The influence of prior subject knowledge, prior ability and work experience on self-efficacy

Journal of Hospitality, Leisure, Sport & Tourism Education 12 (2013) 59–69

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Academic Papers

The influence of prior subject knowledge, prior ability and work experience on self-efficacy Elizabeth M. Ineson a,n, Timothy Jung a,1, Charles Hains b,2, Mincheol Kim c,3 a b c

Manchester Metropolitan University, Hollings Faculty, Old Hall Lane, Manchester M14 6HR, United Kingdom Hotel and Tourism Management Institute (HTMi), CH-6174 S¨ oerenberg, Switzerland Department of Management Information Systems, Jeju National University, Jeju City, South Korea

a r t i c l e i n f o

Keywords: Self-efficacy Subject knowledge Prior ability Work experience

abstract The factors that might enhance the learning achieved by students from a business simulation are examined to determine the extent to which prior ability, and knowledge gained through prior studies and/or work experience impact on self-efficacy. Immediately prior to their participation in a Hotel Operations Tactics and Strategy (HOTS) business simulation course, 326 international students’ prior subject knowledge, prior ability and self-efficacy were measured via an on-line survey. The findings indicate that self-efficacy is influenced positively by prior knowledge and prior ability. Further, it is revealed that work experience does not have any significant moderating effect between either prior knowledge or prior ability and self-efficacy. & 2012 Elsevier Ltd. All rights reserved.

1. Introduction The taxonomy of learning developed by Bloom, Engelhart, Furst, Hill, and Krathwohl (1956) presented educators with a structured plan for creating learning goals for which a strategy of instruction could be developed (Lowe & Holten, 2005). The desire of educators may be to take students in a given programme of study through a cognitive path designed to develop their ability to process information in order to achieve pre-specified educational outcomes. In vocational education, such as hospitality, it is imperative as part of the learning process that as realistic an impression of the hospitality industry as possible is created (Chen & Downing, 2006). This ‘realism’ can be achieved, in part, by linking it to the students’ educational and work experiences. Computer technology is designed to stimulate learning and to promote a higher level of understanding in students within a particular subject area or discipline, than traditional lectures or even case studies (Tompson & Dass, 2000). The advent of new technology has therefore had a great influence on education, instructional delivery and the ways in which students learn (Lowe & Holten, 2005). As such, increasing demands are thus placed upon educators to not only to keep abreast of technological developments, but also to incorporate them within the classroom environment. Computer based business simulations are tools that bridge the gap between learned information and experiential learning and they help to achieve the desired higher level learning outcomes (application, analysis, synthesis and evaluation) identified by Bloom et al. (1956). Benefits noted in early studies using hospitality management simulations included high levels of student motivation, development of technical and interpersonal skills, experiential

n

Corresponding author. Tel.: þ44 161 247 2741; fax: þ 44 161 247 6334. E-mail addresses: [email protected] (E.M. Ineson), [email protected] (T. Jung), [email protected] (C. Hains), [email protected] (M. Kim). 1 Tel.: þ44 161 247 2701; fax: þ44 161 247 6334. 2 Tel.: þ41 41 488 11 25; fax: þ 41 41 488 23 44. 3 Tel.: þ82 64 754 3182; fax: þ82 64 724 3138.

1473-8376/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jhlste.2012.11.002

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motivation and leadership development (Chase, 1983). Later studies suggested that simulations have been shown to improve students’ self-efficacy (Tompson & Dass, 2000). Thus, more organisations are bringing simulations into their curricula to improve both effectiveness and appeal of formal lecture programmes (Aldrich, 2006). Apart from realism, there is a number of contributory factors that influence the learning achieved by students from a business simulation. The present paper is part of a larger study that attempts to measure the experiential learning achieved through the use of a business simulation and subsequently will determine the impacts of both self-efficacy, prior knowledge and ability, and work experience on experiential learning. Here, the focus is on determining the extent to which prior ability, and knowledge gained through prior studies and/or work experience impact on self-efficacy, which determines how people think, feel, motivate themselves and behave, all measured prior to the commencement of the business simulation. 2. Theory 2.1. Experiential learning 2.1.1. Definition A number of definitions of experiential learning has been suggested. Hoover and Whitehead (1975) state that: ‘‘Experiential learning exists when a personally responsible participant cognitively, affectively, and behaviourally processes knowledge, skills, and/or attitudes in a learning situation characterized by a high level of active involvement’’ (p.25). Kolb (1984) defines experiential learning as a ‘‘process whereby knowledge is created through the transformation of experience’’ (p.38) and Feinstein, Mann, and Corsun (2002) define it as ‘‘a participatory method of learning that involves a variety of a person’s mental capability. It exists when a learner processes information in an active and immersive learning environment’’ (p.733). These definitions support the belief that at the centre of experiential learning theory lies the fundamental principle that learning occurs when an individual is engaged with concrete experience; thus experiential learning is a sequence of events that requires active involvement by the student at various points (Walters & Marks, 1981). This principle of learning through doing has its roots in the ancient quote attributed to Confucius ‘‘I hear and I forget, I see and I remember, I do and I understand’’ (cited in Specht & Sandlin, 1991, p.196). The ‘hearing’ and ‘seeing’ are typical of a more traditional classroom that is instructor and content centred whereas the ‘doing’ places the focus on the student and thus learning becomes student centred (Barr & Tagg, 1995). 2.1.2. Experiential learning model Kolb (1984) created a cyclic model of learning that begins with a concrete experience (CE). In other words, when a person learns by doing, the learning is task orientated. Such learning leads to the reflective observation (RO) stage of the cycle where a person reflects on the experience and asks himself/herself the ‘why’ question. The abstract conceptualisation (AC) takes the CE and RO to test existing concepts or to form new ones (Saunders, 1997). The final stage of the cycle, active experimentation (AE) is putting what has been learned into practice then the cycle has gone full circle. As Kolb points out, when the cycle recommences, the learner enters at a higher level of ‘‘cognitive functioning’’ (1984, p.23). Thus the learning cycle follows an upward spiral during the learning process (Saunders, 1997). Kolb’s experiential learning model supports Bloom’s (1956) taxonomy of educational objectives at the higher levels of the cognitive domain. Bloom’s hierarchy begins with the basic level of knowledge (remembering previously learned material) then progresses through comprehension (understand the meaning of material), application (using the material in a new situation), analysis (break down the material into component parts), synthesis (put parts together to form a new whole) and concludes with evaluation (ability to judge the value of the new material). The CE, RO and AC stages of Kolb’s (1984) cycle correspond to the analysis, synthesis and evaluation in Bloom’s (1956) taxonomy with the AE being the application stage and the cycle begins again with CE/analysis but at a higher level of learning (Gopinath & Sawyer, 1999). 2.1.3. Experiential learning using business simulations Kolb and Lewis (1986) suggested that simulations offered learners the best support for active experimentation as the experiential learning environment was far broader than that offered by cases. Current literature reveals that business simulations whether they be used in international relations (Lewis, 2005; Shellman & Turan, 2006), management (Adobor & Daneshfar, 2006), accountancy (Chen & Downing, 2006), entrepreneurship (Marriott, 2004), strategy (Doyle & Brown, 2000; Gopinath & Sawyer, 1999; Kendall & Harrington, 2003 Washbush & Gosen, 1998), or hospitality management (Edelheim & Ueda, 2007; Martin & McEvoy, 2003) are seen as a pedagogical benefit to student learning. Kolb’s (1984) experiential learning cycle has been used by some authors (see, for example, Chen & Downing, 2006; Edelheim & Ueda, 2007; Gopinath & Sawyer, 1999; Marriott, 2004; Saunders, 1997; Tompson & Dass, 2000) as the model to explain the cyclic process students go through when using a business simulation built on an iterative (repetitive with incremental refinements or adjustments) framework, which is typically computer based. 2.1.4. The simulation package The simulation package used for the present research is the ‘‘Hotel Operations Tactics and Strategy’’ (HOTS) computer based business simulation. Developed by the Orange Simulation Company (1998–2005), a British based company focused

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on the services industry, HOTS is a popular computer simulation used globally in over 80 higher education institutions (Current Users, n.d., 2007). HOTS, simulates the operation of a 250 room full service hotel based on a ‘real time’ decision making process leading to cause–effect relationships. The strength of the simulation lies in its ability to integrate key components of strategy, finance, marketing, sales, operations, human resources and service quality (Edelheim & Ueda, 2007: Martin & McEvoy, 2003). The current HOTS package (version 4.1a), can be set in either competitive mode (maximum of eight hotels) where teams compete against each other, or stand-alone mode where teams compete against the simulation model. In either mode students are required to make decisions, covering either one week or one month, on such things as room pricing, room yield, food and beverage (F&B) pricing and costing, staffing levels, training expenditure, advertising, guest comfort, property refurbishment and capital expenditure on extra facilities ranging from mini-bars to the purchase of a conference centre and health club. Throughout the simulation students have access to an assortment of market research to guide their decision-making; following each time period, a detailed analysis of performance is provided through financial reports (according to the Accounting Standards Board) and operational indicator reports. The cyclic process that students go through (plan, decision, result, reflect, draw conclusions, plan) is suggested to mirror Kolb’s (1984) experiential learning model (cf. Chen & Downing, 2006; Edelheim & Ueda, 2007). The skill sets measured by HOTS parallel those skill sets that are seen to be important in the hospitality industry (Nelson & Dopson, 2001). It has been suggested that HOTS is an effective teaching tool as it enhances the learning experience in applying business concepts whilst testing critical and analytical thinking skills (Martin & McEvoy, 2003). However HOTS, and in general computer based business simulations, are only of value when used as part of a continuous process of lecture and evaluation as opposed to being used as ‘‘off the shelf’’ products (Edelheim & Ueda, 2007). 2.2. Self-efficacy 2.2.1. Definition and dimensions of self-efficacy Computer based business simulations have been shown to improve a student’s self-efficacy more so than the traditional case study approach found in many capstone business courses (Tompson & Dass, 2000). Bandura (1997) defined selfefficacy as ‘‘beliefs in one’s capabilities to organise and execute the course of action required to produce given attainment’’ (p.3). He suggests that efficacy beliefs vary on three dimensions according to level, generality and strength. A person’s level of self-efficacy may vary according to the task demanded and the degree of perceived difficulty by that person of the given task such that the ‘‘range of perceived capability for a given person is measured against levels of task demands that represent varying degrees of challenge or impediment to successful performance’’ (p.42). However, a person’s efficacy beliefs also vary in strength based on a conviction in his/her own capability to overcome difficulties and obstacles. A stronger sense of personal self-efficacy leads to greater perseverance such that the probability of performing the chosen activity successfully is higher. Finally, a person may consider himself/herself to be efficacious across either a broad range of activities or within a particular sphere. ‘‘Assessments linked to activity domains and situational contexts reveal the patterning and degree of generality of people’s beliefs in their efficacy.’’ (p.43). 2.2.2. Sources of self-efficacy Bandura (1997) identified four sources of self-efficacy: (i) enactive mastery experience; (ii) vicarious experience; (iii) verbal persuasion; and (iv) physiological and affective states. Of these, enactive mastery experiences are considered as the prime source of informational influence related to a person’s efficacy ‘‘because they provide the most authentic evidence of whether one can muster whatever it takes to succeed.’’ (p.80). Successes build a strong belief in personal efficacy whilst failures weaken it. A person who is unable to judge for himself/herself a given attainment for an activity will look typically at the performances of others on the same activity (vicarious experience) to gauge whether s/he is a relatively good or poor performer. Research has shown that outperforming others raises self-efficacy beliefs whilst being outperformed lowers those of others (Bandura, 1997). Similarly, when faced with difficult tasks, verbal persuasion, about his/her capability to succeed will sustain a person’s efficacy; in contrast, if doubts are raised, efficacy may be affected negatively. ‘‘People who are persuaded verbally that they possess the capabilities to master given tasks are likely to mobilize greater effort and sustain it than if they harbour self-doubts and dwell on personal deficiencies when difficulties arisey.to raise unrealistic beliefs of personal capabilities, only invites failure.’’ (p.101). 2.2.3. The development of self-efficacy Bandura’s (1997) linked the development of self-efficacy to social cognitive theory through the four sources noted in Section 2.2.4. In particular, mastery experiences are associated with actual successes such as passing examinations, are powerful contributors to self-efficacy development as they provide hard, convincing evidence of success (Bandura, 1997; Palmer, 2006). In contrast, Bandura (1997) pointed out that failure can lower self-efficacy, especially before a robust sense of efficacy is developed. A person who is unable to judge for himself/herself a given attainment for an activity will look typically at the performances of others on the same activity (vicarious experience) to gauge whether they are good or poor performances (Bandura, 1997) Observation of others, such as that gained through work experiences, particularly in relation to the on-the-job training which is common at operative level and especially in the hospitality industry (Poulston, 2008), can increase self-efficacy (Schunk, 1987). Although, in line with mastery experiences, observational experiences can be weakened by subsequent failures (Schunk, 1989), the overall impact of mastery experience on self-efficacy

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development is greater than that of vicarious experience (Bandura, 1997). He continues: when faced with difficult tasks, verbal persuasion, about his/her capability to succeed will sustain a person’s self-efficacy; in contrast, if doubts are raised, efficacy may be affected negatively. Finally, Bandura (1997) suggests that when judging capability, a person is reliant to a certain extent on his/her physiological and/or emotional state. Typically people are more likely to consider they are less capable in times of adverse stress and negative mood states; indications of bodily fatigue, aches and pains during activities involving strength and stamina are linked to physical inefficacy. Thus self-efficacy beliefs are altered positively altered when a person’s physical status is enhanced, and stress levels are reduced along with negative emotional tendencies (cf. Cioffi, 1991). 2.2.4. The contribution of prior academic ability and knowledge to self-efficacy van-Dinther, Dochy, and Segers (2011) remarked on the importance of the construct of student self-efficacy in educational research and stressed the accountability of the application of knowledge and skill in influencing students’ selfefficacy. With respect to prior education, numerous studies have linked factors associated with expanding knowledge, developing ability and achievement outcomes with the enhancement of self-efficacy (For example, Alkharusi, 2011; Bandura, 1997; Pajares, 1996; Pajares & Miller, 1997; Schunk, 1996; Schunk & Pajares, 2002). Furthermore, Bandura’s (2006) argument that people’s knowing is directed at building meaning between past and present in order to sustain a proactive view of self is consistent with self-efficacy theory. Familiarity with computers and computer ability were proved to be predictive of self-efficacy by both Cassidy and Eachus (2002) and Hutchison, Follman, Sumpter, and Bodner (2006). The latter, whose studies embraced 1387 first year engineering students in the United States, also cited ‘understanding the materials’ as a strong predictor of self-efficacy and it is acknowledged that prior knowledge and ability are antecedents of understanding (cf. Bloom, 1956). In contrast with the majority, Cantrell, Young, and Moore (2003) found that Turkish preservice elementary teachers’ with low science knowledge level scored moderately on self-efficacy with respect to their perceived ability as teachers. Interestingly, persuasive communication and evaluative feedback are more effective when their providers, such as their teachers, are perceived by students to be knowledgeable and reliable, and the information is realistic (Bong & Skaalvik, 2003). Positive communication and feedback contribute to students’ beliefs about their capabilities, so developing their self-efficacy (Bandura, 1997; van-Dinther et al., 2011). Reviewing the literature to date, van-Dinther et al. (2011) highlighted the importance of the role of self-efficacy in relation to students’ achievements. Although most research to date in relation to ability levels appears to have focused on demonstrating the direct and indirect effects of students’ self-efficacy on their subsequent achievements, that is, with self-efficacy being classed as an independent variable. van-Dinther et al. (2011) concluded that educational programmes had the possibility to enhance students’ self-efficacy. They maintained that higher educational institutions should focus not only on the development of their competencies but also on students’ self-efficacy development. Therefore, in order to provide a baseline against which to measure the impact of on-course learning and performance on self-efficacy development, it is deemed appropriate to examine the relationship between students’ prior knowledge and ability their self-efficacy prior to undergraduate entry. The following hypotheses are out forward: H1. Prior knowledge has a positive influence on self efficacy. H2. Prior ability has a positive influence on self efficacy. 2.2.5. The role of work experience in the development of self-efficacy In addition to the necessary academic entry qualifications, prior work experience is a desirable prerequisite for entrants to vocational management courses (cf. Raybould & Wilkins, 2005). Therefore, the majority of students on such courses have some, often industry relevant and sometimes substantial, work experience. Research, (for example, Barron, 2008) has indicated that such experience is beneficial in terms of students’ subsequent on-course performance, perhaps due in part to its impact on their self-efficacy (Brooks, Cornelius, Greenfield, & Joseph, 1995). In turn, it has been claimed that selfefficacy is central to career choice (Lucas, Cooper, Ward, & Cave, 2009). Although this issue had not been explored to date in a hospitality context, the literature points, in general, to the positive influence of work experience on self-efficacy. Wood and Bandura (1989), using a simulated organization, found that the organizational attainments of managers, including personal goal setting and the implementation of analytical thinking strategies, were improved directly by their perceived managerial self-efficacy, and prior experience in nursing students was seen to contribute positively to self-efficacy (Tresolini & Stritter, 1994). A subsequent meta-analysis of 114 studies conducted by Stajkovic and Luthans (1998) reported a significant positive relationship between work-related performance and self-efficacy. In contrast, Coll, Zegwaard, and Lay (2001) noted that vicarious workplace experiences can lower self-efficacy, exemplified by a demotivating comment made to a student by an experienced colleague. Nevertheless, Judge and Bono (2001) confirmed self-efficacy to be related to work performance. Cassidy and Eachus (2002) developed the computer user self-efficacy (CUSE) scale and established a significant positive relationship experience in using computers and computer self-efficacy. Furthermore, Tang et al. (2004), sampling 107 masters’ level psychology counselling students, found a significant link between both prior related work experience and length of internship and self-efficacy. Pajares (1996) affirmed that work which is related closely to student study areas promotes the enhancement of knowledge and skills, commenting that such experience leads to the development of enhanced self-efficacy through subject mastery within specific task domains. Lucas et al. (2009) further supported the value of work experience in relation to self-efficacy development. Following the industrial placement of 400

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engineering undergraduates in the United Kingdom, they recorded close relationships between undergraduates’ courses of study, their perceptions of their work performance and feedback on their work performance, and self-efficacy, suggesting a potential moderating effect of work experience. On the basis of the fairly scant literature, it appears that work experience can have a positive impact on students’ self-efficacy, particularly if it is linked to the academic course of study. It is therefore hypothesized that: H3. Work experience has a significant moderating effect between the prior knowledge and self efficacy. H4. Work experience has a significant moderating effect between the prior ability and self efficacy. 3. Methods 3.1. Research population and sample frame The research population was defined as all hospitality students who use HOTS during their programme of study. To ensure the sampling frame was as unbiased and representative as possible the following issues were considered: (i) the population came from students studying hospitality as a major and the HOTS business simulation was used as a formal part of their studies; and (ii) the instructors who used HOTS followed similar teaching strategies in terms of outcomes and the syllabus at each institution covered the key skill sets as defined in the HOTS simulation. The sample frame came from undergraduate hospitality majors at three higher education institutions in the UK, Australia and Switzerland. This geographical spread provided a broad and diverse range of students in three education systems. The institutions were chosen as the instructors had used HOTS for more than three years, and there was some similarity in their teaching styles and methods so limiting extraneous influences. The UK based teacher was a previous member of faculty of the Swiss college and the Australian programme is a franchise of the Swiss college, resulting in similar approaches to teaching and learning. The sample consisted of 326 students over the three institutions who had enrolled on courses that utilised HOTS. 3.2. Data collection The data were collected using the open source MoodleTM on-line learning platform via an electronic questionnaire which was supervised by the course leader at each institution. A pilot study was run at the sister campus of the Swiss institution to ensure the on-line instructions were clear, that the MoodleTM platform functioned correctly and that the downloaded data was in a useable format. The original questionnaire used page breaks to separate each section. However, this method created a system glitch that affected the data. Once the page breaks were removed, the questionnaire functioned correctly for all data collection. The average time to complete the questionnaire was between 8 and 10 min. The structured self-reporting questionnaire was broken into five sections. Section A covered basic demographic information such as the student identification number, date of birth, gender, institution and start date of the students’ current programme of study. This format separated data by institution, gave two control variables (gender and age) and served to match with the collection of post-test simulation data (student number and date of birth) to be examined subsequently in a later study. Section B was a measure of the students’ general self-efficacy (GSE) using the new general self-efficacy scale (NGSE) developed by Chen, Gully, and Dov (2001). The 8-item NGSE scale is an alternative to the 17-item General SelfEfficacy Scale (SGSE) developed by Sherer et al. (1982), which has been the most widely used GSE measure and cited in over 200 published studies (Chen et al., 2001). The benefits of the NGSE was that it was shorter, had higher construct validity, demonstrated a higher reliability, and was one-dimensional thus easier to interpret than the multi-dimensional SGSE scale (Chen et al., 2001). The 8-item NGSE scale was scored on a 5-point Likert scale from (1) strongly disagree to (5) strongly agree. Bandura (1997) suggested that ‘‘efficacy beliefs are both products and constructors of experience’’ (p.82) thus Section C recorded details of students’ prior work experience (length of time, full-time or part-time, and field) and prior studies. A part-time position was defined as working fewer than 20 h per week and full-time 20 h or more per week. This range was identical to that used in the NGSE scale study by Chen et al. (2001). The choices given in this section of the questionnaire were specific to the skill sets integrated within the HOTS simulation. However, students were given the opportunity to list other work experience and courses outside the range stated. Sections D and E used 10-point semantic scaling (Saunders, Lewis, & Thornhill, 2007) from poor to excellent over 23 matched questions to measure students’ knowledge and ability related to the skill sets built within the HOTS simulation or apparent within the syllabus of the appropriate course at each institution. The approach was similar to the framework proposed by Tompson and Dass (2000) who measured students’ efficacy both pre- and post-simulation using matched pairs of items based on the core concepts found in the strategic management curriculum. The first paired item measured understanding the concept and the second the application of the concept. The two-part measure of knowledge and application corresponded to measures of self-efficacy for fixed and generative skills (Tompson & Dass, 2000). Referring back to Bandura (1997), who suggested that efficacy beliefs vary on three dimensions according to level, generality and strength, the NGSE assessment in section B could be compared subsequently to the task specific self-efficacy measures in sections D and E with any influence from efficacy related to work experience and/or knowledge, based on the results obtained in section C.

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3.3. Data analysis The data were processed using SPSS with the measures for general self-efficacy (using NGSE) being used as the dependent variable whilst pre-knowledge (PK) and pre-ability (PA) were employed as independent variables. Furthermore, work experience was used as a mediating effect variable. First, a series of exploratory factor analyses was conducted using varimax rotation to examine the dimensionality of the independent variables, prior knowledge and ability, and the dependent variable, self-efficacy. Second, the influence of the independent variable factors on the dependent variable factors was examined via multiple regression analysis. Finally, further multiple regression analysis was conducted to assess the potential moderating effect of work experience between prior knowledge and ability, employing Baron and Kenny’s approach. According to Baron and Kenney (1986), the moderator is a qualitative or quantitative variable that affects the direction of the relationship between an independent variable and a dependent variable. In the analysis of the data, all results reported at p ¼0.001, 0.01, or 0.05 if significant, otherwise differences were reported as not significant (NS). 4. Research model and hypothesis The hypotheses of the research are as follows and the research model can be seen in Fig. 1. H1. Prior knowledge has a positive influence on self efficacy. H2. Prior ability has a positive influence on self efficacy. In addition, this study aims to discover whether work experience has a function as moderate variable and therefore, following hypotheses are proposed as additional hypothesis. H3. Work experience has a significant moderating effect between the prior knowledge and self efficacy. H4. Work experience has a significant moderating effect between the prior ability and self efficacy. 5. Results: Analysis and discussion 5.1. Analysis of demographics, fields of work and subjects The respondents’ demographic profiles were summarised in Table 1 according to institution, gender, age and work experience. The UK sample comprised 57.7% (n¼188) of the total and the Australian and Swiss samples constituted 22.4 (n ¼73) and 19.9% (n ¼65), respectively. The age (in years) of the respondents was calculated based on the day the pre-test questionnaire was completed. The mean age of the respondents was 21.3 years with an age range of 18–41 years. The majority of students were female (63.8%) within all institutions, with the greatest gender disparity in the UK institution (female: male; 7:3). This gender disparity follows a similar trend in undergraduate tourism and hospitality programmes in Austria (Aubke & Amata, 2008) and Australia (O’Mahoney, Whitelaw, & McWilliams, 2008). Of the 326, only 4.6% had no work experience, 61.4% had worked for two or more years. Given that formal work experience is part of all the programmes of study it is not surprising that 61.7% (n ¼192) of the students with work experience had been recruited to full-time positions. In order to test the hypotheses, the length of work experience was measured as quantitative variable and scored on a 5-point scale as follows: no experience (n ¼15); o6 months (n ¼58); Z6 months o12 months (n¼73); Z12 months o24 months (n ¼77); and Z24 months (n ¼123). Students were asked to indicate in which fields they had work experience and which subject areas (courses) they had undertaken prior to the simulation. The choices offered reflected the key skill sets of the HOTS business simulation. 5.2. Exploratory factor analysis prior to hypotheses testing The first step was to perform an exploratory factor analysis on the prior knowledge variable. Reference to Table 2 shows that four factors representing: financial knowledge (F1); management knowledge (F2); marketing knowledge (F3); and business knowledge (F4) were identified; the overall predicted variance was 76.3%. In order to check the reliability of each Prior Knowledge

H1

Self Efficacy H2

Prior Ability

H4

H3

Work Experience Fig. 1. Research model.

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Table 1 Frequencies and cross tabulation of gender, location and work experience. Institution

UK

Australia

Switzerland

Total

Gender

Total

% of total

N

%

Female Male Total Female Male Total Female Male Total

131 57 188 39 34 73 38 27 65

69.7 30.3

Female Male Total

208 118 326 326

Overall

57.7 53.4 46.6 22.4 58.5 41.5 19.9 63.8 36.2 100 15

Work experience period (months) 0

o6

Z6 o12

Z 12o 24

10 4 14 0 1 1 0 0 0

21 5 26 1 2 3 6 3 9

16 10 26 12 7 19 17 11 28

28 12 40 17 9 26 5 6 11

56 26 82 9 15 24 10 7 17

10 5 15

28 10 38 111

45 28 73

50 27 77 200

75 48 123

Z 24

Table 2 Factor analysis of prior knowledge, prior ability, and self efficacy. Variables

Prior knowledge

Prior ability

Loading value (Max–Min) Eigen value Accumulated rate Cronbach’s alpha (reliability coefficient) KMO(Kaiser–Meyer–Olkin measure Bartlett’s test of sphericity

Factor name Financial knowledge

Management knowledge

Marketing knowledge

Business knowledge

0.851–0.567 4.879 21.212 0.928

0.813–0.705 4.849 42.296 0.933

0.784–0.637 4.083 60.048 0.960

0.837–0.688 3.745 76.333 0.890

of sampling adequacy) Approx. Chi-square Factor name Marketing/business ability 0.816–0.653 5.570 23.453 0.957

Loading value (Max–Min) Eigen value Accumulated rate Cronbach’s alpha (reliability coefficient) KMO (Kaiser–Meyer–Olkin measure of sampling adequacy) Bartlett’s test of sphericity Approx. Chi-square Self-efficacy

0.936 7633.461 (p ¼0.001)

Management ability

Financial ability

0.771–0.572 5.169 44.855 0.949

0.822–0.466 4.852 64.672 0.921

Applied financial ability 0.748–0.703 2.763 77.684 0.932

0.950 8821.882 (p ¼0.001)

Factor name Self Efficacy 0.833–0.690 4.794 59.927 0.904

Loading value (Max–Min) Eigen value Accumulated rate Cronbach’s alpha (Reliability coefficient) KMO (Kaiser–Meyer–Olkin measure of sampling adequacy) Bartlett’s test of sphericity Approx. Chi-square

0.916 1370.065 (p ¼ 0.001)

factor, Cronbach’s alpha was calculated and all factors recorded over 0.80 so the resultant coefficients were indicative of high internal consistency, therefore confirming their internal reliability (cf. Nunnally, 1978). With regard to prior ability, four factors also emerged from the analysis: marketing/business ability (F1), management ability (F2), financial ability (F3), and applied financial ability (F4) with an overall predicted variance of 77.7% (see Table 2). However, only the management dimension was a common feature. In contrast to prior knowledge, the marketing and business variables were combined in one factor and the financial variables were subdivided into financial ability and applied financial ability; the latter includes the liquidity and solvency ratios and operating ratios. Cronbach’s alpha of prior ability factor was over 0.90. Therefore, the result shows very high internal consistency.

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The next step was to examine the dimensionality of the dependent variables by means of a final factor analysis, the results of which are reported in Table 2. All of the variables loaded onto one factor with high reliability (a ¼0.904), confirming the validity of the NGSE as a measure of self-efficacy in this context. In addition, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was above 0.9 (meritorious; Kaiser, 1974), and, along with the very highly significant Bartlett’s tests of sphericity, this confirms the validity of the factor analyses (cf. Norusis, 2005) in above factor analyses (Table 2). 5.3. Regression analysis to test Hypotheses 1 and 2 Two regression analyses were conducted with self efficacy as a dependent variable, and the prior knowledge and prior ability factors as the two independent variables, in order to determine their interrelationships. Table 3 shows the significant (p ¼0.001) F-value associated with each regression equation. It is apparent from the data in Table 3 that all of the factors, with the exception of the financial factor in prior knowledge and the applied financial factor in prior ability, are highly statistically significant (p¼ 0.01). The management factors in both prior knowledge and prior ability plus the marketing/business factor in the latter group proved to be the most significant (p¼ 0.001). Furthermore, Table 3 shows that the financial factor in prior ability was statistically significant although the applied financial factor was not significant in prior ability. 5.4. Regression analysis to test Hypotheses 3 and 4 The regression analysis to test Hypotheses 3 and 4 was performed to identify any moderating effect of work experience on the relationships of the prior knowledge and ability factors and self efficacy, using the method of Baron and Kenney (1986) as portrayed in Fig. 2. The present study used the length of work experience in the questionnaire items as moderator variable of work experience. Fig. 2 has three causal paths that connect with the dependent variable of self efficacy: the impact of the prior knowledge and prior ability as a predictor (Path a), the impact of work experience level as a moderator (Path b), and the interaction or product of these two (Path c). The objective of the moderator Hypotheses 3 and 4 is to examine whether the interaction (Path c) is significant. However, moderator variables always have a role as Table 3 Regression analysis: results of tests for Hypotheses 1 and 2. Model

Prior knowledge Constant Financial Management Marketing Business Prior ability Constant Marketing/business Management Financial Applied financial

Non-standardized coefficients

Standardized coefficients

t-value

Significant probability

B

Standard error

Beta

 3.63E  17 .091 .182 .179 .150

.053 .053 .053 .053 .053

– .091 .182 .179 .150

.000 1.708 3.436 3.369 2.827

1.000 N.S. .001 .001 .01

1.036E  17 .199 .169 .138 .070

.053 .053 .053 .053 .053

– .199 .169 .138 .070

.000 3.748 3.173 2.600 1.309

1.000 .001 .01 .01 N.S.

R ¼.310, R2 ¼ .096, Adjusted R2 ¼ .085, F ¼8.518, Sig ¼.001***. R ¼.304, R2 ¼ .092, Adjusted R2 ¼ .081, F ¼8.148, Sig ¼.001***.

Predictor (Prior Knowledge & Prior Ability) Moderator (Work Experience)

(a)

(b)

(c)

Predictor × Moderator (Prior Knowledge & Prior Ability × Work Experience) Source: Baron and Kenny (1986)

Fig. 2. Model for the moderating effect analysis. Source: Baron and Kenney (1986).

Outcome Variable (Self Efficacy)

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Table 4 Regression analysis results of tests for Hypotheses 3 and 4. R

R2

Adjusted R2

Hypothesis 3 test (prior knowledge) 1 0.308(a) 0.095 0.083 2 0.356(b) 0.126 0.112 3 0.372(c) 0.138 0.113 Hypothesis 4 test (prior ability) 1 0.307(a) 0.094 0.082 2 0.356(b) 0.127 0.1130 3 0.368(c) 0.136 0.11

Std. Error of the estimate

Change statistics

Durbin-Watson

R square change

F change

df 1

df 2

Sig. F change

0.957 0.942 0.942

0.095 0.031 0.012

8.025 10.975 1.047

4 1 4

306 305 301

0.001 0.001 N.S.

1.789

0.957 0.942 0.944

0.094 0.033 0.009

7.952 11.478 0.744

4 1 4

306 305 301

0.001 0.001 N.S.

1.852

independent variables and therefore path b has a meaningful implication. In addition to the regression equation (a) of step 1, work experience, which was used as moderating variable (b) in step 2, had the explanatory power (R2) of 12.6% and 12.7% and the R2 was increased by 3.1% and 3.3%, respectively. Therefore, each corresponding F-value (10.975 and 11.478) showed a statistically significant increase (p¼0.01) and it showed that, by injecting work experience, the impact of prior knowledge and ability on self efficacy was increased further. Next, R2 by interaction effect of prior knowledge/prior ability and work experience in a committed step 3 (c) was recorded as13.8% and 13.6%, respectively. As compared to step 2, the R2 was increased by1.2% and 0.9%, therefore the F-value (1.047 and 0.744 in each case) had no significance. Finally, it was found that work experience level had no statistically significant moderating effect on the influence of the prior knowledge and prior ability factors on self efficacy, as shown in Table 4. Nevertheless, path b of Hypotheses 3 and 4 has a significant level (p ¼0.001 in each case) and therefore it is confirmed that work experience level has an independent role (variable) on self efficacy as opposed to being a moderator in this particular regression model. According to the regression analysis, it was confirmed that the management, marketing and business factors in prior knowledge have a positive influence on self-efficacy. However, the financial factor in prior knowledge has no influence on self-efficacy and therefore Hypothesis 1 was partially accepted. With regard to prior ability, the marketing/business, management and financial factors have a positive influence on self-efficacy however, the applied financial factor does not influence self-efficacy and therefore Hypothesis 2 was only partially accepted. Hypotheses 3 and 4 were tested to determine any moderating effect and, in this instance, it was revealed that work experience does not have a significant moderating effect between prior knowledge and self-efficacy. Although there may also be significant main effects for the predictor and the moderator (Paths a & b), Baron and Kenney (1986) proposed that the moderating effect is meaningful only if the interaction (Path c) is significant. It was found that work experience does not have a significant moderating effect between prior knowledge/ability and self-efficacy and therefore H3 and H4 were rejected. 6. Conclusions Interestingly, although the students’ perceptions of their prior knowledge and prior ability in relation to marketing, business and management revealed a statistically significant positive effect on their self-efficacy, the findings with regard to prior financial knowledge and ability were not so clear cut. Although there was a statistically significant relationship for financial ability (Cash Flow; Balance Sheet; Profit & Loss; Break Even Point; Ability: Cost Volume Profit; and Spreadsheets), neither overall prior knowledge of finance, nor ability in relation to the applied financial factors (Liquidity & Solvency; and Operating Ratios), was associated significantly with self-efficacy. These findings suggest that, although the students may judge themselves to be well prepared in terms of marketing business and management, they may lack confidence in their financial knowledge and ability prior to taking a HOTS course. It appears that work experience has no moderating effect between prior knowledge and self-efficacy, confirming the previous literature (for example, Barron, 2008), which means that work experience acts as a relatively independent factor which has a positive influence on self-efficacy rather than moderating factor. There seem to be two options in terms of taking these results forward: First, as it does not influence or contribute to building self efficacy, there should not be too much focus on the applied financial factors within HOTS, particularly if it is employed in the first year of study. It would seem prudent to focus on the marketing, business and management areas, and, ideally encouraging students to acquire some financial-related pre-entry work experience. Second, HOTS could be introduced later on in the course, perhaps following an applied finance course, or re-introduced with a strong focus on the financial aspects of the simulation. The present paper is part of a larger study to measure the students’ experiential learning using a hospitality business simulation. A post-test questionnaire was conducted at the conclusion of the simulation period in each institution; phase 2 of this study combines the pre- and post-data. The focus for the current paper was to measure the influences on learning

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prior to the simulation period in relation to students’ self-efficacy and to determine the extent to which work experience and prior knowledge influenced this learning. The addition of the NGSE scale (Chen et al., 2001) to the self efficacy framework proposed by Tompson and Dass (2000) was an attempt to separate general efficacy beliefs in a broad context, with efficacy beliefs for fixed skills being measured through current knowledge of a concept and generative skills measured through self ability to apply the concept. The results suggest that whilst students perceived themselves to be generally efficacious, their perceptions of their knowledge and ability in finance were relatively lower. It is also noted that students’ ages had no significant impact on any of the measures used in the study. On the basis of these findings a revision of the initial model (Fig. 1) is presented in Fig. 2. One of the limitations to the study is that the NGSE scale is a self-assessed 5-point scale whilst the scale used for knowledge and ability is a 10-point scale. Bandura (1997) suggests that having too few scale points loses information useful for the purpose of differentiation because ‘‘people who use the same response category would differ if intermediate steps were included’’ (p.44). However the fairly large sample size, coupled with a high internal reliability of the data means, indicates that realistic conclusions can be drawn from the data. Bandura (1997) also suggested that minimising ‘‘evaluative concerns over possible social reactions to one’s self appraisal’’ (p.45) leads to an enhanced accuracy of selfefficacy measures. In the present study, students were instructed that their answers to the questionnaire would have no bearing on their grades and, for questions requiring the use of the rating scales; they were advised that there were no right or wrong answers. 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