The common phrase “Birds of a feather flock together” rings particularly true when it comes to high-performing work teams. Substantial organizational research during the past several years suggests that emotional affect — employees’ moods, emotions and disposition — influences job performance, decision-making, creativity and turnover.
Finding candidates with similar affective characteristics early in the screening and recruiting process can yield significant benefits in terms of performance and retention. Moreover, technology can extract affect from recorded candidate interviews, ensuring that the organization focuses on recruiting and hiring employees who exhibit the characteristics that are most likely to drive superior-performing teams.
A significant development in organizational dynamics during the past several years is the notion of “affective processes” and how they affect job performance, turnover, teamwork and leadership. Affective processes encompass a wide range of phenomena including discrete emotions, moods and dispositional traits.
One of the leading researchers in the area of affect in organizations is Sigal Barsade, a professor of management at the University of Pennsylvania’s Wharton School of Business. Her research — and those of her colleagues in the field — establishes links between employees’ affect and how it impacts critical organizational outcomes.
According to Barsade’s research, affect can be represented by a circular graph called a “circumplex” (Figure 1). The coordinates an employee occupies on the circumplex have an impact on his or her teammates, customers and others inside and outside the organization. The circumplex also identifies emotions that are likely to be picked up in verbal communications only and not likely to require or be influenced by nonverbal cues. This is significant in that these emotions can be captured in a variety of ways.
Affect impacts not only how work teams interact with one another but also how individuals interact with others outside the organization. For many companies, such as call centers, retailers, retail banking, hospitality, travel and leisure, this includes how employees interact with customers. In light of recent customer experience initiatives in place with many customer-facing companies, one might argue that these interactions should have higher priority than interpersonal interactions within the organization, and therefore should take on greater importance in the recruiting cycle.
Using customer service operations as an example, critical organizational outcomes typically include things like the customer experience, customer satisfaction, sales conversion and first service call resolution. Influencing these outcomes by identifying agent behavior that affects them helps to improve overall performance.
Additionally, identifying these early in the employee life cycle ensures that the right employees are placed in the right jobs with the right teammates. Taking this example a step further, a customer service professional’s emotional state can often be discerned by a customer, which can have a positive or negative effect on the customer experience.
While emotions can be transitional, everyone has a relatively stable underlying tendency to exhibit positive or negative moods or emotions. This is referred to as dispositional affect and, when measured outside the presence of a specific stimulus — during an interview, for example, where the candidate’s emotional state is relatively stable — provides valuable insight into how a person fits into the organization.
Capturing Emotional Content
Emotions associated with affect are found in verbal interpersonal communications. While these can be supplemented by nonverbal communication cues such as gestures and body language, the emotions in verbal communications tend to be more pure, in the sense that the recipient is not likely to be influenced by nonverbal cues and is therefore less likely to be conflicted about the communication’s emotional content.
Companies can make effective use of this without making substantial changes to their recruiting process. Identifying a job candidate’s affective profile early and acting on recommendations made through advanced audio analysis and machine learning improves recruiting performance by advancing better-qualified candidates to later stages of the process. And it doesn’t require substantial changes to most talent acquisition processes in place.
Most companies, for instance, conduct a telephone screen or interview early in a candidate’s recruitment. Some organizations — especially customer-facing ones — rely on them more than others. Call center agents, for example, are rarely seen by their customers and therefore have to rely exclusively on verbal communication skills, where the emotional content of such communication is more evident.
By recording these early-stage telephone interviews, companies can make use of speech analysis technologies to uncover the candidate’s underlying emotional affect. There are several approaches, including speech analytics, which analyzes the content of the speech and looks for keywords that are indicative of certain emotions.
Others have analyzed the speech energy, pace and timing, which tends to provide a more accurate view of emotion. These methods often also include predictive analytics software that will predict things like job fit, tenure and performance. Recording these interviews has the added benefit of providing a consistent interview experience for all candidates, thereby increasing the fairness of this stage of the recruiting process.
Measuring Business Outcomes
It’s rare to find a company that doesn’t have a well-defined set of desired business outcomes or key performance indicators. For example, customer service operations tend to be one of the most heavily measured departments in any company. Employees often receive personalized scorecards detailing their performance against the broader organization’s goals and their team members.
Collecting these business outcomes at the individual employee level is helpful in creating the predictive models that are used not only to identify excellent performers but also to compare their performance against a variety of pre-employment assessment data — including emotional makeup, which can be collected for each prospective employee.
It sounds complicated, but the task is easier through the use of advanced audio analysis technologies to extract emotion and machine learning techniques to identify relevant patterns that might otherwise be difficult to observe. For those companies using other pre-hire assessments, the candidate selection process can be further improved to increase the likelihood of excellent downstream performance and tenure.
With well-developed predictive models, these software applications can make recommendations about which candidates are likely to perform well in what jobs. Recruiters are skeptical about turning over hiring decisions to a computer. In practice, however, these systems are merely recommending which candidates to examine first based on how well they match the model. The recruiter still has final say over who progresses and who doesn’t. This approach works especially well for those jobs where high volumes of candidates are required.
By formally and regularly matching pre-hire performance with post-hire, business-relevant outcomes, companies create a cycle of what amounts to continuous validation. Most test and assessment validation today is done once and remains static until someone decides the validation window has expired. By constantly correlating business outcomes with pre-hire assessment performance, companies develop a self-improving recruiting process: Those applicants who are predicted to perform well are always at the top of the list, even as the business’ needs change over time.
Using Affect During Recruiting to Identify Best Job Fit
Again using the call center example, it’s fairly easy to see that common types of contact center jobs more or less neatly occupy specific areas of the circumplex. Other job roles can be similarly mapped, providing a significant way of correlating a candidate’s affect with his or her expected job role. Identifying this early in a candidate’s recruitment can reduce or eliminate poor performance due to poor job fit.
Those roles where customers require assistance with problems or issues require employees who are generally more relaxed, exhibit empathy and are less excitable. Roles where the company is trying to sell products or services tend to require employees who are more energetic and pleasant — because who wants to buy something from someone who sounds sad or grumpy?
Collectors don’t necessarily need to be pleasant to effectively do their jobs, but they do need to be assertive.
Other roles in the company can be similarly identified and mapped, creating a comprehensive view of who will fit best into which roles. Additional pre- and post-hire data collected further improves the predictability of the models and the quality of their results.
Companies that adopt this approach during the interview process can ensure that applicants are matched to their best-fit job roles. For example, a candidate applying for a customer service role, but who exhibits the emotional characteristics that are better suited for a sales role, could be encouraged to consider the sales job if one exists. Identifying those candidates who would be well-suited for either role will help improve performance in this area.
A high-performing organization, no matter what its business mission, will always be dependent on its teams of well-skilled, well-trained, motivated and enthusiastic employees. Adapting the pre-hire process to include screening for emotional disposition will help ensure that prospective employees are placed into the job roles they are best suited for, with teammates and supervisors that are most like them and are afforded the greatest opportunity for success.
Find out how to incorporate emotional traits into hiring here.
Todd Merrill is the chief technology officer at HireIQ Solutions Inc., an HR technology firm. He can be reached at email@example.com.