INTRODUCTION
Tourism development contributes to changes in value systems; individual behavior, family relationships, collective lifestyles, community organization as well as economic set-up of the host community. Accordingly, many tourism researchers have studied the attitudes of the host communities towards such impacts. These studies have documented attitudes, opinions and indeed perceptions that residents have of the impacts of tourism development (Pizam & Milman, 1984; Ap, 1990; Snaith & Haley, 1994; Swain, 1995; Wall, 1996; Zalatan, 1998). In all the cases, demographic characteristics of the study samples are solicited and analyzed in order to reveal their relationships with the overall perceptions of the impacts.
A study by King and his co-researchers (1993), for instance, found that tourism-employed residents were more supportive of tourism development than those who were not employed in tourism. Similarly, Snaith & Haley (1994) and Var et al. (1985) concluded that residents who were economically dependent upon tourists were generally more satisfied with tourism development than those who were not. However, Milman & Pizam (1988) found that increased tourist concentration leads to negative attitudes towards tourism development. Various studies have also shown that the hosts perceive tourism's positive impact to be related to its potential as an employment generator (Var et a1.,1985; Milman & Pizam, 1988; Long et al., 1990; Perdue et al., 1990; Snaith & Haley, 1994; O'Connor, 1995; Swain, 1995; Wall, 1996; Haralambopoulus & Pizam, 1996; Swain, 1995; Wall, 1996; Zalatan, 1998; Kibicho, 2002; 2004). However, these researchers have not analyzed gender differences (or lack of it) in
Address correspondence to Wanjohi Kibicho, MOI University, Department of Tourism Management, PO Box 3692, Eldoret, Kenya. Tel: (0254) 0321-63133, Fax: (0254) 0321- 63133/63257. E-mail: kibichowanjohi@yahoo.co.uk
attitudes towards tourism development and its related impacts (Swain, 1995). Consequently, tourism industry has, in limited ways, responded to the suggestion (at least in qualitative studies) that men and women might have different perceptions regarding to tourism impacts (Robinson, 1994; Bruner, 1995 ; O'Connor, 1995 ; Swain, 1995; Scott, 1997; Sinclair, 1997; Bras & Dahles, 1998; Zalatan, 1998; Martin et al., 2002). Nevertheless, a stream of studies is developing based on gender differences in perceptions of such impacts (Robinson, 1994; Bruner, 1995 ; Harvey et al., 1995; O'Connor, 1995 ; Swain, 1995; Wall, 1996; Scott, 1997; Sinclair, 1997; Bras & Dahles, 1998; Zalatan, 1998; Martin et al., 2002). A study by Harvey et al., (1995) revealed non-significant gender differences in perceptions. However, Martin and her co-researchers found significant differences in their study in Hilton Head, South Carolina (2002). The current study, therefore, intends to explore further such differences in perceptions for a community that is economically dependent on tourism in a developing country.
Study Site
The research for this study was conducted in Malindi (Figure 1) between September and November 2003. The town of Malindi serves as the headquarters of Malindi district. It is located between latitudes 3° 15' and 4° 00' South and longitudes 40° 50' and 41 ° 43' East, which is 125 kilometers north of Mombasa city (Figure 1). The importance of tourism to the Malindi economy can not be gainsaid. It is the largest component of the region's economy contributing over 85% of the local economy's activities (Kenya, 1998; Kibicho, 2002). Development of modern tourism dates back to 1931 when an 18 bed hotel was built as the base for deep sea fishing off Malindi Bay (Kibicho, 2002; 2004). This is where the world famous reporter on sport fishing, Ernest Hemmingway, stayed for several weeks in 1934. The hotel, classified as "3 star", is still in existence by the name The Blue Marlin.
In the same year, 1934, tourism industry was formerly launched by the then District Commissioner, Sir Leo Lawfords. He later built a makuti (coconut palm fronds) hotel in 1935 under the name Lawfords Hotel (Kibicho, 2002; 2004). The hotel still stands as a "4 star" establishment. From the local people's viewpoint, Sir Lawfords, is considered a visionary planner for the type of planning he used for Malindi's development. It was based on a semimaster plan that included the establishment of three fundamental commitments: strict control of land use, development of recreational areas, and preservation of two square kilometers of marine reserve (currently known as Malindi Marine National Park). His goal was to maintain the area's natural beauty by developing tourism in such a way that the destination's natural attributes would be enhanced rather than being destroyed. However, with an ever increasing tourism development (mass tourism), it was difficult to maintain the high quality set by Lawfords. By 1968 the resort had almost tripled its bed capacity to about 1,000. Today, Malindi has become a mass tourism destination, largely due to its attractions of sun, sand and sea (Kibicho, 2002; 2004).
METHODOLOGY
The aim of this study, therefore, was to assess the residents' perception of the impacts of tourism. Specifically, the survey sought to investigate and analyze gender perceptual differences on impacts of tourism development in Malindi. It was also aimed at generating some insights into the aspect of differences in gender attitudes on tourism development. To achieve this, the survey strove to answer the following two research questions:
- (i) What are the hosts' attitudes towards the tourism industry?
- (ii) Are there gender differences in perceptions and attitudes towards tourism impacts in Malindi?
Figure 1 The Study Site
The Study Sample
A list of housing within the Malindi area was obtained from the Malindi Municipality Housing Department. The residents from the listed residential houses were randomly selected using computer generated random tables. Then, two representatives of the household (a male and a female) were requested to fill in the questionnaire. In most cases, the chosen respondents were interviewed separately. A total of 140 questionnaires were distributed. One hundred and four usable questionnaires were collected which translated to a response rate of 75%. The sample was fairly even split based on gender (54% = male; 46% = female).
Survey Instrument
Questionnaire was the main survey instrument used for data collection. The instrument was a combination of the Tourism Impact Attitude Scale developed by Lankford & Howard (1994) and the scale developed by Milman & Pizam (1988) (Table 1). It consisted of two sections.
The first part sought demographic information as summarized below. Majority of the respondents; were male (54%), have lived in Malindi area for over 15 years (57%), had a primary level of education (68%) and their median age was between 40-50 years. Forty seven percent (male = 62%; female = 38%) of the respondents were employed full time, 12% (male=58%; female =42%) were employed part-time, 8% (male =43%; female = 57%) were retired, and the rest were unemployed or students. Of those who were employed, 80% (male = 59%; female = 41%) worked in tourism related establishments. Ninety percent (male = 52%; female =48%) of the respondents also reported that at least one family member was employed in the tourism industry. This demographic profile of the study sample is consistent with the population of Malindi area as reported by the National census conducted by the Ministry of Planning, Government of Kenya (Kenya, 2000).
The second section included 29 closed-style items requiring the respondents to rate their level of agreement with a particular item. This was done by indicating their response on a 5-point attitudinal scale with grades ranging from strongly disagree (1) to strongly agree (5). The twenty nine items were as outlined in Table 1.
Table l Survey Items<sup>a</sup>
- 1. You support and you would like tourism to continue as the main industry in Malindi
- 2. Tourism development has enhanced Malindi Image
- 3. Tourism development has created employment opportunities for locals
- 4. Tourism development has increased personal income
- 5. Increased standard of living from tourism
- 6. Tourism development has resulted into the rise in prices of goods and services in Malindi
- 7. Tourism development has affected the local people's attitudes towards work
- 8. Tourism development has led to decreased honesty in any commercial exchange
- 9. Tourism development has enhanced the quality of life in general
- 10. Courtesy and hospitality towards visitors has increased due to tourism development
- 11. Tourism development has brought immorality into our area
- 12. Local people are now sexually permissive due to tourism development in Malindi
- 13. Tourism development is responsible for increased levels of alcoholism in Malindi
- 14. New tourism facilities should be developed which will attract more tourists to Malindi
- 15. Tourism development is associated to prostitution
- 16. Malindi should not try to attract more tourists
- 17. Malindi should become more of a tourist destination
- 18. Gambling and other illegal games has increased in Malindi due to tourism development
- 19. You support local tax levies for tourism development
- 20. Tourism development has improved the local economy
- 21. Tourism development has created recreational opportunities for the local people
- 22. Tourism development has reduced out-migration of the youths
- 23. Long term planning by the local Government can control the negative impacts of tourism
- 24. Tourism development has increased in-migrations of the youths from other areas of the country
- 25. Tourism development has enhanced economic independence of the youth in Malindi
- 26. Tourism development has led to early marriage (of youth)
- 27. You support tourism for having a vital role in Malindi
- 28. You think that tourists are valuable
- 29. Overall, you are in favor of further development of tourism in Malindi
*Respondents rated the items on a 5-Point Scale with 1 = Strongly Disagree; 2 = Disagree; 3 = Indifferent; 4 = Agree; 5 = Strongly Agree.
Limitations
A major and highly unforeseen problem of the current study should be noted. Due to patriarchal nature of the study area, husbands did not allow their wives to independently participate in the study. In other cases, husbands insisted on being present as their wives were interviewed which undoubtedly affected their opinions on some crucial issues.
DATA ANALYSIS AND DISCUSSION
General Attitudes towards the Tourism Industry
Seventy four percent (male = 51 %; female = 49%) of the total respondents are in favor of further development of tourism industry in Malindi. This was represented by an overall mean support score of 4.4 (male = 4.6; female = 4.1) and a standard deviation of 0.7 on the 5- Point Attitudinal Scale.
Majority of the respondents (69%) are of the opinion that the image of Malindi, both nationally and internationally, had improved as a result of tourism development. They all feel that new tourism facilities should be developed to attract more tourists to Malindi. Only 4% of the respondents supported a decrease of the tourist numbers in the area.
Impacts of Tourism
The respondents were requested to express their opinions about the impacts of the current tourism development on various issues as outlined in Table 2.
Table 2 reports that local economy, personal income, standard of living, prices of goods and services, employment opportunities, Malindi's image, quality of life in general and recreational activities were perceived to improve as a result of tourism growth in Malindi. Specifically, the hosts' perceptions seemed very positive about the role played by tourism on economicrelated issues with tourism's contribution to the local economy scoring as high as 4.7 on the 5-Point Scale. However, sexual permissiveness, especially among the youths, was perceived to increase as a result of tourism. This could be attributed to increased close contact between the local youths and tourists when the former work in the tourism industry. In addition alcoholism, prostitution, gambling and other illegal games, and general morality were perceived to worsen as a result of tourism expansion in the area. The respondents reported that out-migration of the locals to other areas has substantially decreased as the local people get employed in the tourism industry (mean = 3.6). By contrast in-migration, mainly by Kenyans from upcountry regions, who come to work in the tourism industry has increased (mean 3.9). Many respondents expressed a willingness to work in tourism-related jobs. Respondents also felt that tourism had been responsible for the weakened community cohesion and cooperation. These social attributes are consistently being replaced by new values which encourage competition and entrepreneurship. This has mainly resulted to increased economic independence of the youths from the family bonds (mean = 3.6). The industry was also perceived to have an influence on the marriage of the youths especially young girls (mean = 4.2). Overall, this suggests that the Malindi people perceive the current tourism development to have both positive and negative effects on their community. Positive impacts of tourism development were noted on economic issues while negative effects were reported on sociocultural settings of the local community. These findings are in-line with those of the early studies on impacts of tourism on a destination (see for example Pizam & Milman, 1984; Var et a1.,1985; Milman & Pizam, 1988; Long et al., 1990; Perdue et al., 1990; Snaith & Hailey, 1994; Kibicho, 2002).
Of interest to the present context, however, is the fact that there were significant gender differences on more than half of the individual items in the survey battery (15 out of 29) (See Table 2). However, it should be noted that although respondents reported differences in their opinions, they did not have opposing views on the 5-point attitudinal scale (strongly agree vis-k-vis strongly disagree).
Table 2 Residents' Perceptions of Tourism Impactsa
| Statementc | Maleb | Femaleb | Combined Meanb |
|---|---|---|---|
| 1d. | 4.5(1.0) | 4.1(1.1) | 4.3 |
| 2d. | 4.5(1.0) | 3.9(1.0) | 4.2 |
| 3. | 4.5(1.1) | 4.5(0.6) | 4.5 |
| 4. | 4.1(0.8) | 4.1(0.7) | 4.1 |
| 5. | 4.0(1.1) | 4.0(1.1) | 4.1 |
| 6d. | 3.0(0.7) | 4.0(1.0) | 3.5 |
| 7d. | 3.3(0.9) | 3.7(0.8) | 3.5 |
| 8d. | 2.9(1.1) | 3.5(1.0) | 3.2 |
| 9d. | 4.3(0.8) | 4.1(1.3) | 4.2 |
| 10. | 3.9(1.0) | 3.9(0.8) | 3.9 |
| 11d. | 2.7(0.5) | 3.0(1.1) | 3.0 |
| 12d. | 3.2(1.0) | 3.4(0.9) | 3.3 |
| 13d. | 3.2(0.6) | 3.6(0.7) | 3.4 |
| 14d. | 3.8(1.0) | 330(1.2) | 3.6 |
| 15d. | 4.0(0.9) | 4.4(1.0) | 4.2 |
| 16d. | 3.2(1.1) | 3.6(1.0) | 3.4 |
| 17d. | 3.9(0.7) | 3.5(1.0) | 3.7 |
| 18d. | 3.4(1.0) | 3.7(0.6) | 3.6 |
| 19. | 4.0(0.8) | 3.7(1.0) | 3.9 |
| 20. | 4.7(1.0) | 4.7(0.5) | 4.7 |
| 21. | 4.1(0.9) | 4.0(1.0) | 4.0 |
| 22. | 3.5(0.6) | 3.6(0.9) | 3.6 |
| 23. | 4.0(0.9) | 4.0(0.8) | 4.0 |
| 24d. | 3.9(1.1) | 3.8(1.0) | 3.9 |
| 25d. | 3.5(0.6) | 3.7(0.9) | 3.6 |
| 26. | 4.1(1.0) | 4.4(0.9) | 4.3 |
| 27. | 3.9(0.5) | 3.8(0.4) | 3.9 |
| 28. | 4.1(1.0) | 4.1(0.6) | 4.1 |
| 29. | 4.6(0.6) | 4.6(0.7) | 4.6 |
Demographic Characteristics and Tourism Impacts
In order to test the correlation between the respondents' socio-economic and demographic characteristics and their opinions on tourism impacts, t - tests were conducted. To examine the perceptual differences between those respondents who were directly economically dependent on tourism and those who were not, a series of t- tests were undertaken (See Table 3).
Table 3 Perceptual Difference Between Residence with Tourism Economic Reliance and Those Who Do Not
| T | Means | |||||
|---|---|---|---|---|---|---|
| Impacts | Economic | Economic Reliance | No Economic- Reliance | |||
| variablesb | Male | Female | Male | Female | ||
| 1. | 4.6(0.9) | 4.3(0.7) | 4.3(1.0) | 4.0(1.1) | 3.4 | |
| 2. | 4.7(1.0) | 4.1(1.0) | 4.2(1.0) | 3.8(0.8) | 3.3 | |
| 3. | 4.9(1.0) | 4.9(0.8) | 4.0(0.9) | 4.1(0.8) | 4.0 | |
| 4. | 4.3(0.5) | 4.4(0.6) | 3.9(1.0) | 3.8(0.6) | 4.2 | |
| 5. | 4.2(0.9) | 4.1(0.9) | 3.9(0.8) | 3.9(0.8) | 3.1 | |
| 6. | 2.5(1.0) | 3.6(1.0) | 3.4(0.8) | 4.3(0.6) | -2.4 | |
| 7. | 3.2(0.7) | 3.6(0.7) | 3.4(1.2) | 3.8(1.2) | -1.1 | |
| 8. | 2.6(1.1) | 3.5(1.1) | 3.2(0.7) | 3.5(0.7) | -1.0 | |
| 9. | 4.8(0.8) | 4.6(0.9) | 3.8(0.9) | 3.6(0.8) | 5.2 | |
| 10. | 4.4(1.1) | 4.5(1.0) | 3.4(0.8) | 3.3(0.8) | 4.5 | |
| 11. | 1.8(1.1) | 2.2(1.0) | 3.6(0.6) | 4.4(0.6) | -6.6 | |
| 12. | 2.2(0.9) | 2.5(0.7) | 4.0(1.1) | 4.3(1.1) | -6.3 | |
| 13. | 2.0(1.2) | 2.4(1.2) | 4.4(1.0) | 4.8(1.0) | 5.8 | |
| 14. | 4.3(1.0) | 3.7(0.7) | 3.2(1.0) | 2.8(1.1) | 2.6 | |
| 15. | 3.8(0.8) | 4.2(0.8) | 4.1(0.6) | 4.5(0.9) | -1.9 | |
| 16. | 2.0(0.6) | 2.4(0.6) | 4.4(1.1) | 4.7(1.0) | -4.6 | |
| 17. | 4.5(1.0) | 4.2(1.1) | 3.3(0.8) | 2.8(1.2) | 2.6 | |
| 18. | 3.8(0.7) | 4.2(0.7) | 2.9(0.6) | 3.1(0.6) | 2.3 | |
| 19. | 4.7(1.1) | 4.4(1.1) | 3.2(1.1) | 2.9(1.0) | 3.3 | |
| 20. | 4.7(0.5) | 4.7(0.8) | 4.7(0.9) | 4.6(0.7) | 0.1 | |
| 21. | 4.4(1.0) | 4.3(1.0) | 3.9(0.9) | 3.8(0.8) | 4.1 | |
| 22. | 4.0(0.9) | 4.0(0.8) | 3.0(1.2) | 3.2(1.1) | 2.9 | |
| 23. | 4.0(1.1) | 4.0(1.0) | 4.0(1.0) | 4.0(1.1) | 0.00 | |
| 24. | 3.1(1.0) | 3.2(1.1) | 4.6(0.8) | 4.4(0.8) | -2.8 | |
| 25. | 4.0(0.9) | 4.2(0.9) | 3.0(1.0) | 3.1(1.0) | 1.9 | |
| 26. | 4.0(1.0) | 4.2(1.0) | 4.2(0.8) | 4.5(0.9) | -1.6 | |
| 27. | 4.3(0.9) | 4.2(0.9) | 3.5(1.0) | 3.4(1.1) | 1.8 | |
| 28. | 4.2(0.9) | 4.2(1.1) | 4.0(0.6) | 4.0(0.6) | 1.2 | |
| 29. | 4.7(1.0) | 4.6(1.0) | 4.4(0.9) | 4.5(1.0) | 1.7 | |
Table 3 indicates that the hosts (both male and female) who had a direct economic reliance on tourism had more positive perceptions towards the industry and its impacts than those who had no direct relation. Significant differences (P <0.005) between the two categories of residents were noted in variables such as tourism's development in Malindi, Malindi's image, standard of living, personal income, and morality. Unsurprisingly, those residents with a direct economic link with tourism were more significantly supportive of the industry compared with those with no tourism linkage.
Significant differences (P <0.005) were also noted regarding the economic related issues. These included employment opportunities, personal income and living standards. Residents (both male and female) directly relying on tourism perceived the industry to impact more positively on these factors than the other group. Other significant differences (P <0.005) between the two groups of respondents were found on variables such as attitude to work, morality, sexual permissiveness, alcoholism, drug taking, gambling and the Malindi's image. Tourism dependent residents perceived it relatively more positively than their counterparts with no reliance on the industry. In general, the respondents are in favor of further
development of tourism in Malindi. This was represented by a mean score of 4.6 on the 5- Point Scale. This answers our first research question on the hosts' attitudes towards the impacts of tourism in Malindi.
A second attempt was made to investigate the possibility of any perceptual difference based on origin and marital status of the respondents (Table 4).
Table 4 Difference in Perceptions Based on Residents' Origin and Marital Status
| Impacts | Originb | Marital Statusb | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| variables | Mali | Malindi Other | Married | Other | ||||||
| Male | Female | Male | Female | t-V | Male | Female | Male | Female | t-V | |
| 1. | 4.4 | 3.8 | 4.6 | 4.4 | -2.1 | 4.5 | 4.1 | 4.5 | 4.1 | .1 |
| 2. | 4.3 | 3.8 | 4.7 | 4.0 | -3.0 | 4.6 | 4.0 | 4.4 | 3.8 | .4 |
| 3. | 4.4 | 4.5 | 4.6 | 4.5 | 1.8 | 4.5 | 4.5 | 4.5 | 4.5 | .00 |
| 4. | 4.0 | 3.9 | 4.2 | 4.3 | -4.8 | 4.1 | 4.1 | 4.1 | 4.1 | .00 |
| 5. | 3.8 | 3.8 | 4.2 | 4.2 | -5.7 | 4.0 | 4.0 | 4.0 | 4.0 | 0.01 |
| 6. | 2.9 | 4.0 | 3.1 | 4.0 | -3.4 | 3.1 | 4.0 | 2.9 | 4.0 | .26 |
| 7. | 3.2 | 3.8 | 3.4 | 3.61 | .09 | 3.3 | 3.7 | 3.3 | 3.7 | .00 |
| 8. | 2.8 | 3.3 | 3.3 | 3.7 | -3.1 | 2.8 | 3.5 | 3.0 | 3.5 | 01 |
| 9. | 4.1 | 4.0 | 4.5 | 4.2 | -3.5 | 4.3 | 4.0 | 4.3 | 4.2 | 01 |
| 10. | 3.3 | 3.8 | 4.5 | 4.0 | -4.7 | 3.8 | 4.0 | 4.0 | 3.8 | 1 |
| 11. | 3.9 | 1.5 | 1.5 | 1.5 | 3.4 | 2.7 | 3.1 | 2.7 | 2.9 | 1 |
| 12. | 3.7 | 3.8 | 2.7 | 3.0 | .12 | 3.2 | 3.4 | 3.3 | 3.4 | 1 |
| 13. | 4.6 | 4.2 | 1.8 | 3.0 | 1.7 | 3.2 | 3.6 | 3.2 | 3.6 | .01 |
| 14. | 3.0 | 3.2 | 4.6 | 3.4 | -1.1 | 3.8 | 3.4 | 3.8 | 3.2 | 1 |
| 15. | 4.5 | 4.4 | 3.5 | 4.4 | 1.2 | 3.9 | 4.3 | 4.1 | 4.5 | 3 |
| 16. | 3.1 | 3.4 | 3.1 | 3.8 | -1.0 | 3.2 | 3.6 | 3.2 | 3.6 | 01 |
| 17. | 3.8 | 3.4 | 4.0 | 3.6 | -1.9 | 4.0 | 3.5 | 3.8 | 3.5 | .1 |
| 18. | 3.6 | 3.8 | 3.3 | 3.6 | .25 | 3.4 | 3.7 | 3.4 | 3.7 | .01 |
| 19. | 4.0 | 3.7 | 4.0 | 3.7 | 0.00 | 4.0 | 3.8 | 4.0 | 3.6 | .07 |
| 20. | 4.6 | 4.7 | 4.8 | 4.7 | 36 | 4.7 | 4.7 | 4.7 | * 4.7 | .1 |
| 21. | 4.2 | 4.1 | 4.0 | 3.9 | 1.7 | 4.2 | 4.0 | 4.0 | 4.0 | .2 |
| 22. | 3.5 | 3.4 | 3.5 | 3.8 | -0.5 | 3.5 | 3.7 | 3.5 | 3.5 | .1 |
| 23. | 4.0 | 3.9 | 4.0 | 3.7 | .31 | 4.0 | 4.0 | 4.0 | 4.0 | .02 |
| 24. | 4.0 | 3.8 | 3.8 | 3.6 | .22 | 3.9 | 3.7 | 3.9 | 3.9 | 02 |
| 25. | 4.0 | 3.1 | 3.0 | 4.3 | -0.2 | 3.4 | 3.7 | 3.6 | 3.7 | 03 |
| 26. | 4.2 | 4.7 | 4.0 | 4.1 | .2 | 4.1 | 4.4 | 4.1 | 4.4 | .02 |
| 27. | 3.9 | 4.0 | 3.9 | 3.6 | 1.9 | 3.9 | 3.9 | 3.9 | 3.7 | .03 |
| 28. | 4.1 | 4.4 | 4.1 | 3.8 | 2.0 | 4.0 | 4.1 | 4.2 | 4.1 | .02 |
| 29. | 4.5 | 4.6 | 4.7 | 4.6 | -1.7 | 4.6 | 4.6 | 4.6 | 4.6 | 0.00 |
Table 4 indicates that there are no statistically significant differences (P <0.005) in attitudes towards tourism and its impacts as far as marital status of the respondents was concerned. It seems that whether one is married or not makes no difference as to his/her perception of tourism impacts. Contrary to these findings, t-test results regarding respondents' origin showed statistically significant differences (P <0.005). It was found that respondents of non-Malindi origin had more positive attitudes towards tourism and presented a higher level of support for the industry. They had more positive perceptions of not only tourism's impacts on the local economy, but also of its effects on certain social impacts such as morality, sexual permissiveness and increased levels of alcoholism. This finding may be attributed to the fact
that majority of the non-Malindi respondents (86%) have a direct economic relation with the tourism industry. And as found out earlier (Table 2), those respondents who were economically dependent on tourism have positive perceptions and attitudes towards the industry. However, no significant differences (A < 0.05) were found between male and female respondents irrespective of their marital status and origin.
To further investigate whether the residents' perceptions and attitudes towards the impacts of tourism development were a function of the respondents' socio-demographic characteristics, Pearson product-moment correlations were computed for selected sociodemographic characteristics and the twenty-nine impact variables (Table 5).
Table 5 Pearson Correlationsa between Impact Variables and Respondents' DemographicCharacteristics
| Impacts | Yearsc | Age | Education | Household Income | ||||
|---|---|---|---|---|---|---|---|---|
| variablesb | Male | Female | Male | Female | Male | Female | Male | Female |
| 1. | 26 | 26 | 28 | 27 | .25 | .26 | .34 | .33 |
| 2. | 22 | 21 | 27 | 27 | .24 | .24 | .39 | .39 |
| 3. | 35 | 35 | .31 | .30 | .28 | .29 | .30 | .32 |
| 4. | 27 | 28 | .19 | .18 | .43 | .42 | .36 | .35 |
| 5. | 22 | 21 | .20 | .20 | .44 | .43 | .40 | .40 |
| 6. | 18 | 18 | .22 | .22 | .26 | .25 | .11 | .12 |
| 7. | 21 | 21 | .25 | .24 | .31 | .31 | .21 | .21 |
| 8. | 05 | 06 | 07 | 06 | .18 | .19 | .20 | .22 |
| 9. | 11 | 10 | 20 | 21 | .22 | .21 | .22 | .21 |
| 10. | 13 | 11 | 22 | 23 | .24 | .24 | .24 | .24 |
| 11. | .05 | .05 | .02 | .10 | .30 | .30 | .31 | .30 |
| 12. | .31 | .30 | .08 | .08 | .15 | .15 | 15 | 14 |
| 13. | 29 | 28 | 31 | 32 | .01 | .01 | .06 | .06 |
| 14. | 25 | 23 | 27 | 27 | .02 | .02 | .02 | .02 |
| 15. | 21 | 20 | 20 | -2.1 | 03 | 02 | 04 | 03 |
| 16. | .06 | .08 | 10 | 10 | 08 | -07 | 08 | 09 |
| 17. | 13 | 13 | 25 | 24 | .23 | .20 | .23 | .23 |
| 18. | 05 | 07 | 12 | 12 | .11 | .10 | .05 | .05 |
| 19. | 21 | 22 | 30 | 31 | .23 | .24 | .30 | .31 |
| 20. | 25 | 25 | 24 | 25 | .14 | .14 | .44 | .44 |
| 21. | 30 | 32 | 33 | 33 | .11 | .11 | .36 | .36 |
| 22. | 12 | 11 | 10 | 11 | 09 | 09 | 12 | 12 |
| 23. | 11 | 13 | 09 | 09 | 11 | 10 | 13 | 14 |
| 24. | .10 | .90 | .09 | .09 | .32 | .31 | .34 | .34 |
| 25. | 03 | 02 | .06 | .08 | .11 | .10 | .43 | .43 |
| 26. | .00 | .01 | 06 | 06 | .14 | .15 | .26 | .26 |
| 27. | 28 | 26 | 27 | 25 | .18 | .19 | .45 | .45 |
| 28. | .04 | .06 | .10 | .14 | .11 | .12 | .30 | .27 |
| 29. | .00 | .00 | .20 | .20 | 07 | 07 | .51 | .49 |
Table 5 presents results with a variety of implications. First, it is clear that the fewer the number of years a respondent lived in Malindi, the more positive attitudes s/he had for the future tourism expansion. The most statistically significant variables were found to be those related to economic related issues followed by social impact variables. Secondly, age was found to correlate negatively with 16 impact variables (See Table 5). The results imply that, the older the respondents, the more negative their perceptions of the tourism industry. The highest correlations were found between
age and variables relating to economic issues such as employment opportunities, personal income, standard of living and prices of goods and services. This was again followed by some social impact variables like alcoholism, prostitution and sex permissiveness. This finding suggests that the older respondents were less disposed towards the tourism industry in Malindi. Thirdly, there are 10 positive correlations between the number of family members in the household and the study's impact variables. It was found that those households with more members showed a relatively more positive attitude towards the industry. They all seemed to support further tourism development in the area. Specifically, high correlations were found between economic issues and Malindi's image. Fourthly, regarding the correlations between the level of education and the twenty-seven impact variables only 13 were found to be statistically significant (See Table 5). The results generally show that, the more educated the respondents were, the more positive their perception and attitudes were towards the tourism industry. Lastly, eighteen impact variables were found to have high statistically significant correlations with the household income (See Table 5). The analysis revealed that the higher the household income of the respondents, the more positive they were in terms of their attitudes and perceptions towards the tourism industry in their area. These positive attitudes and perceptions by the respondents with high incomes were also extended to other issues like tourism development as well as its positive effects on certain social issues like prostitution and morality. Further, these analyses revealed non significant differences between male and female respondents. This implies that some views on tourism impacts in Malindi are joint for both male and female residents and do not depend so much on gender.
Both the t -test and the Pearson product-moment correlations analyses show that the Malindi residents' perceptions and attitudes towards the impacts of tourism development are a function of certain socio-demographic characteristics irrespective of the respondents' gender.
To further explore potential differences in opinion on gender, factor analysis was performed separately for males, females and then combined sample (males and females together). This analysis was necessitated by the fact that, in the recent past some researchers have pointedout the fallacy of performing factor analysis on combined data set, rather than for subgroups that may differ based on the factors (Sheppard, 1996; Toth & Brown, 1997). Moreover, Hair et al., (1995: 375) observe that,
"...applying factor analysis to sample of males and females for a set of items known to differ because of gender is inappropriate... Whenever differing groups are expected in the sample, separate factor analysis should be performed, and the results be compared to identify differences not reflected in the results of the combined sample..."
Consequently, a technique of factoring that involved identifying items with thematic commonalities was used to group the data under factors of perceptions, with each sharing a common description factor. In order to allow for any differences in the factor definitions to emerge, a principal component factor analysis subjected to orthogonal rotation using SPSS+Varimax procedure was used allowing the number of possible factors to vary from 1 to 29. Several criteria were used to determine the appropriate number of factors used as the final solution. These preliminary analyses are omitted here for brevity of space. However, details of these criteria can be found in Nunnally (1978). The first criterion was the amount of variation in the entire data set that was associated with the overall number of factors. Eigenvalues of individual factors, cumulative eigenvalues of a chosen set of factors, and a plot of eigenvalues versus factor number (scree-plot) was employed when determining the final number of factors. The second criterion was the interpretability and understability of the factors. Based on these two criteria, a 4-factor pattern was remarkably consistent across the combined sample and indeed relatively 'pure' solution, that is, each variable heavily loaded on only one factor was adopted. It was the most interpretable and most meaningful for the combined sample, as well as for each gender (Table 6).
Table 6 The Resultant Factor
| Factor Name | Eigenvalue | % of Variance | Cumulative % | Itema |
|---|---|---|---|---|
| Factor1: Increased development | 6.019 | 32 | 32 | 14,16,17,19,29 |
| Factor2: Positive impacts | 3.095 | 10 | 42 | 2,3,4,5,9,10,20,21,22 |
| Factor3: Tourism support | 2.736 | 8 | 50 | 1,23,25,27,28 |
| Factor4: Negative impacts | 1.258 | 6 | 56 | 6,7,8,11,12,13,15,18, |
| - | 2022 | 24,26 |
Table 6 shows the factor solutions for the entire sample (males and females combined). The factors emerged in a fairly consistent and easily interpretable manner, as far as the items contained within each factor were concerned. Thus, it was easier to designate different dimensions into four factors. These factors are: Increased development, Positive impacts, Tourism support and Negative impacts.
Further, factor solutions of these dimensions were performed in relation to respondents' gender. The percentage of variation explained by each factor are presented in Table 7.
Table 7 Factors Solutions for the Combined Sample, Males and Females Separately
| Factor Name | % of Variance | ||
|---|---|---|---|
| Combined (\(N = 104\)) | |||
| Factor1: Increased development | 32 | ||
| Factor2: Positive impacts | 10 | ||
| Factor3: Tourism support | 8 | ||
| Factor4: Negative impacts | 6 | ||
| Males (N = 56) | |||
| Factor1: Increased development | 33 | ||
| Factor2: Positive impacts | 11 | ||
| Factor3: Tourism support | 9 | ||
| Factor4: Negative impacts | 4 | ||
| Females \((N = 48)\) | |||
| Factor1: Tourism support | 30 | ||
| Factor2: Increased development | 8 | ||
| Factor3: Positive impacts | 7 | ||
| Factor4: Negative impacts | 6 | ||
Table 7 reveals that, although the four factors were consistent, the amount of variance explained for each of them was quite different for the combined sample, for males and for females. The factors for the combined sample and the factors for males were significantly identical in the amount of variance explained for each factor. However, for females the amount of variance explained by each factor emerged to be significantly different from the overall sample and from males. The most variation explained for the overall sample and for males is by the Increased development factor. Males, with 33% of the variance explained, are in more disagreement in their attitudes toward growth of the tourism industry in Malindi than their fellow females for whom only 8% of the variance is explained. Females have less agreement in their support of the Malindi's tourism industry (30%) than their male counterparts who were found to be more consistent in their support of tourism (9% of variance explained). Important to note is the fourth factor on Negative impacts due to its consistency for the combined sample (6%), for males (4%) and for females (6%). These percentages indicate more agreement on the negative impacts of tourism than for the other three factors (See Table 7).
These results indicate that the factors defined using the combined sample reflect significant differences (P < 0.05) in variance explained by different factors in the male and female samples. Moreover, analysis of individual survey items also revealed differences between males and females (See Table 2).
CONCLUSIONS
Four principal conclusions from the data analysis are drawn. First, the study revealed that the respondents had both positive and negative attitudes towards tourism in the area. First, the study found that, the majority of the respondents feel that tourism has a positive economic impact both to the individuals as well as to the overall local economy. Among these impacts were job creation, improved living standards, improvement of Malindi's image and personal income. Secondly, despite tourism's economic impact, the respondents reported the existence of some tourism related negative social impacts. Tourism was blamed for the worsening or increased levels of alcoholism, prostitution and sexual permissiveness. Although the respondents were aware of these negative social impacts of tourism, they did not oppose its expansion. However, they felt that the current trend should have been mediated to encourage more local people's ownership of tourism businesses.
Second, this research found that there is a strong relationship between the respondents' socio-economic characteristics and their perceptions and attitudes towards tourism's impact. It is important to mention that economic dependency on tourism is the most significant determinant of the residents' attitudes toward the industry irrespective of their gender. Those residents who were associated with the industry had more positive attitudes than those who had no business interest in it. Certain demographic characteristics also were found to influence the study sample. The most important variables were the residents' occupational status, years living in Malindi, education levels, employment of one or more family members in the tourism industry and area of origin.
Third, males in Malindi felt that there are more benefits to tourism than do females. They were stronger than females in their agreement that tourism had led to; economic benefits, enhanced living standards, more recreational opportunities than females. Consequently, males reported a strong support for tourism than were females. Females, on the other hand, came-out more strongly on the issues related to increased development from tourism. They were more opposed to any increase in tourism facilities, or in attracting more tourists. The survey data, thus, allow us to conclude that there are some gender neutral aspects of tourism impacts in Malindi (for example, the effects of tourism on personal income). However, female residents stress the social impacts while male residents are keen on economic impacts (Table 2).
Lastly but not the least, even though the definitions of the factors in the current study were consistent, the percentages of variance explained (Table 6) and the mean score on items (Table 2) were found to be different between males and females. Female opinions, for example, were found to be different from those of the combined sample. This study, therefore, supports Hair et al's conclusion that, separate factor analyses for males and females should be performed (1995). The main conclusion from this survey is that, female residents are an important segment of tourism development. Consequently, tourism destination managers should always take particular regard to potential gender differences when formulating their development policies.