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To Be Or Not To Be? A Study of Employee Turnover 1 To Be Or Not To Be? A Study of Employee Turnover Topic Area of Submission: Psychology (Organizational Psychology) Key words: Employee turnover, retention, software industry Author: Prof. (Ms. ) Meenakshi Gupta Professor of Psychology Department of Humanities & Social Sciences Indian Institute Of Technology Powai , Bombay 400076 India Email: [email protected] iitb. ac. in Telephone: 91 22 25767360 (Office) 91 22 25706964 (Residence) Fax : 91 22 25723480 Abstract: Employee turnover has been defined as a permanent movement of the employee beyond the boundary of the organization.

Interest in the topic has gained momentum in the recent past among organizational psychologists, economists, and sociologists with different perspectives being adopted to study the phenomenon. Data was collected from 71 employees of a well-known software company (Organization 1) and 36 employees from a finance company (Organization 2). A 30-item questionnaire was developed to study the relationship of company image, pay satisfaction, nature of work, nature of peer group, comparison to peer group, inside career opportunities, expectations-reality match and turnover perceptions with intentions to stay.

A stepwise regression analysis showed that expectation reality match, length of service, turnover perception and outside career opportunity were responsible for causing intention to stay/quit in Organization 1. In Organization 2 the predictor variables identified were nature of work, age, turnover perceptions and peer comparison. The only common predictor identified for the two organizations is turnover perception indicating that employee’s perception of manpower stability in the organization causes intentions to stay.

The findings have implications for redesigning work settings to attract, motivate and retain the best employees. The relationships of some demographic variables like sex, marital status, length of service and designation were also put to test. To Be Or Not To Be? A Study of Employee Turnover 2 Introduction Beginning 1990s, the Indian business environment has undergone remarkable changes. Most organizations viewed the presence of a long serving group of employees as an indication of internal efficiency.

However, with economic liberalization opening up new career horizons for professionals in most industries, and thereby tremendously enhancing their prospects for mobility from one organization to another, turnover has come to be understood as a negative ‘spill over’ effect of industrial growth. This phenomenon commonly called turnover had been of secondary interest to most researchers but increasingly more and more attention is beginning to pour in this direction. As the paradigm of lifetime employment becomes unrealistic, the question ‘who stays with you? ’ has assumed great importance in organizations today.

Simultaneously, there has been an increasing tendency to ‘buy in’ the talents of professionals with crossfunctional skills in order to create a competitive advantage. One visible effect of this has been a consistent rise in the pay packages of most organizations so as to attract and retain the most desirable employees. Such a trend over the last few years has resulted in an unstable labor market, especially for industries such as marketing, advertising, finance and software where the skills are by and large transferable, from one work environment to another.

This paper is an attempt to identify the causes of employee turnover in a software firm and a finance company. The software industry has been the sunrise industry in India. As Bill Gates mentioned: “The software industry will create millions of new jobs in the years ahead. India more than any other developing nation, is seizing this opportunity, and will become a huge exporter of software expertise. In fact, India is likely to be a software superpower…” (Gates, 1997). To Be Or Not To Be? A Study of Employee Turnover 3

India is an important player on the IT map of the world on account of it being an important source of technically qualified and English speaking manpower. The Indian IT sector has enlarged from US $ 1. 73 billion in 1994-95 to a US $ 2.. 81 billion in 2000-01. One of the most distinctive characteristics of those software organizations is that they have only the expertise of their staff as assets with which to trade. The task of a leader in such organizations is therefore to recruit, train, empower and retain the best and the brightest professionals (Narayanmurthy, 1998).

The growth in the finance industry is dependent on the growth of the economy, and on the interlink of a country’s economy with the global environment. Largely due to the economic policies of the government in the past few years, the Indian finance industry is reflected in the development of the capital markets and the sophistication of the financing options. The Indian capital market over the last few years, has attracted significant attention worldwide, making it one of the world’s most exciting emerging market. Though there are still many deficiencies in the Indian markets, at the same time there are many areas of future growth e. . debt market, insurance industry, infrastructure development etc. Employee Retention The undesired loss of competent personnel is costly to an organization in both direct and indirect terms. The estimated cost for hiring a professional for one data processing company were more than $ 9000 for a hypothetical $ 18000/year position, with higher salary positions entailing higher hiring costs (Shapiro, 1989). Before one attempts to seek a comprehensive solution to the problem of retention, it may perhaps be proper to analyze the causative factors that result in heavy migration of scarce human resources.

The various reasons that encourage an employee to leave his present job are usually related with satisfaction level. The ‘push’ factors pertain to the dissatisfaction causes, which an employee uses as primary motivators to severe relations To Be Or Not To Be? A Study of Employee Turnover 4 with his company. They are the work environments, compensation package, low employee benefits, inconsistent HR policies, incorrect work assignment, lack of challenge, lack of career development schemes. Two more push factors identified are ‘fear of being found out’ and ‘level of competence’.

Some employees manage to get the job by projecting more skills than they actually possess. After they have strengthened their knowledge to a certain degree in the organization, they are constantly afraid of being ‘found out’ and tend to leave as soon as a suitable opportunity comes. Level of competence implies that the employee may rise in the organization up to a certain level, after which he feels he may not rise. However, this level of competence may be at a higher position in another organization with slightly lower standing in the industry.

The ‘pull’ factors that lure an employee are higher compensation package and greater technical challenge (Ganesh, 1997). Retention Mechanisms When valuable workers want to quit their job, how does the organization encourage them to reconsider their decision? How does management convince them to work out any problems that might be acting as a ‘push’ factor in their decision to leave; or to reevaluate the benefits of offers acting as a ‘pull’ factor. Some of the retention techniques practiced by software companies today are described below. Retaining through Allowance Fund.

In an industry where most work is done on-site, there may be discontentment among employees who have not been chosen to work at the client’s place especially overseas. Not that they are incompetent but because the work demanded only a few people. To tackle this problem some companies have started in India Allowance Fund, wherein the employees who do not get a chance to go abroad are compensated financially such that he/she earns almost the same amount that his or her counterpart sent abroad would earn. Chennai based Shapre Systems and Mumbai based Mahindra British Telecom started this fund (Nair, 1997). To Be Or Not To Be?

A Study of Employee Turnover 5 Retaining by mentoring. Unlike a counselor a mentor is from within the organization and has executive powers to recommend a person in a suitable spot. Experience in human relations, a cool head and a non-biased approach are some of the attributes of a mentor. Mentors help employees develop self-confidence and their overall personality, which reflects in the employees’ technical capabilities and helps in overall working of the organization. A mentor is dedicated to the mentored and guides them till the objective is achieved. Mentors bring both the organization and the employee closer.

They bring a sense of belonging and loyalty amongst the employees (Chaddah, 1997). Bond as Retention Measure To bond or not to bond? Most companies today are forced to ask themselves this question on an ever-increasing basis. The employer invests a considerable amount of time, money and resources in training and brings the new entrant to a stage where after the employer feels that he can begin to get a decent return on investment. It is at this point of time that the employee decides to leave the firm for better prospects, having gained a degree of experience and expertise at the employer’s expense.

The employer is bound to feel whether the sole purpose of recruiting people is to retain them for a better job opportunity. After all he is not running a training institute and paying people to get trained too. As far as the employee goes he sees no harm in accepting a job offer if one comes his way and the prospects are better. An employer being a businessman needs to take care of his own interests. He cannot afford to be altruistic. If he on the other hand is too hard-nosed about this affair, he can actually prevent the right people from joining his company.

The right part would be the middle path (Bulsara, 1997). Bonds are justified and acceptable if the company is clearly spending significant money and/or time for training the employee and hence expects the employee to spend a minimum prefixed period after getting the training. Bonds are not justified when the person is going abroad or working in India on revenue earning assignment. Some companies claim that the experience gained on an assignment To Be Or Not To Be? A Study of Employee Turnover 6 abroad is actually training and hence the person needs to return and work ith the company in India to pass on his knowledge to other employees. By the same argument every employee working on assignment in India is continuously getting trained and hence needs to continue to work and should also be required to sign a bond (Doshi, 1997). The true HR challenge in is in retaining as is evident from our previous discussion where the demand for a particular skill is high and supply far short of demand. In such a scenario there is bound to be high mobility among such professionals. Before addressing the problem of retention one needs to seek answer to the question why do people quit?

What are the factors one weighs in deciding if one wants to be in an organization or not? Reasons for leaving have been largely speculative. So far there has been no systematic attempt to study the causes of employee turnover among high skilled professionals. The present study is an attempt in this direction. Objectives The purpose of the study was to examine the factors that influence turnover intentions. The specific objectives were: 1. Does company image influence employee turnover intentions? 2. Does pay satisfaction influence employee turnover intentions? 3.

Is nature of work an important determinant of employee turnover intentions? 4. Does nature of peer group influence employee’s intentions to stay with the organization? 5. Does peer-self comparison influence turnover intentions? 6. Does internal career/growth opportunity provided by the organization influence employee’s intentions to leave? 7. Does outside career opportunity influence employee’s intention to stay with the organization? 8. Does the degree of match between what was expected and what was achieved in the present job influence turnover intentions? To Be Or Not To Be? A Study of Employee Turnover 7 9.

Does perception of employee stability influence intentions to stay with the organization? Methodology Two organizations were selected for the study. Organization 1 is a software company. Its chief activities include manufacture of high-end servers; PC line of products and providing networking solutions. It has collaborations with three large US software firms. It received ISO 9001 certification, which represents an external validation and endorsement of their internal processes. This has been implemented within the company over the last few years to ensure the highest level of customer satisfaction and delivery commitments.

The ISO certification also gives an extra edge to the company’s business development activities in the international market. It employs approximately 850 personnel of which 750 are software professionals. Most of their businesses come form Europe and the US. It proposes to expand its client base to the Middle East and the African continent. It also plans to set up its own R&D section so as to be in the state of preparedness for future clients. 71 software professionals formed a sample for this study of which 51 were males and 20 females. The mean age of the sample was 27 years and length of service in the organization 19. 0 months. Of the 71 professionals, 47 were unmarried and 24 married; 43 were at the lower level of the hierarchy and 28 at the middle level. Organization 2 is a finance company set up in the year 1992 in collaboration with a giant American finance company. Its efforts are directed towards building an organization of high integrity and credibility that institutionalizes professional practices in the capital market. The objective is to provide total satisfaction to all constituents by achieving a blend of financial innovation with profit and efficiency.

To achieve these goals the organization focuses on building on its core competencies in the existing line of its business: developing new, innovative and customized products and exploring new and unchartered avenues for sustained growth. The company has consciously opted for a functional flat organizational structure with greater responsibilities to employees for To Be Or Not To Be? A Study of Employee Turnover 8 decision making. 36 professionals from organization 2 formed part of the sample of which 30 were males and 6 females. The mean age of this sample was 28 years and average length of service 23 months, 21 respondents were arried and 15 unmarried. 14 finance professionals were at the lower level of the hierarchy and 22 at the middle level. The study examined the influence of company image, pay satisfaction, nature of work, nature of peer group, comparison to peer group, inside career opportunity, outside career opportunity, expectations-reality match and turnover perception on intentions to say in the organization (retention). It also examined the role of age, sex, marital status, length of service, and designation on employee attitude and behavior at work. The operational definition of the variables is given below. Operational definitions of variables

Independent variables 1. Company image (CI): It is the strength of an individual’s identification with, and pride in an organization. 2. Pay satisfaction (PS): It is the extent of contentment received out of the money, fringe benefits, and other commodities having financial value which organizations give to the employees in return for their services. 3. Nature of work (NW): The extent to which role performance in an organization is stimulating, result oriented, skill demanding and non-repetitive. 4. Nature of peer group (NP): It is the extent to which the peer group in an organization is responsible, intelligent, work oriented and genuine. . Comparison to peer groups (PC): The extent to which the individual identifies himself with his peer group on work related attributes such as skills, ambition and educational qualifications. 6. Inside career opportunities (IC): It is the probability that an individual will be able to occupy roles within the organization that offer greater rewards and growth opportunities. To Be Or Not To Be? A Study of Employee Turnover 9 7. Outside career opportunities (OC): It is the nature and quality of unoccupied roles in an organization’s external environment. 8. Expectations-Reality Match (ER): It is the extent to which the present job easures up to the kind of job that the individual wanted when he first took it up. 9. Turnover Perception (TP): It is the extent to which the employee perceives the organization houses a set of long serving employees. Dependent Variable Intentions to Stay (K): It is an individual’s behavioral intention to stay with the organization, as a direct outcome of company policies, labor market characteristics, and employee perception. Intentions to leave is negatively related to continuance commitment and is a widely agreed upon precursor to turnover (Mobley et. al. , 1979). Tools: Questionnaire

In order to measure the independent and dependent variables a questionnaire was designed with 30 close-ended items measuring all the variables on a 5 point rating scale. Information on demographic characteristics (age, sex, length of service, number of organizations worked with before and designation) was also sought. Results & Discussions Table 1 presents the means and standard deviations obtained on all the questions measured on a five-point scale. The table shows that nature of the peer group had the highest mean value of all the variables for Organization 1 whereas expectation reality match had the highest mean for organization 2.

But this was not significantly different when both the organizations were compared. To Be Or Not To Be? A Study of Employee Turnover 10 Table 1 Mean and SD for Organization 1 & 2 Org 1 (N=71) Org 2 (N = 36) df105 VARIABLE MEAN SD MEAN SD T VALUE Company Image 3. 35 0. 85 3. 53 0. 45 1. 15 Pay Satisfaction 3. 45 0. 69 2. 22 0. 52 9. 46** Nature of Work 3. 17 1. 04 3. 64 0. 45 2. 64** Nature of Peer Group 3. 82 0. 70 3. 68 0. 49 1. 02 Peer Comparison 3. 59 0. 71 3. 68 0. 59 0. 61 Inside Career Opportunity 3. 39 1. 07 2. 28 0. 69 5. 63** Outside Career Opportunity 2. 68 1. 02 3. 39 0. 81 3. 61** Expectations- Reality Match . 60 1. 02 3. 89 0. 88 1. 46 Turnover Perception 2. 80 0. 67 3. 00 0. 59 1. 50 Intentions to Stay 3. 09 1. 03 2. 69 0. 71 2. 11* *p=05 **p=. 01 Results indicate that employees in Org 1 are significantly higher on pay satisfaction than employees in Org 2. Differences also exist on nature of work with the finance organization employees finding their work more satisfying. Inside career opportunity was more pronounced for the software employees indicating that they perceive themselves to be provided with greater growth opportunities. Outside career opportunities were perceived to be higher by the finance professionals.

Organization 1 employees were found to have grater intentions to stay. This finding is in keeping with the slow down in the IT/Software sector today. Organization1 Significant differences were obtained between the two sexes on Peer Comparison (t=3. 09, df 69 p < . 05) and Turnover Perception (t=2. 43, df 69, p < . 05). Males evaluated themselves better in relation to their peer groups (mean = 3. 75) than females who rated themselves at par with their peer group (mean = 3. 20). Females were observed To Be Or Not To Be? A Study of Employee Turnover 11 to be having a more stable perception of the work force (mean = 3. ) with an average employee staying for 4 or more years. On the other hand, males perceived the workforce as unstable (mean = 2. 69) with average employees staying for 1-2 years in the organization. Marital status was as expected significantly related with age (t = 3. 94, df 69, p < . 05). Unmarried employees were observed to fall in a lower age group with their mean being 25 years. Married employees had a significantly higher mean age of 30 years. Differences were also observed on the variable number of organizations worked with before, with married employees having worked for an average of 2. 5 organizations.

Unmarried employees on the other hand have worked for lesser number of organizations (mean = 1. 21). Differences were also observed on the perception of the nature of peer groups (t = 2. 37, df 69, p < . 05). Unmarried employees were found to rate their peer group with more desirable characteristics (mean = 3. 86) than their married counterparts (mean = 3. 02). Table 2 gives the correlation matrix between all the variables of the study. The table shows a significant relationship between age and intention to stay. Researches focusing on individual variables have studied the influence of age on intentions to quit.

A study by Werbel and Bedeion (1989) indicates that age is a significant moderator of relationship between performance and intention to quit. On the other hand the study by Healy et. al. (1995) found a near zero relationship between age and turnover. Turnover models (Price, 1977) also reiterate that age and other demographic variables such as length of service retain powerful independent effects on intentions to stay. Length of service was also found to be significantly associated with intentions to stay. Gerhart (1990) structural model of turnover proposes a direct positive relationship between tenure and intentions to stay.

Assuming that tenure could be used as a proxy variable for length of service, it points towards the amount of firm specific training or specialization received by an employee who has stayed for a long time with the organization. Hence it may be stated that the longer an employee stays with a particular firm, greater will be his To Be Or Not To Be? A Study of Employee Turnover 12 exposure to a specific area of specialization (for e. g. , to a particular programming language) thereby greater would be the intention to stay with the same organization.

Other demographic variables (sex, marital status and designation) were not found to have a direct relationship with intentions to stay. Literature reveals that male-female differences exist, with females shown to have higher intentions to leave, but this difference disappears when job satisfaction is controlled in the analysis. No previous study, however, has explored the relationship of marital status and designation on intentions to say. The present study found a non significant relationship for the two variables. Company image measured the extent of individual’s identification with and pride in an organization.

The variable was positively correlated with intentions to stay. Previous research does not find a direct mention of company image as a variable but if organizational commitment could be used as a proxy variable for company image, then studies (including matching model by Wanous, 1980) indicate that organizational commitment is negatively correlated with turnover intentions and behavior. Pay satisfaction, nature of work and inside career opportunities have also found an important relationship in turnover literature. These three variables were significantly correlated with intentions to stay.

A single study on occupational change by Markey and Parks (1989) found these three factors to be significantly important in determining a job switch. Their results indicated that more than 60% of the sample (10 million workers) switched jobs for better pay, better working conditions and better advancement opportunities. The present study found expectations reality match to be also significantly correlated to intentions to stay. Previous research, though not studying the variable directly, finds a mention of two similar concepts. One deals with Realistic Job Preview (RJP) and another is the proposal put by Human Capital theory.

The importance RJP of the accuracy of job information has been indicated to be an important precursor variable to To Be Or Not To Be? A Study of Employee Turnover 13 voluntary turnover (Wanous, 1980). Hence, lesser the accuracy of job related information, greater are the chances that the employee would have unrealistic expectations, thus resulting in turnover. Along similar lines, Human Capital theory finds relevance there. Study by Tsang et. al. (1991) indicates that workers with education in excess of what their job requires of them has an adverse effect on employee stability in the organization.

Therefore, a mismatch between expectations and reality on the job can have a jeopardizing influence on intentions to stay. Outside career opportunity is the last variable that was significantly negatively correlated with intentions to stay. A number of authors have incorporated this variable in their models, though not necessarily under the same label. Price’s (1977) model of turnover indicates that job satisfaction and opportunity structure interact, and turnover is most likely for dissatisfied individuals in economies of high opportunity.

Along similar lines, Mobley (1977) proposed ‘search for and evaluation of alternatives’ as important mediating steps between dissatisfaction and actual quitting. The matching model by Wanous (1980) also speaks of the role of ‘comparison of present job to others’ for assessing job’s utility in fulfilling employee’s goals and expectations. Hence, an expectation that the present job will help in achieving important intrinsic and extrinsic work goals to a greater degree than an alternative job would, forms an important component of work adjustment process and employment stability.

The results do not show a significant relationship of nature of peer group and peer comparison and turnover perceptions with intentions to stay. This finding is in contradiction with earlier research done in the area. O’Reilly et al. (1989) explored the relationship between social integration of the group and individual turnover. Their finding indicated that lower level of group social integration is negatively associated with individual turnover. For the present study, it is possible that in a software firm nature of peer group, and comparison of peer group does not hold any significant elationship to individual intentions to stay due to homogeneity of the sample. To Be Or Not To Be? A Study of Employee Turnover 14 Table 2 Correlation Matrix (Organization 1) * p = . 05 ** p = . 01 ( ) = indicates negative correlation Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1. Age 2. Sex (0. 42) 3. Marital status 0. 62** 0. 15 4. Length of service (mths. ) 0. 46** (0. 04) 0. 18 5. No. of organizations 0. 47** 0. 17 0. 41** (0. 23) 6. Designation 0. 43** (0. 12) 0. 28* 0. 41** 0. 06 7. Company image 0. 07 0. 17 0. 03 (0. 07) (0. 04) 0. 08 8. Pay satisfaction 0. 23 0. 11 0. 08 0. 13 (0. 07) 0. 00 0. 29* 9. Nature of work 0. 05 (0. 4) (0. 09) 0. 02 0. 09 0. 06 0. 53** 0. 32* 10. Nature of peer group (0. 26) 0. 07 (0. 27) (0. 30)* 0. 09 (0. 08) 0. 18 0. 11 0. 18 11. Peer comparison (0. 16) (0. 35)* (0. 08) (0. 14) (0. 04) 0. 00 (0. 02) (0. 12) 0. 14 (0. 11) 12. Inside career opportunity 0. 00 0. 10 (0. 15) 0. 00 0. 02 0. 00 0. 57** 0. 49** 0. 58** 0. 42** (0. 14) 13. Outside career opportunity (0. 19) (0. 08) (0. 03) (0. 09) (0. 02) (0. 01) (0. 30)* (0. 28)* (0. 24) (0. 06) 0. 32* (0. 27) 14. Expectations reality match 0. 15 0. 05 0. 11 0. 02 0. 14 0. 05 0. 48** 0. 35** 0. 63** 0. 10 (0. 09) 0. 42** (0. 37)** 15. Intentions to stay 0. 34* (0. 03) 0. 19 0. 39** 0. 9 0. 18 0. 45** 0. 38** 0. 42** 0. 02 (0. 07) 0. 36** (0. 40)** 0. 56** 16. Turnover perception 0. 07 0. 28 0. 08 (0. 05) 0. 03 0. 07 0. 22 0. 06 0. 09 0. 19 (0. 20) 0. 23 (0. 01) 0. 03 0. 24 To Be Or Not To Be? A Study of Employee Turnover 15 Turnover perception, though not found to be significantly associated with intentions to stay, emerges as a predictor variable at step no. 3 on stepwise regression discussed later in this section. This may be possible since the correlation coefficient value associated with turnover perception is a borderline value which assumes a significant value when partially correlated with intentions to stay.

This is an interesting observation since it shows the interactional effect of other variables on turnover perceptions. In order to determine the causality effect of the independent variables on employee’s intentions to stay. Stepwise regression was carried out (Table 3). Table 3 Stepwise Regression (Organization1) Variables Dependent Independent Predictor for variable entered on step no. R square b f-ratio Intentions to stay Expectations Reality match 1 0. 318 0. 483 32. 11** Length of service 2 0. 465 0. 381 29. 59** Turnover perception 3 0. 523 0. 239 24. 43** Outside career opportunities 4 0. 552 (0. 184) 20. 9** Constant 0. 55 0. 921 ** Significant at 0. 0000 Variable expectations reality match emerges as a predictor variable on step no. 1 (R square = 0. 32), indicating that 32% of the variance in intentions to stay may be explained by the variable expectations reality match. However, this 32% figure is not to be looked at in isolation, since other significant predictors (on step no. 2, 3 & 4) have an interacting effect on this variable. The next variable (entered on step no. 2) that has the power to explain the variance in dependent variable intentions to stay (K), is the length of service (LOS) of the employees. 7% of the variance in the intentions to stay may be contributed to this variable (after dropping the effects To Be Or Not To Be? A Study of Employee Turnover 16 of ER). However, in this case also, the proportion is not to be interpreted in the absolute sense since other predictors (on step no. 3 & 4) have an interesting effect on the potency of the R square value. The next significant predictor of the dependent K was calculated to be turnover perception (R square = 0. 52). This indicates that after dropping the influence of variables ER & LOS, turnover perception could explain 52% of the variance observed in the intentions to stay.

The last variable entered on step no. 4 (thereafter reaching the p< . 05 limits) is outside career opportunity (OC), with the power to account for 55% of the variance in the intentions to stay. This high explanatory power is achieved after dropping significant predictor variables like ER, LOS & TP but inclusive of the effects of other variables not entered into the equation. Taking the beta (slope) and constant (y intercept) into account, a linear turnover model could be predicted. K = 0. 55 + 0. 48 ER + 0. 38 LOS + 0. 24 TP – 0. 8 OC ………. (equation 1) It may be noted here that many significantly correlated variables (with dependent K) were dropped by the statistical package SPSS while carrying out stepwise multiple regression analysis. This is because only those variables become predictor variables, which have a high partial correlation coefficient with intentions to stay. For e. g. , both NW and ER are significantly correlated with K, but only one was selected to be predictor variable (ER) since it had a higher partial correlation coefficient with K.

Another point to be noted here is that the predictor variable turnover perception, though not significantly correlated with K, emerged as a strong predictor in the regression analysis. As seen form the stepwise regression results expectations reality match was found to be a very important variable (entered on step no. 1) that directly influences intentions to stay. This is an individualistic variable, implying that a gap between an employee’s expectation of the job and To Be Or Not To Be? A Study of Employee Turnover 17 hat the job actually offers is of significant importance in determining, whether an employee intends to stay with the same job. This finding is consistent with the matching model formulated by Wanous (1980). Introducing the concept of RJP as a staffing procedure for enhancing need-reward match process, Wanous proposes that RJP increases the accuracy with which job applicants assess the degree of match between needs and rewards provided by the job in question. If applicants accept the job after receiving and RJP, a better match is expected between applicant work related needs and job rewards.

A strong job reward match results in job satisfaction; and job satisfaction influences employment stability by negatively affecting actions to secure another job. Length of service is the next predictor variable entered in the linear equation. This is in line with the structural model (Gerhart, 1990) which identifies tenure as one of the chief components of intentions to stay. Length of service could be viewed as a proxy for the amount of firm specific training or specialization and also represents an investment in the firm. Also age and length of service being strongly correlated implies that the employee has grown with the organization.

It may be interpreted that this long stay with an organization builds up a sense of complacency within the employee, along with fostering somewhat narrow range of skill acquisition and experience. Hence, it is likely that a long stay with the organization reduces the employee’s confidence in dealing with broad and varied situations thus fostering a longer stay with the organization. Further, family setup stabilizes around this period of time, thereby requiring a greater risk taking ability on the part of the employee to switch his job. Turnover perception, entered on step no. in the equation also forms an important determinant of intentions to stay. Here again, T test result of differences between the two sexes on variable turnover perception can be interpreted in the context of the overall regression results. Hence, it could be said that the female members were found to have a more stable perception of the employees. Though literature finds no mention of the influence of predictor variable turnover perception on intentions to stay, Krackhardt & Porter’s (1986) snowball metaphor may offer an explanation here.

The snowball explanation of turnover suggests that patterns of turnover are To Be Or Not To Be? A Study of Employee Turnover 18 not independently distributed across any work group thereby suggesting that people are not independent actors. At work they effect others and are effected by others. Hence, when an employee perceives that the workforce is stable in the organization, there is a larger probability that he would have greater intentions to stay. General organizational norms concerning work or value of work may also influence turnover perceptions.

Norms can also vary across segments of an organization, for e. g. , in the organization studied women were found to have a better stability perception of the workforce than the men. The last predictor variable, entered on step no. 4, is outside career opportunity bearing a negative relationship with intentions to stay. A number of research work has studied the influence of comparable external job opportunities with intentions to stay. Mobley (1977) model of employee turnover decision process proposes that comparison of alternative jobs with the present job is a precursor to intentions to stay/quit.

Research in psychology and economics suggest a main effect of general labor market conditions on voluntary turnover. Price (1977) used opportunity structure (state of the economy) as a proxy variable for outside career opportunity. He proposed that job satisfaction and opportunity structure interact, and turnover is most likely for very dissatisfied individuals in economies of high opportunity. Gerhart (1990) structural model includes the dimension ‘perceived ease of movement or labor market perceptions’ as a determinant of voluntary turnover.

Whatever the appropriate measure of external job opportunities, implication of the finding is that such conditions may place strong constraints on turnover control programs of the organization. Such programs, designed to alleviate turnover, may appear successful when there is a general dearth of alternative career opportunities. However, as outside career opportunities become more generally favorable (as in the case of a growing economy), employees who intend to leave may actually do so in increasing numbers. Finding may be explained using March & Simon (1958) model which indicated that certain factors e. . , expectations reality mis-match push the employee to look for alternative employment, whereas other factors for example, favorable perception of outside career opportunities may ‘pull’ the employees to consider alternative employment. To Be Or Not To Be? A Study of Employee Turnover 19 Organization 2 No significant differences were obtained between the sexes on any of the variables. Differences on age and length of service with married employees having a higher mean age (30 years) and greater length of service (34. 6 months) than the unmarried employees (age 26 years: length of service 14. months). Significant differences were obtained on pay satisfaction (t=2. 13, df 34, p=. 05) with married being more satisfied (mean=2. 42) than their counterparts(mean=2. 06). Differences were observed on inside career opportunities (t=3. 98, df 34, p=. 01) with the unmarried finding greater career opportunity in the job external market (mean=3. 65) than their married counterparts (mean=3. 02) married employees had a greater intention to stay (mean=3) than the unmarried (mean=2. 48) with a t value of 2. 31, df 34, p=. 05.

The correlational analysis between variables show a positive relationship of length of service, company image and nature of work with dependent variable intentions to stay. This is consistent with previous literature cited in the area (Gerhart 1990). Other variables were not found to be significantly associated with intentions to stay. However an examination of the coefficient values suggest that age, marital status, peer comparison, and expectations reality match would have emerged to be significant if the sample size was marginally larger. To Be Or Not To Be?

A Study of Employee Turnover 20 Table 4 Correlation Matrix (Organization2) * p = . 05 ** p = . 01 ( ) = indicates negative correlation Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1. Age 2. Sex 0. 04 3. Marital status 0. 61** 0. 08 4. Length of service (mths. ) 0. 74** 0. 02 0. 57** 5. No. of organizations 0. 14 (0. 02) (0. 09) (0. 09) 6. Designation 0. 53** (0. 10) 0. 44* 0. 58** 0. 00 7. Company image 0. 05 0. 14 0. 10 0. 26 0. 08 0. 18 8. Pay satisfaction (0. 12) 0. 0 0. 34 0. 00 (0. 27) 0. 15 0. 17 9. Nature of work 0. 07 0. 06 0. 25 0. 32 (0. 28) (0. 03) 0. 33 0. 23 10.

Nature of peer group 0. 24 0. 08 0. 18 0. 00 (0. 66)** (0. 15) 0. 00 0. 39* 0. 60** 11. Peer comparison 0. 18 0. 12 (0. 05) 0. 22 0. 14 0. 24 0. 10 0. 03 0. 24 (0. 11) 12. Inside career opportunity 0. 05 0. 04 0. 56** (0. 03) 0. 02 0. 16 0. 26 0. 62 0. 05 0. 10 0. 01 13. Outside career opportunity 0. 03 0. 06 (0. 39)* 0. 19 0. 22 (0. 23) 0. 06 (0. 47)** 0. 30 (0. 21) 0. 19 (0. 75)** 14. Expectations reality match (0. 01) 0. 06 0. 30 0. 20 (0. 38) 0. 00 0. 30 0. 30 0. 39* 0. 68** (0. 01) 0. 19 0. 33 15. Intentions to stay 0. 35 0. 09 0. 37 0. 44* (0. 10) 0. 14 0. 40* 0. 16 0. 7** 0. 16 (0. 22) 0. 08 0. 06 0. 26 16. Turnover perception (0. 13) 0. 13 (0. 29) (0. 11) (0. 07) 0. 10 0. 29 (0. 12) 0. 00 (0. 08) (0. 14) (0. 12) (0. 04) 0. 03 0. 28 To Be Or Not To Be? A Study of Employee Turnover 21 Regression analysis results portray a better picture of the predictor variables influence on intentions to stay, as it removes the interactional influence by taking a partial correlation coefficient into consideration (Table 5). Table 5 Stepwise Regression (Organization2) Variables Dependent Independent Predictor for variable entered on step no. R square b f-ratio

Intentions to stay Nature of work 1 0. 219 0. 532 9. 56** Peer Comparison 2 0. 336 (0. 379) 8. 36 Age 3 0. 480 0. 414 9. 85* Turnover Perceptions 4 0. 554 0. 275 9. 61** Constant (2. 360) (2. 000) ** Significant at 0. 05 * Significant at 0. 01 Independent variable nature of work emerges as a predictor variable in step 1 (R square=0. 22) indicating that the variance in intentions to stay can be explained by nature of work. However, value 22% is not an independent statistic since it includes the influence of other variables in it. The next variable extracted was peer comparison.

The negative beta sign indicates that higher the employee evaluates himself in relation to his peer group, lower would be his intentions to stay. Age accounted for 48% of the variance in the dependent variable. Table 5 shows that 55% of the variance in intentions to stay can be explained by turnover perception in the organization. Taking the beta value (slope) and the constant (y intercept) into account, a linear turnover model predicted for Organization 2 is: K = -2. 36 + 0. 53 NW + 0. 42 age + 0. 28 TP – 0. 38 PC .. (equation 2) To Be Or Not To Be? A Study of Employee Turnover 22

The results suggest that nature of work is a strong determinant of intentions to stay for organization 2. Hence, poor nature of work in terms of being routinized, not result oriented, low on skill enhancement and low on stimulation results in dissatisfaction with the job, thereby causing turnover. The finding is in agreement with Markey & Parks (1989) study of occupational change where results indicated that workers switched jobs because of better working conditions and advancement opportunities. A number of researchers have focused on the relationship between job characteristics and turnover intentions.

Mobley (1977) identified job satisfaction as the primary variable among the chain of variables that determine turnover. Hackman & Sulltle (1977) with the help of job characteristics model specified how job characteristics and individual differences interact to affect satisfaction and productivity of individuals in the organization. Age appears to be a predictor of intention to stay in organization 2. Werbel & Bedeian (1989) investigated the influence of age as an antecedent of intentions to quit and found age to be a significant moderator of performance and intentions to quit.

However, another study found a small and near zero relationship between age and turnover (Healy et al. , 1995). It is possible that in the present case, age and tenure being very closely correlated, age comes to acquire similar significance as that of length of service. Longer the stay with an organization, narrower becomes the focus for an individual. Hence to have intentions of quitting, one needs to overcome a number of constraints like acquiring new skills. T test result for demographic characteristics of marital status and designation may be discussed in this context.

Married and unmarried individuals were found to be significantly different in age. Similarly middle and lower level professionals were significantly different in age. It may be inferred that the married, middle level professionals who fall in the higher age bracket have greater intentions to stay. Viewing age within the context of marital status and designation offers more plausible explanation for senior employees having greater intentions to stay. It is possible that family commitments have a strong influence on the occupational mobility of an employee. To Be Or Not To Be? A Study of Employee Turnover 3 Turnover perception, was the only common predictor variable between the two organizations. Employee`s perception of workforce stability in the organization is a strong predictor of employees intention to stay. Individuals in an organization are not independent actors. They influence each other, and in turn are influenced by other`s decision making. It can be said that keeping all other factors aside, `group think` implicitly influences an individuals decision to stay on with the organization. Peer comparison negatively influences intention to stay for employees of organization 2.

The higher the individual rates himself in comparison to his peer group lesser would be his intention to stay. Implications With the advent of a work scenario where more and more companies have to concede that their valued employees are leaving them, a new concept of career planning is bound to emerge. The focus of this new paradigm should be not only how to motivate and retain key knowledge workers, but also how to reinvent careers when the loyalty of an employee is to his/her `brainware` rather than to the organization. Career development has to take into account the changing world of employment.

With lifetime employment in one company not on the agenda of most employees, jobs will have to become short term. The present generation wants work to be exciting and entertaining. This suggests designing work systems that leverage the thinking of all employees. Opportunities for continuous up gradation of skills must be provided. The organization must commit to lifelong learning. As employees gain greater expertise and control over their careers, they will reinvest their gain back into their work. To Be Or Not To Be? A Study of Employee Turnover 24 References Bulsara, N. (1997). Bond or not to bond.

Express Computers, Jan. 20, p. 5. Chaddah, J. K. (1997). Mentors: A new way of life. Express Computers, Jan. 20, p. 5. Doshi, S. (1997). Bonds should be used selectively. Express Computers, Jan. 20, p. 6. Ganesh, A. (1997). Strategy: Why do professionals leave? Data Quest, Dec. 15, p. 148-151. Gates, Bill (1997). India: A software superpower? Business Today, May 22-June 6, p. 90. Gerhart, B. (1990). Voluntary turnover and alternative job opportunities. Journal of Applied Psychology, 95 (5), p. 467-476. Hackman, J. R. & Suttle, J. L. (1977) Improving life at work. Glenview I11: Scott Foresman Healy, M. C. t. al. (1995). Age and voluntary turnover: A quantitative review. Personnel Psychology, 48, p. 335-339. Krackhardt D. and Porter, L. W. (1986). The snowball effect: Turnover embedded in communication networks. Journal of Applied Psychology, 71 (1), p. 50-55. March, A. and Simon, J. K. (1958). Organizations. New York: Wiley. Markey, J. P. and Parks, W. (1989). Occupational change: Pursuing a different kind of work. Monthly Labour Review, 112 (9), p. 3-12. Mobley, W. H. (1977). Intermediate linkages in the relationship between job satisfaction and employee turnover. Journal of Applied Psychology, 62, p. 38. Nair, K. S. (1997). Retaining professionals through allowance fund. Express Computers, Dec. 8, p. 5. Narayanmurthy, N. R. (1998). Succeeding in the global software economy-leadership challenge. Indian Management, 37 (3), March, p. 25-30. Natrajan, G. (1997). Strategy: facing the real issues, finding solutions. Data Quest Nov. 30, p. 159. O’Reilly, C. , Coldwell, D. F. and Barnett, W. P. (1989). Workgroup demography, social integration and turnover. Administrative Science Quarterly, 34 (1), p. 21-37. Price, J. L. (1977). Study of turnover. Iowa State Press, Ames, IA. Shapiro, A. (1985).

Managing Professional People: Understanding Creative Performance. The Free Press: London, Collier Macmillan Pub. Tsang, M. C. , Rumberger, R. W. and Levin, H. M. (1991). The impact of surplus schooling on worker productivity. Industrial Relations, 30 (2), p. 209-228. Wanous, J. P. (1980). ‘The Matching Model’, Organizational Entry: Recruitment, Selection and Socialization of Newcomers. Reading, MA: Addison-Wesley. Werbel, J. D. and Bedeion, A. G. (1989). Intended turnover as a function of age and job opportunity. Journal of Organizational Behaviour, 10 (3), July, 275-281. Workforce Turnover in FIFO Mining Operations n Australia: An Exploratory Study A research report by Centre for Social Responsibility in Mining and Minerals Industry Safety and Health Centre Workforce Turnover in FIFO Mining Operations in Australia: An Exploratory Study A research report by CSRM and MISHC Ruth Beach David Brereton David Cliff Published by: Centre for Social Responsibility in Mining, Sustainable Minerals Institute, University of Queensland Brisbane Qld 4072 Australia Telephone: National (07) 3365 3776 International (+617) 3365 3776 Fax (07) 3365 1881 The authors of this report are: Ruth Beach, Centre for Social Responsibility in Mining

David Brereton, Centre for Social Responsibility in Mining David Cliff, Minerals Industry Safety and Health Centre National Library of Australia Cataloguing-in-Publication Data “Workforce Turnover in FIFO Mining Operations in Australia: An Exploratory Study” ISBN 1864 9972 49 © University of Queensland 2003 Acknowledgements The authors would like to thank all of the site-based personnel who assisted with the study. Due to confidentiality requirements we cannot name any individuals. However, without the willing cooperation of the mining sites involved in the study we would not have been able to undertake this research.

We would also like to record our thanks to the Western Australian Mining Occupational Safety and Health Advisory Board (MOSHAB) for making the data from their Safety Behaviour Survey (2002) available to us. Finally, we are grateful to Russell Parks (MOSHAB), Keith Barker (QMC) and Ian Williams (Chair, CSRM Advisory Board) for their timely and helpful comments on a draft of this report. The authors accept sole responsibility for any omissions or errors of fact or interpretation in this report. Workforce Turnover in FIFO Mining Operations in Australia CONTENTS

Executive summary ……………………………………………………………………………………………………………….. i List of Abbreviations……………………………………………………………………………………………………………. iv Glossary……………………………………………………………………………………………………………………………….. v Chapter 1: Introduction …………………………………………………………………………………………………….. 1. 1 Study Background…………………………………………………………………………………………………… 1 1. 2 Project Objectives ………………………………………………………………………………………………….. 1 1. 3 Scope of the Study…………………………………………………………………………………………………… 1 1. 4 Definitional Issues………………………………………………………………………………………………….. 2 1. Why turnover is an important issue…………………………………………………………………………… 2 1. 6 What is known about employee turnover in the Australian Mining Industry? …………………. 5 Chapter 2: Methodology…………………………………………………………………………………………………….. 9 2. 1 Key Questions……………………………………………………………………………………………………….. 9 2. 2 Methodology……………………………………………………………………………………………………….. 0 Chapter 3: Overview of participating mines ……………………………………………………………………… 12 3. 1 Characteristics of participating sites ………………………………………………………………………. 12 3. 2 How employee turnover was defined on site …………………………………………………………….. 15 3. 3 Employee turnover rates for 2002 …………………………………………………………………………… 16 3. 4 Chapter summary …………………………………………………………………………………………………. 7 Chapter 4: Factors Impacting on Turnover ………………………………………………………………….. ….. 18 4. 1 A model of turnover………………………………………………………………………………………………. 18 4. 2 Explaining inter-site differences……………………………………………………………………………… 20 Comparing FIFO Operations ………………………………………………………………………………………….. 0 The Impact of Rosters ……………………………………………………………………………………………………. 21 The External Labour Market…………………………………………………………………………………………… 22 Organisational Culture …………………………………………………………………………………………………… 23 4. 3 Explaining Intra-site Differences ……………………………………………………………………………. 4 Differences between occupational groups…………………………………………………………………………. 24 Trends within sites over time ………………………………………………………………………………………….. 25 4. 4 Summary…………………………………………………………………………………………………………….. 26 Chapter 5: Managing employee turnover ………………………………………………………………………….. 28 5. Preferred Turnover Levels …………………………………………………………………………………….. 28 5. 2 Perceived Costs and Consequences of Turnover……………………………………………………….. 29 5. 3 Use of data by management……………………………………………………………………………………. 32 5. 4 Strategies for dealing with employee turnover………………………………………………………….. 33 5. 5 Summary…………………………………………………………………………………………………………….. 6 Chapter 6: Issues Relating to Contractors…………………………………………………………………………. 37 Workforce Turnover in FIFO Mining Operations in Australia 6. 1 Employee turnover within contractors …………………………………………………………………….. 37 6. 2 The impact of high employee turnover in contractors………………………………………………… 40 6. 3 The contractor-mine company relationship………………………………………………………………. 0 6. 4 Chapter Summary…………………………………………………………………………………………………. 41 Chapter 7: Concluding Comments ……………………………………………………………………………………. 42 7. 1 Key Findings……………………………………………………………………………………………………….. 42 7. 2 Implications for management …………………………………………………………………………………. 43 7. Future Research …………………………………………………………………………………………………… 44 References ………………………………………………………………………………………………………………………….. 46 Appendices …………………………………………………………………………………………………………………………. 48 Appendix 1, Suggested definitions and data recording fields …………………………………………………… 9 Appendix 2, Proposed exit interview data collection form ………………………………………………………. 52 Appendix 3, Seven questions to assess employee turnover on site…………………………………………….. 54 Appendix 4, A framework for estimating separation costs ………………………………………………………. 57 Appendix 5, Data Collection Tools………………………………………………………………………………………. 60 Workforce Turnover in FIFO Mining Operations in Australia

Workforce Turnover in FIFO Mining Operations in Australia Executive Summary i Executive summary Context and Objectives of the Study The primary objective of this research project is to assist the mining industry manage workforce turnover more effectively, especially in fly-in/fly-out (FIFO) operations. A secondary objective is to map out an agenda for further research in the area. The project arose from consultations with mining industry representatives, who identified turnover as an important issue for the industry and drew attention to the adverse economic, operational and social impacts of workforce instability.

The research has been conducted jointly by the Centre for Social Responsibility in Mining (CSRM) and the Minerals Industry Safety and Health Centre (MISHC), with funding provided through the University of Queensland’s Sustainable Minerals Institute (SMI). Methodology The study draws on data from three mines in Western Australia and six in northern Queensland. Seven of these sites are wholly or partly FIFO operations. Two townbased sites were included for comparative purposes. A case study methodology was used; data being collected from interviews, company records, public documents and site visits.

Site based human resource (HR) managers were the primary group of interviewees, however other sections of mine management were interviewed where possible. All participating sites provided the research team with a substantial amount of documentary material and statistical data, in addition to the interview data. Key Findings Turnover rates at participating sites Annual turnover of company employees at the seven FIFO sites, as at June 2002, ranged from 10 to 28 per cent, with the average being 21 per cent. This average is comparable to estimates from other studies.

The two town-based sites had annual turnover rates of 8 per cent and 27 per cent respectively. Within sites, turnover rates tended to be highest amongst professional and managerial staff, and in the mining operations area. Most sites did not record information about the employee turnover rates of contractors, but findings from a recent Western Australia industry survey (MOSHAB 2002) suggest that contractor workforces tend to be less stable than company workforces. We found no evidence of turnover rates stabilising at a ‘natural level once a mine had been in operation for a few years.

Within some sites employee turnover rates varied markedly over time. This appeared to be due mainly to site-specific factors, such as changes in working arrangements and management interventions. Workforce Turnover in FIFO Mining Operations in Australia Executive Summary ii Explaining Turnover Employee turnover rates were influenced by a number of factors, including: • the FIFO roster structure – shorter rosters, such as 9-days on/5-days off (9/5) and 8/6 were generally associated with lower employee turnover rates, although one site demonstrated that the 14/7 could be managed with employee turnover of only 12 per cent the level of management commitment to employee training and skills development • the extent to which management had been successful in creating and maintaining a positive workplace culture • the extent to which management perceived the present rate of employee turnover as inevitable. External factors, such as the state of the labour market, appeared to be of only secondary importance. The Cost of employee turnover None of the participating sites tracked the cost of employee turnover, and no interviewees could estimate the full cost of employee turnover with any confidence.

Using estimation procedures from other industries, we calculated that the cost of ‘average’ employee turnover at an open-cut FIFO mine of 300 employees would be in the order of $2. 8 million. Many interviewees recognised that employee turnover costs could be considerable. These costs include direct recruitment and training costs, as well as loss of productivity during the stages of exit and replacement (loss of site and project knowledge, the orientation of new personnel by fellow workers, interruption of work teams and stalled projects).

It is likely that managers would give greater attention to controlling turnover if they had access to more accurate information about the full cost of turnover for their operations. How employee turnover was managed Most interviewees agreed that a turnover rate above 20 per cent was detrimental to their site’s productivity. Employee turnover at five of the sites in the study exceeded this threshold. At several of these sites managers saw ongoing high turnover as normal or largely due to factors beyond their control. Few sites collected data to monitor and understand trends and patterns in workforce turnover on site.

Only limited demographic information was collected about employees, little use was made of exit interview data, and most sites did not track trends over time or across different areas of the mine. In addition, most sites did not monitor, or have access to, turnover data for major contractors on site (including principal contractors, where they were used). Workforce Turnover in FIFO Mining Operations in Australia Executive Summary iii Implications for management Controlling turnover in FIFO operations is not easy, given the location of these operations, the recurring travel demands on employees and the impact of extended absences from home.

However, as illustrated by some of the sites in this study, high turnover is not an inevitable corollary of FIFO. Specific initiatives that can assist sites to better manage workforce turnover include: • establishing monitoring systems to track turnover trends and patterns within occupational groups on site • improving exit interview procedures and making better use of these data • routinely evaluating management initiatives (such as the introduction of new recruitment practices) for their impact on employee turnover • undertaking periodic ‘organisational climate’ surveys to monitor the workplace culture and employee perceptions of management reviewing existing roster arrangements to ascertain whether there is scope to introduce shorter, more balanced, rosters • monitoring turnover rates amongst major contractors and, where necessary, taking steps to encourage contractors to address workforce stability issues • supporting research to establish a reliable and comprehensive costing of employee turnover specifically for the mining industry. Some practical materials to address some of these issues are included in the appendices to this report. Future Research

The project has identified a number of areas where current levels of knowledge about employee turnover in the mining industry are inadequate. Questions requiring further research include the following. • How much does employee turnover cost? A standard method for valuing direct and indirect costs associated with turnover is needed in the industry. • How does turnover within contractors affect mine operations? Mine management currently has a limited appreciation of the extent and impact of employee turnover within principal contractors and subcontractors. • Can retention of mine professionals and management be improved?

Research is needed to identify factors contributing to the high turnover of professional and managerial personnel and identify effective managerial strategies to improve their retention. • How does workplace culture on site impact on workforce stability? A detailed comparative study is needed to determine the extent to which turnover levels are influenced by the site level organisational culture. Workforce Turnover in FIFO Mining Operations in Australia Abbreviations and Glossary p iv List of Abbreviations ABS Australian Bureau of Statistics AusIMM The Australasian Institute of Mining and Metallurgy

CSRM Centre for Social Responsibility in Mining DIDO Drive-in / drive out FIFO Fly-in / fly-out HR manager Human resource manager MISHC Minerals Industry Safety and Health Centre MOSHAB Mines Occupational Safety and Health Advisory Board SMI The Sustainable Minerals Institute Workforce Turnover in FIFO Mining Operations in Australia Abbreviations and Glossary p v Glossary Casual employee An employee hired on a daily basis or on a per/cycle basis (that is fly in to site and work for the full FIFO or DIDO cycle). Casuals can be sourced from a labour hire company, or have an individual arrangement between the mine company nd the casual employee. Some mines make a point of hiring inexperienced casuals and training them to operate machinery. They then use this pool of casuals to fill vacancies in the permanent workforce as they arise. Casuals can be employed for more than 12 months at the site in a series of casual appointments. Fixed term employee A fixed term employee is usually on a contract of between six and 24 months. Sometimes called ‘temporary full-time’ employees. Permanent employee A permanent employee is a continuous appointment. Company employees Employees of the company that owns the mine. The phrase Company employees’ normally refers to permanent employees, but can include all company employees (permanent, fixed term and casuals). Contractors Variously the companies or the employees of the companies that are associated with the mine, (not the lease holder of the mine). See also ‘sub-contractor’ and ‘subbies’. Daily Commute The workforce travels between home and the work site on a daily basis. Work pattern and shift length can vary widely, but usually based on a 40-42 hour week. Drive-in / drive-out (DIDO) Travel to the mine is by road, and the workforce resides on site for a period of time before returning to the pickup point.

Transport between the pick-up point and the mine is supplied by the company. One or both journeys are paid as work time. Usually operates on a 12 hour shift pattern. DIDO is different to FIFO in that the distances between the mine and the pickup point are shorter and individual employees may have the Workforce Turnover in FIFO Mining Operations in Australia Abbreviations and Glossary p vi option to self-commute to site. Fly-in / fly-out (FIFO) Travel to the mine site is by air, and the workforce resides on site for a period of time before flying back to the pick-up airport. Transport between the pick-up airport and the mine is upplied by the company. Parent company Where the mine operations is subcontracted to a principal contractor, the lease holder of the mine is sometimes referred to as the parent company. Principal contractor Term for the company that is engaged to undertake the actual mining for the lease holder. Principal mine contractors can have a similar employee structure to mining companies. That is, they will have permanent full time, fixed term and casual employees. Owner miner Refers to a mine company that is both the lease holder of the mine and also undertakes the mining operations.

Sub-contractor Term for a company contracted at the mine by either the mining company or the principal contractor. Subbies Variously refers to employees of sub-contracting firms or the sub-contracting firms on site as a group. Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 1 Chapter 1: Introduction This introductory chapter describes the background, objectives, focus and scope of the study, discusses why turnover is an important issue for the mining industry and briefly reviews other research that has been conducted in the area. . 1 Study Background In 2002 the Centre for Social Responsibility in Mining (CSRM) undertook a scanning exercise to identify possible social science research projects that might be of operational benefit to the minerals industry and which addressed some aspects of sustainability. Consultations with industry personnel pointed towards workforce turnover as a potentially significant issue, especially in the FIFO sector, where concern was expressed about the adverse economic, operational and social impacts of high turnover.

Further investigations established that very little research had been undertaken into the extent, causes or consequences of turnover within the industry. Subsequently, the CSRM and the Minerals Industry Safety and Health Centre (MISHC) obtained funding from the University of Queensland’s Sustainable Minerals Institute (SMI) to conduct an initial study of turnover at a limited number of FIFO sites. Several companies were approached to provide access to sites for the purposes of this research. A second stage was also foreshadowed, contingent on funding, which would further develop the practical outcomes of the project. 1. 2 Project Objectives

The project aimed to enhance the mining industry’s understanding of the phenomena of workforce turnover and assist – and encourage – the industry to manage turnover more effectively, especially in FIFO operations. Specific project objectives were to: 1. collect data on the extent and nature of workforce turnover in remote mining operations in Australia and possible costs associated with turnover 2. identify factors which impact on turnover rates and, in particular, to account for significant variations in rates between sites 3. identify workforce management practices that may be effective in reducing undesired turnover of staff . map out an agenda for further research in the area. 1. 3 Scope of the Study The study draws on data from three mines in Western Australia and six in northern Queensland. Seven of these sites are wholly or partly FIFO operations. At the remaining two mine sites the employees live in a nearby town. We chose to focus primarily on FIFO sites because there was some evidence from previous studies that Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 2 FIFO sites tend to have higher levels of turnover than town-based sites.

The two non- FIFO sites were included to provide some points of comparison. All of the sites in the study are engaged in metalliferous mining, which reflects the make-up of the Australian FIFO sector. By limiting the study to metalliferous mines we were able to control for some confounding issues, such as the divergent industrial cultures of the coal and metalliferous sectors of the industry, and the different work patterns and remuneration levels in the two sectors.

Due to cost considerations and the logistical difficulties associated with organising travel and on-site accommodation, we were only able to visit six sites, all of which were in Queensland. Information about the three Western Australian sites was collected through extended telephone interviews and e-mail contact. The study relies primarily on data that sites already had on-hand, or could make available fairly readily. It was not practical to collect data directly from employees or from personnel records, although this would certainly be desirable in a more comprehensive study.

The study focuses on turnover amongst company employees, rather than in the mine workforce as a whole. At some of the sites studied a majority of the workforce were employed by contractors and substantial functions, such as mining operations, were wholly contracted out. However, information about turnover amongst contractors was generally not kept by the companies and it proved difficult, for logistical reasons, to obtain direct access to contractors at most of the sites. 1. 4 Definitional Issues In broad terms, turnover can be defined as “any departure beyond organisational boundaries” (Macy and Mirvis, 1976).

For the purposes of this study, our focus is restricted to employee movements that create a vacancy on site. This definition encompasses both voluntary and involuntary departures (for example, dismissals), but excludes movements that result from positions being made redundant. The turnover rate is simply the number of vacancies that are created by departures in a given year expressed as a proportion of the number of company employees at the site. This approach to defining turnover is consistent with the way in which the term is generally used within the Australian mining industry (although, as discussed in Chapter 3. , sites varied in how this definition was applied in practice). 1. 5 Why turnover is an important issue Some employee turnover is beneficial for workplaces and is also socially desirable because it attracts new skills and ideas to the company (or mine) and creates new employment opportunities. However, there is broad agreement amongst practitioners and researchers that continuing high turnover has a number of negative impacts. First, turnover is a direct financial cost to employers.

Specific costs will vary between industries and occupations, and will depend on the nature of the job and the difficulty in recruiting a suitable replacement, but in broad terms include: Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 3 • separation costs (administration associated with processing resignations and dismissals, time taken up in conducting exit interviews, productivity losses associated with impending departure) • vacancy costs (lost productivity and/or additional costs such as overtime or contractor payments to cover for vacancies created by departures) recruitment costs (advertising, employment of job search agencies, time and resources spent in processing applications, staff time involved in selection interviews, travel costs for short-listed candidates and relocation costs for successful applicants and their families) • training and start-up costs (the time of trainers and staff and of new employees taken up in inductions and on the job training, loss of productivity until the new employee reaches full production potential).

Estimates of the dollar cost of turnover vary widely, in part because researchers use different multipliers for their calculations, and also because they are based on data from different industries. Estimations of the costs of hiring and training new employees vary widely. Replacing a relatively unskilled worker is reported to cost between 300 to 700 times the hourly rate (Moody 2002). Pinkovitz, Moskal and Green (1997) estimated that the replacement cost of an ‘hourly rate’ worker was eight times the weekly wage.

Such estimates have been reported by consultants and key industry organisations in industries as diverse as retailing, the plastic industry, hotels and hospitality, and information technology. No research has been conducted specifically on the costs of turnover at mining operations in general or FIFO sites in particular, but it seems likely that these costs will be greater than the estimates cited above. This is because the costs for replacements of FIFO workers, such as travel, accommodation, training and orientation costs, are routinely higher than those working in an urban setting.

Additionally, there are extra costs associated with lost productivity due to the use of 12-hour shifts and long work patterns. Simply put, a position that remains unfilled on a 56-hour a week roster costs the company more in lost productivity than the same vacancy on a 40-hour a week roster. Second, there is a considerable body of evidence that ongoing high turnover impacts adversely on operational efficiency, especially for complex processes that require close teamwork and high amounts of assumed knowledge. Where there is ongoing instability in the workforce, consequences can nclude increased stress and tension amongst those remaining employees who have to fill the gaps left by departing employees, declining employee morale, and decreased productivity due to loss of work group synergy (Pinkovitz et al. , 1997). In addition, new employees take time to reach full effectiveness and are likely to be more error-prone than their experienced counterparts. According to one study, staff turnover, acquisition, and assimilation rates can extend a project’s cost and duration by as much as 60 per cent (Abdel- Hamid 1989:371).

In a worst case scenario the outcome of continuing workforce 1 Abdel-Hamid’s research entailed modelling the impact of turnover on software design teams, a complex task involving input from multiple professionals. His work explored the orientation period for new recruits, including the assimilation period, which is the time needed to acquaint newly acquired Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 4 instability can be a negative workplace culture of distressed, under-functioning employees which then affects new recruits in a self perpetuating manner (Reese 1992 in Boshoff and Mels 2000).

Third, where turnover is high, it becomes much more difficult to focus on other workforce management objectives, because most of the capacity of human resource personnel is taken up with ‘base level’ tasks of recruiting and training new staff. In particular, there will be less scope to implement staff development initiatives and other strategies to enhance the skills and productivity of existing employees.

Fourth, high turnover amongst employment target groups, such as indigenous employees, makes it much more difficult to make progress against these targets. Again, much of the recruitment effort has to be devoted to replacing departing employees. In addition, where the target population is relatively small (for example, a local Native Title Group) the recruitment pool can be depleted quite quickly. Fifth, there is concern that high rates of workplace turnover can increase the risk of safety incidents.

Reasons for this are that: (a) there will be a greater proportion of recent recruits within the workforce, with consequent communication lapses creating more opportunities for error; (b) the constant concern of human resources personnel with covering for and replacing departing employees reduces opportunities for advanced safety training and refresher training and (c) it is inherently more difficult to build and communicate a positive safety culture if the composition of the workforce is constantly changing.

Sixth, particularly in the case of FIFO operations, high turnover could well be an indicator that employees are experiencing significant conflicts between ‘home’ and work life. Repeated family absence has been shown to contribute to such tensions (Beach 1999; Storey, Shrimpton, Lewis and Clark 1989). Work-home conflict is, in turn, associated with stress, reduced productivity, increased turnover intentions and a range of other negative consequences (Johnson 1995; Rice, Frone and McFarlin 1992). Finally, high turnover can also have an undesirable effect on the communities that are associated with mining developments.

When employees resign, they (and their families) will also leave the community. Where populations are unstable, it is much more difficult to build and maintain a sense of community and to sustain activities such as clubs and associations, which contribute positively to the social life of the community. A further reason why it is important to undertake research on the issue of turnover is that some mining companies (for example, Newmont Australia) are now reporting publicly on employee turnover rates at their sites.

It is likely that reporting against this indicator will increase in the future, as the Global Reporting Initiative has identified employee turnover as a ‘core’ sustainability indicator for companies (GRI 2002:52). If companies are to report publicly on their performance in this area, it is team members with the mechanics of the project, integrate them into the project team, and train them in the necessary technical areas. The length of this assimilation period can vary depending on such things as the nature of the project and the training resources committed.

According to one estimate, it takes 18 months for new employees to become maximally effective (Abdel-Hamid 1989:22). Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 5 important that they have a clear understanding of the factors which impact on turnover rates and are in a position to interpret trends and patterns correctly. 1. 6 What is known about employee turnover in the Australian Mining Industry?

Voluntary employee turnover, and its corollary employee retention, has generated a vast amount of interest for over 50 years, with well over 1,500 academic papers addressing the subject. In addition, numerous articles in the popular business press, by personnel management consultants and others, discuss the extent of the costs of employee turnover and methods for reducing voluntary turnover2. However, much of this literature is of only limited relevance to the mining sector as the focus of turnover research has been on occupations and industries based in major population centres.

Available data about turnover within the Australian mining industry comes from four relevant studies: AMMA (1998); Gillies, Wu and Jones (1997); the AusIMM 2001 membership survey (Venables, Beach and Brereton 2002); and MOSHAB (2002). Some useful data are also contained in the Australian Bureau of Statistics (2002) Labour Mobility Survey. In 1998, the Australian Mines and Metals Association conducted a survey about long distance commuting in the mining and hydrocarbon industries (AMMA 1998).

Respondents were 19 mining companies, 15 companies in oil and gas and three contractor companies (drilling and catering). A lengthy survey was posted to the Human Resources Superintendent at each operation (or company in the case of contractors). One of the conclusions of the report was that: turnover ratios were not alarming… The mining sector’s average turnover across all job categories ranged from between 0-17%. … Turnover ratios for drilling contractors were, as research suggests, quite high and ranged between 25-50%” (AMMA 1998:3).

The questionnaire used in this research did not ask directly about employee turnover rates. Instead, this information appears to have been obtained from conversations with mine management, which casts some doubts on the validity of these data. In 1997 Gillies, Wu and Jones (1997) conducted a series of mail-out surveys of the Australian mining industry focusing on the management of FIFO operations. Questionnaire data were gathered from 14 fly-in mines, 11 of which were operating with a 14 days on/7 days off pattern. The issue of employee turnover was not directly canvassed in the questionnaire.

However, based on other information provided by participating mines, the researchers reported the following: Only six FIFO operations responded that they could quantify their professional employee turn-over rates, with the highest rate being 60 per cent per annum and the lowest being nil in the previous 12 months. The non-professional employee turn-over rates of these operations ranged from the highest rate of 90 per cent to the lower of only two employees in the previous 12 months. No FIFO operations 2 A recent example is an article by R. Turner (2003) in the February issue of the Australian Financial Review BOSS Magazine.

Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 6 were aware of any industry wide data that detailed employee turn-over rates in FIFO operations (Gillies, Wu and Jones 1997:9). The authors considered that individual or personal factors were driving the employee turnover rates at participating sites. They hypothesised that an iterative process of self selection operated whereby employees who liked FIFO stayed, and those who did not left the site – a process which was repeated until the workforce stabilised.

In effect Gillies et al’s hypothesis supported a ‘wait and see’ approach to human resources management on FIFO sites. However, at the time of writing they could only identify two FIFO sites with the expected low employee turnover, and both of these were using even-time FIFO schedules. In 2001, the Australian Institute of Mining and Metallurgy (AusIMM) conducted a membership survey which obtained responses from a broad cross section of mining industry professionals working in regional and remote operations, mainly geologists, mining engineers and metallurgists.

Around one third of the respondents indicated that there were employed in a long distance commuting arrangement (typically a FIFO operation). Of this group, 17. 5 per cent had changed jobs in the last year, compared with 10. 9 per cent of respondents in town-based jobs (Venables et al. , 2002:5). These employees engaged in long distance commuting were more likely to report that their working lifestyle put pressure on personal relationships and that their lives lacked a balance between work, health and relaxation (Venables et al. , 2002:6-7).

In 2001 the Western Australian Mines Occupational Safety and Health Advisory Board (MOSHAB) conducted a survey of the health and safety attitudes and behaviours of employees in the Western Australian mining industry. The results, based on data from 4700 respondents, provide the most recent and robust information about workforce stability in the Australian mining industry. The survey measured employees’ length of service at their current workplace as one of the survey items. The proportion of employees that have been at the workplace less than 12 months is a reasonable proxy measure of the level of employee turnover3.

Relevant findings from the survey are as follows: • The overall proportion of workers with less than 12 months service at their current site was 19. 6 per cent. • Different occupational groups had differing levels of employment stability. Managers and operators appeared to have higher than average levels of employment change. Around 28 per cent of managers had less than 12 months service at their current mine, and one-third of mine employees (mine and plant operators, trades) had less than 12 months service at their current mine4. Length of service includes those instances where hiring for new positions was undertaken in the previous 12 months. This means that a new mine, or a mine with a recent expansion, will have a larger proportion of workers who started in the previous 12 months. For these mines, length of service is a poor proxy for labour turnover. However, given the size of the MOSHAB sample, this factor is unlikely to have skewed the overall data to any great extent. 4 The published results did not provide a more detailed breakdown of occupational groups.

Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 7 • The variation between industry sectors is considerable. Gold and nickel had the lowest levels of workforce retention. These sectors also have the highest number of FIFO operations. Coal and Alumina, which are residentially based, had the highest levels of workforce retention. • Length of service among employees of contractors is, on average, shorter than length of service among employees of mine companies (further discussed in section 6. 1 Employee turnover within contractors, page 37).

Another useful source of data is the Australian Bureau of Statistics (ABS) Labour Mobility5 survey which reports on the mobility of the workforce nationally, as well as providing comparative data across industry sectors (ABS Labour Mobility Report 5 The ABS definition of ‘Labour Mobility’ is much broader than employee turnover. It includes employees who remain with the same employer and change jobs, employees who remain with the same employer and physically move (such as taking an interstate position), all casual employees, all part-time employees, all temporary employees and all full-time permanent employees.

Mining companies normally include only this last category of employees – full-time permanent employees – in their employee turnover rate. Figure 1: Labour mobility by industry, Australia Labour mobility by industry ABS Labour Mobility 6209. 0 (2002) 0 5 10 15 20 25 Ag, Forest & Fish Education Gvt Admin & Def Manufacturing Health & Commt’y E, Gas & Water Cult & Rec Serv Construction Communication Personal & Other Transport & Store WholesaleTrade Retail Trade Property & Bus Finance & Insure Accom, Cafes… Mining % Workforce Turnover in FIFO Mining Operations in Australia Chapter 1, Introduction p 8 6209. 2002). The mobility rates for different industry sectors are represented in Figure 1. The ABS estimates that nationally 15 per cent of employed Australians changed jobs during the year 2000. The job mobility rate in the mining industry as a whole was 21. 7 per cent, giving it the highest mobility rate of any industry sector. This is further evidence that workforce turnover is a significant issue for the industry. Structure of Report The methodology used in the study is discussed in the following chapter. The results and discussions are then presented in four chapters.

Chapter 3 presents descriptive data about the sites and describes how employee turnover was defined and recorded at these sites. Chapter 4 provides some theoretical back ground to the discussion of what factors impact on employee turnover and presents data about inter-site differences, intra-site differences and changes over time. Findings about the management of employee turnover are presented and discussed in Chapter 5. Finally, issues relating to contractors that arose from our analyses are discussed in Chapter 6. The report conclusions are provided in Chapter 7.

Suggested practical tools for management on site are included in the Appendices. Workforce Turnover in FIFO Mining Operations in Australia Chapter 2, Methodology p 9 Chapter 2: Methodology This Chapter provides more detail about the key questions that shaped the research and outlines the methodology that was employed to address these questions. 2. 1 Key Questions The main questions that were used to guide the collection of data from sites were as follows: Characteristics of employee turnover • How is the employee turnover rate calculated? What are the turnover rates at different FIFO sites? • Is there a ‘typical’ turnover rate? • Are there differences in employee turnover between occupational groups? • Do turnover rates for company employees and employees of contractors differ? Influences on turnover • How can inter-site differences in turnover be explained?

• What factors help account for differences in turnover between different areas of a mine? • To what extent and why do turnover rates vary over time within sites? Management practices • How significant an issue is turnover for site management? How actively is turnover managed? • What are the perceived acceptable and optimal rates for turnover? • What are the costs of employee turnover? • To what extent do sites monitor these costs and take account of them when making decisions? • What strategies do managers use to control turnover and/or manage its consequences and how effective are these strategies? Workforce Turnover in FIFO Mining Operations in Australia Chapter 2, Methodology p 10 Ensuring the validity of the research We took the following steps to ensure the reliability and validity of our data and analysis. See Stake, 2000; and Yin, 1995; for further technical detail on rigor in case study methodology. )

1. Wherever possible, we used multiple data sources to verify an observation or interpretation: this is known as the process of’ ‘triangulation’. 2. Detailed records have been kept of how data collection and analysis were undertaken. 3. We submitted site feedback documents to site sources for review. 4. All parts of the document were critically reviewed by each member of the research team. 5. A draft of the final report was reviewed by other researchers and industry ersonnel. 2. 2 Methodology This study utilises a multiple case study design (Yin 1995; Stake 2000:438). Mine sites are well suited to case study methods as each site is a clearly defined geographical and organisational entity.The method enabled qualitatively rich data from interviews, documents and observation to be combined with a range of quantitative data (including human resource data, exit interview data and data from previous studies) to construct profiles of the participating sites. Data collection The six Queensland sites were visited y at least one member of the project team. The interviews were conducted in person at these sites, and a wider range of personnel on site participated in discussions. Interviews and discussion with personnel at the three Western Australian mine sites were conducted by telephone. For all sites, additional data and documents were supplied to the researchers, such as turnover data, exit and entry forms (templates only). Where applicable other documents were supplied, such as management reports, employee incentive schemes and site newsletters.

We also accessed publicly available information about the participating mine sites (for example web-based sources). Standard quantitative and qualitative data collection instruments were used to ensure, as far as possible, that comparable data were collected at participating sites. A confidential site feedback report was provided to each participating mine. This process served to verify the accuracy of the researchers’ findings and provided a point of negotiation about confidentiality and use of the data. Confidentiality of sources

The following steps have been taken to ensure that confidentiality of participating sites and individuals is respected in this report; • not disclosing the State where the mine is located • including additional data in some tables • changing the gender of the reported interviewee where this could identify an individual • deleting the mine name in all quotes Workforce Turnover in FIFO Mining Operations in Australia Chapter 2, Methodology p 11 • not specifying the year of significant events, where that might identify the mine. Limitations

As indicated at the outset of this report, this is an exploratory study. Because we only have data about a relatively small number of sites – and a variable amount of information about these sites – our ability to test competing explanations in any systematic way or assess the relative contribution of different factors is limited. However, by analysing patterns across and within sites and over time, and by drawing on other research where available, we have been able to draw some broad conclusions and identify areas where further research would be most productive.

The main limitations of the study are as follows: • The findings reported here may not be typical for the full population of Australian FIFO sites, although we are satisfied that there is a sufficient spread to enable some broad generalisations to be made. • We included only two non-FIFO ‘control’ sites, which limited our ability to assess the impact of factors unique to FIFO operations (such as the impact of extended absences from home). • With the exception of one site, data were not available on employee turnover amongst contractors.

Contractors play a vital role in the Australian mining industry and clearly warrant attention in a more comprehensive study. • The primary source of information was human resource and management personnel; collecting data from a wider range of employees was outside this project brief. It is possible that management perceptions about the culture of their sites and the factors that impact on turnover may not have been aligned with the experiences of the workforce as a whole, although we have made every effort to ‘triangulate’ verbal sources with data available on site, previous research findings and industry data.

The study focuses almost exclusively on the role played by organisation-level variables such as work scheduling practices, management style, workplace culture, and rates of remuneration. We did not survey employees and former employees in this study, so there is very little discussion of the impact of individual-level factors (such as employees’ expectations and motivations) on employee turnover. The issue of what distinguishes employees who leave from those who stay has been studied extensively by researchers within the context of other industries.

Despite these limitations, we are confident that the key conclusions of the study are defensible and that it has provided valuable insights into the extent, causes and consequences of turnover in FIFO operations within the Australian minerals industry. Workforce Turnover in FIFO Mining Operations in Australia Chapter 3, Overview of mines p12 Chapter 3: Overview of participating mines In this Chapter we present and discuss data on: • the characteristics of participating sites • definitions of employee turnover used at participating sites • turnover