Executive Summary

According to a National Retail Security survey conducted by the National Retail Federation and the University of Florida, total retail losses in 2009 were $36.5 billion, an increase of $2 billion from two years earlier. Digging deeper into the results, the largest component of retail shrinkage was internal employee theft ($15.9 billion). Incredible as it may seem, employee theft represents 44% of shrink within retail organizations. In fact, employees stealing from their employers represent a higher percentage of total losses than shoplifting ($12.7 billion, or 35% of losses).

These astounding figures make employee theft one of the greatest single threats to profitability at the store level. The National Shrink Database polled over 30,000 stores on the most utilized security measures that decrease shrink. While popular options included security cameras, product tagging, in-store loss prevention officers, and an alarm system, there was one glaring omission—making better hiring decisions does not appear on the list! Traditional security measures address the symptom, not the root of the problem, which is adding people to the payroll who eventually choose to steal. To better understand the “people” part of the equation, 53,713 incumbents across a varied group of retail organizations were studied to evaluate the effect, if any, between behavioral fit to the job and a reduction in theft-related terminations.

The research showed that the three-fold process of 1) assessing candidates, 2) matching them against a behavioral profile, and 3) hiring according to the strongest behavioral fit resulted in lower termination rates for theft reasons. In fact, in this study of over 50,000 employees, those NOT matched against a behavioral profile were 463% more likely to be terminated for theft compared to job candidates who were selected against a behavioral profile. Based on the research data studied, this white paper presents hiring strategy improvements that your organization can quickly adopt. These suggested improvements are designed to take a bite out of employee theft.

Details of the Theft Study


To substantiate the findings of the study, it is important to describe the content and parameters of the dataset:

• There were (n = 7) retail companies with international brand-name recognition involved in the study. By design, data was collected from a variety of retail store business models. The retail business models ranged from large full-service locations with a high employee count to smaller locations with a small employee count per location.
• Operationally, three job types were the focus of the study (Sales Associate, Assistant Manager, and Store Manager). In retail, these three job types represent the front lines of the retail market.
• To be included in the study, participants (n = 53,713) had to possess reliable hire dates and the records for those terminated had to include specific “termination reasons.”
• Of those studied there were (n = 31,808) who were assessed and behavioral fit to the job was considered in the selection process. Conversely, there were (n = 21,905) hired with no consideration for behavioral fit to a behavioral profile.
• Hire dates spanned over multiple years and ranged from January 5, 2005, to September 9, 2008.
• Termination dates showed a similar range from April 5, 2005, to Sept. 30, 2009.

Study Findings

According to the FBI, thefts are reported every year by 2.58% of people living in 275 cities (2.18 million people of a total population of 84.58 million.) This number represents a small percentage of the population but collectively adds up to a very large dollar value. Keep in mind that 2.58% represents reported theft, not the cases where thieves are caught. The number of people who actually see a thief caught and convicted is an even lower percentage.

Companies dealing with employee theft often experience the same challenges of actually catching thieves. On one hand, you have to first catch the thief, and on the other hand, you have to keep accurate records of these theft occurrences. Most retailers are in the business of making a profit, not tracking crime rates. Therefore, there is an additional layer of complexity whereby companies are either inefficient in their data tracking or they specifically lump theft in with other involuntary termination reasons. It is these types of challenges that make employee theft data difficult to capture and study. Consequently, most studies addressing employee theft involve a very small percentage of employees who are officially caught and terminated for theft reasons.

For the purposes of this study, behavioral fit was represented by four system recommendations: Not Recommended, Recommended with Reservations, Recommended with Qualifications, and Recommended. Behavioral fit recommendations were provided to perspective employers and used throughout the selection process for (n = 31,808) hires. The remaining group of employees was hired with no consideration for fit to a behavioral profile (n = 21,905).

Strong Fit vs. No Fit

The objective for studying this mountain of data was to understand the impact of using behavioral fit in the early identification of employee theft. This was achieved by comparing the theft termination rates of employees who had received the top two recommendation categories of Recommended and Recommended with Qualifications (a Strong Fit) to those where behavioral fit was not a consideration in the selection process (or No Fit).

When compared to Strong Fit hires (categorized as such using a behavioral profile), No Fit employees showed a statistically significant 463% increase (X2 = 88.52, p = .0000) in the number of terminations due to theft-related reasons. ((0.62% - 0.11%) / 0.11% = 463%).

Strong Fit vs. Weak Fit

The second objective was to understand the effectiveness of the two highest recommendation categories as compared to the two lower recommendation categories. This was achieved by comparing the theft termination rates of Strong Fit employees to Weak Fit employees (comprised of Recommend with Reservations and Not Recommended). Among those selected as a Weak Fit, the data showed a statistically significant 155% increase in the number of terminations due to theft-related reasons (X2 = 32.46, p = .0000) when compared to the Strong Fit hires ((0.28% - 0.11%) / 0.11% = 155%).

Intriguing Numbers, but What Does This Mean
for My Organization?

Making the effort to evaluate job candidates for their behavioral fit to the role pays off in a big way. This study shows that hires made with no consideration for fit to the job were statistically more likely to be terminated for employee theft reasons. Statistically, there is a strong link between low behavioral fit to the job and an increase in theft terminations. Looking beyond these results, it is important to understand in practical terms why and how behavioral fit aides in the reduction of employee theft.

Overlapping Theft Factors

Understanding an employee’s motives in a theft situation is very complicated. It would be difficult to identify all of the negative influences involved in a single employee theft instance. Because there are many negative influences that could lead to situational employee theft, each instance would probably point to a slightly different reason or motivation behind the act. For the sake of this discussion, below is a list of a few common factors that typically play a big role in theft activity. As more factors overlap, the probability for employee theft increases.

• Reward – Employee theft may occur simply due to the reward or material gain that can be realized once the act has been committed. (Ex: the employee has easy access to the most popular seasonal toy or to cash in the register.)
• External Circumstances – These thefts occur due to off-the-job situations an employee might be battling. (Ex: External financial issues such as inflated personal debt, collection accounts, or tax liabilities may cause a person to steal against his/her better judgment.)
• Accessibility – Put simply, the easier the employee theft can be successfully carried out (with little risk of being caught in the act), the higher the probability that employee theft will occur.
• Low Achievement/Satisfaction – This state occurs when an employee is not successful, struggles to keep up with the workload, or is in constant turmoil beyond their comfort level. (Ex: An employee with a low tolerance for a harsh boss may retaliate by stealing; likewise, knowing that termination is imminent for performance reasons, an employee may elect to steal something as a “parting gift.”)
• Peer Support (also Peer Pressure) – These thefts occur when employees are supported or encouraged in their efforts to steal by an unethical peer group. (Ex: “Everyone does it, so I might as well do it too. And if I don’t play along, it could mean trouble for me.”)

The more factors the employee experiences on the job, the higher the probability of employee theft.

Practical Use: Linking Behavioral Fit to Theft

By design, a person with a strong behavioral fit to the job comfortably produces more, which often leads to more positive social interactions as well as increased success in the physical and mental duties associated with the job. The benefits of these successes often manifest in these areas: more recognition, positive attitude, enjoyment in the role, fulfillment from the role, and increased job satisfaction. In some cases, when the fit is strong over an extended period, employees begin to value their jobs as an extension of themselves.

• Best fit employees are less likely to jeopardize a good thing (more motivated to maintain a good situation)
• Contented workers have less of a need to participate in retaliatory actions
• By considering behavioral fit, an employer can select candidates who naturally thrive in a certain type of job (while avoiding internal stress due to being mismatched to a job)
• Reducing the number of bad fits to the job improves morale, productivity, and positive identification among everyone within the role

Making an Impact Using Fit as Your Guide

Reducing employee theft can be boiled down to how well you shift the odds in your favor. In other words, the more overlapping factors you remove from the workplace environment, the lower the probability that an employee will steal.

1. Remove Accessibility – By putting traditional security measures in place, which most retailers do, you effectively reduce the accessibility variable in the employee theft equation. It sounds simple, but methods such as security cameras, better record keeping, a loss prevention department, and other programs make it more challenging for the thief and cause habitual thieves to think twice before applying for employment. Deploying these techniques will reduce accessibility, and subsequently the level of employee theft—to a certain extent.

Important Point: Do not stop at traditional security programs--go beyond physical security and focus on the fit of the people to the job.

2. Remove Low Achievement/Satisfaction – It is important to note that behavioral fit to the role will not tell you if someone will or will not steal. Background checks, criminal investigations, and other methods of detecting historical criminal activity are used to reduce the probability of a thief being hired. Instead, behavioral fit matches the person to the role and the company culture, which in turn increases achievement and satisfaction and, eventually, reduces turnover due to theft-related activity. The study discussed here (n = 53,713) showed that hiring those with a stronger behavioral fit to the job reduced theft-related terminations. Practically, according to the data studied, by including a behavioral profile in your selection process to determine fit as a consideration in the overall employment decision, you will likely see a quantifiable decrease in the number of theft-related terminations.

3. Remove Peer Support – When an organization implements standard security precautions AND hires employees with a better fit to the job and culture, it removes both the accessibility aspect of theft as well as the achievement and satisfaction issues that an employee might encounter. With those two components under control, you effectively neutralize the peer support issue (for one thing, it is harder to steal, plus the peer group does not condone or model a stealing mentality). By focusing on these areas, you will not only position your organization as one that reduces the temptation to engage in theft, but also increases buy-in to the organization’s overall business goals.

Naturally, applying a behavioral fit model to the organization will not necessarily affect all of the external circumstances surrounding each employee. A strong fit will not change the potential reward of stealing on the job. However, combined with strong preventative processes, a strong job fit helps to minimize three of the five major theft components through better hiring decisions.


It is not realistic to expect a complete removal of employee theft from the day-to-day concerns of a business. However, it makes sense to stack the odds in your favor any way you can. You can determine job fit by using a good behavioral assessment that works in tandem with a customized performance profile for a specific job. In fact, when you adopt such a hiring process, you will have the behavioral insight necessary to hire employees who are less likely to be terminated due to theft, which will decrease your shrink figures and keep that value where it belongs, which is in your inventory as potential revenue.

Author's Bio: 

Jason Taylor is passionate about using sound science and scalable technology to design and create innovative and sophisticated tools that bring a fresh perspective to the selection and talent management field. Annually, the technology tools under Taylor’s direction match several million employees to employers while providing quantified results to board rooms across industries.

As chief science officer at PeopleAnswers, Taylor ensures that his talent assessment software stays ahead of the marketplace with cutting-edge capabilities.

A pioneer in human capital systems development, editors from several scientific research publications distinguish Taylor for his research on web-based selection systems which he has developed. His historical perspective, expertise and track record of delivering bottom-line results to companies of all sizes from early stage start-ups to Fortune 500 companies have established Taylor as a thought leader in behavioral-based technology tools.

Taylor often speaks on talent management and selection technology at conferences across many industries including human resources, retail, hotel and restaurant, real estate and industrial and organizational psychology.

Taylor is an active member of the American Psychological Association (APA) and the Society for Industrial and Organizational Psychology (SIOP). He earned his Ph.D. in leadership education and development from Texas A&M University.