Every year, the number of students graduating is very large while the labor demand of the market is always fluctuating, especially when the economic situation is not very positive. Not only that, in such a difficult situation, employers also set higher recruitment requirements to find good employees, one person can do many jobs, limiting the recruitment of many employees to reduce the pressure on salaries. Is this the cause of the "oversupply" of "virtual" human resources, leading to the current "crisis" of surplus? Therefore, it is necessary to have solutions and government policies to partly solve the common situation of students today.
5.5.4. Measuring the influence of local attractiveness factor on students' career orientation
The analysis results show that the local attractiveness factor has a relatively high influence on students' career orientation. Specifically, the average values of the factors range from 3.57 to 4.04. In which, the factor with the greatest influence and the most decisive factor for students is the income factor V4 (Mean = 4.04). The above results also partly reflect the desire of students to have a job with a high income to have a more comfortable life. This is completely consistent with the analysis results in the statistical descriptive table on the reasons for choosing a workplace.
4.1
4
3.9
3.8
3.7
3.6
3.5
3.4
3.3
DP has many jobs Convenient in work
Higher income
4.04
3.73
3.57
Figure 5.9: Chart of the influence level of local attractiveness factor
The least influential factor in the group of local attractiveness factors is the locality has many job opportunities V1 (Mean = 3.57). When there are too many things in life, especially for new graduates, the problem of finding a job with a high income is the top priority. When you have met your minimum needs, then you can think about higher needs. The remaining factor with Mean value is: Working locally has many advantages in work V2 (Mean = 3.73).
5.5.5. Measuring the impact of company characteristics on
student career orientation
The results of measuring the influence of company characteristics on students' career orientation show that the factor Company size V19 (Mean = 3.67) is identified by students as having the most influence on them. Next is Company culture V21 (Mean = 3.65); and finally Company location V20 has the smallest mean value (Mean = 3.54).
It is necessary to have knowledge about the company to make the right choices. In each profession, we need to know at least the job requirements, career prospects, salary, etc. In addition, we must also learn about the career challenges, difficulties and advantages in the career that we may encounter during the working process.
As a student myself, I am preparing to step out of the micro-context of school to join a larger environment... I also realize that I have set certain career goals for myself. What will my company be like? Where will I work? What is my income? After how many years will I be promoted? etc. A series of questions will be the criteria for us to determine the right goals to strive for in the future.
3.67
3.65
3.54
COMPANY SCALE
COMPANY LOCATION
COMPANY CULTURE
Figure 5.10: Chart of the influence level of company characteristics
5.5.6. Measuring the influence of family factors on students' career orientation
The measurement results show that the family condition factor has a relatively low level of influence on students' career orientation. The average influence level is between 3.20 and 3.62. In which, the most influential factor is Family Economy V24 (Mean = 3.62). At a lower level of influence is the Family Origin factor V25 (Mean = 3.32) and the least influential is the Family Tradition factor V26 (Mean = 3.20). Each student has a different family situation, but the common point is that they are all strongly influenced by the family circumstance factor. They will tend to choose a place with a higher income to work.
3.7
3.6
3.5
3.4
3.3
3.2
3.1
3
2.9
Family economics
Family background
Family Tradition
3.62
3.32
3.2
Figure 5.11: Chart of the level of influence of family conditions
5.5.7. Measuring the impact of preferential policy factors on students' career orientation
The analysis results show that the preferential policy factor has a great influence on students' career orientation. The factor that has the greatest influence and is the most decisive factor for students is the promotion opportunity factor V23 (Mean
= 4.40). At the same time, the salary and benefits factor V22 also has a great influence on students (Mean = 4.28). The above results reflect the desire of students to have a job with high income and have the opportunity to be promoted at work. Therefore, these are factors that businesses need to pay attention to if they want to attract many talents to the company.
4.42
4.4
4.38
4.36
4.34
4.32
4.3
4.28
4.26
4.24
4.22
Salary and benefits policy
Promotion opportunities
4.4
4.28
Figure 5.12: Chart of the impact level of preferential policies
5.6. Analysis of factors affecting student employment
5.6.1. Testing the relationship between hometown and workplace (Table 6.1, Appendix 6, page 115)
To assess whether students differ between their hometown and workplace, we conduct a Chi-square test to find out this relationship.
Hypothesis Ho: Hometown has no relationship with workplace.
Chi-Square Test table, Asymp Sig. value = 0.008 < 0.05 shows that the test result is significant with 95% confidence level, which means we reject the hypothesis. Thereby, we can conclude that there is a relationship between hometown and income.
Student's hometown and workplace. It can be understood that hometown is the place where students were born and grew up, so students have a lot of affection for their hometown and want to contribute their efforts to build their hometown.
5.6.2. Test the relationship between the growing area and the workplace (Table 6.2, Appendix 6, page 116)
Being born in urban areas will tend to choose to work in big cities because there are many conditions for self-development. Whether or not there is a relationship above. We will conduct a Chi-square test.
Hypothesis Ho: There is no relationship between place of birth and place of work.
Asymp Sig. value = 0.371 > 0.05. With a significance level of 95%, this test is not statistically significant. We accept the null hypothesis, meaning that there is no relationship between the region of birth and the place of work. This shows that regardless of where students are born, urban or rural, the decision to find a place to work is the same among students.
5.6.3. Testing the relationship between gender and workplace (Table 6.3, Appendix 6, page 116)
Hypothesis Ho: There is no relationship between gender and workplace.
Through the test results, we have the value of Asymp Sig. = 0.015 < 0.05, so we reject the Ho hypothesis with a confidence level of 95%. Therefore, men and women tend to choose different workplaces.
This relationship can be explained as follows: Male students often choose to work in their hometown more than female students. And most female students choose to work in big cities such as Can Tho City and Ho Chi Minh City.
5.6.4. Testing the relationship between family income and type of company employed (Table 6.4, Appendix 6, page 117)
Hypothesis Ho: There is no relationship between household income and type of company employed.
The test results show that we should accept the Ho hypothesis with 95% confidence because the Asymp Sig. value = 0.878 > 0.05. That means that there is no relationship between household income and the type of company that students want to work for. There is no difference in choosing the type of company even if one student has better economic conditions than another.
5.6.5. Testing the relationship between university type and company type (Table 6.5, Appendix 6, page 117)
Hypothesis Ho: There is no relationship between type of university and type of company desired to work for.
The Chi-square test results indicate that we can accept the hypothesis with 95% confidence because the Asymp Sig. value = 0.334 > 0.05. This means that there is no real relationship between the type of university and the type of company that students want to work for. A good working environment and reasonable employee benefits will attract many students to work.
5.6.6. Testing the relationship between gender and desired job (Table 6.6, Appendix 6, page 118)
Hypothesis Ho: There is no difference in job type choice when students have different genders.
Asymp Sig. value = 0.065 > 0.05. Accept the null hypothesis with 95% confidence. From there, we can conclude that there is no difference between men and women in choosing the type of job they want. Their ability to choose a job is the same.
5.7. Measuring the factors necessary for students' job search process
5.7.1. Factor analysis
The theoretical part of exploratory factor analysis has been presented in the above section, so in this section the author only focuses on the analysis results.
The result of Bartlett's test ( Table 7.1, Appendix 7, page 118 ) has a Sig value.
= 0.000 < 0.05 and KMO coefficient (Kaiser Meyer Olkin) = 0.805 > 0.5 so the hypothesis is valid.
In this analysis, “Correlation between observed variables is 0” will be safely rejected with 95% confidence. This means that the observed variables in the population are related to each other and EFA analysis is appropriate.
The Total Variance Explanation results ( Table 7.2, Appendix 7, page 119 ) show that there are 3 factor groups greater than 1 , so there will be 3 factor groups drawn from 13 factors. The Cumulative % column shows that these 3 factor groups explain 57.9% of the variation in the data.
After rotating the factors, we remove the variable Qualities (X2) because the factor coefficient (Factor Loading) is less than 0.5. Based on the Factor Rotation Matrix table ( Table 7.4 , Appendix 7 , page 120 ), we divide the factors into the following 3 groups of factors :
O Factor 1: Knowledge and skills (S1)
STT
Variable name | Element | |
1 | X3 | Socio-economic knowledge |
2 | X5 | Teamwork skills |
3 | X6 | Presentation skills |
4 | X7 | Communication and behavioral skills |
5 | X8 | Learning skills – self-study |
6 | X9 | Foreign language and computer skills |
7 | X10 | Planning and management skills |
8 | X13 | Field trip |
Maybe you are interested!
-
Identify Rating Levels and Rating Scales
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of the islanders. Therefore, this indicator will be divided into two sub-indicators:
a1. Natural tourism attractiveness a2. Cultural tourism attractiveness
b. Tourist capacity
The two island communes in Quan Lan have different capacities to receive tourists. Minh Chau Commune is home to many standard hotels and resorts, attracting high-income domestic and international tourists. Meanwhile, Quan Lan Commune has many motels mainly built and operated by local people, so the scale and quality are not high, and will be suitable for ordinary tourists such as students.
c. Time of exploitation of Quan Lan Island Commune:
Quan Lan tourism is seasonal due to weather and climate conditions and festivals only take place on certain days of the year, specifically in spring. In Quan Lan commune, the period from April to June and from September to November is considered the best time to visit Quan Lan because the cultural tourism activities are mainly associated with festivals taking place during this time.
Minh Chau island commune:
Tourism exploitation time is all year round, because this is a place with a number of tourist attractions with diverse ecosystems such as Bai Tu Long National Park Research Center, Tram forest, Turtle Laying Beach, so besides coming to the beach for tourism and vacation in the summer, Minh Chau will attract research groups to come for tourism combined with research at other times of the year.
d. Sustainability
The sustainability of ecotourism sites in Quan Lan and Minh Chau communes depends on the sensitivity of the ecosystems to climate changes.
landscape. In general, these tourist destinations have a fairly high level of sustainability, because they are natural ecosystems, planned and protected. However, if a large number of tourists gather at certain times, it can exceed the carrying capacity and affect the sustainability of the environment (polluted beaches, damaged trees, animals moving away from their habitats, etc.), then the sustainability of the above ecosystems (natural ecosystems, human ecosystems) will also be affected and become less sustainable.
e. Location and accessibility
Both island communes have ports to take tourists to visit from Van Don wharf:
- Quan Lan – Van Don traffic route:
Phuc Thinh – Viet Anh high-speed boat and Quang Minh high-speed boat, depart at 8am and 2pm from Van Don to Quan Lan, and at 7am and 1pm from Quan Lan to Van Don. There are also wooden boats departing at 7am and 1pm.
- Van Don - Minh Chau traffic route:
Chung Huong high-speed train, Minh Chau train, morning 7:30 and afternoon 13:30 from Van Don to Minh Chau, morning 6:30 and afternoon 13:00 from Minh Chau to Van Don.
f. Infrastructure
Despite receiving investment attention, the issue of infrastructure and technical facilities for tourism on Quan Lan Island is still an issue that needs to be resolved because it has a direct impact on the implementation of ecotourism activities. The minimum conditions for serving tourists such as accommodation, electricity, water, communication, especially medical services, and security work need to be given top priority. Ecotourism spots in Minh Chau commune are assessed to have better infrastructure and technical facilities for tourism because there are quite complete and synchronous conditions for serving tourists, meeting many needs of domestic and foreign tourists.
3.2.1.4. Determine assessment levels and assessment scales
Corresponding to the levels of each criterion, the index is the score of those levels in the order of 4, 3, 2, 1 decreasing according to the standard of each level: very attractive (4), attractive (3), average (2), less attractive (1).
3.2.1.5. Determining the coefficients of the criteria
For the assessment of DLST in the two communes of Quan Lan and Minh Chau islands, the students added evaluation coefficients to show the importance of the criteria and indicators as follows:
Coefficient 3 with criteria: Attractiveness, Exploitation time. These are the 2 most important criteria for attracting tourists to tourism in general and eco-tourism in particular, so they have the highest coefficient.
Coefficient 2 with criteria: Capacity, Infrastructure, Location and accessibility . Because the assessment area is an island commune of Van Don district, the above criteria are selected by the author with appropriate coefficients at the average level.
Coefficient 1 with criteria: Sustainability. Quan Lan has natural and human-made ecotourism sites, with high biodiversity and little impact from local human factors. Most of the ecotourism sites are still wild, so they are highly sustainable.
3.2.1.6. Results of DLST assessment on Quan Lan island
a. Assessment of the potential for natural tourism development
For Minh Chau commune:
+ Natural tourism attractiveness is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined as average (2 points) and the coefficient is quite important (coefficient 2), then the score of Capacity criterion is 2 x 2 = 4.
+ Exploitation time is long (4 points), the most important coefficient (coefficient 3) so the score of the Exploitation time criterion is 4 x 3 = 12.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is assessed as good (3 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 3 x 2 = 6 points.
The total score for evaluating DLST in Minh Chau commune according to 6 evaluation criteria is determined as: 12 + 4 + 12 + 4 + 4 + 6 = 42 points
Similar assessment for Quan Lan commune, we have the following table:
Table 3.3: Assessment of the potential for natural ecotourism development in Quan Lan and Minh Chau communes
Attractiveness of self-tourismof course
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
CommuneMinh Chau
12
12
4
8
12
12
4
4
4
8
6
8
42/52
Quan CommuneLan
6
12
6
8
9
12
4
4
4
8
4
8
33/52
b. Assessment of the potential for humanistic tourism development
For Quan Lan commune:
+ The attractiveness of human tourism is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined to be large (3 points) and the coefficient is quite important (coefficient 2), then the score of the Capacity criterion is 3 x 2 = 6.
+ Mining time is average (3 points), the most important coefficient (coefficient 3) so the score of the Mining time criterion is 3 x 3 = 9.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points.
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is rated as average (2 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 2 x 2 = 4 points.
The total score for evaluating DLST in Quan Lan commune according to 6 evaluation criteria is determined as: 12 + 6 + 6 + 4 + 4 + 4 = 36 points.
Similar assessment with Minh Chau commune we have the following table:
Table 3.4: Assessment of the potential for developing humanistic eco-tourism in Quan Lan and Minh Chau communes
Attractiveness of human tourismliterature
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Quan CommuneLan
12
12
6
8
9
12
4
4
4
8
4
8
39/52
Minh CommuneChau
6
12
4
8
12
12
4
4
4
8
6
8
36/52
Basically, both Minh Chau and Quan Lan localities have quite favorable conditions for developing ecotourism. However, Quan Lan commune has more advantages to develop ecotourism in a humanistic direction, because this is an area with many famous historical relics such as Quan Lan Communal House, Quan Lan Pagoda, Temple worshiping the hero Tran Khanh Du, ... along with local festivals held annually such as the wind praying ceremony (March 15), Quan Lan festival (June 10-19); due to its location near the port and long exploitation time, the beaches in Quan Lan commune (especially Quan Lan beach) are no longer hygienic and clean to ensure the needs of tourists coming to relax and swim; this is also an area with many beautiful landscapes such as Got Beo wind pass, Ong Phong head, Voi Voi cave, but the ability to access these places is still very limited (dirt hill road, lots of gravel and rocks), especially during rainy and windy times; In addition, other natural resources such as mangrove forests and sea worms have not been really exploited for tourism purposes and ecotourism development. On the contrary, Minh Chau commune has more advantages in developing ecotourism in the direction of natural tourism, this is an area with diverse ecosystems such as at Rua De Beach, Bai Tu Long National Park Conservation Center...; Minh Chau beach is highly appreciated for its natural beauty and cleanliness, ranked in the top ten most beautiful beaches in Vietnam; Minh Chau commune is also home to Tram forest with a large area and a purity of up to 90%, suitable for building bridges through the forest (a very effective type of natural ecotourism currently applied by many countries) for tourists to sightsee, as well as for the purpose of studying and researching.
Figure 3.1: Thenmala Forest Bridge (India) Source: https://www.thenmalaecotourism.com/(August 21, 2019)
3.2.2. Using SWOT matrix to evaluate Quan Lan island tourism
General assessment of current tourism activities of Quan Lan island is shown through the following SWOT matrix:
Table 3.5: SWOT matrix evaluating tourism activities on Quan Lan island
Internal agent
Strengths- There is a lot of potential for tourism development, especially natural ecotourism and humanistic ecotourism.- The unskilled labor force is relatively abundant.- resource environmentunpolluted, still
Weaknesses- Poorly developed infrastructure, especially traffic routes to tourist destinations on the island.- The team of professional staff is still weak.- Tourism products in general
quite wild, originalintact
general and DLST in particularalone is monotonous.
External agents
Opportunity- Tourism is a key industry in the socio-economic development strategy of the province and Van Don economic zone.- Quan Lan was selected as a pilot area for eco-tourism development within the framework of the green growth project between Quang Ninh province and the Japanese organization JICA.- The flow of tourists and especially ecotourism in the world tends toincreasing
Challenge- Weather and climate change abnormally.- Competition in tourism products is increasingly fierce, especially with other localities in the province such as Ha Long, Mong Cai...- Awareness of tourists, especially domestic tourists, about ecotourism and nature conservation is not high.
Through summary analysis using SWOT matrix we see that:
To exploit strengths and take advantage of opportunities, it is necessary to:
- Diversify products and service types (build more tourism routes aimed at specific needs of tourists: experiential tourism immersed in nature, spiritual cultural tourism...)
- Effective exploitation of resources and differentiated products (natural resources and human resources)
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Factors Measuring International Tourists' Evaluation of Cultural Tourism Resources' Attractiveness Are Differently Influenced by Distance -
Human Ecological Pyramid for Studying the Impact of Local Buffer Zone Communities on Forest Resources -
Reliability Testing of Variables Measuring Input Factors and Tourism Attractiveness -
Environmental impact assessment of Thanh Minh industrial cluster infrastructure investment project, Phu Tho town, Phu Tho Province - 2
0 Factor 2: Experience (S2)
STT
Variable name | Element | |
1 | X11 | Experience from part-time work |
2 | X12 | Experience from group exercises – topics |
ΠFactor 3: Capacity (S3)
STT
Variable name | Element | |
1 | X1 | Education |
2 | X4 | Understanding the company |
5.7.2. Scale evaluation
Table 5.12: Cronbach Alpha analysis results (Q18)
TT
Observation variable | Average of the scale if variable | Variance of ladder measure if variable type | Correlation with total variable | Coefficient Cronbach Alpha if variable type | Coefficient Cronbach's Alpha | |
Factor 1: Knowledge and skills | 0.846 N = 8 | |||||
1 | Socio-economic knowledge | 28.92 | 12.62 | .523 | .835 | |
2 | Teamwork skills | 28.98 | 11.90 | .591 | .827 | |
3 | Presentation skills | 29.01 | 11.73 | .638 | .821 | |
4 | Communication skills | 28.64 | 11,12 | .692 | .817 | |
5 | Learning skills – self-study | 29.04 | 12.10 | .529 | .835 | |
6 | Foreign language and computer skills | 28.97 | 11.72 | .639 | .821 | |
7 | Planning and management skills | 28.96 | 11.68 | .596 | .826 | |