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Applied Ergonomics 42 (2011) 225e232

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Precarious employment, working hours, work-life conflict and health in hotel work Maria McNamara a, b, *, Philip Bohle a, Michael Quinlan b a b

Work and Health Research Team, Ageing, Work and Health Research Unit, Faculty of Health Sciences, The University of Sydney, PO Box 170, Lidcombe NSW 1825, Australia School of Organisation and Management, Australian School of Business, The University of New South Wales, Sydney NSW 2052, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 February 2010 Accepted 15 June 2010

Precarious or temporary work is associated with adverse outcomes including low control over working hours, work-life conflict and stress. The rise in precarious employment is most marked in the service sector but little research has been done on its health effects in this sector. This study compares permanent and temporary workers in the hotel industry, where working hours are highly variable. Survey data from 150 workers from eight 3-Star hotels in urban and regional areas around Sydney were analyzed. Forty-five per cent were male and 52 per cent were female. Fifty four per cent were permanent full-time and 46 per cent were temporary workers. The effects of employment status on perceived job security, control over working hours, and work-life conflict are investigated using PLS-Graph 3.0. The effects of control over working hours, on work-life conflict and subsequent health outcomes are also explored. Temporary workers perceived themselves as less in control of their working hours, than permanent workers (b ¼ .27). However, they also reported lower levels of work intensity (b ¼ .25) and working hours (b ¼ .38). The effects of low hours control (b ¼ .20), work intensity (b ¼ .29), and excessive hours (b ¼ .39) on work-life conflict (r2 ¼ .50), and subsequent health effects (r2 ¼ .30), are illustrated in the final structural equation model. Ó 2010 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Keywords: Precarious employment Working hours Work-life conflict Health

1. Introduction Precarious employment is characterized by insecurity, a lack of control over work processes, poor social protection, low income, and a general lack of benefits associated with more secure employment (Menendez et al., 2007; De Cuyper et al., 2008; Louie et al., 2006). Job and income insecurity are major concerns for precarious workers due to insufficient hours, intermittent scheduling and inability of employers to guarantee hours (Lewchuk et al., 2003; Louie et al., 2006). In many organisations, work schedules for the following week are posted at most a week in advance, thus allowing workers very limited opportunity to balance work, social and family responsibilities (Zeytinoglu et al., 2004). There is now a substantial body of research linking precarious employment with adverse health and safety outcomes (Quinlan and Bohle, 2008a; De Cuyper et al., 2008). It indicates that precarious employment is associated with poorer mental health

* Corresponding author at: Work and Health Research Team, Ageing, Work and Health Research Unit, Faculty of Health Sciences, The University of Sydney, PO Box 170, Lidcombe NSW 1825, Australia. E-mail address: [email protected] (M. McNamara).

(De Cuyper et al., 2008), increased cardiovascular morbidity (Ferrie et al., 2008), and poorer self-rated health (Kim et al., 2008). There is a growing body of medical and psychological research linking work organisation to health outcomes using the Job Strain (Karasek, 1979) and Effort/Reward Imbalance (Siegrist, 1996) models. These models predict that jobs characterized by low levels of control over work and high expenditure of psychosocial effort in completing assigned tasks expose workers to mental and physical health risks (D’Souza et al., 2003; Tsutsumi and Kawakami, 2004). However, they do not accommodate broader labour market and other institutional factors influencing the impact of precarious employment (Lewchuk et al., 2008). Such factors are now being incorporated in the Employment Strain (Lewchuk et al., 2008) and PDR (Quinlan and Bohle, 2008a) models. However, the mechanisms underlying the causal links in the association between precarious employment and OHS outcomes remain poorly understood. Unfortunately, there is limited knowledge regarding health and safety in the burgeoning service industries (Quinlan and Bohle, 2008a). This is disturbing when we consider that the shift towards precarious employment has been most discernible amongst women, immigrants and younger workers in industries such as tourism and hospitality (Polivka, 1996; Mayhew and Quinlan, 2002; Seifert and Messing, 2006). The hospitality

0003-6870/$ e see front matter Ó 2010 Elsevier Ltd and The Ergonomics Society. All rights reserved. doi:10.1016/j.apergo.2010.06.013

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industry has its own unique risks and hazards. Research highlights the vulnerability of hospitality workers to specific types of adverse outcomes including stress, burnout, “emotional injury”, violence and physical injuries (Hoel and Einarsen, 2003; Gleeson, 2001; Seifert and Messing, 2006). Stress in hotel work may be attributed to a combination of long and irregular working hours, bad job design and duties, insufficient training, excess workloads, outsourcing/work intensification and poor management (Hoel and Einarsen, 2003; Lo and Lamm, 2005; Seifert and Messing, 2006). There are few studies which empirically investigate stress in the hotel industry (Exceptions include Brymer et al. (1991) and Zohar (1994)). In both Brymer et al. (1991) and Zohar’s (1994) studies, autonomy and worker control are important mediating factors when considering stress in the hotel industry. There are a large number of studies showing that low levels of control are associated with outcomes such as distress, disease, high absenteeism, and increased turnover (Parkes and Sparkes, 1998; Zeytinoglu et al., 2004). Control over work has been found to improve stress, health and well-being, while reducing work-life conflict (Mauno et al., 2006). There is substantial evidence that temporary workers have less control over the timing of their work, work methods and the variety of tasks performed (Letourneux, 1998; Goudswaard and Andries, 2002; Paoli and Merillie, 2001). However, they may also experience fewer demands compared to permanent workers (Goudswaard and Andries, 2002), which may explain the findings of Saksvik et al. (2005) that, despite lower levels of control, temporary workers still reported lower stress levels than permanent employees. Nevertheless, studies on nonpermanent workers have had inconsistent results (Benavides et al., 2000; Saksvik et al., 2005). Faulkner and Patiar (1997) note that the autocratic management style, emphasizing managerial control, traditionally used in the hospitality industry may contribute to worker stress because of inadequate feedback on performance and lack of consultation and communication. Hotels have characteristics typical of lean service systems, which are significantly correlated with lower control, higher work demands and employee strain (Sprigg and Jackson, 2006). There are standardised ways of performing tasks, which are often repetitive, allow low discretion and are highly pressurized (Sprigg and Jackson, 2006). The issue as to what exactly constitutes precarious employment has been the subject of widespread debate. Measurement issues abound in current research. The use of taxonomic measures, such as employment status, has resulted in mixed findings (Quinlan and Bohle, 2008a,b; Louie et al., 2006). More perceptual assessments of employment arrangements have also been utilized (Probst, 2005). The debates continue towards a more comprehensive measure of precarious employment. This study utilizes employment status but also measures perceptual variables and examines their impact on workers outcomes. The value of employment status as an indicator of precariousness and subsequently of health outcomes is investigated in this study. The comparative value of worker perceptions of job security as a predictor of elements of precariousness and subsequent health outcomes is also investigated helping to determine which indicator is more useful. The relationship between employment status and perceptions of job insecurity is also investigated. The hypotheses of this study are outlined in the Hypothesised Structural Model (see Fig. 1). The direction of hypotheses is represented by arrows between constructs.

Generally permanent full-time workers work longer hours than temporary workers (ABS, 2006) who are used to meet fluctuating demands (Lai and Baum, 2005). Permanent full-time workers in the hotel industry often work excessively long hours (TTF, 2006; Hoel and Einarsen, 2003). Alternatively, those who perceive a lack of control may also experience excessively long shifts and too many weekly hours. These alternative pathways are illustrated in the hypothesised structural model (See Fig. 1). Hypothesis 3. Temporary workers will report lower levels of control over their hours. Hypothesis 4. Those who perceive themselves as less secure will report lower levels of control over their hours. Precarious workers are mostly used to meet changeable demands in the hotel industry. On-call work is common (Lai and Baum, 2005) and increased use has been made of temporary employment agencies in areas like cleaning and functions. The implications of working in such a manner are often highly variable and irregular hours with no control over the timing and duration of such hours. Qualitative research on permanent and temporary employees in five-star hotels illustrates the irregular and highly variable nature of the hours worked by temporary employees and consequences for work-life conflict and health (Bohle et al., 2004). Temporary workers reported much greater variation in working hours than full-time, permanent staff. Their hours ranged from zero to 73 h per week and shift lengths ranged from two to 18 h. Many were only advised of starting times, and never given finishing times for their shifts. In contrast, most permanent full-time workers reported much more regular hours (often fixed 8-h shifts with limited overtime). Bohle et al. (2004) found that temporary workers had less control than permanent workers over working hours, due to their weak labour market position and tenuous employment. The limited organisational power held by many temporary workers results in reduced capacity to exert control over work schedules and protect themselves from the most undesirable shift allocations (Aronsson, 1999). Bohle et al. (2004) also found that the combination of high work intensity, variable and unpredictable working hours, and work-life conflict resulted in dietary, exercise and sleeping problems for temporary workers. In contrast, more securely employed workers reported much more regular hours and did not experience the same degree of disruption to their work-life balance and subsequent negative effects. They also reported that, although they sometimes worked long hours, they had a satisfactory level of control. These findings suggest that some forms of precarious employment can carry significant disadvantages in terms of working hours, work-life conflict and health. Hypothesis 5. Temporary workers will report higher levels of work intensity. Hypothesis 6. Those who perceive themselves as less secure will also report higher levels of work intensity.

Hypothesis 1. Temporary workers will perceive greater job insecurity than permanent workers.

As stated previously, findings in relation to work intensity and temporary workers have been mixed, although a study of hotel cleaners by Seifert and Messing (2006) found ‘flexible’ employment relationships and outsourcing worsened the workloads of this predominantly immigrant and female group. This study hypothesises that temporary and more insecure workers will report greater work intensity.

Hypothesis 2. Permanent workers will report working longer hours (Excessive Hours).

Hypothesis 7. Those who report lower levels of control over their hours, will also be more likely to experience increased levels of interpersonal conflict and violence.

M. McNamara et al. / Applied Ergonomics 42 (2011) 225e232

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Fig. 1. Hypothesised structural model.

A lack of control may lead to workers acceptance of undesirable and unsociable shifts which may be more conducive to exposure to abuse or violent behaviour. Hotel and restaurant industry work has been linked to increased risks of violence (Houtman et al., 2003). Factors such as working hours (evening and night), dress code and a suggestive physical environment are all conducive to unwanted attention from customers (Hoel and Einarsen, 2003). A lack of control on the part of the worker has been shown to worsen the impact of the experience (Hoel and Einarsen, 2003). Increased levels of work intensity, or working under pressure, may also be conducive to increased levels of conflict with guests or co-workers. Hypothesis 8. Those who report lower levels of control over their hours will also report higher levels of work-life conflict. Hypothesis 9. Work-life conflict will have a negative effect on psychological well-being. Recent research indicates that control over working hours is important (Pisarski et al., 2002, 2006; Bohle et al., 2004). In fact, the Tourism and Transport Forum found that working hours and low pay were the main reasons for turnover in the Australian hotel industry (TTF, 2006). Recent reviews also identified a tendency in current research to neglect working hours issues (Louie et al., 2006; Quinlan and Bohle, 2008a). Past research on full-time shift workers indicates that low control over work hours leads to greater work-life conflict resulting from long or socially undesirable working hours, particularly in the evenings or at weekends (Pisarski et al., 2002). A lack of control over working hours has been found in nurses to exacerbate work-life conflict and in turn health outcomes (see Pisarski et al., 2002). Work-life conflict results in greater fatigue and poorer physical and psychological well-being (Pisarski et al., 2002).

2. Method 2.1. Participants This study is part of a larger study carried out for the first authors’ PhD thesis. Survey data from 150 hotel workers from eight 3-Star hotels in urban and regional areas around Sydney were analysed in this study. Of these, 81 (54%) were permanent full-time, and 69 (46%) were casual. Another 12 ‘Permanent Part-time’ workers were excluded from the current data analysis due to their small number. There were just two labour hire workers who were included in the category ‘casual/ temporary’ for the purpose of analysis. This is a little different to what was found by the ABS in August 2004 e Over half of the employees in the Accommodation, cafes and restaurants industry (59%) were casual

employees (ABS, 2006). (This is because casual workers were less likely to participate and had to be targeted). Sixty eight (45.3%) of the respondents were male and 78 (52%) were female (4 did not specify). The mean age of the total sample was 31.1 (SD ¼ 11.4) with a range of 17e66 years. The mean age of permanent full-time workers was 31.6 (SD 9.8), and that of casual workers was 30.6 (SD 13.0) but the difference was not significant. More than two thirds (67%) were under 35 years of age and 36% was in the 18e24 age range, which is consistent with evidence that the hospitality industry employs younger workers (Lo and Lamm, 2005; Mayhew and Quinlan, 2002). Almost two thirds (65%) of respondents aged between 18 and 24 stated that they were casual or temporary workers. These proportions correspond with Australian labour force statistics for August 2004, in which young people (aged 15e24 years) made up 21% of all employees but comprised 40% of casual employees (ABS, 2006). Permanent full-time workers worked an average of 44.2 h (SD 5.7) per week and casuals worked an average of 29.0 h (SD 9.8) with a minimum of 7 and maximum of 60. The mean tenure of permanent full-time workers was 2.63 years (SD ¼ 3.2) and that of casuals was 2.8 years (SD ¼ 2.2) There may be cultural and organisational differences across different hotel groups. However, the fact that the hotels in this study were from different urban and regional locations adds to the generalizability of the results to other 3-Star hotels. 2.2. Procedure The survey was distributed to hotel workers in operational areas in 3-Star hotels in New South Wales, Australia. The hotels used in the study are comparable in that they are all standard 3-Star hotels belonging to the same hotel group. The researcher arranged to distribute the surveys wherever convenient or possible. This method of sampling provided more responses from temporary workers than could otherwise be obtained. Participants completed the survey via the hotel’s staff intranet, where available, during their shift. Workers from two hotels completed the survey using the paper and pencil method of survey distribution. These surveys were returned by individual workers to their hotel managers in sealed envelopes which were later forwarded to the chief researcher. All participants gave informed consent and were informed that participation was voluntary. Ethical approval was gained from the Human Research Ethics Committee of The University of New South Wales (Approval #036081). 2.3. Measures The reliability and validity of the measures used in this study were investigated. The composite reliability and Average Variance

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Extracted (AVE) for each construct, are presented in Table 1. All data are self-report. Employment status is operationalized with two levels, Temporary or Permanent. The ABS uses a bifurcated approach which divides the workforce into temporary and permanent on the basis of whether or not the worker receives the benefits associated with permanent work (holiday pay, paid leave and superannuation). If a worker does not receive these benefits they are categorized as temporary. In Australia the term casual worker is more commonly used. According to the ABS, a ‘casual’ employee is someone who is not entitled to either paid holiday leave or sick leave while any other employee is permanent (ABS, 2006). Employment status was coded as a dichotomous variable (0 ¼ permanent full-time, 1 ¼ casual). Job security was measured using three items developed by Bohle et al. (2001) e.g. ‘I expect my job to provide steady work in the future’ (see McNamara, 2009). These were rated on a five-point scale ranging from 1 ¼ Strongly Disagree to 5 ¼ Strongly Agree. The Average Variance Extracted (AVE) by this variable was .717 and the composite reliability was .884. Work Intensity was measured by a six item scale also developed by Bohle et al. (2001) e.g. ‘There is

Table 1 Individual item loadings, construct reliabilities, & convergent validity coefficients. Construct/Item Job Security JS1 JS2 JS3 Excessive Hours EH1 EH2 EH3 Low Hours Control HC1 HC2 HC3 Work Intensity WI1 WI2 WI3 WI4 WI5 WI6 Interpersonal Stress IS1 IS2 IS3 IS4 IS5 Work-life Conflict WLC1 WLC2 WLC3 WLC4 WLC5 WLC6 WLC7 GHQ12 GHQ1 GHQ2 GHQ3 GHQ4 GHQ5 GHQ6 GHQ7 GHQ8 GHQ9 GHQ10 GHQ11 GHQ12 Note: *p < .05, **p < .01.

Loading

Composite Reliability

AVE

.884

.717

.871 .843 .827 .885

.918

33.524** 59.298** 14.565**

.036 .050 .045

22.010** 14.607** 18.050**

.041 .041 .034 .049 .026 .051

18.388** 18.282** 24.013** 15.020** 33.479** 15.402**

.035 .022 .044 .074 .056

23.653** 39.623** 16.923** 8.173** 11.843**

.033 .046 .019 .030 .035 .021 .015

24.026** 16.465** 44.257** 26.538** 23.543** 41.393** 60.689**

.055 .060 .070 .054 .058 .041 .054 .080 .026 .048 .072 .072

12.896** 11.247** 8.957** 12.990** 11.951** 19.624** 12.627** 6.923** 30.063** 15.843** 9.276** 9.037**

.700

.801 .759 .874 .804 .822 .882 .907 .713 .677 .626 .704 .698 .799 .687 .553 .791 .764 .668 .650

.026 .016 .052

.553

.817 .859 .745 .604 .664 .942

33.075** 29.541** 23.083**

.606

.752 .754 .822 .734 .865 .735 .859

.026 .029 .036

.615

.805 .736 .810 .902

T-Statistic

.720

.867 .915 .757 .827

Standard Error

.486

often not enough time to do the jobs I am allocated without rushing’ (See McNamara, 2009), Items were rated on a five-point scale ranging from 1 ¼ Strongly Disagree to 5 ¼ Strongly Agree. There were two other questions which asked respondents how often they experienced; ‘Pressure on breaks due to workload’, and ‘Insufficient time to do my job well’. These were rated on a five-point scale ranging from 1 ¼ Never, to 5 ¼ Always. These items were standardised and load highly on the latent construct. The AVE was .606 and the composite reliability was .902. These items reflect Nichols (1997) discussion of the intensification of labour, whereby workers have increased work to do in the same number of hours, or the definition of their work has expanded. Low hours control was assessed using a three item scale developed by Bohle et al. (2001). Respondents were asked how often they experienced; ‘Unpredictable hours from week to week’, ‘Short notice about shift changes’, and ‘Not getting time off requested’. Responses were rated on a five-point Likert scale ranging from 1 ¼ Never, to 5 ¼ Always. The AVE was .615 and the composite reliability was .902. Excessive hours were assessed using a three item scale developed by Bohle et al. (2001). Respondents were asked how often they experienced; ‘Excessively long shifts’, ‘Too many hours per week’, and ‘Too much overtime’. Responses were rated on a five-point Likert scale ranging from 1 ¼ Never, to 5 ¼ Always. The AVE was .720 and the composite reliability was .885. Interpersonal conflict and violence was assessed using five items developed by Bohle et al. (2001); e.g. ‘Inappropriate behaviour from guests’ (See McNamara, 2009). These items were scored with a fivepoint scale ranging from 1 ¼ Never to 5 ¼ Always. The final item is dichotomous. It asks respondents whether or not they had experienced violence in the workplace (0 ¼ No, 1 ¼ Yes). Work-life conflict was measured using items from the Work/Non-work Conflict Scale devised by Shamir (1983) which was later used and validated by Bohle and Tilley (1998). Seven items described by Frone and Yardley (1996) were also included. The AVE for this scale was .700 and the composite reliability was .942. Psychological wellbeing was measured using the 12-item General Health Questionnaire (GHQ-12, Goldberg, 1972) with the Likert scoring method (see Banks et al., 1980). 2.4. Data analysis strategy SPSS Version 17 for Windows was used to examine the frequency distributions of each item for missing data and univariate outliers. Skewness and kurtosis indices were also examined to assess normality. Respondents’ age was positively skewed. The hospitality industry is known for its employment of younger workers (Lo and Lamm, 2005; Mayhew and Quinlan, 2002). However, the fact that the sample is a ‘young’ one, may have contributed to the skewness of the well-being variable (GHQ12), which was transformed. Age, sex, department, and hours of work were potentially important between-subjects variables so their effects were examined using dummy variables. As no significant effects were identified, it was not necessary to add them to the final model. ‘Excessive hours’ is used instead of ‘working hours’ in the structural model because it has more predictive value. ANOVAs indicated that there were some categorical differences between permanent and temporary workers (results not shown). Permanent workers perceived themselves as significantly more secure, as having more control over their hours, more work-life conflict, greater work intensity and greater interpersonal stress. However, when the effects of other variables (such as Excessive Hours) are partialled out in structural modelling, some different outcomes are observed (See Fig. 2). It is important to note that PLS Structural Equation Modelling controls for the effect of other variables in the model.

M. McNamara et al. / Applied Ergonomics 42 (2011) 225e232

PLS-Graph 3.0 Build 1126 (Chin, 2001, 2003), was used to estimate the structural model using the bootstrapping resampling procedure (with 500 resamples). PLS is a second generation structural equation modelling technique (SEM) developed by Wold (1982), which employs a component based approach for estimation purposes. PLS is used for several reasons. Unlike covariance-based procedures, PLS places minimal restrictions on measurement scales, sample size and residual distributions (Chin et al., 2003). In PLS, constructs may be measured by a single item whereas in covariance-based approaches, at least four items per latent variable are required (Bontis et al., 2007). The PLS approach is also geared more for exploration and model development (Chin, 1998). Given the lack of research relating to the interaction of precariousness and working hours, such an approach is suitable for this study. Component-based PLS also avoids two serious problems which are often encountered by more well known factor-based covariance fitting approaches, inadmissible solutions and factor indeterminacy (Fornell and Bookstein, 1982). PLS requires at least ten times the number of cases as the larger of either 1) the maximum number of indicators for any construct or 2) the maximum number of incoming links to any construct (Chin et al., 2003). A minimum of 120 cases is therefore required for this analysis, which the present sample of 150 exceeds comfortably. Chin (1998) recommends analysis of the PLS model in two stages; first, the assessment of measurement, followed by an evaluation of the structural path models. Results from the measurement model are presented first followed by an examination of the hypothesised relationships between constructs. Traditional parametric techniques for significance testing or evaluation are not appropriate as PLS makes no distribution assumptions other than predictor specification in its procedure for estimating parameters (Chin, 1998). Therefore, the R2 for dependent latent variables, and the Average Variance Extracted (AVE) was used to assess predictiveness of the model (Fornell and Larcker, 1981; Chin, 1998). 3. Results 3.1. Assessment of the measurement model To assess measurement and factorial validity, it is necessary to assess construct validity by examining convergent and divergent validities of the latent constructs, which capture aspects of the

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goodness of fit of the measurement model, i.e. how well the measurement items relate to the constructs. Convergent validity is shown when each of the measurement items loads with a significant t-value on its latent construct (Gefen and Straub, 2005). Typically, the t-value should be significant at least at the .05 alpha protection level (Chin, 1998; Gefen and Straub, 2005). The significance of the loadings was checked with a bootstrap resampling procedure (500 subsamples) for obtaining t-values, as recommended (Chin, 1998). These were all significant (p < .01). The individual reflective-item reliability for each item (indicator) is given by the loadings or correlations between the indicator and the construct. The minimum acceptable level recommended by Falk and Miller (1992) is .55. As may be seen in Table 1, all items in the model load above Falk and Miller’s (1992) recommendation for minimum value. Internal consistency or construct reliability is indicated by composite reliability scores, generated in PLS (see Santosa et al., 2005). These are used instead of Cronbach’s alpha (SanchezFranco, 2006). As shown in Table 1, the composite reliabilities for latent variables ranged from .827 (Low Hours Control) to .942 (Work-Life Conflict), well over the minimum recommended level of .7 (Nunnally, 1976). Overall the results from the PLS measurement model indicate that the constructs exhibit satisfactory reliability and convergent and discriminant validity (Hall and Smith, 2009; Tenenhaus et al., 2005). Discriminant validity of the measures at the indicator level is shown when the measurement items load more highly on their theoretically assigned factor than on other factors (Gefen & Straub). The loadings were inspected and satisfied this condition. At the construct level, discriminant validity can be assessed in PLS by conducting an AVE (Average Variance Extracted) analysis (Gefen and Straub, 2005; Tenenhaus et al., 2005), the amount of variance captured by the construct in relation to the amount of variance attributable to measurement error (Santosa et al., 2005). AVE is generated automatically in the bootstrap procedure. Discriminant validity of the measures is evaluated by examining the square root of the AVE for each measure (Tenenhaus et al., 2005; Fornell and Larcker, 1981). As shown in Table 2, the square root of the AVEs (on the diagonal), for each construct, is greater than the correlations between it and all other constructs in the model. An AVE score of .5 also indicates an acceptable level (Fornell and Larcker, 1981; Chin, 1998; Hair et al., 1998). Table 1 shows that the average variance extracted (AVE) by our measures is very satisfactory. They are all greater than .5 except for GHQ12

Fig. 2. Final PLS structural model.

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Table 2 Discriminant validity coefficients (the square root of average variance extracted is on the diagonal in bold, & means with standard deviations in brackets for each construct).

Job Security Excessive Hours Low Hours Control Work Intensity Interpersonal Conflict & Violence GHQ12 Work-life Conflict Employment Status

Mean-Permanent

Mean-Casual

Job Security

Excessive Hours

Low Hours Control

Work Intensity

Interpersonal conflict & violence

12.08 (2.17) 6.97 (2.78) 6.17 (2.38) 17.86 (4.98) n/a

11.22 (2.12) 5.32 (1.97) 7.67 (2.29) 14.72 (4.11) n/a

(.847) .172 .421 .367 .340

(.837) .446 .499 .437

(.697) .349 .606

(.849) .450

(.784)

12.56 (6.52) 21.8 (7.08) n/a

9.29 (4.67) 19.00 (6.04) n/a

.410 .388 .152

.461 .631 .265

.371 .465 .222

.517 .544 .289

.509 .464 .206

GHQ12

(.778) .543 .218

Work-life Conflict

Employment Status

(.747) .210

n/a

Note: off-diagonal elements are weighted correlations between constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements in the same row and column.

(.486). The GHQ12 is a well validated measure and its composite reliability is high at .918. Therefore this is not considered problematic. 3.2. Assessment of the structural model The structural path model was evaluated by examining the path coefficients (similar to standardized beta weights in regression analysis), the significance of the path coefficients and the proportion of the variance in the latent variables explained by the indicators (R2). Falk and Miller (1992) recommend a minimum of .10 for R2. Chin (1998) recommends that standardized paths should be around .20 and ideally above .30 to be considered meaningful. The significance can be evaluated by examining the t-values produced by bootstrapping or jackknifing (Chin, 1998; Santosa et al., 2005). In general, resamples of 500 tend to provide reasonable standard error estimates (Chin, 1998). The hypothesised structural model (See Fig. 1) was tested and most of the path coefficients were significant. The fit indices obtained indicate that the model fits the data very well. However, testing failed to find significant coefficients for two of the pathways (those between employment status and job security, and between job security and excessive hours). These pathways were deleted from the final model. The finding of a non-significant pathway from employment status to job security is very interesting. The final model, with path coefficients and R2 values, is presented in Fig. 2. Job security was significantly negatively associated with low hours control (b ¼ .256, p < .01). Employment status was also significantly related to low hours control with temporary workers more likely to report lower levels of control (b ¼ .270, p < .01). Job security is not significantly associated with excessive hours. However, employment status is significantly negatively associated with this variable indicating that permanent full-time workers are more likely to report excessively long hours (b ¼ .382, p < .01). In summary, less secure or temporary workers are more likely to report lower levels of control over their hours but permanent full-time workers are more likely to report excessive working hours and greater work intensity. Employment status is also significantly associated with work intensity indicating that temporary workers report lower levels than permanent workers (b ¼ .250, p < .01). Thus, permanent workers are reporting higher levels of work intensity as well as excessively long shifts. Job security is negatively associated with work intensity (b ¼ .344, p < .01). Work intensity is negatively associated with low hours control (b ¼ .338, p > .01). Low levels of hours control are positively and significantly related to excessive hours (b ¼ .531, p < .01), which in turn is significantly related to work intensity (b ¼ .371, p < .01). This illustrates an important nexus between hours control and work

intensity. Excessive hours (b ¼ .392, p < .01), work intensity (b ¼ .289, p < .01) and low hours control (b ¼ .197, p < .01) are all positively associated with work-life conflict. Those with low hours control, who work excessively long hours and report greater work intensity, unsurprisingly report higher levels of work-life conflict (r2 ¼ .50). Work-life conflict, in turn, is significantly associated with GHQ12 (b ¼ .544, p < .01) explaining 29 per cent of its variance. These findings are consistent with the findings from previous research on psychological well-being and control over the working environment (for example Pisarski et al., 2006). Work intensity (b ¼ .266, p < .01) and low hours control (b ¼ .513, p < .01) are significantly associated with interpersonal conflict and violence. Workers who are less in control, or who have less power to refuse, are also more likely to be working the least desirable or unsocial shifts. There may be other factors, such as the hotel department the participant works in, that may also be influential. This was investigated in initial analysis but small cell sizes made it difficult to draw definitive conclusions. No significant effects were found. 4. Conclusions The results above largely support the proposed structural model and the hypotheses. However, the first and most prominent finding is that employment status did not affect workers perceptions of job security. (Therefore this pathway is not included in the final model e See Fig. 2). It is quite surprising that Hypothesis 1 is not supported. In terms of measuring precarious employment, the weak relationship between employment status and perceived job security suggests the taxonomic approach has limited value. Direct measures (e.g. powerlessness, job insecurity) may be more effective. In the literature on precariousness, there is a key pathway from employment status to job insecurity, to health effects (Benavides et al., 2000; Quinlan et al. 2001; Virtanen et al., 2005). However, it seems that perceptions of job insecurity are generally more pervasive (Louie et al., 2006). Even people holding permanent jobs feel insecure, which may be linked to repeated rounds of downsizing, restructuring and outsourcing. Some industries are characterised by more insecurity than others. For example, the seasonal nature of agricultural and hospitality work contributes to insecurity. The concept of security may be envisaged as a continuum of effects ranging from formally insecure to traditionally secure forms of employment. Therefore the key effects of employment status may not be as significant as first imagined and more explicit account may need to be taken of broader changes in the labour market and business practices affecting labour management. Seeing as the effect of employment status on perceptions of job insecurity has not been previously investigated in the context of the hotel industry, it may be that this finding is quite typical of this

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industry. Perhaps insecurity is not only felt by casual workers but is rather a feature of the hotel industry in general (Louie et al., 2006). A ‘turnover culture’ has perhaps led workers to have different perceived job security, and a more short-term perspective, to those in other industries (Iverson and Deery, 1997). Casual workers are commonly used to meet variable demands in the hotel industry. The high rate of turnover, work intensity and burnout in this industry, ensures that jobs are readily available (Hoel and Einarsen, 2003; TTF, 2006). In fact, 78 per cent of hotels surveyed reported experiencing staff shortages and recruitment issues (TTF, 2006). Therefore job insecurity is less of a problem and the overriding issues are over-load and burnout. Other industries characterised by high levels of intensity, turnover and burnout include the trucking and call centre industries (Sprigg and Jackson, 2006; Mayhew and Quinlan, 2006). Despite the finding that there was no significant relationship between employment status and job security, there were some important individual effects. Employment status had a strong effect on working hours. Permanent workers were more likely to report excessively long shifts thus supporting Hypothesis 2. However, permanent workers were also more likely to report greater control over their hours as proposed by Hypothesis 3. In terms of job security, workers who perceived themselves as most insecure reported least control over their hours thus supporting Hypothesis 4. This is as expected. Workers with lower control over their hours were more likely to report increased levels of Interpersonal Conflict and Violence (as proposed by Hypothesis 7). The findings in relation to work-life conflict and psychological well-being are also as expected (i.e. Hypotheses 8 and 9 are supported). The findings of this study indicate that temporary or insecure workers have lower levels of control and are therefore at a disadvantage in terms of work-life balance and health outcomes. Even though there are potential benefits associated with temporary or flexible work in terms of work-life balance, the critical issue may be who determines the amount or timing of work. It appears to be a question of whether or not workers have control over the hours they work. This supports the findings of prior studies on the importance of control (Pisarski et al., 2002). Permanent workers reported greater work intensity than temporary workers, which was contrary to Hypothesis 5.Those who perceived themselves as insecure also reported lower levels of work intensity which was contrary to Hypothesis 6. As stated earlier, research findings in relation to work intensity have been mixed. These findings raise some interesting questions in terms of work organisation in the hotel industry. Are the trade-offs for having a permanent job longer hours and greater work intensity? A related question is whether the presence of casual workers is associated with employment practices by which permanent workers work longer hours and casuals work shorter but more irregular hours. There are complex interactions which could vary across industries, which has implications for future research in other industries. The use of temporary workers may also affect the working hours (and workloads) of permanent workers and simply comparing outcomes for the two groups would overlook this interaction or spillover effects. Complexity in terms of potential spillover effects should also be considered, such as when greater work-life conflict is produced by lack of control over working hours. This study is a valuable contribution to the literature as it focuses on workers who have been largely ignored by the wider working hours research field. It has largely focused on permanent full-time workers who are less exposed to the irregular unpredictable and anti-social work hours of many precarious workers. Due to the crosssectional design of this study, it does not provide a strong basis for demonstrating the causality implied in the hypothesised model.

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