CC BY
38
DOI 10.18551/rjoas.2019-10.28
THE EFFECT OF NUMBER OF TOURISTS AND GENERAL BANK CREDITS ON HOTEL ROOM OFFERS, WOMEN WORKERS AND GROWTH OF TOURISM SECTOR
IN THE PROVINCE OF BALI
Purwanta I Ketut Suparta Danu*, Dewi Made Heny Urmila
Faculty of Economics and Business, University of Udayana, Denpasar, Indonesia *E-mail: indonesiaketut.suparta@bni.co.id
ABSTRACT
This research was conducted with the aim to determine the effect of the number of foreign tourists and commercial bank credit on the offerings of hotel rooms and female workers as well as the growth of the hotel and restaurant sector in Bali Province. The data used are two, namely primary data which is data from informants who are selected as resource persons and secondary data which is the main data in the discussion of research material sourced from several institutions that exist and are related to data used in research such as BI Regional Bali, Banks General, BPS-Regency / city, BPS - Bali Province, BPS Indonesia, Regional Tourism Office (Diparda) Bali Province, and existing libraries such as FEB-UNUD-Denpasar library. Data collection methods used is direct structured interview techniques which are assisted by using a questionnaire and library study. The data analysis method used is descriptive and inferential statistical methods, as well as path diagrams (Path Diagrams). The results of this study concluded that: (1) The number of tourists has a positive and significant effect on the number of room offers and the development of GDP - the tourism sector; (2) The amount of working capital loans and investment in commercial banks has a positive and significant effect on the number of room offers; (3) The number of room offers has a positive and significant effect on the number of female workers in the accommodation business; (4) The number of tourists staying at other hotels and accommodations and the amount of working capital loans and investment in commercial banks has a positive indirect effect on the number of female workers through variable number of accommodation rooms; (5) The number of tourists staying in hotels and other accommodations and the amount of working capital loans and investment in commercial banks, with number of rooms offers as intervening variable has positive indirect effect on the development of GDP - the tourism sector; (6) The number of tourists staying in hotels and other accommodations, through number of accommodation rooms and the number of female workers as intervening variables, has a positive indirect effect on the development of GRDP -the tourism sector.
KEY WORDS
Foreign tourists, bank credit, growth in the PHR sector.
Sustainable tourism can be simply defined as tourism that takes full account of current and future economic, social and environmental impacts, meeting the needs of visitors, industry, environment and local communities (UNWTO, 2016). Even. Sustainable tourism is said to be an important economic resource for many developing countries, especially small island nations (Stefan Gossling, 2000). Likewise, tourism can be an easy means of accessing capital and can be used by tour operators to attract capital from banks and investors (Iwona Niedziotka, 2010). According to Jussi Ranet at al. (2005), tourism is, one of the fastest growing industries globally. Dandang Rizki Ratman (2018) stated that, tourism is one of the largest industries in the world with very rapid development. Mari Elka Pangestu (Kompas.com; Jakarta, 2014) stated that it cannot be denied, that the development of the tourism sector can bring in a lot of jobs, business opportunities and increased regional income.
The Indonesian government in developing tourism is actively promoting tourist destinations in the area. The increase in tourist arrivals to Bali during this period, apparently
decreased when seen through its growth rates. In 2008 the growth of foreign tourists to Bali was 24.97 percent, then dropped to 4.34 percent during the economic crisis in 2012, and again increased to 15.62 percent in 2017 (BPS-Province of Bali. 2018). The fluctuation of foreign tourists visiting Bali is believed to have an impact on various other types of businesses, especially those included in the tourism sector, such as; wholesale and retail trade, hotels and restaurants (PHR). When looking at the supply side of tourism in Bali, many businesses that are developing are accommodation businesses (star and non-star hotels and other lodging), including here the number of rooms provided, restaurants (eating and drinking), and others. In Bali the development of tourism supporting businesses is very rapid, until the end of 2017. For example, the accommodation business, the number of star hotels in Bali in 2008 - 2017 recorded an increase, namely from 150 units to 551 units, as well as for non-star hotels and other accommodations up from 1,656 units to 4,323 units. The increase in accommodation facilities in Bali is seen increasing every year, in line with the increasing number of tourist arrivals.
The rapid development of the number of hotels in Bali was followed by an increase in the number of hotel rooms, both star and non-star hotels and other accommodations. In Bali during the year (2008 - 2017) BPS-Bali Province data (2018), showed; the number of star hotel rooms continues to increase, from 20,240 units to 66,277 units, while for non-star hotels and other accommodations also increased from 19,849 units to 58,617 units. According to Tjokorda Oka Artha Ardana Sukawati (Tribunnews.com; Denpasar, 2017), the number of rooms in Bali until 2017 is not directly proportional to the number of tourist arrivals to Bali (Tribunnews.com; Denpasar, 2017).
If the indicator of the development of the accommodation business is considered, according to Sulastiyono (2008), it can be seen through the room occupancy rate (TPK), both for star hotels and non-star hotels and other accommodations. Sulastiyono also stated that if the ROR of hotels is high, this will indirectly affect the income and profits of the hotel business. In practice, the condition is caused by the income received by the accommodation entrepreneur is sourced from the proceeds of the sale of existing rooms.
In the context of providing hotel facilities in Bali, this type of investment in the tourism sector is largely supported by Financial Institutions - Commercial Banks (LK-BU) through the provision of funding facilities. LK-BU in the Bali area until now continues to experience development, in line with the development of the tourism sector (PHR). LK-BU has a function as an intermediary for public funds, among others as the provision of funds for the accommodation and restaurant business and other businesses. LK-BU in the regions so far has facilitated many of these business activities, in the form of granting investment loans and working capital. In the area of Bali during the period 2008 - 2007 has realized the disbursement of large amounts of credit and continues to increase every year, from Rp. 15,568 trillion to Rp. 73.04 trillion (BI-Regional Bali, 2018).
When further observed the realization of LK-BU credit distribution in the tourism sector by regency / city in Bali, it turns out that between one region and another is very lame. In the Province of Bali, the realization of LK-BU loans in the tourism sector in 2017 was the highest and was concentrated in the world's tourist destinations. It is known that in 2017, the realization of credit in Bali experienced a slight slowdown, as a result of the global economic turmoil that has not yet recovered. For example, investment credit growth is only 1.39 percent, while working capital loans have increased with acceleration reaching 5.16 percent in 2017.
Until 2015, according to the Indonesian Ministry of Tourism, the tourism sector has experienced a rapid increase in the number of tourist destinations, and this provides broad opportunities for investors to make investments. The Ministry of Tourism also revealed that until 2015, the tourism sector was also a core business for the national economy, especially in terms of: export earnings, employment, corporate development and regional infrastructure development (Dadang Rizki Ratman, 2018). In terms of employment, referring to the Economic and Financial Review (BI-Bali Province, 2018) revealed, the trade, hotels and restaurants (PHR) sector was the biggest contributor to employment in Bali. Understanding the above review can be revealed several problems, namely; (i) until the end of 2017, foreign
tourist visits to Bali experienced a significant decline and quite influential on the regional economy of Bali, (ii) the contribution of the tourism sector in the GRDP of the Province of Bali was still positive, (iii) LK-BU credit in the Bali region played quite a role in the sector tourism, but is concentrated in three world tourist destinations, (iv) in the tourism sector the inequality of female workers with men (or gender) still exists.
Associated with women's involvement in economic activity that is still low, revealed by the pre-researchers as follows. Monther Jamhawi, Abdulla Al-Shorman, et al. (2015), revealed that in one of the tourist sites in Jordan (Madaba) it provided limited access to jobs, and opportunities for women to create entrepreneurship in small and medium income generating activities. In Indonesia there is still a wide range when looking at gender in the contribution of the economy. Based on data from the World Economic Forum (Kompas.com. Jakarta, 2018) revealing the Global Gender Gap problem, in 2017. Here it is stated that Indonesia ranks 109 out of 144 countries regarding women's involvement in economic participation and business opportunities (Kompas.com.2018). Likewise, by taking the example of Denpasar City, the problem of gender inequality in the economic sector still exists and appears to occur in various occupations. For example, in the tourism sector the number of women workers absorbed 30.4 percent, as hotel owners reached 16.70 percent, in the restaurant and restaurant businesses by 50.10 percent (Arjani, Ni Luh, et al. 2018).
According to Sri Mulyani Indrawati (Republika.co.id, Surabaya. 2018), siding with women can increase added value to economic performance (Republika.co.id, Surabaya. 2018). According to him, women and men must be in the same position so that the economy can grow inclusively (Sindonews. Jakarta. 2018). When looking at the results of women's development as measured by the Gender development index (IPG), women's development in Bali showed improved conditions in 2017. This is due, IPG in Bali showed an increase, namely from 92.71 in 2015 to 93.70 in 2017 However, if you pay attention to the economic indicators of the IPG in Bali, namely per capita expenditure, women's per capita expenditure is still unequal to men's per capita expenditure. In 2017 in Bali, women's per capita expenditure reached IDR 13,180 thousand per month, compared to men's per capita expenditure which reached IDR 16,369 thousand per month. Gender inequality in the economic sector would have an impact on the economy of Bali as a whole. It has been noted that in recent years Bali's economic growth has decreased from 6.03 percent in 2015 to 5.59 percent in 2017. Based on this background, the research framework in this study can be seen in Figure 1.
K = Realization of Commercial Bank Loans in the PHR Sector
H = Hotel Room Offers p = Female Workersin Hotel Business
X
Number of foreign tourists staying at the hotel £-Ï G = Tourism Sector Growth [PHR)
Figure 1 - Path Hypothesis Model
Based on Figure 1, then the hypothesis can be formulated as follows: 1) Hypothesis 1: The number of foreign tourists staying at hotels and the realization of commercial bank loans in the PHR sector has a positive and significant effect on hotel room offerings.
2) Hypothesis 2: The number of foreign tourists staying at hotels and hotel room offerings has a positive and significant effect on the number of female workers in hotel businesses.
3) Hypothesis 3: The number of tourists staying in hotels, hotel room offerings and the number of female workers in hotel businesses has a positive and significant effect on the growth of the tourism sector (PHR).
4) Hypothesis 4: There is a positive and significant indirect effect of: the number of tourists staying at hotels that are intervening in hotel room offerings and the realization of commercial bank loans intervening in hotel room offerings, on the number of female workers in hotel businesses.
5) Hypothesis 5: There is a positive and significant indirect effect of the number of foreign tourists staying at hotels that are intervened in hotel room offerings, the number of foreign tourists staying at hotels that are intervened by the number of female workers in hotel businesses, the realization of commercial bank loans intervening in hotel room offerings and hotel room supply intervened by the number of female workers in the hotel business, towards the growth of the tourism sector (PHR).
6) Hypothesis 6: There is a positive and significant indirect effect of foreign tourists staying at hotels that are intervening in hotel room offerings and the number of female workers in the hotel business, the realization of commercial bank loans in the PHR sector intervening in hotel room deals and the number of female workers in hotel businesses, towards the growth of the tourism sector (PHR).
METHODS OF RESEARCH
This research was conducted in Bali Province, as a sample of research areas in 3 (three) regions, namely Denpasar City, Badung Regency and Gianyar Regency. The objects raised in this study are working capital and investment credit from LK-BU and the number of tourists staying at hotels, which are then associated with hotel room offers, absorption of female workers in the accommodation business (star hotels, non-star hotels and other accommodations) and its impact on the GRDP growth of the tourism sector (specifically the provision of accommodation and restaurants). Primary data collection methods from informants using structured direct interview techniques, which are assisted by using a list of questions that have been prepared previously. The questions raised in the questionnaire are subject to the research material. Secondary data collection techniques are as follows; library research (library research). According to Danang Sunyoto (2016), literature study is a technique of collecting data through various journals (domestic and foreign), books, archives or year reports from various data source agencies, and those related to the object of research. In addition, this kind of data collection is also carried out by utilizing information and technology (IT) namely; internet media, newspapers and others. Data analysis methods used are descriptive and inferential statistical methods, as well as path diagrams (Path Diagrams). Each of these analysis methods has different objectives according to their purpose.
RESULTS OF STUDY
By using the results of the coefficient analysis using the estimation results of standardized coefficients (Ghozali Imam, 2008), with the attachment sources I, II and III, the following estimation equations are prepared.
The regression coefficient data used to compile the following estimation equation:
H = al W+ a2 K H = 0,800 W + 0,294 K
The estimation results of the simple linear regression equation above each coefficient can be explained as follows.
Coef. a1 = 0,800 and a2 = 0,294. The estimation results are statistically significant, which is proven by the results of the statistical test table t. The results of t analysis of each coefficient are 10.084 and 3.705 which is greater than the value of the t table (a = 5%; n-k =33-2 = 31) which is equal to 2.039
The estimated linear regression coefficient above is interpreted economically, as follows: Coef. a1 = 0,800 which means that every time there is an increase in the number of tourists staying at hotels and other accommodations by 1 percent per year, the number of hotel room and other accommodation offers will increase by 0.8 percent per year.
Coef a2 = 0,294 which means that every time there is an increase in the amount of working capital loans plus commercial bank investment by 1 percent per year, the number of offers for the number of hotel rooms and other accommodations will increase by about 0.2 percent per year.
The results of the analysis of the coefficient R indicate 0.903, this means that the relationship between the dependent and independent variables is statistically very strong. That is, if the number of tourists staying together with the amount of working capital loans plus investment increases, this will increase the number of hotel room and other accommodation offers.
With the basis of the results of the analysis of the correlation coefficient R, the next can be determined the magnitude of the coefficient of determination of R square. In accordance with the results of the analysis on the summary model it is known that the large R square coefficient is 0.815. The estimation results indicate that changes in the dependent variable (number of room offers) are determined by changes in the independent variable (number of tourists staying overnight and the number of working capital and investment loans from commercial banks) of 0.815 times 100 percent = 81.50 percent. This means that other variables besides the two independent variables determine the change in the independent variable by (100 -81.50) percent or by 18.50 percent.
The next analysis is Anova. F value {5%; (n1) (n2)} i.e. = 4,116. The next step, namely the calculated F value compared to the table F value, and the results of the comparison stated that the calculated F (67.463) is greater than the table F value = 4.116. This means that the effect of the independent variable on the dependent variable is statistically significant.
Thus it is said that the number of tourists staying together with the amount of working capital and investment loans has a positive and significant effect on the variable number of accommodation room offers.
The regression coefficient data in Figure 2 is then used to construct the following estimation equation:
P = piw + p2H P = - 0,005 W + 0,955 H
The estimation results of the simple linear regression equation above each coefficient can be explained as follows.
Coef. pi = - 0,005 and p2 = 0,955. Estimation results for coefficients pi statistically declared insignificant and for p2 is significant. The test results were proven through table t test (a = 5%; n-k =33-2 = 31), where t table is 2.039 while t arithmetic of each coefficient namely; for pi is - 0,049 which is smaller than the value of table t and p2 is 8.696 and is greater than the value of t table t. The estimated linear regression coefficient above is interpreted economically, as follows:
Coef. pi = - 0,005 which means that every time there is an increase in the number of tourists staying in hotels and other accommodations by 1 percent per year, the use of female workers in hotels and other accommodations will decrease by 0.0 percent per year or relatively small or even insignificant.
Coef. p2 = 0,962 which means that every time there is an increase in the number of hotel rooms and other accommodation offers by 1 percent per year, the use of female workers in hotels and other accommodations will increase by 0.9 percent per year.
The results of the analysis of the coefficient R is 0.951, this means that the relationship between the dependent and independent variables is statistically very strong. This means that changes that occur in the number of room offers and also the number of tourists staying overnight, can jointly affect the use of labor in the accommodation business.
On the basis of the results of the analysis of the correlation coefficient R, the next can be determined the magnitude of the coefficient of determination R square (or R2). The analysis shows that R square is 0.905 (see the summary model), and this result indicates that the change in the number of female workers is determined by the change in the number of accommodation room offers and the number of tourists staying overnight at 90.50 percent (i.e.: 0.905 times 100 percent). This means that other variables besides the two independent variables determine the change in the dependent variable by 9.50 (ie: 100 - 90.50 percent).
The next analysis is Anova. The results of the analysis based on Anova calculations show the calculated F value = 143.373 and significant at a significance level of 5 percent. The value of F table with df is calculated as follows: i) df (n1) = k -1 = 2 -1; ii) df (n2) = n - k = 33 - 2 = 31.
With the above conditions, the value of F table or F {(5%); (n1) (n2)} = 4.16. By comparing the calculated F value with the F table value, it can be stated that the calculated F of 141.237 is greater than table F value = 4,116. The results of this analysis mean that the effect of the independent variable on the dependent variable is significant. Thus the results of the analysis of the data above show that the number of accommodation room offers and the number of tourists staying together can significantly influence the number of women employed in the accommodation business.
The regression coefficient data in Figure 2 is then used to compile the following estimation equation.
G = y1 W + Y2 H + Y3 P G = - 0,192 W + 1,981 H - 1,351 P
With the basis of the results of the multiple linear regression coefficient analysis above, the relationship between variables statistically and economically can be explained as follows. Coef. y1 = - 0,192, y2 = 1,981 and y3 = - 1,351. Estimated results y1 not statistically significant, while estimation results y2 and y3 are statistically significant. The significance test results were proven through the results of the statistical test table t. The analysis results of t calculate each coefficient as follows; for y1 is -0.728 which is smaller than the value of table t (with a = 5%; nk = 33-2 = 31) which is equal to 2,039, for y2 is 4,007 which is greater than the value of table t (with a = 5%; nk = 33-2 = 31) that is 2,039, and for y3 is - 3,087 which is smaller than the value of table t (with a = 5%; nk = 33-2 = 31) which is equal to 2,039.
The estimated linear regression coefficient above is interpreted economically, as follows. The coefficient y1 = - 0.192 which means that an increase in the number of tourists staying in hotels and other accommodations by 1 percent per year, the growth of the tourism sector (provision of accommodation, food and drink) will decrease insignificantly by 0.1 percent per year.
The coefficient y2 = 1.981 which means that every time there is an increase in the number of hotel and other accommodation offers by 1 percent per year, the growth of the tourism sector (provision of accommodation, food and drink) will increase significantly by 1.981 percent per year.
The coefficient y3 = - 1.351 which means that every time there is a decrease in the use of female labor in hotels and other accommodation by 1 percent per year, the growth of the tourism sector (provision of accommodation, food and drink) will decrease significantly by 1.3 percent per year.
Furthermore, the relationship or correlation between the dependent variable (GRDP growth of the business sector in the tourism sector and the independent variable (number of accommodation rooms, number of tourists staying overnight and the use of female workers), is totally known through the results of the analysis of the coefficient R in the model summary.
The results of the analysis of the coefficient R = 0.683, this means that the relationship between the dependent and independent variables is statistically quite strong. That is, if there is a change in the number of accommodation room offers, the number of tourists staying and the use of female workers in the accommodation business, then this condition has a positive and strong enough relationship with changes in the GRDP of the tourism sector. Anova analysis results can be seen through the large value of F, namely F arithmetic = 8.454 and the results of this analysis are significant.
With some provisions that underlie the determination of the table F value above, the F table value with a significance level of (5%) and degrees of freedom (degrees of freedom) or df: (n1) (n2) equal to 3.32. Furthermore, the F table value is compared with the calculated F value, the results show that the calculated F value = 8.45 is greater than the F table value = 3.32. The results of the comparison of the F value means, the effect of the independent variable on the dependent variable is significant.
By using the path diagram in Figure 2, a simple linear regression estimation equation (4) model can then be prepared to test the fourth hypothesis proposed earlier. The form of regression equation in question is:
P = (o1p2)WH_^ p+ (o2p2)KH_ p
Where: P = Total use of female workers in hotels and other accommodation; WH p = Number of foreign tourists staying at hotels and other accommodations (W), intervening in the number of hotel room and other accommodation (H) offers; KH_^p = Total loans (ie working capital plus investment) Commercial Banks (K), intervening in the number of hotel room and other accommodation (H) offers; oip2 = Intervening variable coefficient WH_^p; o2p2 = Intervening variable coefficient KH p.
Previously, the results of the estimation of multiple linear regression coefficients have been obtained from the following equations:
Equation (1): H = ai W + o2 K or H = 0,800W + 0,294 K Equation (2): P = piW + p2H or P = - 0,005 W + 0,955 H
Equation (3): G = y1W + y2H + y3P or G = - 0,192 W + 1,981 H - 1,351 P
Where the magnitude of the multiple linear regression coefficient is known, respectively:
01 = 0,800 p2 = 0,955 with the result that (o1p1) = (0,800)(0,955) = 0,764
02 = 0,294 p2 = 0,955 with the result that (o2p2) = (0,294)(0,955) = 0,281
By substituting the results of the calculations above, it can then be arranged multiple linear regression calculations from indirect relationships between variables. Namely, the amount: (oip2) = 0,764 and (o2p2) = 0,281, then the above calculation is stated:
P = (olpl)WH^ p+ (o2p2)KH_ p
P = 0,764 WH p+ 0,281 KH p.
The estimation results of the multiple linear regression equation are interpreted as follows: that there is a positive indirect effect of the number of tourists staying overnight and the number of working capital loans and investment banks on the use of female workers in the accommodation business, through intervening the number of accommodation rooms.
Indirect Relationship Analysis Results of the Multiple Linear Regression Model:
G = (O1Y2)WH^g + (plY3)WP^ G + (O2Y2)KH^ G + (P2Y3)HP_^ G
Where: G = Growth in the tourism sector (i.e. hotel and food and beverage supply); WH_^g = Growth in the tourism sector (i.e. hotel and food and beverage supply); WP .g = The number of foreign tourists staying at hotels and other accommodation (W), in
the intervening variable the number of female workers in the hotel and other accommodation businesses (P); KH^g = Total credit (ie working capital plus investment) Commercial Bank (K), in the intervening variable number of hotel room and other accommodation (H) offers; HP g = Number of hotel room and other accommodation (H) offers, in the intervening variable the number of female workers in the hotel and other accommodation business (P); alY2 = Intervening variable coefficient WH_^G; P1y3 = Intervening variable coefficient WP^G; a2Y2 = Intervening variable coefficient KH_^G; P2y3 = Intervening variable coefficient HP_^G.
Previously, the results of the estimation of multiple linear regression coefficients have been obtained from the following equations:
Equation (1): H = al W + a2 K or H = 0,800W + 0,294 K Equation (2): P = piW + p2H or P = - 0,005 W + 0,955 H Equation (3): G = y1W + y2H + y3P or G = -0,192 W + 1,981 H - 1,351 P
By using the results of the estimation of the multiple linear regression coefficients from equations (1), (2), 3) above, the multiple linear regression coefficients (5) can be calculated as follows.
ai = 0,800 and y2 = 1,981 ; with result that (alY2) = (0,800)(1,981) = 1,585 pi= - 0,005 and y3 = - 1,351 ; with result that (piy3) = (- 0,005)(- 1,351) = 0,007
a2 = 0,294 and y2 = 1,981 ; with result that (a2Y2) = (0,294)(1,981) = 0,582 p2 = 0,955 and y3 = - 1,351; with result that (P2y3) = (0,955)(- 1,351) = - 1,290
Furthermore, the results of the calculation of the regression coefficient above are substituted into equation (5), so that the estimated equation results are obtained as follows.
G = (alY2)WH^G+ (P1Y3)WP^g + (a2Y2)KH^ + (P2Y3)HP^g G = 1,585WH^g + 0,007 WP^g + 0,582 KH^g - 1,290 HP^g
The results of estimated multiple linear regression (5) can be interpreted as follows:
There is a positive indirect effect of the number of tourists staying in hotels and other accommodations, which intervened the number of hotel room offers and other accommodations on the growth of the tourism sector (ie the provision of accommodation, tomb-drinking).
There is a positive indirect effect of the number of tourists staying overnight intervening in the number of women using the labor force, on the growth of the tourism sector (ie the trading business and the provision of accommodation, grave-drinking).
There is a positive indirect effect of credit (ie working capital plus investment) of commercial banks intervening in the number of hotel room offerings and other accommodations, on the growth of the tourism sector (ie the provision of accommodation, tomb-drinking).
There is an indirect negative effect on the number of hotel rooms and other accommodation offers intervened by the number of female workers employed in the hotel business and other accommodations, towards the growth of the tourism sector (ie the provision of accommodation, tomb-drinking).
G = (a1p2Y3)WHP_^ G + (a2p2Y3)KHP_^G
Where: G = Growth in the tourism sector (provision of accommodation and food and drink); WHP .g = The number of foreign tourists staying at hotels and other accommodation (W), in the intervening variable the number of hotel room and other accommodation offers along with the total use of female workers in hotel and other accommodation businesses (P); KHP_G = The number of loans (ie working capital plus investment) Commercial Banks (K), in the intervening variable the number of hotel room offers and other accommodation along with the total use of female workers in hotel and other accommodation businesses (P);
o1P2y3 = Intervening variable coefficient WHP g; o2P2y3 = Intervening variable coefficient KHP^g.
Previously, the results of the estimation of multiple linear regression coefficients have been obtained from several equations namely equations (1), (2), (3) n as follows.
(1) Equation (1): H = o1 W + o2 K or H = 0,800 W + 0,294 K
(2) Equation (2): P = p1W + p2H or P = - 0,005 W + 0,955 H
(3) Equation (3): G = y1W + y2H + y3P or G = -0,192 W + 1,981 H - 1,351 P
By using the results of the estimation of the multiple linear regression coefficient from equations (1), (2), 3) above, it can be calculated the magnitude of the multiple linear regression coefficient (6), namely:
• (o1P2y3); where each size is: o1 = 0,800, P2 = 0,955, y3 = - 1,351. With the result that (o1P2y3) = (0,800)(0,955)(- 1,351) = - 1,032;
• (o2P2y3); where each size is: o2 = 0,294, P2 = 0,955, y3 = - 1,351. With the result that (o2P2y3) = (0,294)(0,955)( - 1,351) = - 0.379.
Using the results of the multiple linear regression coefficient analysis above, the following estimation equation (6) can be arranged:
G = (o1p2Y3) WHP_^G+ (o2p2Y3) KHP_^G G = - 1,032 WHP - 0,379 KHP_^G
The coefficients of the above regression equation have the following meanings:
There is a negative indirect effect of the number of foreign tourists in hotels and other accommodations that are intervened in the number of accommodation rooms and the amount of use of female workers, on the development of the tourism sector (ie; provision of accommodation and food and drink).
There is a negative indirect effect of the realization of the number of loans (working capital + investment) of Commercial Banks intervening in the number of accommodation rooms and the number of female workers used in the hotel and other accommodation businesses, towards the development of the tourism sector (namely, the provision of accommodation and eating and drinking).
CONCLUSION AND SUGGESTIONS
Based on these results, it can be concluded several things, namely the direct effect of the number of tourists staying at hotels and other accommodations and the amount of lending (ie working capital plus investment) of commercial banks is significant to the number of hotel room and other accommodation offers. The direct effect of the number of tourists staying at hotels and other accommodations is not significant, on the use of female workers. Meanwhile, the direct effect of the number of hotel room offerings and other accommodation is significant, on the use of female workers in hotel and other accommodation businesses. The direct effect of the number of hotel room offerings and other accommodations and the use of female workers is significant on the growth of the tourism sector (ie the provision of accommodation and eating - drinking). Meanwhile, the direct effect of the number of foreign tourists staying at hotels and other accommodations is not significant in increasing the growth of the tourism sector (ie the provision of accommodation and food and drink). This last possibility was caused by the length of stay of tourists who on average declined during the last five years. There is a positive indirect effect of the variable number of tourists staying and the number of working capital loans and investment of commercial banks on the use of female workers in the accommodation business, through intervening the number of accommodation rooms. There is a negative indirect effect of the number of tourists staying at hotels and other accommodations as well as credit (ie working capital plus investment) Commercial Banks, which intervened the number of hotel room offerings and other accommodations on the growth of the tourism sector (ie the provision of accommodation,
tomb-drinking). And, there is a positive indirect effect of the number of tourists staying in hotels and other accommodations and a negative indirect effect of the number of hotel room offerings and other accommodations intervening in the amount of use of female labor, on the growth of the tourism sector (ie the trade and supply accommodation, tomb-drinking). There is a negative indirect effect of the number of foreign tourists staying at hotels and other accommodations as well as the amount of credit (ie working capital plus investment) of commercial banks intervening in the number of accommodation rooms and the use of female workers, on the development of the tourism sector (ie, providing accommodation and eating and drinking).
There are a number of suggestions made in this section, namely the accommodation business is very concerned with tourist visits and the number of tourists staying at hotels and other accommodations continues to increase. To that end, in the future in an effort to increase tourists need to support quality promotion, namely by the use of information and communication technology effectively and proactively. Utilization of information and communication technology should receive priority in the development of accommodation businesses, such as the provision of free Wi-Fi internet and other things that are needed by tourists. Such efforts must be balanced with an increase in the quality of human resources, so that the use of workers, especially female workers, can be increased. Efforts to increase tourist visits by utilizing information and communication technology are the right way to increase the income of the accommodation business and the tourism sector in general, without neglecting sustainable human development.
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