The destination's attractiveness is superior economic benefits, or access to resources to develop a competitive advantage for businesses

ho are difficult to train ... Thirdly, seeking tourism resources capable of developing investment, attracting tourists for businesses. Such as natural landscape, climate, cultural heritage, events and impressive festivals ... 2.2.2.2 Market-Seeking a. Concept Dunning and Lundan (2008) argue that “Motivation of Market-Seeking are the motivation for businesses to invest in a specific country or region to provide goods or services to markets in countries this or in neighboring countries”. b. Components of Market-Seeking Dunning and Lundan (2008) think that there are two main reasons why businesses seek markets: Firstly, due to tariffs or other barriers that increase costs imposed by the host country. Secondly, seeking markets is to maintain and exploit existing or new markets. According to Dunning and Lundan (2008) a normally selected market must consider the following conditions: 1. Market size 2. Market growth prospects 3. Near suppliers or customers 4. Competitors 5. The company's global marketing and business strategy 6. To approach and understand customers' tastes needs in accordance with local customs, habits, lifestyles and legal aspects. 2.2.2.3 Efficiency-Seeking a. Concept Dunning and Lundan (2008) state that: “The motivation of efficiency-seeking is the motives to rationalize the investment structure based on the resources or markets they target. In other words, reduce costs and increase profits for businesses”. b. Components of Efficiency-Seeking Dunning and Lundan (2008) suggest that there are two types of Efficiency-Seeking: Firstly, the availability and the relative cost of natural resources and labor in the country they seek, are lower than the country currently operating in the business. Secondly, taking advantage of various factors in business environment, institutions, infrastructure, laws ... thereby creating lower costs, more beneficial for businesses. Such as: preferential investment policies, tax breaks, transportation costs, infrastructure ... Thusly, based on the two types of efficiency-seeking engines, experimental researchers also pointed out the components of the efficiency-seeking engine including three main components in the study of attracting capital. That tourism is: (1) cost advantage; (2) advantage in infrastructure; (3) advantage of an investment environment. 2.3 Relationship between investment destination attractiveness and investment intent 8 There are many theories explaining the relationship between destination attractiveness and investment intent, but the most prominent is Ajzen (1991) with the intended behavior theory. Attitude refers to one's opinion about whether a behavior is positive or negative (Ajzen, & Fishbein, 1980). Attitude is defined as a psychological and cognitive behavior that individuals exhibit by assessing any particular factor with a degree of relevance or nonconformity (Eagly and Chaiken, 1993). In this study, the attitude refers to the investor's assessment of the attractiveness of the destination. From the attitude of the attraction of the destination will affect the intention to invest. The subjective criterion refers to a cognitive social pressure arising from one's perception (Ajzen, & Fishbein, 1980). Cognitive behavioral control refers to individual perceptions of the ease / difficulty of performing a behavior of interest (Ajzen, 1991). In the author's study, only the attitude and belief of investors about the factors that make up the attractiveness of the investment destination affect the investment intention. The factors of "subjective standard" and "cognitive behavioral control" were not considered in this study. Many studies have confirmed the significant and positive influence of attitude on intentions of behavior (Teo and Pok, 2003; Shih and Fang, 2004; Ramayah and Suki, 2006). Many studies on investment intentions have also shown that the same attitude has the greatest positive impact on investment intentions (Alleyne and Broome, 2010; Ali, 2011; Shanmugham and Ramya, 2012; Ali. et al., 2014; Sudarsono, 2015; Cuccinelli et al., 2016). With the theory of behavior intended to affirm investors' attitude and beliefs about the factors that make up the attractiveness of destinations that affect investment intentions. However, the intended behavior theory only points to the impact of investor attitudes and beliefs but has not yet shown the factors that make the destination of investment attractive. Therefore, the intended behavior theory will be combined with the investment motive theory that will contribute to the author's research. Attitude toward action Subjective Norm Intention Behavior Perceived Behavioral Control Figure 2.2: The intended behavioral theory (Ajzen, 1991) 9 2.4 Some empirical research on destination attractiveness in attracting investment in the field of tourism - hotel. 2.4.1 A number of empirical studies on the attractiveness of destinations in attracting investment capital based on the theory of international production sites Table 2.8: Summary of empirical studies - international production sites Group Impact factor Experimental study Market Seeking Potential tourism market Dunning and Kundu (1995); Kundu and Contractor (1999); Dunning (2002); Du Plessis (2002); Aykut and Ratha (2004); Johnson and Vanetti (2005); Newell and Seabrook (2006); Naude and Krugell (2007); Duanmu and Guney (2009); Masron and Shahbudin (2010); Anil et al. (2014); Assaf et al. (2015); Santos et al. (2016); Tomohara (2016); Kristjánsdóttir (2016); Puciato et al (2017); Li et al. (2017). Looking for cost advantages 1. Quality of human resources Assaf & Josiassen (2012); Assaf et al. (2015b); Kristjánsdóttir (2016). 2. Availability and cost Dunning (2002); Endo (2006); Masron and Shahbudin (2010); Lu et al. (2011); Anil et al. (2014); Puciato et al (2017); Falk (2016). 3. Infrastructure Dunning and Kundu (1995); UNESCAP (1991); Urata and Kawai (2000); Endo (2006); Nguyen Manh Toan (2010); Dunning (2002); Aykut et al. (2004); Beerli and Martin (2004); Assaf et al. (2015); Lu et al. (2011); Kristjánsdóttir (2016); Puciato et al (2017). 4. Attracting policies and investment incentives Aykut and Ratha (2004); Johnson and Vanetti (2005); Endo (2006); Duanmu and Guney (2009); Masron and Shahbudin (2010); Lu et al. (2011); Kristjánsdóttir (2016); Puciato et al (2017). 5. Restrictions and regulations Brouthers et al (2000); Johnson and Vanetti (2005); Villaverde and Maza (2015); Assaf et al. (2015); Falk (2016). 6. Political stability Dunning and Kundu (1995); Urata and Kawai (2000); Anil et al. (2014). 7. Investment environment Kundu and Contractor (1999); Dunning (2002); Endo (2006); Santos et al. (2016); Tomohara (2016); Li et al. (2017); Li et al. (2018). Source: Author summarizes previous empirical studies The empirical studies mostly use quantitative research with exploratory factor analysis, a small number of SEM analysis and table data analysis. Mostly, the 10 search engine of cost advantage has the difference in the impact factors in different studies. Although there are differences, but these factors all show the incentive to find cost advantages for businesses. The author can group the groups of factors from the above cost-seeking engine into three main groups: (1) availability and cost of using cheap physical and human resources; (2) infrastructure; (3) the investment environment includes favorable policies and investment incentives, regulations and restrictions, political stability ... Basically, the above studies divide investors' motives into these two main groups. is a market search engine and a cost advantage search engine. 2.4.2 A number of empirical studies on destination attractiveness in attracting tourist investors according to investment incentive theory Table 2.9: Summary of empirical studies - investment motives Group Impact factor Experimental study Market Seeking Potential tourism market Snyman and Saayman (2009); Polyzos and Minetos (2011); Yang and Fik (2011); Ussi and Wei (2011); Guillet et al (2011); Zhang et al. (2012); Adam and Amuquandoh (2013); Villaverde and Maza (2015); Puciato (2016). Efficiency Seeking 1. Labor and costs Snyman and Saayman (2009); Ussi and Wei (2011); Zhang et al. (2012); Adam and Amuquandoh (2013); Villaverde and Maza (2015); Puciato (2016) 2. Infrastructure Snyman and Saayman (2009); Polyzos and Minetos (2011); Ussi and Wei (2011); Adam and Amuquandoh (2013). 3. Laws and regulations Yang and Fik (2011); Guillet et al (2011); Adam and Amuquandoh (2013); Zhang et al. (2012); Puciato (2016). 4. Business environment Polyzos & Minetos (2011) tourism resources Seeking 1. Natural resources (landscape, flora and fauna, beach ...) Snyman and Saayman (2009); Polyzos and Minetos (2011); Ussi and Wei (2011); Adam and Amuquandoh (2013). 2. Cultural heritage and major events Polyzos and Minetos (2011); Yang and Fik (2011); Guillet et al (2011); Zhang et al. (2012); Puciato (2016). Source: Author summarizes previous empirical studies Basically, the investment motive theory with the addition of motivation for tourism resources-seeking is completely consistent and more complete than the theory of plant location. the theory of plant location refers to the search for physical resources: the 11 materials of cigarettes, oil, gold, metals ... however, it is classified as a search engine of cost advantage. 2.5 Research model and research hypotheses 2.5.1 Research model 2.5.2 Research hypotheses H1: The advantage of tourism resources has a positive impact on the attractiveness of tourism in attracting tourism investment H2: Tourism infrastructure has a positive impact on the attractiveness of tourism in attracting tourism investment. H3: Economic advantage (potential tourism market) has a positive effect on the attractiveness of tourism in attracting tourism investment H4: The investment environment has a positive impact on the attractiveness of tourism in attracting tourism investment. H5: The cost advantage has a positive effect on the attractiveness of tourism in attracting tourism investment. H6: The overall attractiveness of investment destinations has a positive impact on tourism investment intentions. Chapter 3 RESEARCH DESIGN 3.1 General overview Based on the research model and the hypotheses proposed by the author in the research overview, in this section, the research focuses on two main issues. Firstly, design research process Second, present the scale development results 3.2 The process of studying the attractiveness of destinations in attracting tourism investment To achieve the research goal, the author conducted the study in two stages: preliminary research and formal research. For the official study, the author conducted a survey of 500 samples, obtaining 359 valid observations. Attitude about the overall attractiveness of the tourism destination Potential tourism market Advantage of tourism resources Tourism infrastructure Investment environment (PCI) Cost advantage Investment intent Source: author proposed Figure 2.3: Proposed research model 12 Objectives of the study Qualitative research (In-depth interview with expert) Research background theory and recent experimental research synthesis Proposal of research model and scale Preliminary research Calibrate models and scales Proposing measurement variables for preliminary quantitative research Preliminary quantitative research (N = 162) Evaluate the scale reliability by Cronbach’s Alpha coefficient Exploratory Factor Analysis EFA Proposal of formal measurement variables (questionnaire) Official quantitative research (N =359) Evaluate the scale reliability by Cronbach’s Alpha coefficient Exploratory Factor Analysis EFA Confirmatory Factor Analysis CFA Testing models and research hypotheses SEM Post-quantitative interview to confirm research results Source: Author's proposal Figure 3.1: Research sequence 13 3.3 Research results development scale Table 3.35: Preliminary EFA analysis -Rotated Component Matrixa Source 1 2 3 4 5 MT3 ,883 The Government of Ontario (2009) stop at qualitative research. MT5 ,868 UNCTAD (2006); Masron & Shahbudin (2010); Lu et al. (2011); Villaverde & Maza (2015). MT7 ,866 The Government of Ontario (2009); Villaverde & Maza (2015). MT4 ,850 UNCTAD (2006); Masron & Shahbudin (2010); Lu et al. (2011); Villaverde & Maza (2015). MT2 ,818 The Government of Ontario (2009) stop at qualitative research. MT6 ,806 The Government of Ontario (2009) stop at qualitative research. TN2 ,810 Aykut et al. (2004); Polyzos (2002); Snyman & Saayman (2009). TN3 ,743 Survey form TN1 ,686 Papeditodorou (2001); Polyzos & Arabatzis (2006); Polyzos & Minetos (2011) TN5 ,668 Yang & Fik, (2011); Zhang et al. (2012); Puciato (2016) TN7 ,645 In-depth interview TN4 ,629 Komilis (1986); Polyzos & Minetos (2011) TN6 ,591 Survey form CP4 Dunning (2002) KT6 Dunning (2002) KT3 ,778 Dunning (2002) KT5 ,766 Dunning (2002) KT2 ,753 Dunning (2002) KT4 ,711 Dunning (2002) MT8 ,662 Dunning (2002); Snyman & Saayman (2009); Villaverde & Maza (2015); Assaf et al. (2015) KT1 ,630 Dunning (2002) HT3 Kayam (2009); Artuğer et al. (2013) ,836 MT1 UNCTAD (2006); Masron & Shahbudin (2010). ,818 HT2 Aykut et al. (2004); Dunning (2002) ,815 HT1 Aykut et al. (2004); Dunning (2002) ,798 HT4 Kayam (2009) ,745 CP1 Dunning (2002); Vichea (2005); Anil et al. (2014); Puciato et al. (2017) ,740 CP2 Dunning (2002); Snyman & Saayman (2009); Assaf et al. (2015); Puciato et al. (2017) ,701 CP3 Dunning (2002); Snyman & Saayman (2009); Assaf et al. (2015); Puciato et al. (2017) ,638 MT9 Dunning (2002); Questionnaire PCI Vietnam 2018. ,507 MT10 The Government of Ontario (2009) stop at qualitative research. Source: EFA analysis results from SPSS 22.0 software 14 With the above EFA analysis results, we realize that variable MT1; MT8; MT9 was retained but transferred to measurement for another factor, variables MT10, CP4 and KT6 were removed. Continued verification of the scale by Cronbach’s Alpha analysis showed that the scales were satisfactory. The destination attractiveness scale is inherited from the original scale of Ajzen (1991); Carpenter and Reimers (2005); Paramita et al. (2018). No EFA and Cronbach’s Alpha analysis were included. The scale of investment intent is inherited from the original Ajzen scale (1991); Paramita et al (2018); Ali (2011). No EFA and Cronbach’s Alpha analysis were included. So, with the result of testing the scale by Cronbach's Alpha coefficient and analyzing the discovery factor, basically the scale of "Measuring the attractive factors of destination in attracting tourism investment" is very good. This set of scales can basically meet the quantitative standards used for official quantitative research. Chapter 4 QUANTITATIVE RESEARCH RESULTS 4.1 Testing the scale by Cronbach’s Alpha analysis Table 4.0.0: Summary of scale verification data by Cronbach’s Alpha analysis NO. Factor Number of observed variables Cronbach ’s Alpha Corrected Item-Total Correlation (smallest) Cronbach's Alpha if Item Deleted (biggest) 1 Resources (TN) 7 0,934 0,784 0,925 2 Market (KT) 6 0,944 0,739 0,944 3 Infrastructure (HT) 5 0,931 0,776 0,923 4 Environment (MT) 6 0,912 0,653 0,910 5 Cost (CP) 4 0,809 0,611 0,769 6 Interesting (HD) 5 0,903 0,671 0,900 7 Investment intent 3 0,825 0,655 0,782 Source: Results from SPSS 22.0 software The result shows that only the resource advantage scale is measured by 7 observed variables, eliminating TN6 and the remaining 6 measuring variables for Crobach’s alpha coefficient is 0.934. All other scales are satisfactory, without any additional variables. 4.2 Testing the scale by exploring factor analysis 4.2.1. KMO and Bartlett test results The test result of KMO coefficient = 0.918 proves that this research data is very good and meets the requirements for EFA analysis (Kaiser, 1974; Kaiser & Rice, 1974). Bartlett test results have Sig coefficient = 0.000 <0.05, which means that the observed variables used to measure the total variables are correlated with each other (Bartlett, 1937; Bartlett, 1950). 15 4.2.2 Analyze discovery factors with official data Table 4.0.1: Variables and indicators measuring destination attractiveness to attract investors Variables and indicators (items) Loading factor 1. Advantage of tourism resources TN1. The land has a system of beaches and many beautiful islands with the potential to develop sea and island tourism. ,813 TN2. Diverse forest and animal ecosystems with tourism development potential ,787 TN3. The land has a cool and fresh climate suitable for tourism development. ,814 TN4. Historic sites, museums, impressive monuments capable of attracting and developing tourism ,820 TN5. Unique and interesting cultural events and festivals attract many visitors. ,805 TN6. Diverse and attractive cuisine attracts many visitors creating tourism investment opportunities fail TN7. Attractive nightlife attracts many visitors creating tourist investment opportunities (nightlife, restaurants, casinos, night markets ...) ,855 2. Potential tourism market KT1. The number of tourists visiting the locality is large ,844 KT2. The area has high tourism returns statistics ,848 KT3. The growth of tourism is high ,865 KT4. Easy access to regional and global markets ,879 KT5. Local welcome to tourists and investors ,862 KT6. The level of competition in that locality is low and equal ,781 3. Tourism infrastructure HT1. The local transportation system (bridges, wharves, yards, vehicles ...) is convenient for tourism development. ,800 HT2. Transportation system that connects the locality with other areas convenient for tourism development (waterway, aviation, railway ...) ,853 HT3. Local public equipment is good (electricity, water, health, sanitation, public service, ATM ...) ,889 HT4. There are many local banks offering a full range of international payment and transaction methods. ,866 HT5. The province has available land and land and always facilitates the allocation of land for long-term lease. ,872 4. Tourism investment environment MT1. Local governments and courts resolve disputes and process complaints quickly and fairly. ,816 MT2. Local governments are active and flexible in legal activities, administrative procedures ... in order to facilitate business enterprises. ,737 MT3. Supportive services of the government to facilitate tourism business enterprises (legal advice, market search, trade promotion, technology support, security ...) ,723 16 Variables and indicators (items) Loading factor MT4. Transparency and accessibility to information on investment, land, policies, services ... locally are very easy. ,840 MT5. Cost of time to implement short-term state regulations (administrative procedures, inspection ...) ,860 MT6. Informal costs in this area are low ,671 5. Cost advantage CP1. Enterprises easily access to cheap input materials ,774 CP2. Localities have many incentives on the budget (income tax, VAT, clearance ...) ,779 CP3. The locality has preferential land rent and business premises is better than other localities. ,770 CP4. The quality of local labor is well trained to meet the needs of businesses. ,718 6. The attractiveness of the destination attracts investors HD1. I think the company's revenue will grow as expected. ,859 HD2. I think the company's profit will reach as expected ,900 HD3. I think investing in tourism in the locality is a good idea ,842 HD4. Overall, I think our company is very pleased with this local investment. ,862 HD5. In general, that locality is very attractive for tourism investment. ,782 7. Intention to invest in tourism AT1. I think our company will invest or continue investing in long-term business in this locality. ,890 AT2. I would recommend this locality to friends and relatives who want to invest. ,845 AT3. I will speak well about this locality to anyone who wants to find out. ,846 Source: EFA analysis results from spss 22.0 The analysis results show that Eigenvalue factor extract factor for both independent and dependent variables is greater than 1. Total Variance Explained coefficient = 71,547% proves that 5 independent variables solved preferable to the variation of the dependent variable is 71,547%. The results of EFA analysis for two independent variables, HD and AT, both yield satisfactory results, and the measurement variables for the independent variable have a Total Variance Explained coefficient greater than 0.7. Therefore, we can confirm that the above scale is satisfactory for conducting CFA analysis. 4.3 Confirmatory Factor Analysis CFA 4.3.1 Testing unidirectional properties The unidirectional test results show that the index P = 0.000 <0.05 satisfactory; CMIN / df = 1,975 0.8; CFI = 0.939, TLI = 0.945 are greater than 0.9; RMSEA = 0.052 <0.08 were satisfactory (Taylor et al., 1993; Hair et al., 2010). With the above results, the unidirectional verification of the scale is satisfactory. At the same time, the research model is in accordance with the actual research data. 17 4.3.2 Results of measuring reliability of scales in CFA analysis Table 4.36: Test results of reliability of scales in CFA analysis Factor Observed variables Estimate Reliability scale Total reliability Average Variance Extracted Environment (MT) MT1 ,827 0,913 0,639 MT2 ,745 MT3 ,757 MT4 ,864 MT5 ,890 MT6 ,696 Market (KT) KT1 ,812 0,944 0,738 KT2 ,886 KT3 ,914 KT4 ,929 KT5 ,849 KT6 ,750 Resources (TN) TN1 ,850 0,935 0,706 TN2 ,823 TN3 ,862 TN4 ,824 TN5 ,829 TN7 ,854 Infrastructure (HT) HT1 ,807 0,933 0,735 HT2 ,845 HT3 ,898 HT4 ,872 HT5 ,864 Cost (CP) CP1 ,728 0,810 0,516 CP2 ,743 CP3 ,695 CP4 ,706 Interesting (HD) HD1 ,841 0,905 0,657 HD2 ,891 HD3 ,780 HD4 ,814 HD5 ,716 Investment intent (AT) AT1 ,829 0,828 0,616 AT2 ,750 AT3 ,773 Source: Analysis results by Amos With the results in the table above, we can see that the total reliability coefficient of 6 factors is greater than 0.6 and the extracted coefficients of 6 groups of factors are greater than 18 0.5. This proves that the scale of 6 groups of factors is satisfactory (Gerbing and Anderson, 1988; Hair et al., 2010). 4.3.3 Test of convergence and discrimination in CFA analysis Based on the results of CFA analysis (Figure 4.1), it is shown that all observed variables have regression normalization coefficient for 6 factors greater than 0.5 and less than 1. The lowest variable is CP3. with a value of 0.695; at the same time all P-values are less than 0.001 (requiring less than 0.05), which proves that all measurement variables reach the convergence value in the scale (Gerbing and Anderson, 1988; Hair et al., 2010). The results of discriminating value testing are shown in the following table: Table 4.37: Estimated results of correlation between variables Estimate S.E. C.R. P Label KT TN ,228 ,030 7,601 *** KT MT ,193 ,033 5,862 *** KT HT ,117 ,028 4,232 *** KT HD ,246 ,029 8,601 *** KT CP ,151 ,030 5,076 *** KT AT ,200 ,025 8,141 *** TN MT ,324 ,041 7,880 *** TN HT ,137 ,032 4,312 *** TN HD ,210 ,031 6,766 *** TN CP ,174 ,034 5,066 *** TN AT ,213 ,028 7,588 *** MT HT ,249 ,039 6,394 *** MT HD ,171 ,034 4,967 *** MT CP ,230 ,041 5,646 *** MT AT ,186 ,031 6,064 *** HT HD ,125 ,029 4,265 *** HT CP ,213 ,036 5,955 *** HT AT ,094 ,025 3,758 *** HD CP ,097 ,030 3,221 ,001 HD AT ,257 ,028 9,199 *** CP AT ,126 ,027 4,661 *** Source: Analysis results by Amos With the above results, we realize that the correlation coefficients are all smaller than 1. The highest correlation coefficient is 0.332 is less than 0.695 (the smallest correlation coefficient measured for a concept in the figure). 4.1). As such, this indicator basically meets the requirements for the value that distinguishes between concepts (Bagozzi and Foxall, 1996). The above result satisfies two requirements that the correlation between the factors must be less than 1, and the correlation between the factors must be smalle