Dinh Trong An
© 2023 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
The private economy is regarded as one of the nation's most important economic components. Local governments take a variety of steps to attract private investment. In this study, the methodology is based on a SEM (structural equation modeling) model utilizing data gathered from 265 private businesses. According to the research findings, all six factors influence the investment intentions of businesses: the infrastructure, labor, administrative procedures, land policy, transparency, support from local government. The method is based on a SEM (structural equation modeling) model with 265 private enterprise data. According to the findings of the study, the following six factors influence the investment intentions of businesses: the infrastructure, labor, administrative procedures, land policy, transparency, support from the local government. Moreover, fsQCA (fuzzy set qualitative comparison analysis) with the same dataset reveals that investment intention is composed of two groups of factors. The infrastructure, labor, support from the local government, administrative procedures, and transparency comprise the first group. The second group includes labor, support from the local government, land policy, administrative procedures, and transparency.
private enterprise, investment intention, investment environment, SEM model, fsQCA
As the local government implements plans to improve the investment climate, private investors will become interested in the area. In regions with developed and stable infrastructure, enterprise production and business grow and prosper. This will result in future increases in labor productivity, profits, and the number of private investors [1]. Private investment can be viewed as one of the most important factors in promoting economic growth, increasing output, and creating a large number of employment opportunities. Ultimately, this will play a significant role in the formation of investment capital and the maintenance of steady, sustainable growth [2]. Local governments have a significant impact on the investment climate by constructing infrastructure, ensuring public safety, making the law clear, and so on. In addition, the local government has implemented policies to attract private businesses; administrative procedures must be enhanced to be more effective and efficient, and inefficient or unnecessary procedures must be eliminated [3].
Local governments can improve the investment environment in a variety of ways, including by reforming administrative services, promoting local potential, and increasing infrastructure investment in the locality. The favorable investment climate will attract businesses that are interested in and actively seeking new investment opportunities [4]. Furthermore, because each locality has unique strengths, connecting them will improve the investment climate and promote comparative advantages in the region [5].
The purpose of this study is to determine the role of local government in enhancing the investment climate in order to attract private investors based on the infrastructure, labor, administrative procedures, land policy, transparency and support from the local government. By means of structural modeling (SEM) and fuzzy set qualitative analysis (fsQCA), the research hypotheses are tested using survey data collected from private businesses (fsQCA). In this way, the study has contributed to an important document for determining the role of local government in private companies' investment intentions. In the context of limited local resources, local governments are able to prioritize the implementation of crucial factors. The article talked about the theoretical foundations, research hypotheses, research methods, analysis and comparison of results, and finally, implications and conclusions.
2.1 Local competition theory
The relationship between local competition and firm-level competition is based on three levels: (1) local availability factors; (2) local competitiveness; and (3) enterprise-level competitiveness [6-9].
Due to natural factors including natural resources, geographical location, and local scale, the first level experiences the least change or the slowest change. Both of these factors are advantageous, but they can also create disadvantages for the region, particularly resource issues. If you rely too heavily on natural advantages, you may lose the motivation to create and endure adversity.
The second level is macroeconomic competitiveness, or the general environment in which all business activities are organized. This group of factors includes the quality of macro-policies, infrastructure, and political institutions. The quality of this group of factors largely lies in the structure and sequence of policy making. From an institutional perspective, good policies are often created in an inclusive institutional environment in which most stakeholders participate actively and with responsibilities in the policy-making process. Good policies are often created when there is a harmonious coordination between the three main pillars of this structure, namely local government, businesses and socio-political organizations, self-organizing social organizations, and communication [10].
If a municipality wishes to improve its competitiveness, it must first improve the ability of its businesses, which is determined by factors such as the business environment, the level of development of the industry, and the business development strategy. This demonstrates the close relationship between enterprise and local competition [6].
2.2 The OLI paradigm
The OLI paradigm has shown three decisive factors when investing abroad [11]. First: the advantage of ownership (Ownership-O), which means that the business must have an advantage over enterprises from other countries in terms of size, technology, ability to access low-interest loans, and intangible assets that have the characteristics of the enterprise. Second: The Advantage of Location (Location-L). When manufacturing in foreign countries, enterprises get advantages in natural resources, investment promotion policies, cheap labor sources, etc. Third, benefits of internalization (internalize-I), that is, using those benefits within the enterprise rather than selling or renting them to other businesses.
There are four decisive factors for choosing an investment location when investing abroad. First: basic resources, second: market foundation, third: efficiency foundation, and fourth: strategic asset foundation [12, 13]. In addition, there are many studies that confirm the great role of investment location on the decision to invest abroad [14-16].
2.3 Local marketing theory
Previous studies have shown that to choose the locations of enterprises based on input materials, labor, transport, and energy [17, 18]. Placing businesses close to raw materials, especially those that are difficult to store, will help reduce production costs [19]. Firms producing consumer goods tend to locate factories in cities or densely populated areas [20]. Therefore, it is common to choose economically advantageous positions in order to maximize profits [21, 22]. The choice of investment location is based on factors such as business characteristics, business environment, and development trends outside the enterprise [23]. This also leads to competition between localities, including five aspects, first: supporting factors and resources; second: core resources and attraction factors (attractors); third: destination management; fourth: destination policy, planning, and development; and fifth: determining qualifying and amplifying determinants [24].
Locality is regarded as a commodity in the market economy, with customers being businesses with capital, techniques, and technology, the exploitation of which will exploit the potential of the local to produce goods to meet the market's needs [25]. As a result, businesses will constantly relocate to other locations in search of investment opportunities where they can gain a competitive advantage [26, 27].
The establishment of a locally recognized brand is an essential strategy for attracting financial investment and fostering economic expansion. Through advertising and investment promotion, the government brings to the public's attention opportunities such as infrastructure, policies, human resources, and so on. Information is readily available to businesses, and they primarily rely on business strategies when making location decisions investment [28]. In order for communities to stimulate economic development and build brands, they need to construct their own competitive advantages and construct brands [29].
3.1 The infrastructure
Before making investment decisions, investors frequently evaluate local infrastructure, including communication systems, transportation systems, electricity systems, water supply systems, and drainage systems [30]. Infrastructure is one of the most influential factors on private enterprise investment capital flows and investment decisions [6]. In addition, as communication improves, businesses will be able to use modern industry to manufacture goods, giving them a competitive advantage [31, 32].
H1: Private enterprise investment intentions are positively impacted by infrastructure.
3.2 Labor
When making investment decisions, the cost of employees is always a crucial factor for businesses. Localities with low labor costs will attract the most investment capital, particularly in labor-intensive manufacturing industries [32, 33]. The level of competition in the market economy is intensifying, and businesses are placing high demands on labor quality in order to master modern technology, boost productivity, and produce goods that satisfy consumer demand and preferences [31, 34].
H2: Labor has a positive effect on private enterprises' investment intentions.
3.3 Administrative procedures
The local economy will grow over time with the effective implementation of investment policies. This will be the primary factor influencing investors' investment decisions in the area [35]. Reforming administrative procedures is one of the significant measures taken by the local government to enhance the area's attractiveness. It seeks to expedite investment licenses, production-related permits, and permits required by authorities... so that businesses face no obstacles when executing investment procedures [36, 37]. Also, the government must take steps to protect the interests of investors, such as making sure everyone knows what their responsibilities and goals are and making it easier and cheaper to put investment procedures into place [38].
H3: Administrative procedures positively affect the investment intentions of private enterprises.
3.4 Land policy
Land is one of the most influential factors in determining the location of an investment because it affects factory construction time, production scale, land rent, and related costs. Land is viewed as a tool for attracting investment [39]. As a result, numerous local governments have increased the land supply, lowering the cost for businesses. In addition, land is a financial instrument because businesses can use it as collateral for bank loans. Many businesses desire large tracts of land because, in addition to serving production activities, they can construct housing and infrastructure to serve their employees [40]. Local governments that distribute land funds unequally, on the other hand, will cause an economic imbalance. Businesses lose connections within and beyond the region, resulting in higher production costs [41].
H4: Land policy positively affects the investment intentions of private enterprises.
The research model is presented in Figure 1.
Figure 1. Research model
3.5 Transparency
The legal system will aid in investment promotion, but first it is necessary to ensure logic and avoid duplication and confusion. Information is accessible to the public; businesses will comply with the established regulations [42]. In addition, local authorities will receive feedback from stakeholders, which will be used to regularly review and adjust regulations and policies to reflect the current circumstances [43]. For ineffective policies, this presents an opportunity for self-interested behavior. Costing a great deal of time and money, it will present a formidable obstacle for businesses. Enterprises will be eager to investigate locations where they can receive numerous investment incentives and reduce costs during the implementation of investment procedures and the production and business processes [44].
H5: Transparency has a positive effect on the investment intentions of private enterprises.
3.6 Support from the local government
Each location has unique conditions. Consequently, local governments employ aiding tools to attract investors for economic development and job growth [45]. The government supports businesses by reducing taxes, land rent, loan interest rates, and implementing administrative procedures, among other measures. This will assist businesses in reducing production costs and increasing investment for growth [46]. In addition, with the aid of these tools, local governments will adjust the structure of the economy, focusing on the sectors and regions where they anticipate the greatest growth [47, 48].
H6: Support from the local government has a positive effect on the investment intentions of private enterprises.
4.1 Data
The study's population consists of all private enterprises planning to invest in Vietnam. Comrey and Lee's principles are used to determine the sample to be studied [49]. The study surveyed 280 businesses and collected data through pre-prepared questionnaires sent to private businesses looking to invest. The survey respondents are the Board of Directors of companies; if they are not the Board of Directors, their responses are deemed invalid. The survey resulted in the collection of 280 answer sheets, 265 of which were valid. There are 182 domestic enterprises and 85 foreign enterprises among the valid votes.
Table 1. Description of business characteristics
|
|
Frequency |
Percent |
|
|
Type of business |
Domestic |
182 |
68,7 |
|
Foreign |
85 |
31,3 |
|
|
Firm age |
Less than 5 years |
55 |
20,8 |
|
6-10 years |
104 |
39,2 |
|
|
11-15 years |
62 |
23,4 |
|
|
More than 15 years |
44 |
16,6 |
|
|
Investment sector |
Agriculture |
35 |
13,2 |
|
Industrial |
128 |
48,3 |
|
|
Services |
102 |
38,5 |
|
Table 1 indicates that enterprises that have been in operation for 6-10 years have the highest proportion (104 enterprises, 39.2%), while enterprises that have been in operation for more than 15 years have the lowest proportion (44 enterprises, 16.6%). Industry has the highest proportion of investment with 128 enterprises (48.3%), followed by services with 102 enterprises (38.5%), and agriculture with 35 enterprises. (13.2%).
4.2 Variables
Based on previous research, the questionnaire was created. The author sends the manuscript to ten investment experts to ask pertinent questions. Following that, the author conducted a pilot survey with 20 businesses, adjusting the questions to fit the research context. The scale used to evaluate the observed variables is a Likert scale ranging from 1 (very poor) to 5 (very good). The infrastructure factor group is based on the work of Loc and Tuyet [31]. Dao and Luan inspired the group of labor factors, government assistance, and administrative procedures [30]. Hamudy and Rifki provided the transparency factor [37], and Hang provided the land policy [34].
4.3 FsQCA method
Ragin developed the fsQCA method [50], which is now widely used in social science research [51]. Traditional methods rely on variables for analysis, whereas fsQCA relies on survey data from specific case. As a result, fsQCA will provide detailed results. This allows us to identify all cases in which a particular combination of conditions results in a specific outcome. Using this method allows you to look for other ways to achieve the same result [52]. Data for the study should be temporal or nominal, then corrected for fuzzy sets ranging from 0.0 to 1.0, where 0.0 is incomplete, 1.0 is complete, and 0.5 is ambiguous.
The fsQCA method is commonly used in the social sciences to investigate complex cases of causality [53]. Traditional statistical methods check the net effect, but fsQCA looks for combinations of conditions that result in the same result. If only two or three variables are used in a SEM study, the results will be limited [52]. Furthermore, using fsQCA will overcome the phenomenon of multicollinearity and nonlinearity, which is difficult to overcome with SEM [54].
5.1 Results from SEM
According to Table 2, the reliability can be confirmed when Cronbach’s Alpha coefficients for all the latent variables were found to be well above the accepted threshold value of 0.7 [55]. Item-Total Correlation coefficient is greater than 0.3 [55]. This result indicates that the data can be utilized with the EFA model. For this study, CFA and SEM models are utilized in addition to EFA to analyze the data.
Table 2. Cronbach's Alpha coefficient
|
Items |
Cronbach’s Alpha |
Corrected Item- Total Correlation |
Cronbach’s Alpha if item deleted |
|
IN |
0.802 |
|
|
|
IN1 |
|
0.632 |
0.749 |
|
IN2 |
|
0.603 |
0.759 |
|
IN3 |
|
0.618 |
0.752 |
|
IN4 |
|
0.620 |
0.751 |
|
LA |
0.800 |
|
|
|
LA1 |
|
0.573 |
0.769 |
|
LA2 |
|
0.605 |
0.753 |
|
LA3 |
|
0.721 |
0.696 |
|
LA |
|
0.556 |
0.777 |
|
AP |
0.752 |
|
|
|
AP1 |
|
0.563 |
0.688 |
|
AP2 |
|
0.566 |
0.684 |
|
AP3 |
|
0.610 |
0.633 |
|
LP |
0.831 |
|
|
|
LP1 |
|
0.716 |
0.741 |
|
LP2 |
|
0.671 |
0.786 |
|
LP3 |
|
0.686 |
0.771 |
|
TR |
0.791 |
|
|
|
TR1 |
|
0.605 |
0.744 |
|
TR2 |
|
0.611 |
0.738 |
|
TR3 |
|
0.681 |
0.662 |
|
LS |
0.826 |
|
|
|
LS1 |
|
0.713 |
0.752 |
|
LS2 |
|
0.586 |
0.810 |
|
LS3 |
|
0.749 |
0.734 |
|
LS4 |
|
0.565 |
0.819 |
|
II |
0.771 |
|
|
|
II1 |
|
0.617 |
0.679 |
|
II2 |
|
0.623 |
0.672 |
|
II3 |
|
0.577 |
0.723 |
The indicators are suitable for conducting research using the Amos 20.0 software and the confirmatory factor analysis (CFA) model. According to Gerbing and Anderson [56], the CFA loading coefficients are greater than 0.5. In the study of Fornell and Larcker [57], the composite reliability (CR) ≥ 0.7 and the average variance extracted (AVE) ≥ 0.5. According to Table 3, the study's CFA model's data are thus appropriate.
Table 3. Standardized CFA loadings
|
Item |
CFA Loading |
CR |
AVE |
|
LS |
|
0,832 |
0,558 |
|
LS1 |
0.793 |
|
|
|
LS3 |
0.873 |
|
|
|
LS2 |
0.655 |
|
|
|
LS4 |
0.643 |
|
|
|
IN |
|
0.802 |
0.504 |
|
IN3 |
0.703 |
|
|
|
IN1 |
0.719 |
|
|
|
IN2 |
0.697 |
|
|
|
IN4 |
0.720 |
|
|
|
LA |
|
0.801 |
0,509 |
|
LA3 |
0.897 |
|
|
|
LA2 |
0.665 |
|
|
|
LA1 |
0.632 |
|
|
|
LA4 |
0.625 |
|
|
|
LP |
|
0.831 |
0.623 |
|
LP1 |
0.812 |
|
|
|
LP3 |
0.783 |
|
|
|
LP2 |
0.772 |
|
|
|
TR |
|
0.793 |
0.563 |
|
TR3 |
0.829 |
|
|
|
TR2 |
0.702 |
|
|
|
TR1 |
0.714 |
|
|
|
AP |
|
0.753 |
0.504 |
|
AP3 |
0.739 |
|
|
|
AP2 |
0.688 |
|
|
|
AP1 |
0.703 |
|
|
|
II |
|
0.771 |
0.530 |
|
II2 |
0.718 |
|
|
|
II1 |
0.755 |
|
|
|
II3 |
0.709 |
|
|
Figure 2. Structural equation model
The measurement model fits with satisfactory value: the Tucker & Lewis index (TLI) is 0.984, the comparative fit index (CFI) is 0.987, and the goodness-of-fit index (GFI) has a value of 0.927. This is satisfactory: χ2/df = 1.130, p = 0.000, df = 231, χ2 = 260,941. The root mean square error of approximation (RMSEA) index is 0.022.
Table 4. Structural path
|
|
B |
Std |
β |
P-Value |
|
IN → II |
0.188 |
0.071 |
0.206 |
0.008 |
|
LA → II |
0.164 |
0.063 |
0.187 |
0.009 |
|
AP → II |
0.157 |
0.059 |
0.174 |
0.008 |
|
LP → II |
0.219 |
0.078 |
0.212 |
0.005 |
|
TR→ II |
0.235 |
0.087 |
0.213 |
0.007 |
|
LS → II |
0.175 |
0.065 |
0.181 |
0.007 |
The structural equation model is shown in Figure 2. The results from the SEM model are shown in Table 4. The Infrastructure has a positive effect on the investment intention of private enterprises with a value of β=0.206 (p<0.01). Labor has a positive impact on investment intention with a value of β=0.187 (p< 0.01). Administrative procedures has a positive impact on the investment intention of private enterprises with a value of β = 0.174 (p <0.01). Land policy has a positive impact on investment intention with a value of β=0.212 (p <0.01). Transparency is positively related to investment intention with a value of β= 0.213 (p< 0.01). Support from the local government has a positive effect on investment intention with a value of β= 0.181 (p< 0.01).
5.2 Results from fsQCA
Based on research by Ragin [58], this study has chosen a consistent threshold of 0.8. The data were analyzed using fsQCA 3.0 software and obtained the results as shown in Table 5.
According to Table 5, there are two methods for combining factors that comprise private enterprise investment intentions, referred to as a group of factors. The first group of factors consists of: IN, LA, AP, TR, and LS with a raw coverage index of 0.87167 > 0.4. The second group of factors consists of: LA, AP, LP, TR, and LS with a raw coverage index of 0.88262 > 0.4. The fsQCA results are reliable and acceptable [59]. Enterprises that intend to invest but pay little attention to the land factor tend to concentrate in less developed areas where land is easily fenced and inexpensive to rent. For businesses that are less interested in local infrastructure in areas with a relatively developed economy, the infrastructure is synchronous and always prepared to provide for businesses when they decide to invest.
Table 5. FsQCA results (consistency threshold: 0.8)
|
COMPLEX SOLUTION |
|||
|
Model: II= f(IN, LA, AP, LP, TR, LS) |
|||
|
Frequency cutoff: 5 |
|||
|
Consistency cutoff: 0.994824 |
|||
|
|
Raw coverage |
Unique coverage |
Consistency |
|
IN*LA*AP*TR*LS |
0.87167 |
0.0129644 |
0.994901 |
|
LA*AP*LP*TR*LS |
0.88262 |
0.0239142 |
0.992611 |
|
Solution coverage: 0.895584 |
|||
|
Solution consistency: 0.992718 |
|||
Note: IN: The Infrastructure; LA: Labor; AP: Administrative Procedures; TR: Transparency; LP: Land Policy; LS: Support from the local government
With the rapid growth of the economy, the role of private businesses is growing in significance. This study's objective is to identify the factors that influence the investment intentions of private companies, including: the infrastructure, labor, land policy, transparency, administrative procedures, support from local government.
Local infrastructure has a positive correlation with investment intent, according to research results. This is also demonstrated by Dao and Luan study’s [30]. To encourage private investment in the area, the government must prioritize the development of land transport infrastructure, the connection of economic centers, and the establishment of a regional economic network. deploying synchronously modern communication, electricity, water, and waste treatment systems that are always in good working order so that businesses can immediately implement investment projects.
High technology has been widely applied to production by the private sector. Consequently, businesses frequently place high quality, self-study, and research requirements on employees' ability to operate modern equipment. The local government has devised a plan to develop human resources, thereby creating a cost-competitive, attractive environment for workers. This will help businesses reduce expenses and encourage local investment plans [31, 33].
Administrative procedures and transparency must be carefully combined by municipal governments. Inappropriate legal regulations require a great deal of time and effort to be removed and replaced with alternative regulations. During the process, businesses require transparency, clear working steps, authority responsibilities, details on prepared documents, and processing time. This allows businesses to reduce investment costs, have a clear implementation plan, and be willing to invest in communities that meet the aforementioned criteria [37, 38, 44].
In order to improve their ability to attract private businesses, a number of communities have constructed industrial parks, which are large areas with competitive land rental costs, completed infrastructure, and investment readiness. private. There are business support centers for information, loan assistance, and administrative procedures in industrial parks. With the support and good policies on local land, businesses can establish ties with other businesses within and outside the region, easily import input materials, and consume output products [41, 48].
The study includes an introductory section on the role of local government in fostering an investment climate that influences the investment intentions of private companies. Before making investment decisions, future research can identify more internal factors of businesses. In addition, this study was conducted at a fsingle point in time and does not show how factors have changed and been ranked over time. This study focuses solely on how private businesses in Vietnam intend to invest. It does not compare businesses across nations.
This study is a part of the author's dissertation.
The Contents of Items in the Questionnaire
|
Code |
Contens |
Level |
||||
|
IN |
The Infrastructure |
|
|
|
|
|
|
IN1 |
Electrical system |
1 |
2 |
3 |
4 |
5 |
|
IN2 |
Wastewater supply and treatment system |
1 |
2 |
3 |
4 |
5 |
|
IN3 |
Communication system |
1 |
2 |
3 |
4 |
5 |
|
IN4 |
Traffic system |
1 |
2 |
3 |
4 |
5 |
|
LA |
Labor |
|
|
|
|
|
|
LA1 |
Labor Supply |
1 |
2 |
3 |
4 |
5 |
|
LA2 |
Technical level of labor |
1 |
2 |
3 |
4 |
5 |
|
LA3 |
Labor cost |
1 |
2 |
3 |
4 |
5 |
|
LA4 |
Learning and improving |
1 |
2 |
3 |
4 |
5 |
|
AP |
Administrative Procedures |
|
|
|
|
|
|
AP1 |
License related to production and business |
1 |
2 |
3 |
4 |
5 |
|
AP2 |
Appraisal of investment documents |
1 |
2 |
3 |
4 |
5 |
|
AP3 |
Investment license time |
1 |
2 |
3 |
4 |
5 |
|
LP |
Land Policy |
|
|
|
|
|
|
LP1 |
Land rental price |
1 |
2 |
3 |
4 |
5 |
|
LP2 |
Land Supply |
1 |
2 |
3 |
4 |
5 |
|
LP3 |
Location to invest |
1 |
2 |
3 |
4 |
5 |
|
TR |
Transparency |
|
|
|
|
|
|
TR1 |
Clarity in the law |
1 |
2 |
3 |
4 |
5 |
|
TR2 |
Monitoring the implementation of legal regulations |
1 |
2 |
3 |
4 |
5 |
|
TR3 |
Time and cost of carrying out administrative procedures |
1 |
2 |
3 |
4 |
5 |
|
LS |
Support from the local government |
|
|
|
|
|
|
LS1 |
LS1: Copyright and trade-mark support |
1 |
2 |
3 |
4 |
5 |
|
LS2 |
Financial Assistance |
1 |
2 |
3 |
4 |
5 |
|
LS3 |
Information assistance |
1 |
2 |
3 |
4 |
5 |
|
LS4 |
Support to carry out administrative procedures |
1 |
2 |
3 |
4 |
5 |
|
II |
Investment Intention |
|
|
|
|
|
|
II1 |
Finding production resources |
1 |
2 |
3 |
4 |
5 |
|
II2 |
Effective search |
1 |
2 |
3 |
4 |
5 |
|
II3 |
Market discovery |
1 |
2 |
3 |
4 |
5 |
[1] Abimanyu, A. (2005). Format Anggaran Terpadu Menghilangkan Tumpang Tindih. Bappeko Depkeu, Jakarta, Indonesia.
[2] Hamid, E.S. (2006). Ekonomi Indonesia: Dari Sentralisasi ke Esentralisasi. Yogyakarta: UII Press.
[3] Murwito, I.S., Rheza, B., Mulyati, S., Karlinda, E., Riyadi, I.A., Darmawiasih, R. (2013). Interregional cooperation on trade as alternative policy for improving regional economic. Komite Pemantauan Pelaksanaan Otonomi Daerah, Jakarta, Indonesia.
[4] Kuncoro, M. (2004). Regional Autonomy and Development: Reform, Planning, Strategy and Opportunity. Wisnu Chandra Kristiaji, Jakarta, Indonesia.
[5] Snieskaa, V., Zykiene, I. (2015). City attractiveness for investment: Characteristics and underlying factors. Procedia-Social and Behavioral Sciences Jounal, 213: 48-54. https://doi.org/10.1016/j.sbspro.2015.11.402
[6] Porter, M.E (1990). The Competitive Advantage of Nations. New York: The Free Press.
[7] Porter, M.E (1998). Clusters and Competition: New Agendas for Companies, Governments, and Institutions. In On Competition. Boston: Harvard Business School Press.
[8] Porter, M.E. (1998). Measuring The Microeconomic Foundations of Economic Development. The Global Competitiveness Report, Geneva, Switzerland: World Economic Forum.
[9] Porter, M.E. (2008). The five competitive forces that shape strategy. Harvard Business Review, 86: 79-93.
[10] Acemoglo, D., Robinson, J. (2012). Why Nations Fail: The Origins of Power, Prosperity and Poverty. New York: Crown.
[11] Dunning, J.H. (2002). Theories and Paradigms of International Business Activity. USA: Edward Elgar Publishing.
[12] Dunning, J.H. (1977). Trade, Location of Economic Activity and the MNE: A Search for an Eclectic Approach. In: Ohlin, B., Hesselborn, PO., Wijkman, P.M. (eds) The International Allocation of Economic Activity. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-03196-2_38
[13] Dunning, J.H. (1998). Location and the multinational enterprise: A neglected factor? Journal of International Business Studies, 29: 45-66. https://doi.org/10.1057/palgrave.jibs.8490024
[14] Buckley, P.J., Buckley, P.J., Cross, A., Tan, H., Liu, X., Voss, H. (2010). Historic and emergent trends in Chinese outward direct investment. Foreign direct investment, China and the World Economy, 119-162. https://doi.org/10.1057/9780230248328_7
[15] Cheng, S. (2006). The role of labour cost in the location choices of Japanese investors in China. Regional Science, 85(1): 121-138. https://doi.org/10.1111/j.1435-5957.2006.00066.x
[16] Makino, S., Lau, C.M., Yeh, R.S. (2002). Asset-exploitation versus asset—seeking: Implications for location choice of foreign direct investment from newly industrialized economies. Journal of International Business Studies, 33(3): 403-421. https://doi.org/10.1057/palgrave.jibs.8491024
[17] Djwa, P.D.K. (1960). An analysis of industrial location factors with particular reference to Indonesia. Faculty of Commerce and Business Administration, The University of British Columbia. https://doi.org/10.14288/1.0102437
[18] Rawstron, E.M. (1958). Three principles of industrial location. Transactions and Papers (Institute of British Geographers), 25: 135-142.
[19] Renner, G.T. (1947). Geography of industrial localisation. Economic Geography, 23(3): 167-189. https://doi.org/10.2307/141510
[20] Jones, L.L., Woods, M.D. (2002). Economic location theory and practice (unpublished paper). Texas A and M University.
[21] Isard, W. (1949). The general theory of location and space-economy. The Quarterly Journal of Economics, 63(4): 476-506. https://doi.org/10.2307/1882135
[22] Smith, D.M. (1966). A theoretical framework for geographical studies of industrial location. Economic Geography, 42: 95-113. https://doi.org/10.1080/00130095.1966.11729842
[23] Van Noort, E.A., Reijmer, I.A. (1999). Location choice of SMEs: The most important determinants. EIM Small Business Research and Consultancy: Zoetermeer.
[24] Crouch, G.I. (2011). Destination competitiveness: An analysis of determinant attributes. Journal of Travel Research, 50(1): 27-45. https://doi.org/10.1177/0047287510362776
[25] Kotler, P., Hamlin, M.A., Rein, I., Haider, D.H. (2001). Marketing Asian Places, Attracting Investment Industry and tourism to cities, states and nations. John Wiley&Sons (Asea), Singapore.
[26] Leigh, N., Blakely, E.J. (2013). Planning local economic development: theory and practice (4th ed.). Washington: Sage.
[27] Chung, W., Alcacer, J. (2002). Knowledge seeking and location choice of foreign direct investment in the United States. Management Science, 48(12): 1534-1554. https://doi.org/10.1287/mnsc.48.12.1534.440
[28] Hall, T. Hubbard, P. (1996). The entrepreneurial city: new urban politics, new urban geographies? Progress in Human Geography, 20: 153-174. https://doi.org/10.1177/030913259602000201
[29] Pasquinelli, C. (2013). Competition, cooperation and co-opetition: unfolding the process of interterritorial branding. Urban Research and Practice, 6(1): 1-18. https://doi.org/10.1080/17535069.2012.727579
[30] Dao, T.T, Luan, N.V. (2019). Identify factors affecting foreign direct investment capital in the southern key economic region. Science & Technology Development Journal, 22(2): 275-288. https://doi.org/10.32508/stdj.v22i2.1051
[31] Loc, L.T, Tuyet, N.T. (2013). Factors affecting the satisfaction of foreign direct investment enterprises; Case study in Da Nang. Research and Exchange, 11(21): 73-78. https://vjol.info.vn/index.php/kttc/article/view/12331/11302.
[32] Wu, X., Strange, R. (2000). The location of foreign insurance companies in China. International Business Review, 9: 381-398. https://doi.org/10.1016/S0969-5931(00)00007-X
[33] Oum, T.H., Park, J.H. (2004). Muntinational firms’ location preference for regional distribution centers: focus on the Northeast Asian region. Transportation Research E: Logistics and Transportation Review, 40(2): 101-121. https://doi.org/10.1016/S1366-5545(03)00036-X
[34] Hang, V.T.T. (2010). Some solutions to strengthen State management of FDI enterprises in Vietnam. State Management Review, 176: 22-30.
[35] Dejuan-Bitria, D., Mora-Sanguinetti, J.S. (2021). Which legal procedure affects business investment most, and which companies are most sensitive? Evidence from microdata. Economic Modelling, 94: 201-220. https://doi.org/10.1016/j.econmod.2020.09.023
[36] Glebova, I.S., Sadyrtdinov, R., Rodnyansky, D. (2013). Impact analysis of investment attractiveness of the Republic of Tatarstan on fixed investments of its leading companies. World Applied Sciences Journal, 26(7): 911-916. https://doi.org/10.5829/idosi.wasj.2013.26.07.13520
[37] Hamudy, M.I.A., Rifki, M.S. (2021). Tanjung Lesung special economic zone: Expectation and reality. Policy & Governance Review, 5(1): 50-65. https://doi.org/10.30589/pgr.v5i1.337
[38] Zheltenkov, A., Syuzeva, O., Vasilyeva, E., Sapozhnikova, E. (2017). Development of investment infrastructure as the factor of the increase in investment attractiveness of the region. IOP Conference Series: Earth and Environmental Science, 90(1): 012122. https://doi.org/10.1088/1751315/90/1/012122
[39] Huang, Z., Du, X. (2017). Strategic interaction in local governments' industrial land supply: Evidence from China. Urban Studies, 54(6): 1328-1346. https://doi.org/10.1177/0042098016664691
[40] Ke, S., He, M., Yuan, C. (2014). Synergy and co-agglomeration of producer services and manufacturing: A panel data analysis of Chinese cities. Regional Studies, 48(11): 1829-1841. https://doi.org/10.1080/00343404.2012.756580
[41] Huang, Z., Du, X. (2017). Government intervention and land misallocation: Evidence from China. Cities, 60(Part A): 323-332. https://doi.org/10.1016/j.cities.2016.10.006
[42] Al Halbusi, H., Williams, K.A., Mansoor, H.O., Hassan, M.S., Hamid, F.A.H. (2020). Examining the impact of ethical leadership and organizational justice on employees’ ethical behavior: Does person-organization fit play a role? Ethics & Behavior, 30(7): 514-532. https://doi.org/10.1080/10508422.2019.1694024
[43] Logunova, N., Glechikova, T., Kotenev, A. (2021). Investment attractiveness of the area for marine farming and marine aquaculture target species. Transportation Research Procedia, 54: 76-83. https://doi.org/10.1016/j.trpro.2021.02.051
[44] Wright, M., Pruthi, S., Lockett, A. (2005). International venture capital research: From cross-country comparisons to crossing borders. International Journal of Management Reviews, 7(3): 135-165. https://doi.org/10.1111/j.1468-2370.2005.00113.x
[45] Cullen, M., Calitz, A., Chandler, L., (2014). Business incubation in the eastern Cape: A case study. International Journal for Innovation Education and Research, 2(5): 76-89.
[46] Greene, F. (2012). Should the focus of publicly provided small business assistance be on start-ups or growth businesses?. Ministry of Economic Development, New Zealand.
[47] Bosma, N., Schutjens, V. (2009). Mapping entrepreneurial activity and entrepreneurial attitudes in European regions. International Journal of Entrepreneurship and Small Business, 7(2): 191-213. https://doi.org/10.1504/IJESB.2009.022806
[48] Wonglimpiyarat, J. (2011). Government programmes in financing innovations: Comparative innovation system cases of Malaysia and Thailand. Technology in Society, 33(1): 156-164. https://doi.org/10.1016/j.techsoc.2011.03.009
[49] Comrey, A.L., Lee, H.B. (1992). Interpretation and application of factor analytic results. A First Course in Factor Analysis.
[50] Ragin, C.C. (2000). Fuzzy-Set Social Science 1st Edition. University of Chicago Press.
[51] Wagemann, C., Schneider, C.Q. (2010). Qualitative comparative analysis (QCA) and fuzzy-sets: Agenda for a research approach and a data analysis technique. Comparative Sociology, 9(3): 376-396. https://doi.org/10.1163/156913210X12493538729838
[52] Skarmeas, D., Leonidou, C.N., Saridakis, C. (2014). Examining the role of CSR skepticism using fuzzy-set qualitative comparative analysis. Journal of Business Research, 67(9): 1796-1805. https://doi.org/10.1016/j.jbusres.2013.12.010
[53] Apetrei, A., Paniagua, J., Sapena, J. (2016). Do financial crises moderate entrepreneurial recipes? A comparative fuzzy analysis. Journal of Promotion Management, 22(4): 482-495. https://doi.org/10.1080/10496491.2016.1190222
[54] Phung, M.T., Ly, P.T.M., Nguyen, T.T., Nguyen-Thanh, N. (2020). An FsQCA investigation of eWOM and social influence on product adoption intention. Journal of Promotion Management, 26(5): 726-747. https://doi.org/10.1080/10496491.2020.1729318
[55] Nunnally, J.C., Bernstein, I.H. (1994). Psychometric theory. McGraw-Hill series in psychology, New York: McGraw-Hill.
[56] Gerbing, D.W., Anderson, J.C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2): 186-192. https://doi.org/10.2307/3172650
[57] Fornell, C., Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. https://doi.org/10.2307/3151312
[58] Ragin, C.C. (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press. https://doi.org/10.7208/chicago/9780226702797.001.0001
[59] Emmenegger, P. (2011). Job security regulations in western democracies: A fuzzy set analysis. European Journal of Political Research, 50(3): 336-364. https://doi.org/10.1111/j.1475-6765.2010.01933.x
