot

Prot for the year

Interest receivable and similar

income

Interest payable and similar

expenses

Foreign currency translation

losses

Total comprehensive income

for the year

2022

(24.000)

(48.000)

(110.000)

(158.000)

(182.000)

7.368.000

7.186.000

6.930.000

6.923.000

(7.000)

(256.000)

2023

(123.000)

(96.000)

(219.000)

258.000

(193.000)

65.000

(39.000)

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1.843.000

375.000

2.218.000

(1.073.000)

(20.000)

1.125.000

1.303.000

(178.000)

1.125.000

(1.093.000)

Trade and other receivables

Cash and cash equivalents

Tax asset

Total assets

Tax liabilities

Shareholders’ funds

Retained earnings

Foreign exchange reserve

Net assets

Capital and reserves

Current liabilities

Trade and other payables

15.340.000

873.000

90.000

16.303.000

7.368.000

(15.255.000)

(185.000)

1.048.000

1.233.000

1.048.000

(15.255.000)

799.000

18.000

192.000

1.009.000

(5.000)

(221.000)

2.000

855.000

853.000

855.000

(216.000)

Total

Note. Mace Consultancy Peru, subsidiary of Mace Limited, report in accordance with (ISAs

(UK)) and applicable law. Registration number 10874751. (GOV.UK, 11 August 2023).

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Table 5

GLEEDS ADVISORY LIMITED Balance Sheet 2021-2023

NOTE 2021

22.364.001.000 34.944.876.000 38.380.803.000

(20.440.425.000) (32.797.850.000) (35.189.109.000)

1.923.576.000

(1.806.730.000)

13.140.000

(5.982.000)

(23.606.000)

100.398.000

5.121.618.000

1.677.517.000

(3.444.101.000)

Total

124.004.000

Turnover

Cost of sales

Gross prot

Administrative expensive

Other operating income

Operating prot

Finance costs (net)

Prots on ordinary activities

before taxation

Tax on prot on ordinary

activities

Prot for the nancial year-

income for the year

Current assets

Debtors-due within one year

2022

2.147.026.000

(1.748.284.000)

36.165.000

434.907.000

(18.926.000)

415.981.000

321.555.000

12.437.876.000

1.999.072.000

(10.438.804.000)

(94.426.000)

2023

3.191.694

(1.744.989)

35.315

1.482.020.000129.986.000

(30.788.000)

1.451.232.000

1.091.853.000

7.726.649.000

3.090.925.000

(4.635.724.000)

(359.379.000)

Cash at bank and in hand

Total current assets

Net current assets

Total assets less current

liabilities, being net assets

Amounts falling due within

one year

Note. Gleeds Advisory Limited, report in accordance with (ISAs (UK)) and applicable law.

Registration number 06472422. (GOV.UK, 12 October 2023).

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The Financial Statements of the GLEED AMERICAS (PERU) HOLDING LIMITED

2021-2023, Company number 06213794, Nature of Business (SIC) 74909-Other professional,

scientic, and technical activities not elsewhere classied, where the rst part of the FS/TS

variable will be identied and collected.

Table 6

Gleeds Americas (Peru) Holding Limited

Balance Sheet 2021-2023

NOTE 2021

- - -

(92.000)

(25.814.000)

490.373.000

335.278.000

359.424.000

(11.527.000)

35.500.000

173.000

24.146.000

Total

516.187.000

Turnover

Cost of sales

Gross prot

Administrative expensive

Operating (loss)

Income from shares in group

undertakings

(Loss)/ prot on ordinary

activities before taxation

Tax on (loss)/prot on

ordinary activities

(Loss)/ prot for the

nancial year

2022

(56.000)

335.278.000

359.368.000

(11.526.000)

35.500.000

116.000

24.090.000

2023

(59.000)

- 516.279.000

(59.000)

335.221.000

359.309.000

(11.620.000)

35.556.000

152.000

24.088.000

Fixed assets

Current assets

Investments

Total assets less current

liabilities, being ne assets

Creditors: amounts falling

due within one year

Debtors – due within one year

Cash at bank and in hand

Net current assets

Note. Gleeds Peru Holding Limited, subsidiary of Gleeds Advisory Limited, report in accordance

with (ISAs (UK)) and applicable law. Registration number 06213794. (GOV.UK, 12 October 2023).

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Results

On one hand, the correlation coecient measures the linear relationship between

strategic key drivers of nancial and marketing models with values between 1 or -1 with the

aim of establishing more eective strategies for companies’ campaigns based on the level of

association of the data variables. In this sense, (1) a correlation coecient of 0 means no linear

relationship. (2) 1 indicates a perfect correlation or that the variables are the same size. (3) -1

constitutes a perfect negative linear relationship among variables. (4) Values between 0 - 0,3

means a poor positive linear relationship. (5) Values between 0,3 – 0,7 demonstrate a moderate

positive linear relationship. (6) Values between 0,7 – 1 indicate a high positive linear relationship

among variables (Ratner, 2019).

On the other hand, Uyanık and Güler (2013) note that linear regression analyzes the

relationship between a dependent variable and one or more independent variables. This approach

enables the establishment of a mathematical model based on the correlation line, allowing for

the prediction of interactions between variables. Moreover, it’s a step further for the association

establishment between values in comparison with the correlation coecient, it not only analyzes

the interaction between variables, but also enables one to predict its interaction through the

establishment of a mathematical model based on the correlation line. In this sense, the Regression

counts with several parameters, (1) the Adjusted R Square, which determines the percentage of

the variance explained by de independent variable regarding the total variability of the data can

be measured on a similar range to the correlation coecient, (2) the Regression Critic provides

insight into the model’s overall signicance, suggesting whether the regression coecients are

likely to be zero (values below 0.05 indicate at least one coecient may be non-zero). (3) The

Independent Value Probability oers similar insight into the variance analysis, with values below

0.05 suggesting potential associations worth exploring further.

For the analysis, the following formulas will be applied, through SPSS Statistics, for

each of the companies during the three-year period, concerning the correlation coecient and the

regression model of the DOI variables in relation to the Performance Relationship variable.

Figure 3

Pearson Correlation Coecient Formula

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Figure 4

Multiple Linear Regression Formula

Note. The addition of multiple independent variables is the main dierence between a simple

linear regression and a multiple linear regression formula. (Uyanık and Güler, 2013).

Correlation Coecient and Regression Analysis 2021

Table 7

Financial Ratio Denitions and Classication Ranges for 2021

Note. First mathematical formula proposed by Pearson in 1895. (Rodgers and Nicewander, 1988).

Firms

GLEEDS

MACE

ARUP

DOI

FATA 2021

0,0012751

0,0023218

0,2142595

DOI

FSTS 2021

0,0009359

0,0000528

0,0230812

DOI

FATA2 2021

0,0000612

0,0532011

0,0003283

PERF. REL.

ROA 2021

0,0001659

0,0000440

0,2923207

Note. This classication table of the variables DOI (FATA - FSTS - FATA2) and Performance

Relationship Ratio (ROA), both for calculation of Correlation and Regression Coecient based

on the results of the 2021 nancial statements consulted on the GOV.UK website, self-elaboration.

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Table 8

Regressi on Model of the FATA 2021 Variable Based on the ROA 2021 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FSTS 2021 data

of the three companies (independent variable) and the ROA 2021 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FATA 2021

and the Performance Relationship nancial variable ROA 2021 of the three British companies is

0,99 (High positive linear relationship), whereas the Regression’s parameters indicate that, (1) the

Adjusted R Square is 0,99 (Highly viable data model), (2) the Regression Critic Value is 0,002

(Inferior to 0,05, a reliable regression model), and (3) the Independent Value Probability is 0,002

(Inferior to 0,05, a reliable variable to do prediction).

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Table 9

Regression Model of the FSTS 2021 Variable Based on the ROA 2021 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FSTS 2021 data

of the three companies (independent variable) and the ROA 2021 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FSTS 2021

and the Performance Relationship nancial variable ROA 2021 of the three British companies is

0,99 (High positive linear relationship), whereas the Regression’s parameters indicate that, (1)

the Adjusted R Square is 0,99 (Highly viable data model), (2) the Regression Critic Value is 0,02

(Inferior to 0,05, a reliable regression model), and (3) the Independent Value Probability is 0,02

(Inferior to 0,05, a reliable variable to do prediction).

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Table 10

Regression Model of the FATA2 2021 Variable Based on the ROA 2021 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FATA2

2021 data of the three companies (independent variable) and the ROA 2021 data of the three

companies (dependent variable), which comprises the correlation coecient and the regression

data (the Adjusted R Square, the Regression Critic Value and the Independent Variable Probability),

self-elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FATA2

2021 and the Performance Relationship nancial variable ROA 2021 of the three British companies

is 0,49 (Moderate positive linear relationship), whereas the Regression’s parameters indicate that,

(1) the Adjusted R Square is -0,5 (Negative viable data model), (2) the Regression Critic Value is

0,6 (Superior to 0,05, a no reliable regression model), and (3) the Independent Value Probability

is 0,6 (Superior to 0,05, a no reliable variable to do prediction).

Correlation Coecient and Regression Analysis 2022

The content of each of the variables (FATA-FSTS-FATA2-ROA) responds to the three

British companies selected, based on the 2022 nancial results obtained by both, its global

operations and those of its subsidiary in Peru.

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Table 11

Financial Ratio Denitions and Classication Ranges for 2022

Note. This classication table of the variables DOI (FATA - FSTS - FATA2) and Performance

Relationship Ratio (ROA), both for calculation of Correlation and Regression Coecient based

on the results of the 2022 nancial statements consulted on the GOV.UK website, self-elaboration.

Table 12

Regression Model of the FATA 2022 Variable Based on the ROA 2022 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FATA 2022 data

of the three companies (independent variable) and the ROA 2022 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Firms

ARUP

MACE

GLEEDS

DOI

FATA 2022

0,00341908

0,01669563

0,17976741

DOI2

FSTS 2022

0,00538177

0,00379693

0,00000160

DOI3

FATA2 2022

0,00002214

0,00723793

0,00032835

PERF. REL.

ROA 2022

0,00029957

0,00708973

0,00002801

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Based on the correlation coecient ranges, the association of the DOI variable FATA 2022

and the Performance Relationship nancial variable ROA 2022 of the three British companies is

0,47 (Moderate positive linear relationship), whereas the Regression’s parameters indicate that,

(1) the Adjusted R Square is -0,5 (Negative viable data model), (2) the Regression Critic Value is

0,69 (Superior to 0,05, a no reliable regression model), and (3) the Independent Value Probability

is 0,69 (Superior to 0,05, a no reliable variable to do prediction).

Table 13

Regression Model of the FSTS 2022 Variable Based on the ROA 2022 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FSTS 2022 data

of the three companies (independent variable) and the ROA 2022 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FSTS 2022

and the Performance Relationship nancial variable ROA 2022 of the three British companies is

0,26 (Poor positive linear relationship), whereas the Regression’s parameters indicate that, (1) the

Adjusted R Square is -0,86 (Negative viable data model), (2) the Regression Critic Value is 0,83

(Superior to 0,05, a no reliable regression model), and (3) the Independent Value Probability is

0,83 (Superior to 0,05, a no reliable variable to do prediction).

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Table 14

Regression Model of the FATA2 2022 Variable Based on the ROA 2022 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FATA2 2022 data

of the three companies (independent variable) and the ROA 2022 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FATA2

2022 and the Performance Relationship nancial variable ROA 2022 of the three British companies

is 0,99 (High positive linear relationship), whereas the Regression’s parameters indicate that, (1)

the Adjusted R Square is 0,98 (Highly viable data model), (2) the Regression Critic Value is 0,05

(Not superior to 0,05, a reliable regression model), and (3) the Independent Value Probability is

0,05 (Not superior to 0,05, a reliable variable to do prediction).

Correlation Coecient and Regression Analysis 2023

The content of each of the variables (FATA-FSTS-FATA2-ROA) responds to the three

British companies selected, based on the 2023 nancial results obtained by both, its global

operations and those of its subsidiary in Peru.

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Table 15

Financial Ratio Denitions and Classication Ranges for 2023

Note. This classication table of the variables DOI (FATA - FSTS - FATA2) and Performance

Relationship Ratio (ROA), both for calculation of Correlation and Regression Coecient based

on the results of the 2023 nancial statements consulted on the GOV.UK website, self-elaboration.

Table 16

Regression Model of the FATA 2023 Variable Based on the ROA 2023 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FATA 2023 data

of the three companies (independent variable) and the ROA 2023 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Firms

ARUP

MACE

GLEEDS

DOI

FATA 2023

0,00349444

0,00096424

0,11622830

DOI2

FSTS 2023

0,00305602

0,00010947

0,0000015372

DOI3

FATA2 2023

0,00002159

0,11694747

0,00032841

PERF. REL.

ROA 2023

0,00088544

0.00018444

0,000019088

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Based on the correlation coecient ranges, the association of the DOI variable FATA

2023 and the Performance Relationship nancial variable ROA 2023 of the three British companies

is 0,63 (Positive linear relationship), whereas the Regression’s parameters indicate that, (1) the

Adjusted R Square is -0,19 (Negative viable data model), (2) the Regression Critic Value is 0,56

(Superior to 0,05, a no reliable regression model), and (3) the Independent Value Probability is

0,56 (Superior to 0,05, a no reliable variable to do prediction).

Table 17

Regression Model of the FSTS 2023 Variable Based on the ROA 2023 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FSTS 2023 data

of the three companies (independent variable) and the ROA 2023 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FSTS 2023

and the Performance Relationship nancial variable ROA 2023 of the three British companies is

0,98 (High positive linear relationship), whereas the Regression’s parameters indicate that, (1)

the Adjusted R Square is 0,95 (Highly viable data model), (2) the Regression Critic Value is 0,09

(Superior to 0,05, a no reliable regression model), and (3) the Independent Value Probability is

0,09 (Superior to 0,05, a no reliable variable to do prediction).

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Table 18

Regression Model of the FATA2 2023 Variable Based on the ROA 2023 Variable

Note. This table shows the results of the Excel Multiple Regression apply to the FATA2 2023 data

of the three companies (independent variable) and the ROA 2023 data of the three companies

(dependent variable), which comprises the correlation coecient and the regression data (the

Adjusted R Square, the Regression Critic Value and the Independent Variable Probability), self-

elaboration.

Based on the correlation coecient ranges, the association of the DOI variable FATA2

2023 and the Performance Relationship nancial variable ROA 2023 of the three British companies

is 0,33 (Poor positive linear relationship), whereas the Regression’s parameters indicate that, (1)

the Adjusted R Square is -0.77 (Negative viable data model), (2) the Critical Regression Value is

0,78 (Superior to 0,05, a no reliable regression model), and (3) the Independent Value Probability

is 0,78 (Superior to 0,05, a no reliable variable to do prediction).

Descriptive Statistics

As can be seen in the following graph correlation table, there is a positive linear trend for the ARUP

company with respect to its FATA variable throughout its participation in the G2G agreement, as

for the MACE company, an exponential trend line prevails during the rst two years, while for the

GLEEDS company, a negative linear trend is observed in terms of its FATA variable throughout

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its participation in the G2G model. Therefore, it is observed that only for the ARUP and MACE

participation in the G2G agreement between the UKDT and the ARCC has represented a relatively

positive and sustained development throughout the rst 3 years of the project.

Figure 5

Evolution of FATA Variable Companies From 2021 to 2023

Note. This gure shows the variation between the FATA variables of the ARUP, MACE and

GLEEDS companies throughout 2021, 2022 and 2023, that is, the signicance of their participation

in the G2G agreement during the last 3 years, self-elaboration.

As identied in the following graph, there is evidence of an exponential trend line for the

companies ARUP and MACE during the rst two years with respect to its FSTS variable, while for

the GLEEDS company, a marked negative linear trend is observed in terms of its FSTS variable.

Therefore, it is observed that only the ARUP and MACE participation in the G2G agreement

between the UKDT and the ARCC has represented a partial positive development throughout the

rst 3 years of the project.

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Figure 6

Evolution of FSTS Variable Companies From 2021 to 2023

Note. This gure shows the variation between the FSTS variables of the ARUP, MACE and

GLEEDS companies throughout 2021, 2022 and 2023, that is, the signicance of their participation

in the G2G agreement during the last 3 years, self-elaboration.

As observed in the correlation table below, there is a negative linear trend for the ARUP

company with respect to its FATA2 variable. As for the MACE company, it has been identied a

marked positive exponential trend line of the FATA2 variable in the year 2022-2023, while for the

GLEEDS company, a static linear trend is observed regarding its FATA2 variable throughout their

participation in the G2G model. Therefore, it can be stated that participation in the G2G agreement

between the UKDT and the ARCC only has represented a positive or sustained development for

the MACE company during 2021-2023.

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Figure 7

Evolution of The FATA2 Variable Companies From 2021 to 2023

Note. This gure shows the variation between the FATA2 variables of the ARUP, MACE and

GLEEDS companies throughout 2021, 2022 and 2023, that is, the signicance of their participation

in the G2G agreement during the last 3 years, self-elaboration.

As can be seen in the correlation table below, there is a positive but imperceptible

linear trend for both the company ARUP and MACE with respect to its ROA variable, as for the

company GLEEDS, a fairly marked negative linear exponential trend is observed between the

year 2021-2022 in terms of its ROA variable. Therefore, it is observed that there is no company

in G2G agreement between the UKDT and the ARCC with a positive development throughout the

rst 3-year period.

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Figure 8

Evolution of The ROA Variable of Companies From 2021 to 2023

Note. This gure shows the variation between the ROA variables of the ARUP, MACE and

GLEEDS companies throughout 2021, 2022 and 2023, that is, the signicance of their participation

in the G2G agreement during the last 3 years, self-elaboration.

Discussion

Once the nancial statements have been determined, the DOI and Performance

Relationship has been calculated and both the Correlation Coecient and the Regression

Model have been applied to the DOI and Performance Relationship sub-variables, as well as the

statistical analysis of each of the variables over the three-year period have been introduced. It can

be stated that, although it is true that the participation of ARUP, MACE and GLEEDS in a G2G

agreement, precisely in a country where its presence or experience is not relevant, has become at

least positive, given the characteristics of the contract, which oers a planned work structure with

medium and long-term goals, as can be seen in the details of the analysis of the following results.

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Concerning the analysis of the annual evolution of the DOI FATA variable , it reveals

a poor, generalized linear trend during the rms’ participation in the G2G model. This nding

suggests that, overall, the agreement has not guaranteed improved asset eciency for most of

the participating organizations. However, it highlights the performance of ARUP and MACE,

which showed positive and sustained development during the rst three years of the project. This

exceptional performance is further reinforced by the DOI FSTS variable, where again only these

two companies achieved partial positive progress, setting them apart from the other participants

in the UKDT-ARCC agreement.

A closer look at protability reveals an even closer concentration of success around

MACE. Regarding the DOI FATA2 variable, it is evident that only this organization managed to

maintain positive or sustained growth during the 2021-2023 period. This performance is consistent

with the ROA (Return on Assets) analysis, which shows that MACE was the only entity able to

capitalize on its participation in the G2G agreement to improve its return on assets.

It is also observable that, based on the statistical outputs derived from the study dataset,

although the three companies did not participate equally in the contracts included in the analysis,

no consecutive periods of low activity were observed within the available data window, and most

variables exhibited positive linear or exponential trends, with the exception of FATA2; however,

these ndings are exploratory and limited to the scope of the sample analyzed. Within this

empirical boundary, the results suggest that participation in the UKDT–ARCC G2G framework

coincided with increased operational activity for ARUP, MACE, and GLEEDS in Peru, although

this study does not construct a contract-level participation dataset nor establish causality.

Consequently, interpretations regarding market consolidation or regional diversication should be

viewed as indicative rather than conclusive, while broader claims concerning prestige acquisition

or positioning within the Latin American public sector are presented as contextual observations

informed by existing literature and industry sources, rather than outcomes directly.

Conclusion

As regression level analysis, except for the Correlation Coecient of the FSTS variable

in 2022 and the FATA2 in 2023, the rest of the coecients indicate a moderate / high level of

association among variables. Therefore, a moderate to high level of association exists between the

ratio of FSTS and the performance of companies participating in G2G contracts. However, this

relationship is not constant over time, as a signicant exception was identied in the Correlation

Coecient for 2022, where the link lost the strength observed in the rest of the analyzed period. This

indicates that, while the international sales structure is generally aligned with corporate performance

in this contractual model, there are temporary or situational factors that can weaken this association.

Additionally, concerning the Regression Model, in 2021 one-third of the regression

analysis represent inconsistent gures to be able to predict association among variables (the

FATA2/ROA analysis), in 2022 two-thirds of the regression analysis represent inconsistent gures

to be able to predict association among variables (the FATA/ROA and the FSTS/ROA analysis),

and in 2023 all the regression analysis present inconsistent gures to be able to predict association

among variables (the FATA/ROA, the FSTS/ROA and the FATA2/ROA analysis). Therefore, it is

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concluded that the relationship between the foreign asset ratio (FATA and FATA2) and business

performance lacks long-term predictability, as the regression model showed inconsistent gures

for predicting its impact on ROA in 2022 and a total lack of predictive power in 2023. From

this, three sub-conclusions are derived: rst, the explanatory power of assets on protability

deteriorated progressively from the second year; second, by the end of the period, both ratios

ceased to be reliable predictors; and third, the international asset structure does not fully guarantee

a projectable performance pattern under the G2G contract framework.

It is particularly relevant that the ROA ndings contradict previous reports on nancial

performance in this type of contractual framework. While earlier literature or preliminary

reports suggested broader benets, this research demonstrates that the positive impact has been

selective. This divergence underscores that success in the G2G model is not an inherent outcome

of the agreement, but rather appears to be contingent on the specic management and execution

capabilities of each rm, with MACE being the benchmark for greater nancial sustainability

during the three-year period analyzed.

As development uctuation implication, except for the decient trend shown by the

Gleeds company, regarding the four sub variables throughout the rst three years of the project,

the companies Arup and Mace have presented a positive linear, an exponential growth or a static

linear trend in almost all the sub-variables development during the period 2021-2023. This

responds to the exible nature of the G2G agreement in which the companies participate, which

although it has allowed the organizations to intervene in all the 118 projects, not all have had the

same degree of immersion in each of them. Furthermore, despite the lack of predictability during

the project, the G2G agreement provides a framework that is considerably nancially protable in

the short term for the participating companies, which exponentially increase the opportunities to

obtain new contracts or extend the current ones with public or private entities.

Acknoledgment

This research did not receive external funding.

Conict of interest statement

Author reported no conict of interest

Author Contributions

Conceptualization: Jherson Manuel Jesús Apaza Gallardo

Data curations: Jherson Manuel Jesús Apaza Gallardo

Formal analysis: Jherson Manuel Jesús Apaza Gallardo

Investigation: Jherson Manuel Jesús Apaza Gallardo

Methodology: Jherson Manuel Jesús Apaza Gallardo

Project administration: Jherson Manuel Jesús Apaza Gallardo

Supervision: Jherson Manuel Jesús Apaza Gallardo

Validation: Jherson Manuel Jesús Apaza Gallardo

Visualization: Jherson Manuel Jesús Apaza Gallardo

Writing – original draft: Jherson Manuel Jesús Apaza Gallardo

Writing – review & editing: Jherson Manuel Jesús Apaza Gallardo

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Data Availability Statement

Data available in a publicly accessible repository that does not issue DOIs

This study analyzed publicly available datasets. These data can be found here:

[https://www.gov.uk// Company number 01312454]

[https://www.gov.uk// Company number 12686337]

[https://www.gov.uk// Company number 10874751]

[https://www.gov.uk// Company number 06472422]

[https://www.gov.uk// Company number 06213794]

https://www.gov.uk//[ Company number 02410626]

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Jherson Manuel Jesús Apaza Gallardo

Correo electrónico:607635@students.esic.university