One-Sided Matching: The Most Common Hiring Mistake and Error

Employers make lots of mistakes in the process of recruiting, interviewing and hiring new employees. The Harvard Business Review points out that as much as 80% of employee turnover is due to bad hiring decisions.

Our observation study* to 5,000+ employees, across five different industries - two different countries, has shown that 'One-Sided Matching' is the most common hiring mistake and error:

? Most managers pick candidates whose background is similar to theirs. In this case, we are bound to create an imbalanced organization. An employee with a predominance of our strengths and virtues will also share our limitations. 

? Most managers believe that pirating an employee from a competitor provides an enormous head start without realising the result of pirating is nothing more than the recirculation of mediocrity.

? Most managers are too much relying solely on CV and interviews to pick candidate without considering other critical non-verbal factors: How that individual’s personality and behavior patterns might fit into the job and our company’s culture?

Make hiring decisions with data, not with gut feelings!

Getting the who right is the single most important thing we can do to grow our business. Three steps to improve hiring decisions:

? Step 1. Define critical abilities, skills, experience, knowledge and personal attributes required to be successful in the job.

? Step 2. Conduct a comprehensive competency assessment, which cover organisation value, technical, behavioural and leadership skills, that enables to tell us whether an individual possesses the qualities needed to make it in management, supervisory, front/back office and similar high-level positions within our company.

? Step 3. Use Talent Analytics to build a competency model; a 'two-sided matching' model of competencies that together define successful performance in a particular work setting, that can be used for hiring, promotion and retention. Talent analytics is the application of business analytics techniques to human resources data (the skills, behaviors, and attitudes) that lead to high performance. With the correct size of sample and population variability, we can achieve at least 80% confidence level. A larger sample size normally will lead to a better estimate of the population parameter.

* This study has been acknowledged by Prof. Wayne F Cascio on June 12, 2015 at University of Geneva. Dr. Cascio is a Distinguished University Professor at the University of Colorado and earned Ph.D. in industrial/organizational psychology. His work is featured regularly in business media, including The Wall Street Journal, Newsweek, Time, The New York Times, and Harvard Business Review, among others. He has authored or edited 28 books on human resource management.