Using attitude towards the careers in tourism

Using fsQCA Models to Predict School Students’ Attitude towards the Career Intention in the Tourism Industry: the Case of North Central Province, Sri Lanka AbstractProblems associated with the manpower in tourism industry has been long debated. The present study further addresses this issue through assessing school students’ attitude towards the careers in tourism industry.

This study takes place in the North Central Province of Sri Lanka with a sample of 562 school students, whose attitude towards the careers in the tourism industry was collected through a self-administered questionnaire. In contrast to the previous studies, the present study employs fuzzy sets Qualitative Comparative Analysis (fsQCA) software to identify the combination of different factors with high or low values that constitute the career intention of the school students. Accordingly, the present study identified highest career intention is caused by the combination of high pay / benefits, high industry person congeniality, low social status and low awareness of the industry. The study further identified that, low industry person congeniality, low social status, low nature of work and high awareness of the industry together constitute low career intention of tourism industry.Keywords: attitude, career intention, fuzzy sets, Qualitative Comparative Analysis, tourismIntroduction The tourism and hospitality industry worldwide, and in Sri Lanka in particular, has been confronted with the problem of attracting and retaining quality employees. This has led to a shortage of skilled personnel to staff the ever-growing number of tourism and hospitality businesses (Andorka, 1996; Bonn & Forbringer, 1992; Lokuhetty, 2014). On the other hand, as a service oriented industry, majority of services in the tourism industry are produced and consumed simultaneously in direct service encounters where employees and customers are physically and psychologically close enough to influence each other. Therefore, the importance of well-educated, well-trained, skilled, enthusiastic and committed employees cannot be underestimated (Kusluvan and Kusluvan, 2000).

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

However, Poor image and lack of understanding on job offer has long been debated in the tourism industry (Pavesic & Brymer, 1990; Kusluvan and Kusluvan, 2000). This situation is a complex one with many different factors contributing to the problem. These factors include a young transient workforce; low pay; low levels of formal qualifications; high levels of female, students, part-time, and casual workers; a high proportion of low-skilled jobs; a large proportion of hours worked outside normal business hours; a negative industry image in the eyes of potential employees; poor utilization of student labor and high levels of staff turnover (Baum, 2006; Brien, 2004; Deery & Shaw, 1999; Freeland, 2000). In Sri Lankan context, employment in the tourism and hospitality has been identified as a less preferred career option for a variety of reasons: economic reasons, lack of general awareness of the sector, work hours and environment, and sociocultural pressures (Ministry of Tourism and Christian Religious Affairs, 2017). Although various initiatives have been taken to educate and train future tourism professionals, one can never deny the importance of understanding how different attitudes affecting on career intention in the tourism industry.In Sri Lanka, tourism education is basically offered at four levels; certificate level, diploma level, degree level, and post graduate level. Although, tourism has been identified as a main driving force to develop the country, no tourism related knowledge is given to the students at the school level. If today’s students are to become the effective practitioners of tomorrow, it is fundamental to understand their career intentions.

Recent statistics further emphasizes the labor gap in the industry. According to Sri Lanka Tourism Development Authority, there is a considerable vacuum in the tourism industry (Ministry of Tourism and Christian Religious Affairs, 2017). This may be due to the fact that the school leavers are not aware of the true potentials of the tourism industry which keep them away from the tourism and hospitality industry.

Therefore, identifying their career intention through providing a direct experience is paramount. Different studies undertaken at different levels have brought light to this area of study. The study done by Ross (1997) revealed that the students with a high career intention towards the tourism industry tends to undergo tertiary level education and training in related to tourism to achieve their career objectives. Another comparative study about the attitudes of secondary school students towards tourism careers in Greece and the United Kingdom conducted by Airey and Frontistis (1997) shows that students in United Kingdom has a less positive attitude towards tourism than the students in Greek . Further, Cothran and Combrink (1999), based on their survey of high school students in Arizona, argue that high school students have more interest in tourism and hospitality careers though they possess a less knowledge on tourism and hospitality industry.

On the other hand, Fazio and Williams (1986) also states perceptions and attitudes based on direct experience leave people with a stronger and more realistic view of the industry and will therefore be more predictive of future behavior.Kusluvan ; Kusluvan (2000) highlights that a very limited number of research studies have been conducted to investigate the career intention of individuals who are more than casually interested in the tourism and hospitality industry. They further categorizes the scholarly works on attitudes of individuals on career intention into three groups; attitudes of secondary or high school students towards careers in the tourism and hospitality industry; attitudes of employees who are already working in the tourism and hospitality industry; and attitudes of students who are currently studying tourism and hospitality management at the undergraduate level (Kusluvan ; Kusluvan, 2000). In this study, it is focused on the attitude of secondary school students which is in the first category which has little evidence on investigating their career intentions in tourism industry. However, attitude itself cannot predict the human behavior all the time due to the impact of many moderating variables, conditions and situational constraints causing attitude-behavior discrepancy (Ajzen, 1993; Eagly ; Chaiken, 1993; McGuire, 1985). Simultaneously, there are many scholars who have studied theories on student attitudes, expectations and career choices from numerous viewpoints.

According to Domonte and Vaden (1987), interesting work, advancement potential, secure future, good salary, opportunity for service to society, and social prestige have a great influence on career intention of students in tourism and hospitality industry. Similarly, Blumenfeld et al., (1987) ranked the most influential factors on career intention: (1) type of work; (2) advancement opportunities; (3) company reputation; (4) salary; (5) job security; (6) hours of work; (7) bene?ts; (8) working conditions; (9) nature of co-workers; and (10) nature of supervisors. Although there are many factors affecting the career intention, the literature is silent on how combination of different factors determine the career intention. Thus, it is a timely requirement to assess and identify what constitute to the phenomena.

Therefore, this empirical study answers following questions: what factors (i.e., sufficient conditions) affect the positive attitudes and career intentions towards the tourism industry? What pattern of attitudes offer causal configurations that are sufficient to indicate desirable career intentions and undesirable career intentions of school students? What type of attitudes are necessary conditions for achieving the desired responses of school students? The study applied complexity theory as the framework of the research model. The study also used systematic and innovative analytical approaches including fuzzy set qualitative comparative analysis (fsQCA), and necessary condition analysis (NCA), to investigate significant sufficient attitudes that constitute the career intention of tourism and hospitality industry among the school students2. Literature Review 2.1 Attitude and career intentionEmployee attitude, performance, and behavior are key factors of service quality, which has a direct impact on customer satisfaction and loyalty (Heskett et.

al, 1994). Many scholars have argued that in the service industries, without the positive attitude of employees toward their career and industry, it is impossible to achieve customer satisfaction and loyalty (Rosentbluth, 1991; Zeithaml ; Bitner, 1996). The responsibility of providing such a work force to the industry mainly lies with the public and private educational institutes (Kusluvan ; Kusluvan, 2000).

Although there is no universally accepted definition, attitude is commonly defined as tendencies to evaluate an entity with some favor or disfavor, ordinarily expressed in cognitive, affective and behavioral responses (Eagly ; Chaiken, 1993). Ajzen (1993) defined attitude as “an individual’s disposition to react with a certain degree of favorableness or unfavorable to an object, behavior, person or event to any other discernible aspect of the individual’s world”. Attitudes play a vital role in explaining and predicting individuals’ behavior. The relationship between attitudes and behavior is controversial where there are many empirical findings showing small to moderate positive relationship between attitudes and behavior (McGuire, 1985). This indicates that studying about attitude will assist to predict the behavior and therefore can be used to evaluate the students’ attitude on tourism industry and thus, their intention to work in the tourism industry.

Career decisions of young people, including students, are influenced by their particular skills, knowledge and experiences, which are not alone provide them with a profession. Awareness of opportunities available in the job market and external factors such as attitudes also play a vital role (Lewis and Airey, 2001). As Super (1957) has mentioned ‘People with certain attitudes are more likely to be attracted to certain types of work than to others; those who have certain values are likely to see more opportunity to achieve them in some fields of work than others’. Though, there are numerous studies to show that high school students are interested in careers in tourism industry (Ross, 1994; Choy 1995; Purcell and Quinn, 1996; Ross, 1997; Cothran and Combrink, 1999), some studies reveal that tourism and hospitality professions perceived to be unattractive (Getz, 1994; Koko and Guerrier, 1994; Cooper and Shepherd, 1997), tedious and low level (Sindiga, 1994), physically repetitive, poorly paid, controlled by task oriented managers and providing limited opportunities for participation and development (Koko and Guerrier, 1994), and offer low-status careers (Cooper and Shepherd, 1997). These findings coincide with Getz (1994) who also implied that tourism jobs are perceived to be undesirable by the students in Spey Valley, Scotland. Many researchers including Kusluvan and Kusluvan (2000), Richardson (2008) indicated that the students’ attitudes can be categorized under six main variables: awareness of the industry (aware), nature of the work (work), social status (soso), industry person congeniality (ipc), pay and benefits (pay), career intention (ci).

The present study also use the similar method to categorize the factors.However, individuals’ attitude towards careers in tourism are positive when community involvement in tourism is high in terms of owning or operating tourism businesses by providing a range of career opportunities to local residents (Murphy, 1985). Murphy (1985) further stated that there is a tendency for more positive attitudes to the industry by the individuals who have some involvement or contact with the industry than by those who have no direct attachment with the industry or benefit from tourism. On the other hand, most influential factor that attracts individuals into the careers in tourism industry is their positive experience and attitudes with the industry (Purcell and Quinn, 1996).

Based on the findings of their study, Lewis and Airey (2001) stated that secondary school students in Trinidad and Tobago have favorable and positive attitudes towards engaging in careers in tourism industry, which is strongly influenced by both their work values and information about the industry. The comparative results of United Kingdom and Greece student also fall in the same line where as UK students had less positive attitudes towards tourism careers than their Greek counter parts who have a more realistic view of nature and demand of tourism career (Airey ; Frontistis, 1997).Considering these contradictory research findings, it is questionable when students are given a realistic awareness on tourism industry and career prospects or whether they acquire a less positive attitude towards the tourism industry. However, Airey and Frontistis (1997) argued that the quality of tourism education system in a country plays a vital role in forming students’ perceptions and attitudes towards tourism. Moreover, stakeholders in the tourism industry have to face the challenge of inculcating positive attitudes and providing sufficient and accurate information, guidance and assistance in order to lead the students to the tourism industry (Lewis and Airey, 2001).

However, they further emphasis that the information should not unnecessarily diminish the enthusiasm of the students. 2.1 Complexity theoryComplexity theory has been adopted in many disciplines including organization behavior (Anderson, 1999; Fiss, 2007), marketing (Gummesson, 2008; Woodside, 2014, 2015a; and Wu et al., 2014) and social sciences (Ragin, 2000; Urry, 2005) which has enabled the researchers to build and test theories.

Complexity theory focuses on a pattern phoneme that presents a more in-depth understanding of the relationships between causal antecedents’ conditions and outcome conditions (Olya ; Altinary, 2016). Tourism is consisted of different complex notions of interactions and that its dynamics are chaotic non-linear and unstable (McKercher, 1999). Although complexity theory is used in understanding and solving different tourism phenomenon its wide contribution to the field is limited (Stevenson, Airey, Miller, 2009). Newtonian approach in tourism suggests that knowledge is well established in certain parts of the tourism system but there is little knowledge of the relationships and interactions between these parts (Farrell ; Twining-Ward, 2004). Stevenson, Airey and Miller (2009) further condense that implementation of complexity theory has provided an opportunity for researchers to re-examine the interconnected nature of tourism phenomena and to develop methods and models that attempt to encompass multiple relationships, turbulence and changeThe complexity theory is formed under six main tenets.

As Figure 1 shows, the proposed conceptual model for this study has been developed based on complexity theory using a Venn diagram, and the results of fsQCA have been evaluated based on the key tenets of this theory. Woodside (2014) lists the six main tenets of complexity theory as follows:Tenet 1: A simple antecedent condition may be necessary, but a simple antecedent condition is rarely sufficient for predicting high or low scores in an outcome condition.Tenet 2: The recipe principle: A complex antecedent condition of two or more simple conditions is sufficient for a consistently high score in an outcome condition.Tenet 3: The equifinality principle: A model that is sufficient is not necessary for an outcome having a high score to occur.Tenet 4: The causal asymmetry: Recipes indicating a second outcome (e.

g., rejection) are unique and not the mirror opposites of recipes of a different outcome (e.g., acceptance) principle.Tenet 5: An individual feature (attribute or action) in a recipe can contribute positively or negatively to a specific outcome depending on the presence or absence of the other ingredients in the recipes.Tenet 6: For high Y scores, a given recipe is relevant for some but not all cases; coverage is less than 1.00 for any one recipe.

A few exceptions occur for high X scores for a given recipe that works well for predicting high Y scores. 84772528330600Figure 1: Conceptual model of the study3. Methodology3.1 Research design The data collected from the Advanced Level school students in North Central province. Primary data were collected through self-administered questionnaires. Secondary data were also accessed to develop the research problems and the questionnaire for this study. The questionnaire was consisted of two sections.

The first part consisted of questions concerned with students’ socio-demographic information. The second part contained 31 item inventory on five scale Likert type ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5) to identify the school students’ career intention. The items were factored under six main variables including awareness of the industry (aware), nature of the work (work), social status (soso), industry person congeniality (ipc), pay and benefits (pay), career intention (ci) as identified by Kusluvan and Kusluvan (2000) and Richardson (2008).The population for this research is the advanced level school students in North Central province, Sri Lanka. The data were collected before a workshop conducted by the researchers to enhance school students awareness about the career opportunities in the tourism and hospitality industry. Two workshops were conducted, one is Anuradhapura district and the other in Polonnaruwa district. Ten schools were invited to attend each workshop in each district which were recommended by the North Central Province Educational Department.

Around thirty number of students participated in the workshop representing each school that made a total of 600 objects in both workshops. 600 questionnaires were distributed among the school students and all the questionnaires were collected (100% response rate). However, only 562 (93%) questionnaires were found to be usable.

All participants were given the questionnaires by the research assistants which were self-administered. The sample included 360 (64.1%) male students and 202 (35.9%) female students. Out of the respondents 478 (85%) of them were Buddhists, 49 (8.8%) were Islam, 24 (4.2%) were Hindus.

272 (48.4%) students were studying in Arts stream, 118 (21%) were in Commerce stream, 70 (12.4%) were from Biological Sciences, 67 (11.9%) were from Physical Sciences and 35 (6.2%) were from Technology stream. Of all the respondents only 3% had either of their parents working in the tourism and hospitality sector. Further, 82.7 % stated that they were willing to continue their higher studies in the tourism industry, while 87.

1% of the respondents mentioned that they would like to select a career in tourism and hospitality industry.Construct validity and reliability were applied to determine the convergent validity and internal consistency of the constructs used in the instrument. For the purpose of reliability, principle component analysis and reliability test based on Cronbach’s alpha were used and their results are presented in the Table 1.

According to Table 1, results reveal that sample size was adequate to proceed with analysis to the given constructs. Moreover, communities generated by the analysis were well over 0.5 indicating the construct validity of the measures.

Cronbach’s alpha coefficients which were ranging from 0.725 to 0.887 confirm the reliability of the constructs used in the instrument. Table 1: Results of principle component analysis and reliability testVariable No. of Items KMO Bartlett’s Test Cronbach’s alphaChi Square Value Sig Awareness (aware) 06 .856 1148.082 .000 .

887Nature of the work (work) 05 .842 655.709 .000 .840Social status (soso) 05 .838 976.953 .000 .

884Industry person congeniality (ipc) 05 .747 195.619 .000 .725Pay and benefits (pay) 05 .755 156.

737 .000 .699Career intention (ci) 05 .738 276.392 .

000 .7813.2 Data analysis This study uses fsQCA method to explore how awareness of the industry, nature of the work, social status, industry person congeniality, pay and benefits affect career intention (see Fig. 1). The fsQCA is a case-oriented technique that focuses on combinatorial e?ects.

This method assumes asymmetry relationships between independent and dependent variables, and equi?nality in which multiple pathways and solutions lead to the same outcome. This method also allows for multi?nality in which identical conditions can lead or contribute to di?erent outcomes at different circumstances, and conjunctural causation where causal con?gurations of conditions can be either necessary or su?cient to achieve the outcome while their constituent conditions might be neither su?cient nor necessary (Rihoux ; Ragin, 2009; Woodside, 2013). Although this is a new methods, the researchers have grown their interest towards the same to handle complex situation.

Further, the fsQCA uses the concept of set membership, and thus the raw data must be transformed into fuzzy sets ranging from zero (full exclusion from a set) to one (full inclusion) (Ragin, 2008). Three steps of fsQCA – namely, calibration of data variables, analysis of fuzzy truth table algorithm, and counterfactual analysis of the causal conditions that lead to the school students’ career intention outcomes were performed based on Ragin’s (2008) guidelines.However, the study calculates an index for each construct before calibrating the variables by performing the average of the corresponding indicators. The calibration process requires specifying three anchors: full membership, full non-membership, and a crossover point (Ragin, 2008).

For all constructs (conditions and outcome), the study uses the direct method for calibrating the fuzzy sets (Ragin, 2008) based on the theoretical anchors (Ordanini, Parasuraman, ; Rubera, 2014; Silva ; Gonçalves, 2016). Thus, in this study the rating of ?ve (calibrated value of 0.99) is full membership; the rating of one (calibrated value 0.1) is full non-membership; and the rating of three (calibrated value 0.

5) is the crossover point. 4. Analysis and discussion4.

1 Analysis of necessary conditionsAlthough the analysis of su?cient conditions is at the core of fsQCA, it should always be preceded by the identi?cation of necessary conditions (Schneider ; Wagemann, 2010). This study analyzes only one dependent variable, career intention (see Fig. 1), as the outcome condition. The fsQCA analysis considers ?ve antecedent conditions for the outcome return (aware, work, sos, ipc, pay).

To identify whether any of the ?ve conditions are necessary for career intention, the study analyzes whether the condition is always present (or absent) in all cases where the outcome is present (or absent) (Rihoux ; Ragin, 2009). Therefore, career intention is achievable if the condition in question occurs. The degree to which the cases conform to this rule re?ects “consistency.” A condition is “necessary” or “almost always necessary” when the corresponding consistency score exceeds the threshold of 0.

9 or 0.8, respectively (Ragin, 2000). Table 2 presents the results of the fsQCA test on the necessity of the conditions related to the career intention outcomes. The results show that work, ipc and pay are necessary conditions for career intention, whereas aware and soso are almost always necessary conditions for career intention.Table 2: The result of necessary condition analysisCondition Necessary conditions for the outcomeConsistency Coverageaware 0.859654 0.863596~c_aware 0.514392 0.

926310work 0.937347 0.814429~c_work 0.371765 0.

929822soso 0.895234 0.872164~t_soso -3.921400 1.000000ipc 0.933769 0.

842844~c_ipc 0.393859 0.889331pay 0.925574 0.836192~c_pay 0.404180 0.9106104.2 Analysis of su?cient conditionsThe analysis of su?cient conditions starts with the construction of a truth table (Ragin, 2008).

The truth table has 2k rows (k = number of conditions), and each row in the table corresponds to a con?guration of conditions. Based on the set membership cores, each observation is in a particular row. The study uses the fsQCA algorithm to produce the truth table for the outcomes, career intention (see Fig. 2). To reduce the truth tables to meaningful con?gurations, the study uses a frequency threshold of fifteen observations to exclude less important con?gurations (Rihoux ; Ragin, 2009). In addition, the QCA literature also recommends that at least 80% of the cases in the sample should remain after imposing the frequency restriction (Ragin, 2008).

The frequency threshold ensures that 81% of the cases in the sample are part of the analyses for career intention. In the next step, to identify which con?gurations are su?cient for achieving the outcomes, the study applies a consistency threshold that is greater than or equal to 0.80 (Ragin, 2008) with a PRI score threshold that is greater than or equal to 0.67 to avoid simultaneous subset relations of attribute combinations in both the outcomes and their negations (Schneider ; Wagemann, 2010). Further, when applying these threshold values, the fsQCA software provides three solutions: an intermediate solution, a parsimonious solution, and a complex solution. This study analyzes the intermediate solutions (Table 3 – 4) for the outcomes, as these solutions make no simplifying assumptions (Ragin, 2008). The consistency and coverage values for each complex solution and their respective con?gurations surpass the minimum acceptable values (Ragin, 2008).

Figure 2: Truth table without logical remainders for the outcome condition4.3 Casual recipes for the career intentionThe fsQCA results show that the configurations were sufficient to predict high and low scores in the study’s outcomes, based on the calculation of the complex combination of five conditions, which are presented in Tables 3 – 4. Whilst Ragin (2006) suggest to forget the combination if the consistency is less than 0.75 (better close to 1). Further Ragin (2006) and Woodside and Zhang (2012) identified the coverage in between 0.2 – 0.6 as satisfactory. Accordingly, the results are informative.

Ordanini, Parasuraman, and Rubera (2014) noted that recipes based on the combination of five conditions are more important than the outcome, i.e., career intention. According to the results, eight causal recipes described the condition of high career intention (coverage: 0.90, consistency: 0.86). Coverage and consistency in asymmetrical modeling, which are analogous to the coefficient of determination and the correlate/on in symmetrical modeling, respectively, are two probabilistic measures used to confirm the calculated recipes that are sufficient and consistent causal configurations. The cutoffs for coverage and consistency are 0.

20 and 0.8, respectively (Ragin, 2008). As shown in Table 3 the high career intention in tourism industry was achieved when they provide high pay and benefits, high industry person congeniality, low social status and low awareness of the industry (M3). The second model (M5) indicated that low industry person congeniality, high social status, high nature of work, high awareness of the industry resulted in high career intention.

The third model (M2) low industry person congeniality, low social status, high nature of work, high awareness of the industry resulted in high career intention. Additionally, M4 and M8 are also showing significant results.Table 3: Configural model for career intentionCasual models for high career intention Raw Coverage Unique Coverage ConsistencyA. ci = f(pay, ipc, soso, work, aware) M1: pay*ipc*work 0.859 0.059 0.

891M2:~ipc*~soso*~work*aware 0.281 0.001 0.9443M3:pay*ipc*~soso*~aware 0.395 0.000 0.9581M4:ipc*~soso*work*aware 0.440 0.

002 0.9335M5:~ipc*soso*work*aware 0.379 0.000 0.

9452M6:pay*ipc*soso*aware 0.781 0.009 0.926M7:pay*work*aware 0.815 0.013 0.

891M8:pay*~ipc*~soso*aware 0.336 0.000 0.9384Solution coverage: 0.900368 Solution consistency: 0.865965 Frequency cutoff: 2.00Consistency cutoff: 0.

93 4.4 Casual recipes for the negation intentionThis empirical study explored the causal recipes leading to low career intention (see ~ A in Table 4). These results are line with Kan, Adegbite, El Omari, and Abdellatif (2016), who recognized fsQCA as a method for generating knowledge based on set theory.

According to the fsQCA results, eight casual recipes explained low career intention (coverage: 0.82, consistency: 0.76).

Model 1 (M1) indicated that low career intention resulted from low industry person congeniality, low social status, low nature of work and high awareness of the industry (Table 4). The second model (M6) proposed that high pay/benefits, low industry person congeniality, low social status and high awareness of the industry led to the low career intention of school students (Table 4).Table 4: Configural model for career intention (negation)Casual models for high career intention Raw Coverage Unique Coverage Consistency~A. ~ci = f(pay, ipc, soso, work, aware) M1: ~ipc*~soso*~work*aware 0.499670 0.

006498 0.9223011M2: pay*ipc*~soso*~aware 0.636312 0.004897 0.848443M3: ipc*~soso*work*aware 0.704398 0.

003767 0.821617M4: ~ipc*soso*work*aware 0.624353 0.004709 0.857364M5: pay*ipc*soso*~work*aware 0.565966 0.019493 0.

852845M6: pay*~ipc*~soso*aware 0.589698 -0.000000 0.9066172M7: pay*~soso*work*aware 0.711178 0.000659 0.823107M8: pay*~ipc*work*aware 0.

643940 -0.000000 0.858937solution coverage: 0.823430 solution consistency: 0.764737 frequency cutoff: 2.000000 consistency cutoff: 0.824577 5. ConclusionThis empirical study helps to enrich the understanding of school students’ career intention of the tourism industry which has a considerable value for the development of the tourism industry in North Central province of Sri Lanka.

Current study explored the factors affecting to the career intention of tourism and hospitality industry in North Central province of Sri Lanka using fsQCA models. Five factors were identified affecting students’ career intention using the previous literature. Unlike the past studies conducted on career intention, the present study took place with the involvement of school students in Sri Lanka, which has not been previously studied in Sri Lanka, and used fsQCA method that focuses on combinatory effects.

The results have important implications for policy makers and practitioners since attracting and retaining qualified employees is a challenge with the high inter-industrial competition. In order to increase the school students’ career intention in tourism industry, both the authorities and potential employers should increase their awareness on pay and benefits that includes, free hotel stays, foreign tours, ability to earn a lot of money and ability to make lot of foreign friends. Further, high awareness of nature of work influence high career intention. Thus, it is important to communicate the information regarding interesting nature of the tourism job one’s industry person congeniality matters. Therefore, the students should be assisted to identify their personality, skills, and what the industry expected of them that includes dedication and commitment. The present study recommends the future researchers to use a bigger sample that covers a wider geographical area, to use complexity theory in data analyzing using both fsQCA and structural equation modeling (SEM) to increase the richness of the findings of this nature of studies.

ReferencesAirey, D. and Frontistis, A. (1997). Attitudes to careers in tourism: an Anglo Greek comparison. Tourism Management, 18(3), 149-158.

Ajzen, I. (1993). Attitude theory and the attitude-behavior relation. New directions in attitude measurement, 41-57.Anderson, P. (1999).

Complexity theory and organization science. Organization Science, 10, 216–232.Baum, T. (2006).

Human resource management for tourism, hospitality and leisure: An international perspective. London: Thomson Learning.Blumenfeld, W.

S., Kent, W. E., Shock, P. J., & Jourdan, L. F. (1987).

Job attribute preferences of a group of potential hospitality industry managers (what makes a job good or bad). Hospitality Education and Research Journal, 11(1), 79-91.Bonn M.A. & Forbringer, L.R. (1992).

Reducing turnover in the hospitality industry: An overview of recruitment, selection and retention. International Journal of Hospitality Management, 11, 47–63.Brien, A.

(2004). Do I want a job in hospitality? Only till I get a real job. In: Smith KA, Schott C, (Eds).

Proceedings of the New Zealand Tourism and Hospitality Research Conference in New Zealand. 2004: 23 – 31.Choy, D. J.

(1995). The quality of tourism employment. Tourism Management, 16(2), 129-139Cothran, C.C., & Combrink, T.E. (1999). Attitudes of minority adolescents toward hospitality industry careers.

Hospitality Management, 18, 143-158.Cooper, C. and Shepherd, R. (1997). The relationship between tourism education and the tourism industry: Implications for tourism education. Tourism recreation research, 22(1), 34-47.

Cothran, C. C. and Combrink, T. E.

(1999). Attitudes of minority adolescents toward hospitality industry careers. Hospitality Management, 18, 143-158.Damonte, T. and Vaden, A.

G. (1987). Career decision in hospitality management. Hospitality Education and Research Journal, 11(2): 51–63.Deery, M. & Shaw, R.

(1999). An investigation of the relationship between employee turnover and organizational culture. Journal of Hospitality & Tourism Research, 23(4), 387 – 400.Eagly, A.

H. & Chaiken, S. (1993). The Psychology of Attitudes. Journal of Loss Prevention in the Process Industries, 8(5), 299 – 305. Farrell, B. & Twining-Ward, L.

(2004). Re-conceptualizing Tourism. Annals of Tourism Research, 21(2), 274-295.Fazio, R. H., & Williams, C. J.

(1986). Attitude accessibility as a moderator of the attitude–perception and attitude–behavior relations: An investigation of the 1984 presidential election. Journal of personality and social psychology, 51(3), 505.Fiss, P. C. (2007).

A set-theoretic approach to organizational configurations. Academy of management review, 32(4), 1180–1198.Freeland, B.

(2000). Demands of training: Australian tourism and hospitality. Adelaide, Australia: National Centre for Vocational Education Research.Getz, D. (1994). Students work experiences, perceptions and attitudes towards careers in hospitality and tourism: A longitudinal case study in Spey Valley, Scotland.

International journal of hospitality management, 13(1), 25-37. Gummesson, E. (2008). Extending the service-dominant logic: From customer centricity to balanced centricity. Journal of the Academy of Marketing Science, 36(1), 15–17.Heskett, J. L.

, Jones, T. O., Loveman, G.

W., Sasser Jr., W. E.

, & Schlesinger, L. A. (1994). Putting the service-profit chain to work. Harvard Business Review, 72, 164–174.Kan, A. K. S.

, E. Adegbite, S. El Omari, and M.

Abdellatif. (2016). “On the Use of Qualitative Comparative Analysis in Management.” Journal of Business Research, 69(4), 1458–63.Koko, J.

and Guerrier, Y. (1994). Over-education, underemployment and job satisfaction: A study of Finish hotel receptionists. International journal of hospitality management, 13(4), 357-386.Kusluvan, S., & Kusluvan, Z. (2000). Perceptions and attitudes of undergraduate tourism students towards working in the tourism industry in Turkey.

 Tourism management, 21(3), 251-269.Lewis, A. and Airey, D. (2001).

Tourism careers in Trinidad and Tobago: Perceptions of secondary school students. Tourism and hospitality research, 3(1), 7-20.Lokuhetti, A. (2014). Refreshingly Sri Lanka. Colombo: Ceylon Sights (Pvt.) Ltd.

.McKercher, B. (1999). A Chaos Approach to Tourism. Tourism Management. 20(3), 425-434McGuire, W. J. (1985).

Attitudes and attitude change. In Handbook of Social Psychology, (Eds.) Gardner, L and Aronson, E. Vol 2, Random House, New York.Ministry of Tourism and Christian Religious Affairs. (2017). Sri Lanka tourism strategic plan 2017 – 2020.

Colombo: Sri Lanka Tourism Development Authority.Murphy, P. E.

(1985). Tourism: A community approach. New York, Plenum.Olya, H. G., & Altinay, L.

(2016). Asymmetric modeling of intention to purchase tourism weather insurance and loyalty. Journal of Business Research, 69(8), 2791-2800.Ordanini, A., Parasuraman, A., & Rubera, G. (2014). When the recipe is more important than the ingredients a qualitative comparative analysis (QCA) of service innovation configurations.

Journal of Service Research, 17(2), 134–149.Pavesic, D.V. & Brymer, R. A. (1990). Job satisfaction: What’s happening to the young managers? Cornell Hotel and Restaurant Administration Quarterly, 30(4), 90–96.

Purcell, K. and Quinn, J. (1996).

Exploring the education-employment equation in hospitality management: A comparison of graduates and HND’s. International journal of hospitality management, 15(1), 51-68.Ragin, C. C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.

Ragin, C. C. (2006).

Set relations in social research: Evaluating their consistency and coverage. Political Analysis, 14(3), 291-310.Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond (Vol. 240). Chicago: University of Chicago Press.

Ragin, C. C. (2009). Qualitative comparative analysis using fuzzy sets (fsQCA). In B. Rihoux, & C.

Ragin (Eds.), Configurational comparative methods (pp. 87-122). Thousand Oaks, CA: Sage.Richardson, S. (2008). Undergraduate tourism and hospitality students’ attitudes toward a career in the industry: a preliminary investigation. Journal of Teaching in Travel ; Tourism 8(1), 23-46.Rihoux, B. ; Ragin, C.C. (2009). Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques. California: Sage.Rosentbluth, H. (1991). Tales from a nonconformist company. Harvard Business Review, 69 (July/August), 26-36.Ross, G .F. (1994). What do Australian school leavers want of the industry? Tourism Management, 15(1), 62-66.Ross, G. F. (1997). Travel agency employment perceptions. Tourism management, 18(1), 9}18.Schneider, C. Q., ; Wagemann, C. (2010). Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comparative Sociology, 9, 397–418.Silva, G. M., ; Gonçalves, H. M. (2016). Causal recipes for customer loyalty to travel agencies: Differences between online and offline customers. Journal of Business Research, 69(11), 5512–5518.Sindiga, I. (1994). Employment and training in tourism in Kenya. Journal of tourism studies, 5(2), 45-52.Stevenson, N., Airey, D., ; Miller, G. (2009). Complexity theory and tourism policy research. International Journal of Tourism Policy, 2(3), 206-220.Super, D. E. (1957). The Psychology of Careers. Harper Row, New York.Urry, J. (2005). The complexity turn. Theory Culture and Society, 22(5), 1.Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463e472.Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463–472.Woodside, A. G. (2014). Embrace• perform• model: Complexity theory, contrarian case analysis, and multiple realities. Journal of Business Research, 67(12), 2495–2503.Woodside, A. G. (2015a). Visualizing? matching? generalizing: Case identification hypotheses and case-level data analysis. Australasian Marketing Journal; AMJ, 23(3), 246–258.Woodside, A. G. (2015b). Iconic studies relevant for research in marketing and the Journal of Global Scholars of Marketing Science. Journal of Global Scholars of Marketing Science, 25(3), 259–278.Woodside, A. G., ; Zhang, M. (2012). Identifying x-consumers using causal recipes: “Whales” and “jumbo shrimps” casino gamblers. Journal of Gambling Studies, 28(1), 13-26.Wu, P. L., Yeh, S. S., ; Woodside, A. G. (2014). Applying complexity theory to deepen service dominant logic: Configural analysis of customer experience-and-outcome assessments of professional services for personal transformations. Journal of Business Research, 67(8), 1647–1670.Zeithaml, V. A., ; Bitner, M. Jo. (1996). Services marketing. New York: McGraw-Hill.


I'm Casey!

Would you like to get a custom essay? How about receiving a customized one?

Check it out