How Do International Contractors Choose Target Market Based on Environmental, Social and Governance Principles? A Fuzzy Ordinal Priority Approach Model

Abstract

Overseas market choice is very important for the survival and sustainable development of transnational construction enterprises. However, in previous studies, little attention has been given to overseas market choice models, particularly under the ESG (environmental, social and governance) goals. To bridge this gap, the study combined ESG principles and organizational ecology theory to construct an overseas market choice model for international contractors. Firstly, 17 influencing factors were identified based on a literature review. Then, a market choice model was conducted by using the fuzzy ordinal priority approach (OPA-F). Finally, this paper took Chinese international engineering consulting enterprises as an example to use in the proposed model. This study will help international contractors choose overseas markets more scientifically and rationally.

1. Introduction

In recent years, people have gradually realized the impact of the construction industry on society and environment, and contractors have taken responsibility for minimizing the negative impact on the environment and society and strive to promote sustainable development [1]. Affected by the COVID-19 epidemic, the Russian–Ukrainian conflict, the Fed’s interest rate hike and other events, the overall business level of international projects has stabilized or even declined in recent years [2,3]. According to the top 250 international contractors listed by Engineering News-Record, the total overseas revenue of international contractors declined from 2017 to 2021. In 2021, the top 250 international contractors achieved a total operating income of USD 2065.7 billion, up 13.1% more than the previous year, while the total operating income in overseas markets was USD 397.85 billion, down 5.35% compared to the previous year.

Overseas market selection is one of the most prominent and complicated decisions that an organization should make in the process of transnational expansion [4,5]. The correct decision greatly improves the possibility of profit; on the contrary, entering the wrong market would cause a lot of losses for a company [6,7].

Previous studies on overseas market choice can be divided into two categories. The first category is those that explore factors influencing overseas market choice. For example, Chen et al. (2016) identified factors that affect international market selection through a logistic regression analysis [8]; Viswanathan et al. (2019) proposed six factors influencing international marker choice, i.e., geographical proximity, market potential, host country risk, cultural proximity, firm’s experience and firm size [9]. The second category is those that establish models for overseas market selection. For example, Zolfani et al. (2021) used an MABA analysis and the EDAS method to develop a new decision-making tool to evaluate potential markets [10]. Shipley et al. (2013) proposed a Fuzzy Attractiveness of Market Entry (FAME) model based on knowledge by dealing with limited or unclear information [11]. However, these models are mainly based on the economic benefits of selecting a market [12]. In the era of sustainable development, international contractors should comprehensively consider their ESG (environmental, social and governance) performance.

To bridge the knowledge gap, the study aims to establish an overseas market choice model based on ESG goals for international construction firms. To achieve this goal, a literature review was first carried out to identify influencing factors in international contractors’ market choices. Then, a market choice model was constructed by using the fuzzy ordinal priority approach (OPA-F). Finally, this paper took Chinese international engineering consulting enterprises as an example to use in the proposed model. This model would help decision makers quickly evaluate the potential market, hence providing a decision-making basis for market selection.

The structure of this paper is organized as follows: Section 2 provides a comprehensive review of the literature and establishes the theoretical foundation by identifying the factors that influence international contractors’ market choices and the specific impact of ESG performance on market choice. Section 3 outlines an overview of the market choice model by using OPA-F. Section 4 presents a case study using the model presented. Section 5 discusses the findings, including key influencing factors, the advantages of the proposed model and the applications of the model. Finally, Section 6 provides a conclusion.

2. Theoretical Background

The recognized market choice model consists of three steps: (1) excluding inappropriate countries based on country-specific factors; (2) identifying suitable countries based on industry-specific factors; and (3) selecting target markets based on firm-specific factors [8]. To establish a market choice model, multi-level factors should be identified first. Then, these factors should be judged based on their contribution to ESG performance to achieve the goal of this paper.

2.1. Factors Influencing Market Choice for International Contractors

There are many studies that have explored the factors influencing market choice for international contractors. For example, Yan et al. (2020) investigated the role of home country institutions in international market selection [13]. Li et al. (2021) investigated the impact of the host country’s environmental uncertainty on the choice of entry mode [14]. Viswanathan et al. (2019) analyzed and studied the important factors that affect international market choices in the Indian construction market [9]. Isa C M M et al. (2014) investigated the factors that affect international market choices for Malaysian construction firms [15]. Li et al. (2022) found that contractors’ market choices are affected by institutional factors [16]. Chen et al. (2016) identified factors that influence contractors’ choice of international markets, including market size, political stability and cultural distance [8]. Based on these previous studies and the template of international market selection developed by Ozturk et al. [17], this study selected and altered influencing factors by combining them with the characteristics of international contractors. Finally, 17 influencing factors were identified and are shown in

Table 1. Factors influencing market choice for international contractors.

Category		No.	Factor	Reference	Influence
A	Political law (A1)	X1	Diplomatic relations	[18,19,20]	Positive
		X2	Political stability	[8,21,22]	Positive
		X3	Government credit	[9,23,24]	Positive
		X4	Regulatory environment	[25,26,27]	Positive
	Economic (A2)	X5	Foreign exchange reserve	[25,28,29]	Positive
		X6	Inflation	[30,31,32]	Negative
		X7	Government debt level	[31,33,34]	Negative
		X8	Bilateral economic and trade level	[16,35,36]	Positive
	Culture and public security (A3)	X9	Security level	[21,37,38]	Negative
		X10	Cultural distance	[8,39,40]	Negative
B	International engineering enterprise market (B1)	X11	Market size	[8,25,41]	Positive
		X12	Market growth rate	[18,25,42]	Positive
	Resource availability (B2)	X13	Labor supply	[30,43,44]	Positive
		X14	Fund supply	[45,46,47]	Positive
	Developments in related industries (B3)	X15	Information industry	[46,48,49]	Positive
C	Competitor (C1)	X16	Number of competitors	[15,50,51]	Negative
		X17	Competitiveness	[8,15,52]	Negative

. Due to the similarities between the international engineering market environment and natural habitats, this paper divided 17 factors into three categories according to the organizational ecology theory, i.e., climate, soil and biology.

In this paper, “climate” is classified as a Class A factor, which refers to the national macroenvironment and other factors and consists of three secondary indicators: A = {politics and law (A1), economy (A2), culture and public security (A3)}. The term “soil” is classified as a Category B factor, referring to the development of various industries and including three secondary indicators: B = {international engineering consulting enterprise market (B1), resource supply (B2) and the development of related industries (B3)}. “Biology” is classified as a Class C factor, which refers to the situation of market participants. Class C factors include a secondary indicator: C = {competitor (C1)}.

2.2. The Effect of ESG Performance on Market Choice

ESG is a comprehensive evaluation system released by the United Nations Global Compact in 2004, including environmental, social and governance principles [53]. This system aims to guide enterprises to practice the concept of sustainable development and fulfill their social responsibilities while pursuing economic benefits [54].

Environmental performance (E) covers issues such as carbon emissions and pollutant treatment. For international contractor enterprises, environmental performance mainly examines whether enterprises have environmental awareness and assume responsibilities in the production and operation processes, including formulating environmental protection strategies at the enterprise level and reusing abandoned building materials. Focusing on environmental performance enables contractors to reduce costs and increase their profit margin, thus gaining a competitive advantage in the international market and contributing to environmental improvement [55].

Social performance (S) covers issues such as diversity, human rights and animal protection. For international contractors, social principles mainly focus on workers’ rights and interests, public welfare behavior and other factors that will play a role in social development. Most research has suggested that corporations which effectively address social responsibilities are more competitive. In the construction industry, the facilitation of communication and cooperation among various major stakeholders (including shareholders, contractors, owners, communities and employees) can reduce CSR obstacles caused by problems associated with capital, materials and human resources [56]. The promotion of social performance can reduce the reputation and financial risks of enterprises, and at the same time, it can reduce the hidden costs of enterprises and reduce the total cost level.

Governance performance (G) focuses on organizational structure, employee relationships, etc. Effective corporate governance can create positive results for corporate profitability [57]. The improvement in governance performance shall inevitably enhance the competitiveness of international contractors, promote the continuous development of enterprises, expand their scale and occupy a larger market share.

Overall, ESG performance plays an important role in market selection for international contractors. ESG performance has a great influence on corporate risk-taking, firm performance and even the healthy development of capital markets [58]. If contractors meet the ESG principles, it may be advantageous not only for overseas financing but also for entering overseas markets [59]. Based on the ESG principles, this paper divides the evaluation indexes into three dimensions: environmental performance, social performance and economic performance.

3. Model Development

3.1. Problem Statement

Multi-Attribute Decision Making (MADM) can be regarded as a process of selecting or ranking the best alternative from all possible alternatives [60]. MADM is composed of a scheme set, an attribute set and a weight set and has been widely used in other fields, such as supplier selection. In this paper, environmental performance, social performance and economic performance derived from the ESG principles were taken as the first-level attributes, and 17 habitat factors were taken as the second-level attributes to construct the MADM problem model (as shown in Figure 1).

3.2. Weighting Values among Criteria through OPA-F

In order to solve the MADM model with fuzzy properties, a series of calculations, such as fuzzy language transformation, are needed. This paper adopted OPA-F to assign a weight to each criterion. Compared with fuzzy AHP and fuzzy BWM, OPA-F does not necessitate pairwise comparisons between the factors, and the alternative solutions can be automatically ranked, leading to enhanced efficiency. Moreover, the utilization of group decision-making renders the decision-making process more scientifically sound. In contrast to fuzzy TOPSIS and fuzzy VIKOR, OPA-F does not require the creation of a decision matrix. Most importantly, OPA-F can be used to address the issue of incomplete data [61]. In light of the limited data in the selection of international markets [45], OPA-F integrates both qualitative and quantitative techniques to enhance the accuracy of decision-making.

As a new method, OPA-F has been practically applied. For example, this method was used to evaluate the risks of blockchain technology in construction organizations [62]. The priority requirements of blockchain technology in building supply chains to achieve a circular economy have been clearly defined [63]. Scholars have worked out how to evaluate and select suppliers [61] and measure the performance of construction suppliers [64]. To summarize, OPA-F can solve MADM problems with fuzzy language.

To conduct OPA-F, this paper used triangular fuzzy numbers (TFNs). Compared with other types of membership functions, TFNs are more universal, simple and flexible [61].

Definition 1. 

A TFN can be expressed as q = (l, m, u) and l ≤ m ≤ u, and its membership function can be expressed as Formula (1):

$$\mu (x)\left\{\begin{array}{c}0, x<l\\ \frac{x-l}{m-l}, l\le x\le m\\ \frac{u-x}{u-m}, m\le x\le u\\ 0, x>u\end{array}\right.$$

(1)

Definition 2. 

Suppose there are two fuzzy numbers, A = (l, m, u) and B= (l′, m′, u′), and the operation between them is shown in Formulas (2) to (5):

$$A+B=\left(l+l^{\prime },m+m^{\prime },u+u^{\prime }\right)$$

(2)

$$A-B=(l-l^{\prime },m-m^{\prime },u-u^{\prime })$$

(3)

$$A\times B=(l\times l^{\prime },m\times m^{\prime },u\times u^{\prime })$$

(4)

$$A÷B=(l÷l^{\prime },m÷m^{\prime },u÷u^{\prime })$$

(5)

Definition 3. 

The formula for calculating the GMIR score of fuzzy number q = (l, m, u) is Formula (6):

$$R\left(q\right)=\frac{1+4m+u}{6}$$

(6)

Definition 4. 

Fuzzy linguistic variables are scaled terms that can be converted into fuzzy numbers.

Table 2. Corresponding standard values of fuzzy language.

Linguistic Variables	TFN for Criteria	The Rank (r)
Very Low (VL)	(0,0,0.1)	1
Low (L)	(0,0.1,0.3)	2
Medium Low (ML)	(0.1,0.3,0.5)	3
Medium (M)	(0.3,0.5,0.7)	4
Medium High (MH)	(0.5,0.7,0.9)	5
High (H)	(0.7,0.9,1)	6
Very High (VH)	(0.9,1,1)	7

Note: in calculations, to avoid invalid constraints caused by multiplication with 0, 0 in TFNs is taken as 0.01.

shows the fuzzy linguistic variables used in this study:

Definition 5. 

The fuzzy linear programming model of TFNs can be transformed into model (7):

$$\mathrm{Max}(\mathrm{Min})\left\{{\sum }_{j=1}^n(p_j,q_j,r_j)\otimes (x_j,y_j,z_j)\right\}$$

This is subject to

$$\sum _{j=1}^n\left(a_{ij},b_{ij},c_{ij}\right)\otimes \left(x_j,y_j,z_j\right)\genfrac{}{}{0pt}{}{<}{\begin{array}{c}=\\ >\end{array}}\left(b_i,g_i,h_i\right) \forall \mathrm{i}=1,2,\cdots ,\mathrm{m}$$

(7)

where$\left(x_j,y_j,z_j\right),\left(p_j,q_j,r_j\right) and (a_{ij},b_{ij},c_{ij})$are non-negative fuzzy numbers.

To solve Model (9), it should be transformed into a traditional linear programming model, and then the simplex algorithm should be used to solve it. For the transformation method, see Model (8):

$$\mathrm{Max}(\mathrm{Min})\left\{{\sum }_{j=1}^n(p_j,q_j,r_j)\otimes (x_j,y_j,z_j)\right\}$$

This is subject to

$$\begin{gathered} {\sum }_{j=1}^n{a}_{ij}{x}_i+s_i=b_i \forall \mathrm{i}=1,2,\cdots ,\mathrm{m} \\ {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{b}}_{\mathrm{i}\mathrm{j}}{\mathrm{y}}_{\mathrm{i}}+{\mathrm{t}}_{\mathrm{i}}={\mathrm{g}}_{\mathrm{i}} \forall \mathrm{i}=1,2,\cdots ,\mathrm{m} \\ {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{c}}_{\mathrm{i}\mathrm{j}}{\mathrm{z}}_{\mathrm{i}}+{\mathrm{f}}_{\mathrm{i}}={\mathrm{h}}_{\mathrm{i}} \forall \mathrm{i}=1,2,\cdots ,\mathrm{m} \end{gathered}$$

(8)

where (${\mathrm{x}}_{\mathrm{j}}, {\mathrm{y}}_{\mathrm{j}}, {\mathrm{z}}_{\mathrm{j}}$), $({\mathrm{p}}_{\mathrm{j}},{\mathrm{q}}_{\mathrm{j}},{\mathrm{r}}_{\mathrm{j}}), ({\mathrm{a}}_{\mathrm{i}\mathrm{j}},{\mathrm{b}}_{\mathrm{i}\mathrm{j}},{\mathrm{c}}_{\mathrm{i}\mathrm{j}})$ are non-negative fuzzy numbers.

$$x_j\le y_j\le z_j$$

$$s_i\le t_i{\le f}_i$$

Table 3. Information on letters in OPA-F.

Sets
I	Set of experts, ∀ i ∈ I
J	Set of criteria/attributes, ∀ j ∈ J
K	Set of alternatives, ∀ k ∈ K
Indexes
i	Index of the experts (1, 2, …, p)
j	Index of the preference of the attribute (1, 2, …, n)
k	Index of the alternatives (1, 2, …, m)
Parameters
aij	Fuzzy linguistic variables for attribute j by expert i
r	The rank of the linguistic variable
aijk	Fuzzy linguistic variables for attribute j by expert i for alternative k
Variables
Z	Fuzzy objective function
Wij	Fuzzy weight of attribute j for expert i
Sijk	Fuzzy score of the alternative k based on attribute j and expert i
TSk	Total fuzzy score of the alternative k

describes information about the variables, parameters, indexes and sets involved in the OPA-F algorithm.

Step 1: Solve the attribute model of OPA-F and obtain the weight of each ESG attribute:

$$\begin{gathered} \mathrm{M}\mathrm{a}\mathrm{x} \tilde{\mathrm{Z}}=\frac{{\mathrm{z}}_{\mathrm{l}}+2{\mathrm{z}}_{\mathrm{m}}+{\mathrm{z}}_{\mathrm{u}}}{4} \\ {\mathrm{a}}_{\mathrm{i}\mathrm{j}}^{\mathrm{r}}\left({\mathrm{W}}_{\mathrm{i}\mathrm{j}}^{\mathrm{r}}-{\mathrm{W}}_{\mathrm{i}\mathrm{j}}^{\mathrm{r}+1}\right)\ge \tilde{\mathrm{Z}} \forall \mathrm{i},\mathrm{j},\mathrm{k} \\ {\mathrm{a}}_{\mathrm{i}\mathrm{j}}^{\mathrm{m}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}^{\mathrm{m}}\ge \tilde{\mathrm{Z}} \forall \mathrm{i},\mathrm{j} \\ {\sum }_{\mathrm{i}=1}^{\mathrm{p}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}=(0.8,1,1.2) \\ 0 {\le \mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{w}}\le {\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{w}}\le {\mathrm{u}}_{\mathrm{i}\mathrm{j}}^{\mathrm{w}} \forall \mathrm{i},\mathrm{j} \end{gathered}$$

(9)

Step 2: Solve the OPA-F alternative model to obtain the scores of each potential market:

$$\begin{gathered} \mathrm{M}\mathrm{a}\mathrm{x} \tilde{\mathrm{Z}}=\frac{{\mathrm{z}}_{\mathrm{l}}+2{\mathrm{z}}_{\mathrm{m}}+{\mathrm{z}}_{\mathrm{u}}}{4} \\ {\mathrm{a}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{r}}\left({\mathrm{S}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{r}}{-\mathrm{S}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{r}+1}\right)\ge \tilde{\mathrm{Z}} \forall \mathrm{i},\mathrm{j},\mathrm{k},\mathrm{r} \\ {\mathrm{a}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{m}}{\mathrm{S}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{m}}\ge \tilde{\mathrm{Z}} \forall \mathrm{i},\mathrm{j},\mathrm{k} \\ {\sum }_{\mathrm{i}=1}^{\mathrm{p}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\sum }_{\mathrm{k}=1}^{\mathrm{m}}{\mathrm{S}}_{\mathrm{i}\mathrm{j}\mathrm{k}}=(0.8,1,1.2) \\ 0 {\le \mathrm{l}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{S}}\le {\mathrm{m}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{S}}\le {\mathrm{u}}_{\mathrm{i}\mathrm{j}\mathrm{k}}^{\mathrm{S}} \forall \mathrm{i},\mathrm{j},\mathrm{k} \end{gathered}$$

(10)

Step 3: Calculate the total ambiguity score for each potential market:

$$\mathrm{T}{\mathrm{S}}_{\mathrm{k}}={\sum }_{\mathrm{i}=1}^{\mathrm{p}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}\times {\mathrm{S}}_{\mathrm{i}\mathrm{j}\mathrm{k}} \forall \mathrm{k}$$

(11)

Step 4: After calculating the total fuzzy score of each market, deblurring is performed using Formula (6) to calculate the ranking.

3.3. Measurement of Criteria

3.3.1. Climatic Factors

Diplomatic relations between countries can be divided into five categories, from high to low, according to intimacy and friendliness. In order, these are an alliance, a partnership, a normal relationship, a hostile relationship and an adversarial relationship. Good diplomatic relations mean that there are fewer conflicts between bilateral governments, which can reduce the risk of entry.

Political stability means that a political system maintains dynamic order and continuity [22]. The Worldwide Governance Indicators (WGI) issued by the World Bank released data on political stability and the absence of violence/terrorism. The lower the index, the worse the political stability.

Government credit was evaluated by using the national sovereign credit rating method issued by Dagong Global Credit Rating Co., Ltd. (Beijing, China). The national sovereign credit rating is used to evaluate the political, economic and credit ratings of various countries using certain procedures and methods.

The regulatory environment was assessed based on the ranking of the “Ease of doing business rank” published by the World Bank. The business environment refers to the sum of external factors and conditions such as the government environment, market environment, legal environment and humanistic environment involved in the processes of access, production, operation and withdrawal by market participants [26]. A good business environment can attract investors to enter the market to a great extent.

The foreign exchange reserve refers to the foreign current assets held by the foreign exchange management authorities of a country or region, which is a means to balance domestic and foreign economies [28]. These data are calculated according to the foreign exchange reserve data collected by TRADING ECONOMICS on its official website, and the per capita foreign exchange reserve is used as the evaluation standard for this indicator.

Inflation refers to the shrinking of the real purchasing power of money and the rising of prices caused by the fact that the supply of money in the market exceeds the actual needs of the economy [32]. These data are based on the inflation data disclosed by the official website of TRADING ECONOMICS.

The level of government debt refers to the government debt formed by bonds issued by the government at home and abroad or borrowed from foreign governments and banks, which affects the debt and investment of enterprises [23]. According to TRADING ECONOMICS’s official website, the ratio of government debt to GDP is evaluated.

The bilateral trade with the home country refers to the amount of mutually beneficial trade between the home country and the host country [35]. This factor is evaluated by the amount of bilateral trade per capita. The greater the trade volume, the higher the trade frequency, and the more favorable it is for enterprises to enter.

Public security refers to social stability and order. In recent years, with the development and improvement in social security assessment, the status of public security indicators has gradually improved and become the core system for evaluating social security [38]. This paper intends to use the “terrorism index” to evaluate the overall security situation of a country or region. The data come from the terrorism index of 163 countries in the world, regularly published by the Institute of Economics and Peace (IEP) every year. Its size is evaluated according to the number of terrorist attacks, casualties and economic losses in various countries. The higher the score, the lower the public security level.

Cultural distance refers to the degree of cultural differences between countries, with language as the main feature, mainly including language differences, living habits differences, social and cultural differences and so on [39]. Regarding the measurement of cultural distance, the study uses the Euclidean distance method to calculate the total cultural score based on the six dimensions disclosed in the Hofstede Centre database, namely power distance (PDI), uncertainty avoidance (UAI), individualism (IDV), masculinity vs. feminization (MAS), indulgence (IVR) and long-term orientation (LTO). The larger the value, the greater the difference between the two sides, and the greater the resistance for enterprises to enter.

3.3.2. Soil Factors

The market scale refers to the overall scale of international contractor enterprises, and a large market scale helps enterprises obtain higher returns [41]. These data are evaluated by the percentage of overseas income of countries in the total GDP of the country in Engineering News-Record.

The market growth rate refers to the growth rate of international income of the international engineering industry in each country in the previous two years [42]. The higher the growth rate, the better the development trend of the international engineering industry in that country, which is more conducive to the entry of enterprises from other countries. This value is calculated based on the overseas income data of ENR countries in 2021 and 2022.

The labor supply refers to the number of people who can be supplied by the international engineering industry in the labor market of various countries [43]. The more workers, the lower the labor costs and the higher the profit. This value is based on the data on the labor population on the official website index of TRADING ECONOMICS multiplied by the habitat factor of market size, which leads to a rough estimate of the labor supply quantity.

The capital supply refers to the amount of funds that a state can allocate according to demand, which is conducive to market entry with the support of policy funds [47]. According to the data on government revenue disclosed by the official website of TRADING ECONOMICS multiplied by the habitat factor of market size, this indicator represents the funds that can be used for international contractor enterprises from government revenue.

The information industry refers to the industry that uses information means and technologies to collect, process, retrieve and transmit information and other information technologies and services [49]. In the era of the Internet, the more developed the information industry is, the more information and convenience it can obtain, which is measured by the Internet speed under the official website index of TRADING ECONOMICS.

3.3.3. Biological Factors

The number of competitors refers to the number of other enterprises that are like the products or services provided by an enterprise and serve similar target customers [51]. Areas with many competitors will form Red Sea competition, which is not conducive to the entry and development of enterprises. This value is evaluated by the number of enterprises from various countries in the top 250 international contractors on the ENR list.

The competitiveness of competitors refers to whether international contractor enterprises can surpass other competitors in terms of quality, price and service during project construction [52]. The stronger the competitiveness of local enterprises in the host country, the greater the risk of entering the region. This value is measured by the competitiveness ranking disclosed by the official website of TRADING ECONOMICS.

The above factors have a positive or negative impact on the entry of enterprises. In order to achieve uniformity in scoring, after collecting the original data for each habitat factor, Formula (12) or Formula (13) was selected for normalization processing according to the characteristics of each habitat factor.

$$\mathrm{Y}=\frac{\mathrm{a}-\mathrm{m}\mathrm{i}\mathrm{n}}{\mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{m}\mathrm{i}\mathrm{n}}\left(\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\right)$$

(12)

$$\mathrm{Y}=\frac{\mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{a}}{\mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{m}\mathrm{i}\mathrm{n}}\left(\mathrm{N}\mathrm{e}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\right)$$

(13)

• Y—normalized data;
• a—data to be normalized;
• min—minimum value of this type of data;
• max—maximum value of this type of data.

4. Case Study

4.1. Case Profiles

This paper took Chinese international engineering consulting enterprises as an example to use in the proposed model. At present, Chinese contractors are famous for their construction speed, but Chinese engineering consulting enterprises have not performed well in the international market. In the increasingly complex international environment, choosing the right market can help the development of Chinese international engineering consulting enterprises.

4.2. Data Collection

According to the regional data disclosed by ENR, this paper studied the habitats of the international engineering consulting industry in 11 target countries. The data were collected and normalized as shown in

Table 4. Summary of data sources.

No.	Factor	Data Sources
X1	Diplomatic relations	The official website of the Chinese Ministry of Foreign Affairs
X2	Political stability	The Worldwide Governance Indicators
X3	Government credit	Dagong International Credit Rating Co., Ltd.
X4	Regulatory environment	The World Bank
X5	Foreign exchange reserve	TRADING ECONOMICS
X6	Inflation	TRADING ECONOMICS
X7	Government debt level	TRADING ECONOMICS
X8	Bilateral economic and trade level	TRADING ECONOMICS
X9	Security level	Institute of Economics and Peace
X10	Cultural distance	The Hofstede Centre database
X11	Market size	Engineering News-Record and TRADING ECONOMICS
X12	Market growth rate	Engineering News-Record
X13	Labor supply	TRADING ECONOMICS
X14	Fund supply	TRADING ECONOMICS
X15	Information industry	TRADING ECONOMICS
X16	Number of competitors	Engineering News-Record
X17	Competitiveness	TRADING ECONOMICS

.

4.3. Results of the Case Study

4.3.1. Weighting Results among Criteria

The questionnaire results of five experts were collected, and OPA-F was used to calculate the weight of each attribute, as shown in

Table 5. Normalized data of various habitat factors in various countries.

No.	U.S.A.	Australia	Britain	The Netherlands	France	Germany	Italy	Spain	Japan	South Korea	Canada
X1	0.0000	0.6667	0.6667	0.3333	0.6667	0.6667	0.6667	0.6667	0.3333	1.0000	0.6667
X2	0.0000	0.6848	0.3902	0.8295	0.2326	0.5995	0.4289	0.4393	1.0000	0.5142	0.8424
X3	0.2500	1.0000	0.5000	0.7500	0.5000	0.7500	0.0000	0.2500	0.5000	0.7500	0.7500
X4	0.9000	0.7000	0.8000	0.1000	0.2000	0.6000	0.0000	0.3000	0.4000	1.0000	0.5000
X5	0.0000	0.2018	0.2487	0.3409	0.0263	0.3335	0.3576	0.1832	1.0000	0.8113	0.2621
X6	0.5478	0.6261	0.3826	0.0000	0.7217	0.3565	0.2261	0.6261	1.0000	0.7739	0.6609
X7	0.5611	1.0000	0.7399	0.9291	0.6655	0.8556	0.5002	0.6438	0.0000	0.9530	0.6655
X8	0.1589	1.0000	0.0820	0.7139	0.0299	0.2285	0.0287	0.0000	0.2467	0.7569	0.1407
X9	0.0000	0.5081	0.0383	0.5806	0.0806	0.0464	0.2560	0.4234	0.7056	1.0000	0.2177
X10	0.1535	0.0215	0.3600	0.0000	0.6894	0.8412	0.7965	0.7181	0.7531	1.0000	0.3523
X11	0.0984	0.5738	0.2623	0.9016	0.1148	0.0000	0.0492	0.1148	0.0164	0.0328	1.0000
X12	0.6841	0.7128	0.8872	0.6759	0.8021	0.8472	0.7477	1.0000	0.5713	0.0000	0.5323
X13	0.8963	0.3785	0.4340	0.4137	0.1495	0.0000	0.0340	0.0951	0.0691	0.0286	1.0000
X14	1.0000	0.4561	0.5809	0.7842	0.3143	0.0104	0.0861	0.0778	0.0000	0.0112	0.7405
X15	0.4930	0.0957	0.3986	0.4227	0.0799	0.3147	0.0000	0.3245	0.5667	1.0000	0.3616
X16	0.0000	0.9867	0.9733	0.9867	0.9733	1.0000	0.9067	0.9467	0.9200	0.9200	1.0000
X17	0.0000	0.8000	0.4000	0.1000	0.7000	0.3000	1.0000	0.9000	0.2000	0.5000	0.6000

.

4.3.2. Data Analysis by Country

Table 6. OPA-F results.

Attribute	Total Weight Value
Economic performance	0.609882
Social performance	0.286477
Environmental performance	0.103641
Habitat Factor	Total Weight Value
Diplomatic relations	0.0571502
Political stability	0.1149121
Government credit	0.1689370
Regulatory environment	0.0431405
Foreign exchange reserve	0.0349679
Inflation	0.0635414
Government debt level	0.0278217
Bilateral economic and trade level	0.0746296
Security level	0.0304554
Cultural distance	0.0888628
Market size	0.0270842
Market growth rate	0.0293476
Labor supply	0.0255764
Funding	0.0844996
Information industry	0.0597126
Number of competitors	0.0320588
Competitiveness	0.0373022

shows the weight value of each attribute and habitat factor. After multiplying the normalized data of each country by the weight of the corresponding habitat factor, the score of each country can be obtained and shown in

Table 7. Scores by country.

Country	Climatosphere			Pedosphere			Biosphere	Goal
	A1	A2	A3	B1	B2	B3	C1
South Korea	0.2861	0.1605	0.1193	0.0009	0.0017	0.0597	0.0481	0.6764
Australia	0.3159	0.1493	0.0174	0.0365	0.0482	0.0057	0.0615	0.6345
Canada	0.2832	0.0802	0.0379	0.0427	0.0881	0.0216	0.0544	0.6082
Netherlands	0.2454	0.0910	0.0177	0.0443	0.0768	0.0252	0.0354	0.5358
Japan	0.2357	0.1169	0.0884	0.0172	0.0018	0.0338	0.0370	0.5308
Germany	0.2596	0.0752	0.0762	0.0249	0.0009	0.0188	0.0432	0.4987
Britain	0.2019	0.0597	0.0332	0.0331	0.0602	0.0238	0.0461	0.4580
Spain	0.1438	0.0641	0.0767	0.0325	0.0090	0.0194	0.0639	0.4093
France	0.1579	0.0675	0.0637	0.0266	0.0304	0.0048	0.0573	0.4083
U.S.A.	0.0811	0.0623	0.0136	0.0227	0.1074	0.0294	0.0000	0.3166
Italy	0.0874	0.0429	0.0786	0.0233	0.0081	0.0000	0.0664	0.3067

. Figure 2 shows the proportion of each country’s score in the Climatosphere, Pedosphere, Biosphere and Goal.

Analysis of Climate Indicators

In terms of politics and law in the macroenvironment, Australia scored the highest. Australia’s society is relatively democratic, and it is the country with the lowest risk of political instability in the Asia–Pacific region. At present, the diplomatic relationship between China and Australia is a strategic partnership, and it is still actively promoting the further development of bilateral relations, which is a favorable entry signal for enterprises. In addition, the country’s business convenience index ranks high, and it has a business environment that is convenient for attracting investment, which is conducive to China’s international engineering consulting enterprises investing. Scores greater than 0.25 are also found in Korea, Canada and Germany. The government credit ratings of these countries are relatively high, reaching the AA level. The countries with scores between 0.1 and 0.25 include the Netherlands, Japan, Britain, France and Spain. These countries scored low on some indicators, such as diplomatic relations between China and Japan, political turmoil in France, social unrest caused by Brexit and the decline of government credit caused by the deep recession in Spain. Finally, Italy and the United States ranked as the bottom two for this score. Affected by the COVID-19 epidemic, the Italian government’s financial situation continued to deteriorate, so the Italian government’s credit was downgraded, and its score was relatively low. Influenced by many factors, such as the Sino–US trade war and the different ideologies of the two sides, China–US relations rapidly declined. In addition, the United States is a two-party system, and the political situation often changes greatly, so it scored the lowest.

In terms of economy, Australia, South Korea and Japan scored more than 0.1. The economic structures of China and these countries are highly complementary, and the industrial chains and supply chains of the two countries can be deeply integrated and help each other, so their per capita bilateral economic and trade level is superior to that of other countries. The countries with a score of 0.05–0.1 are the Netherlands, Canada, Germany, France, Spain, the United States and the United Kingdom. The proportion of government debt in these countries is relatively large. For example, Japan’s recent national debt and per capita debt have set a record, showing a financial structure with expanding debt and weak resistance to interest rate hikes. Italy scored very low in this category because of its high inflation rate and the high amount of debt caused by the COVID-19 epidemic.

In terms of culture and public security, South Korea and Japan scored higher because South Korea, Japan and China belong to the East Asian cultural circle and the cultural distance between them is small. At the same time, South Korea and Japan have a relatively small difference between their national incomes, and there are few crimes, so their public security level is significantly ahead of other countries. The countries with a score of 0.03–0.08 are Italy, Spain, Germany, France, Canada and Britain. The level and quality of education of people in these countries are relatively high, crimes are relatively rare and social security is relatively stable. The last three countries are the Netherlands, Australia and the United States because these countries have western European or American cultures, which are far from our culture. Among them, the liberalization of guns in the United States makes it difficult for the social security level to be politically adequate, resulting in the lowest score.

Analysis of Soil Indicators

In terms of the market situation, the Netherlands and Canada scored the highest. The international engineering income of these two countries accounts for a relatively large proportion, and there are many opportunities in the international market, with many engineering projects for international engineering consulting enterprises to choose from. Countries with scores between 0.02 and 0.04 include Australia, Britain, Spain, France, Germany, Italy and the United States. The industries in these countries are diversified, and the international engineering industry is not their pillar industry, so the engineering consulting industry accounts for a small proportion. And due to the epidemic, their economies have been affected by many factors, and their market growth rates have slowed down. Finally, Japan and South Korea ranked as the bottom two, with 0.0171 and 0.0009, respectively. These two countries are in a weak position in international competition because of their small land areas and small markets for the construction industry. In addition, in the increasingly competitive environment, the market share of these two countries is relatively low, showing a downward trend.

In terms of resource provision, the United States scored the highest. As a superpower, the United States has far more labor force and fiscal revenue than other countries, so it scored higher than other countries. Canada and the Netherlands are still among the best in this category. These two countries have a relatively greater labor force than is needed to serve international engineering consulting enterprises, and there is more talent and rich experience in the engineering industry in China. In addition, there are many domestic policies that support the construction industry; for example, the Vancouver municipal government officially announced their plans to encourage the development of multi-storey buildings. Britain, Australia and France ranked very low in contrast. These three countries are mainly engaged in the financial and information industries, with a small proportion of engagement in the construction industry, resulting in relatively little fiscal revenue in the engineering industry. The scores are lower in Spain, Italy, Japan, South Korea and Germany. The population aging of these countries is more serious, the working population is decreasing, and the number of workers used in the engineering industry is even lower. Moreover, these countries are relatively developed, the government revenue will be biased towards high-tech industries and the policy funds for the construction industry will be less supported.

Analysis of Biology Indicators

As competitors, Italy, Spain, Australia and France have very close scores. The number of international engineering consulting enterprises listed in these countries is relatively small on the ENR list, and the number of competitors in the market is relatively small. These countries have relatively weak overall competitiveness and are relatively suitable for the survival of new enterprises. Canada, South Korea, Britain, Germany, Japan and the Netherlands scored between 0.001 and 0.002. There are many international engineering consulting enterprises in these countries which have developed on a relatively large scale and have strong competitiveness. In the end, the score of the United States is 0, because the United States has the largest number of enterprises in ENR, many competitors occupy the market and several of them are among the best all year round and have strong competitiveness, making it unsuitable for enterprises to enter the competition.

Analysis on Candidate Markets

Based on all the indicators, the most suitable countries for China to enter are South Korea, Australia and Canada, and their comprehensive scores are all greater than 0.6. The macroenvironments of South Korea and Australia are outstanding. The market of international engineering consulting enterprises in Canada is developing well, and there are more engineering opportunities. By entering these countries, enterprises can set up local subsidiaries or branches to conduct business activities. The countries with comprehensive scores of 0.4–0.6 are the Netherlands, Japan, Germany, Britain, Spain and France. These countries are more suitable for Chinese enterprises to enter, and all indicators are relatively good, with attention being paid to market changes at any time. When entering these countries, it is suggested that certain risks can be avoided by cooperating with local enterprises to from joint ventures and learning about local conditions. Finally, the countries that are relatively unsuitable for entry are the United States and Italy. The macroenvironment of the United States is relatively complex, and the number of local competitors is large, so it is not recommended to enter into competition with them. Italy’s engineering industry has been affected by the epidemic, and its development is poor. According to the latest data from Italy’s National Bureau of Statistics, the production index of the construction industry has declined for four consecutive months after seasonal adjustment, including a 3.0% month-on-month decline in July. Before entering the country, enterprises must wait for an improvement in the construction market environment.

5. Discussion

5.1. Priority of Criteria Based on ESG

According to the ESG standards, enterprises should focus on economic performance, considering social performance and environmental performance. Good ESG performance is beneficial to promoting the high-quality development of enterprises and market selection decisions [58,59]. Enterprises are for-profit organizations engaged in economic activities such as production, circulation and service, so the priority of enterprises is to make profits. However, in recent years, all manner of industries have begun to pay attention to the impact of enterprises on society and the environment, so enterprises still need to make a difference in terms of social performance and environmental performance to shape a good corporate brand.

Among the weight values of various habitat factors, government credit and political stability have the highest weights. Political risk is one of the most prominent dangers for international contractors to deal with in overseas countries [65], whereas labor supply has the lowest weight. The host country’s government credit and political stability are important indicators for the entrance of international contractor enterprises. Although the government and enterprises have signed the rights and obligations of both public and private cooperation parties, some government departments have repeatedly broken their promises, the political situation has often changed, and they do not recognize the contracts signed by the government before, which has a serious impact on a project’s income. According to the conditions stipulated in the contract, an international contractor enterprise is a legal person organization that focuses on the export of labor services, drives the export of goods, completes project projects abroad and aims to expand exports and earn foreign exchange. Therefore, enterprises do not need the host country to provide many workers who can engage in this industry, so the supply weight of labor is small.

It is necessary for contractors to make market choices in accordance with the ESG principles when operating internationally. As for enterprise decision-makers, they should pay attention to the risks brought by political instability, the influence of host regulation on contractors, market potential, the profound influence of cultural factors, etc. In order to better explore the international market, the proposed strategies should match the market that meets these factors.

5.2. Advantage of the Proposed Model

The proposed model has advantages in terms of criteria selection. Several prior studies tried to identify factors influencing market choice. For example, Chen et al. (2016) introduced a market choice model comprising seven factors, such as market potential [8]. Li et al. (2023) expanded the factors influencing market choice to 21 [25]. Ashley et al. (2022) developed a market choice model incorporating 34 factors from five aspects, including economy, politics and law [30]. Compared to these studies, the proposed model includes influencing factors identified from multiple studies, and all the factors are closely relevant to the characteristics of international contractors. These factors not only include macroeconomic and political indicators but also include cultural indicators.

Moreover, the proposed model has an advantage in terms of its approach to the weighting criterion. Market selection models can be categorized into two types: qualitative and quantitative models [45]. In terms of qualitative modeling, Chen et al. (2016) and Li et al. (2023) utilized logistic regression analyses to assess the impact of each factor in their respective market selection models [8,25]. However, this method only determines whether each factor has a positive or negative impact on market selection without providing the weights of each factor within the entire model. Ashley et al. (2022) identified the importance of factors through interviews [30]. However, interviews do not quantify the weight of factors or present their importance in a data-driven manner. In contrast, the model proposed in this article offers repeatability, enabling a comprehensive comparison and calculation of all the factors together to obtain their objective weights.

In terms of quantitative models, Baena et al. (2023) proposed factor weights based on the relevant literature and practitioner-provided data [66]. This approach demands extensive familiarity with the research field, substantial effort and presents challenges. Duong and Thao (2021), as well as Christian et al. (2016), conducted decision analyses using the TOPSIS model and calculated factor weights using the entropy weight method [67,68]. However, the weights obtained through the entropy weight method are sample-dependent and may change with variations in the sample, potentially limiting their applicability. In comparison to quantitative models, the calculation method of the model presented in this article integrates qualitative and quantitative techniques, making it applicable to diverse and complex scenarios. The weights of each factor remain fixed after the calculation, facilitating multiple uses of the same model and ensuring verifiability.

The model has strong applicability and can evaluate the conditions of the target market completely. Enterprises can select several of them or add several other habitat factors for market selection according to their own characteristic strategic objectives. In addition, the evaluation of various habitat factors can use fuzzy language, which is very convenient for decision makers. The OPA-F algorithm is much simpler than other algorithms for solving the problem of MADM, and respondents can provide incomplete data for a calculation, which makes it suitable for all kinds of situations.

5.3. Application of the Model

International contractors can use the developed model and the available data from international research institutes or other sources to make target market selections. In addition, decision makers in other industries can also combine the characteristics of different industries to increase or decrease the impact factors, form a model that meets their decision-making requirements, and then calculate and select the optimal market entry.

The developed model will be applicable to most enterprises. The process of target market selection consists of five stages: (1) determining factors: select the influencing factors according to the characteristics of the industry; (2) qualitative analysis: judge the relative importance of each factor through interviews; (3) quantitative weighting: calculate the weight of the factors through the OPA-F algorithm; (4) metric scoring: assign scores to each factor according to the collected data and information; and (5) determine the potential market; the specific data of each target country are quantitatively analyzed to obtain the specific score of each target country.

6. Conclusions

Guided by ESG theory, this study constructed a market choice model through 17 factors, calculated the weight using the OPA-F algorithm and took Chinese international engineering consulting enterprises as the case study. Firstly, under the ESG theory system, international engineering enterprises sought economic performance first and foremost, followed by social performance, while environmental performance was relatively low. Secondly, among all the factors, the political stability and government credit weights were the highest, which showed that international contractors pay more attention to the macroenvironment of the host country.

The novelty of this study is building a market choice model based on the OPA-F algorithm for international contractors. The OPA-F algorithm is suitable for a variety of cases, and the whole model has strong applicability. It would provide insights for international contractors to select suitable overseas markets to achieve optimal ESG performance. Also, the proposed model is not fixed, and the factors can be altered to match circumstances in distinct fields when considering particular contexts in applying performance evaluations in practice.

However, there are two limitations to this study. Firstly, most of the country raw data used in the case studies in this paper are published annually by the institutions, which may not reflect real-time changes. Future studies can focus on developing a dynamic model. Secondly, this paper only preliminarily applies the model to international contractors in China. Therefore, further studies can be carried out to further test the model by using data from more countries.

Due to the flexibility of the model developed in this paper, it can respond to the needs of various industries by changing the impact factors. In future research, this model can be developed to suit industries that have a large demand for overseas market expansion, such as manufacturing and retail. More extended work can be developed when enterprise-level data are accessible. For example, we can develop detailed market entry strategies for obtaining a specific project. Also, we can collect more regional data to provide a better basis for market selection.