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A Leading Washington DC Employer & Their Challenges

I wanted to talk about a Washington DC employer which I’m very familiar with to say the least (which I can’t name for many reasons). This Washington DC employer, which employs thousands of Washington, DC residents and is an integral part of the District of Columbia, Virginia, and Maryland life. They are specialist in a particular industry and require a very technical as well as operationally savvy staff to maintain their existence. Because of their governing structure this organization which we will name the “American Resource Network” (ARN) faces a sort of identity crisis that affects internal culture in clear and identifiable ways. The challenges of this organization do not stem strictly from the internal organizational perspective, but from an external one as well.

ARN –  A Fictional/Real Washington DC Employer

For example, ARN faces challenges in being able to hire the right talent for their open positions. And because their positions require high levels of specialization, they usually attempt to network as the primary sourcing tool. That’s more or less a referral hiring program which is equivalent to many organization’s corporate referral programs, but the thing with ARN is that as this practice continued it became a counter-productive practice, and here are some of the reasons.

As the organization grew and still did not implement a structured and fair hiring process specifically over the last 5 years, what had begun as a necessity in order to hire qualified individuals began to take on the shape of mild cronyism—in that managers were no longer hiring qualified individuals but were hiring “people they liked”.

The response of corporate HR professionals was to institute a system of panels to ensure that some objective interviewers were “in the room” when hiring decisions were being made. Organically, ARN managers began selecting interview panel members that directly reported to them and would subtly point them in the direction of hiring candidates the managers knew. HR responded by instituting that every panel should include an HR generalist to ensure fairness and equity. Organically (and on their own), ARN managers responded by writing the job descriptions to fit the “desired” candidate before it was to be posted and before sourcing other candidates and presenting a sample of applicant where one was clearly most qualified and the rest were “not even close” as one manager communicated in an investigative interview. Of course all this took part over a period of a number of years while hiring continued and more managers hired more people that they “liked”.

The result was tumultuous at best, illegal at worst. At least the “technical” side of the organization began to look extremely homogeneous, most likely in violation of the 1964 Civil Rights Act and its later 1991 amendments. In fact, my analysis, which was based on simply statistical methods coupled with qualitative data showed reasonable evidence that direct discrimination could have taken place, but that at least disparate impact did take place.

Of course, in an organization that is as large as ARN these things are never secret. They are well-known, self-evident, and become part of the culture over a long period of time.

To add, it did not only affect the culture or the hiring process, this Washington Dc employer is now an organization internally known for cronyism, myopia, a little nepotism, and of course a lack of motivational systems and processes. In fact, one manager shared with me that “things will never change here. We reward people for seniority and for sticking around… not for performance, so why should I work or make suggestions. I’m much better off keeping my head down and doing my own thing.”

What ARN was missing was a formal and structured approach to hiring that was beginning to become a much larger problem and permeated an entire culture.

And although ARN has a gamut of grievance and “voice” processes and procedures, none of it could undo what has already been done by a lack of a formal hiring process and pro-active culture management.

This as the literature clearly shows, affects employee motivation and performance. In the absence of a counteracting performance management system, which is also the case for ARN—that is the lack of a performance management systems that reward good performance.

One can even make the argument that structured processes and controls for the hiring process are not so important that they should overpower quality performance management systems, but ARN, being in a union environment and run by executives who value loyalty above performance did and does not have the quality performance management system to clean out bad hires.

The result of a lack of structured hiring process, performance management systems, accountability, and top down leadership in this regard to conclude a toxic combination of bad organizational behavior practices that can be seen in the performance of an entire organization’s services and offerings.

And although revenue is not how this organization measures itself, the quality of service metric should be a clear and effective metric—which of course they don’t use. In their case, customer service ratings are so low, they don’t even release them.

However, it is important to note, that even with all the challenges faced by this Washington Dc employer, that they still possess a relatively positive culture. “People get along pretty well here” as one manager answered when prompted about culture. This cultural feature is likely a remnant of a past culture when the organizational culture was different from today, and also could be a natural reaction to ARN receiving a multitude of negative media attention that had forced them to develop closer bonds.

Whether there is direct or indirect discrimination, it is my opinion that this organization is experiencing an identity crisis because of years of bad leadership and that discrimination is not institutionalized.

About The Author
Dr. Joseph A.E. Shaheen
Computational Social Scientist with a twist of network science, social network analysis, data science, and random thoughts.
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