There is growing evidence that the use of “compound” traits that are more tied to particular work situations and particular criteria can enhance prediction above what can be derived from the traditional FFM instruments. Many forms of personality, dispositional, or motivation assessment attempt to focus on either particular problems or criteria characteristic of the workplace. Examples are the prediction of voluntary turnover and the prediction of employee theft. One instrument attempts to measure job compatibility in order to predict turnover. Other new instruments are designed to address particular employment issues or situations, such as customer service, violence, or accident proneness.
Predicting (and Reducing) Voluntary Turnover
Employee turnover can be a serious and costly problem for organizations. You may recall the discussion of Domino’s Pizza. They found that the cost of turnover was $2,500 each time an hourly employee quit and $20,000 each time a store manager quit. Among other things, Domino’s implemented a new and more valid test for selecting managers and hourly personnel that was aimed at predicting both job performance and voluntary turnover. As of 2008, the program was a success on all counts. Turnover was down, store profits were up, and the stock was doing well in an otherwise terrible market. Attracting and keeping good employees was a key factor in their turnaround. There are numerous other examples of companies that have expensive and preventable high levels of turnover that can be reduced with better HR policy and practice. Recall the discussion of SAS, the North Carolina software company. Even at the height of the so-called “high-tech” bubble in the late 1990s, SAS had turnover rates that were well below the industry average. Attracting and keeping good employees is considered a key to the SAS success story. As of 2008, SAS remained one of Fortune’s “Best Companies to Work For” and reported their usual very low turnover rate among its core personnel.
One study revealed guidelines regarding methods that have been shown to be effective at reducing voluntary turnover. A summary of the findings merged with previous research on turnover is presented in Figure 6-6. This most recent research drew several conclusions. First, voluntary turnover is less likely if a job candidate is referred by a current employee or has friends or family working at the organization. Candidates with more contacts within the organization are apt to better understand the nature of the job and the organization. Such candidates probably have a more realistic view of the job that may provide a “vaccination effect” that lowers expectations, thereby preventing job dissatisfaction and turnover (realistic job previews can also do this). Also, current job holders are less likely to refer job candidates who they feel are less capable or those who (they feel) would not fit in well with the organization’s culture.
Another argument for an employee referral system is that having acquaintances within the organization is also likely to strengthen an employee’s commitment to the firm and thus reduce the probability that he or she will leave. Of course, this argument also applies to the employee who made the referral.
Another reliable predictor of voluntary turnover is tenure in previous jobs. In general, if a person has a history of short-term employment, that person is likely to quit again. This tendency may also reflect a lower work ethic (lower Conscientiousness), which is correlated with organizational commitment and turnover. As discussed earlier, tenure in previous jobs, measured in a systematic manner as a part of a weighted application blank (WAB), is predictive of turnover. Intention to quit is also a solid predictor of, and perhaps the best predictor of, quitting. Believe it or not, questions on an application form such as “How long do you think you’ll be working for this company?” are quite predictive of voluntary turnover. Prehire dispositions or behavioral intentions, derived from questions such as this one or from interview questions, work quite well.
Measures of the extent of an applicant’s desire to work for the organization also predict subsequent turnover. However, almost all of the research on WABs has involved entry-level and nonmanagerial positions, so applicability to managerial positions is questionable. This is not true for biodata (or BIBs). Disguised-purpose attitudinal scales, where the scoring key is hidden, measuring self-confidence and decisiveness have been shown to predict turnover for higher-level positions as well, including managerial positions. Answers to questions such as “How confident are you that you can do this job well?” or responses to statements like “When I make a decision, I tend to stick to it” also predict turnover quite well. In addition, there is little evidence of adverse impact against protected classes using these measures. This research also revealed that disguised-purpose measures added incremental validity to the prediction of turnover beyond what could be predicted by biodata alone.
Another example of a disguised-purpose dispositional measure is the Job Compatibility Questionnaire (JCQ). The JCQ was developed to determine whether an applicant’s preferences for work characteristics matched the actual characteristics of the job. One theory is that the compatibility or preference for certain job characteristics will predict job tenure and performance. Test takers are presented groups of items and are instructed to indicate which item is most desirable and which is least desirable. The items are grouped based on a job analysis that identifies those characteristics that are descriptive of the job(s) to be filled. Here is an example of a sample group: (a) being able to choose the order of my work tasks, (b) having different and challenging projects, (c) staying physically active on the job, (d) clearly seeing the effects of my hard work.
The items are grouped together in such a way that the scoring key is hidden from the respondent, reducing the chance for faking. Studies involving customer service representatives, security guards, and theater personnel indicate that the JCQ can successfully predict employee turnover for low-skilled jobs. In addition, no evidence of adverse impact has been found. BA&C incorporated the JCQ in their test for security guards. The JCQ has never been used or validated for managerial positions and is not recommended for the selection of managers.
Can We Predict Employee Theft?
It is estimated that employee theft exceeds $400 billion annually. In response to this huge problem and in addition to more detailed background and reference checks, more than 4 million job applicants took some form of honesty or integrity test in 2008. These tests are typically used for jobs in which workers have access to money, such as retail stores, fastfood chains, and banks. Integrity or honesty tests have become more popular since the polygraph, or lie detector, test was banned in 1988 by the Employee Polygraph Protection Act. This federal law outlawed the use of the polygraph for selection and greatly restricts the use of the test for other employment situations. There are some employment exemptions to the law, such as those involving security services, businesses involving controlled substances, and government employers.
Integrity/honesty tests are designed to measure attitudes toward theft and may include questions concerning beliefs about how often theft on the job occurs, judgments of the punishments for different degrees of theft, the perceived ease of theft, support for excuses for stealing from an employer, and assessments of one’s own honesty. Most inventories also ask the respondent to report his/her own history of theft and other various counter productive work behaviors (CWBs). Sample items typically cover beliefs about the amount of theft that takes place, asking test takers questions such as the following: “What percentage of people take more than $1.00 per week from their employer?” The test also questions punitiveness toward theft: “Should a person be fired if caught stealing $5.00?” The test takers answer questions reflecting their thoughts about stealing: “Have you ever thought about taking company merchandise without actually taking any?” Other honesty tests include items that have been found to correlate with theft: “You freely admit your mistakes.” “You like to do things that shock people.” “You have had a lot of disagreements with your parents.”
The validity evidence for integrity tests is fairly strong, with little adverse impact. Still, critics point to a number of problems with the validity studies. First, most of the validity studies have been conducted by the test publishers themselves; there have been very few independent validation studies. Second, few of the criterion-related validity studies use employee theft as the criterion. A report by the American Psychological Association concluded that the evidence supports the validity of some of the most carefully developed and validated honesty tests. The most recent studies on integrity tests support their use. Although designed to predict CWBs, especially employee theft, integrity tests have also been found to predict job performance. One major study found that integrity tests had the highest incremental validity (of all other tests) in the prediction of job performance over GMA. Scores on integrity tests are also related to Conscientiousness, Emotional Stability, and Agreeableness of the FFM. It has been proposed that a trait represented on integrity tests is not well represented by the FFM. “Honesty-Humility (H-H)” has been proposed as the sixth factor defined as “sincerity, fairness, lack of conceit, and lack of greed.” There is evidence that this sixth factor can enhance the prediction of CWBs or workplace delinquency
Can We Identify Applicants Who Will Provide Good Customer Service?
Considerable research demonstrates that employees’ customer orientation is a good predictor of customer-related outcomes such as customer- and supervisory-ratings of service performance, customer-focused organizational citizenship behaviors, and customer satisfaction. Thus, identifying employees who would have such an orientation would be advantageous for organizations with a strong customer-focused strategy. The Service Orientation Index (SOI) was initially developed as a means of predicting the helpfulness of nurses’ aides in large, inner-city hospitals. The test items were selected from three main dimensions: patient service, assisting other personnel, and communication. Here are some examples of SOI items: “I always notice when people are upset” and “I never resent it when I don’t get my way.” Several other studies of the SOI involving clerical employees and truck drivers have reported positive results as well. The Job Compatibility Questionnaire has also been used to predict effective customer service.
Can We Identify Bad and Risky (and Costly) Drivers?
Driving accidents by employees can be a very costly expense for employers where driving to and from jobs is an essential function of the job. Think cable companies, UPS, FedEx, and exterminators for a few examples of companies that should pay careful attention to the “accident proneness” of the drivers they hire. In addition, employers are often held responsible for the driving behavior of their employees when they are on the job. A plethora of negligent hiring lawsuits have looked at what screening procedures were used to hire the guy who committed a driving infraction while on the job and caused a serious accident.
So, first off, is there such a thing as “accident proneness,” and if so, can we predict it in job applicants? The answers to these two key questions are in fact “yes” and “yes.” Research shows that a person’s previous driving record is the single best predictor of the on-the-job record and an essential screening tool. But personality is a correlate of risky driving behavior and future traffic violations and accidents. For young drivers (18–25), one study found that a high level of “thrill-seeking” and aggression, combined with a low level of empathy, was a predictor of subsequent risky driving and speeding violations. The researchers measured these subfactors from the “Big-Five” traits. The subfactors derived from the Emotional Stability (anger/aggression), Extraversion (“thrill-seeking”), and Agreeableness (low empathy) components of the FFM.
Other research also shows that personality factors are an important influence on risk perceptions and driving behavior. Traits labeled as “sensation seeking,” “impulsiveness,” and “boredom proneness” have also been shown to predict of aggressive and risky driving using the “Driving Anger Scale.”
Another test developed to predict (and prevent) accidents is the Safety Locus of Control Scale (SLC), which is a paper-and-pencil test containing 17 items assessing attitudes toward safety. A sample item is as follows: “Avoiding accidents is a matter of luck.” Validity data looks encouraging across several different industries, including transportation, hotels, and aviation. In addition, these investigations indicate no adverse impact against minorities and women.
Results with older drivers also suggests that a “sensation-seeking” personality and low levels of emotional stability are related to risky driving among older drivers in addition to cognitive and motor abilities. The perception of reckless driving as acceptable and desirable or as negative and threatening and the risk assessment related to cell phone usage are other predictors of driving behavior and accidents. There apparently is such a thing as “accident prone” in the sense that the people most “prone” to be involved in accidents can be identified with a background check and a personality inventory.
Figure 6-6 Predictors of Voluntary Turnover and How to Avoid It
1. Rely on employee referrals
Voluntary turnover is less likely if a job candidate is referred by a current employee or has friends or family working at the organization.
Candidates with more contacts within the organization are apt to better understand the nature of the job and the organization.
Having friends or family within the organization prior to hire is likely to strengthen the employee’s commitment to the firm and reduce the likelihood that he or she will leave.
2. Put weight on tenure in previous jobs
A past habitual practice of seeking out short-term employment predicts future short-term employment.
Short-term employment may reflect a poor work ethic, which is correlated with lack of organizational commitment and turnover.
3. Measure intent to quit
Intention to quit is one of the best (if not the best) predictors of turnover.
Despite their transparency, expressions of intentions to stay or quit before a person starts a new position are an effective predictor of subsequent turnover (e.g., how long do you plan to work for the company?).
4. Measure the applicant’s desires/motivations and job compatibility for the position
New employees with a strong desire for employment will require less time to be assimilated into the organization’s culture.
Job compatibility is correlated with job tenure.
5. Use disguised-purpose dispositional measures
Persons with high self-confidence should respond more favorably to the challenges of a new environment.
Employees with higher confidence in their abilities are less likely to quit than those who attribute their past performance to luck.
Decisive individuals are likely to be more thoughtful about their decisions, more committed to the decisions they make, and less likely to leave the organization.
Decisiveness is a component of the personality trait of Conscientiousness from the five-factor model.
Decisiveness affects organizational commitment and, indirectly, turnover.