amazon recruitment case study

Dr John Sullivan Talent Management Thought Leadership

Amazon recruiting – a case study of a giant among children.

January 17, 2022

Compare their results to all others, and you too will call Amazon… A Giant Recruiting Machine.

Note this case study is designed for quick scanning.

Yes, Amazon recruiting is in a class by themselves because they relentlessly hire when others cry for applicants. Of course, I don’t loosely use the phrase “A giant among children.” However, after doing numerous corporate case studies over the years covering other recruiting powerhouses (including Google, Apple, and Facebook). I quickly found that their record recruiting volumes across a broad range of jobs and locations could only be labeled as breathtaking. And just by chance, if you think that I’m not giving enough credit to most other corporate recruiting functions (even Google pales in comparison). You should realize that only a mere 18% of HR professionals even describe their own recruiting function as “top-notch” or “advanced.”

The Six Pillars Of Recruiting Excellence At Amazon

This Amazon case study reveals the many factors that cause Amazon’s recruiting function to be so far ahead of the competition. They are truly a giant because they excel in each of the six pillars of excellence in recruiting. The six pillars that make Amazon so successful are:

• Their recruiting impacts business results
• Their proven capability of handling huge recruiting volumes across a wide range 
• Their fanatical insistence on quality hires
• A scientific data-driven recruiting approach is the foundation of their success
• They utilize a one-size-fits-one agile hiring process 
• Their targeted recruiting sub-programs are second to none

Let’s jump immediately to the first and most important strategic pillar – Amazon’s record-breaking strategic business and recruiting results. 

Pillar #1. Amazon’s Recruiting Impacts Business Results

Amazon recruiting is aiming to go beyond simply producing recruiting results. And to also directly impact their corporation’s business results. Those results include:

• Hiring is the single most important element in Amazon’s business success – Jeff Bezos made it clear. “Setting the bar high in our approach to hiring has been, and will continue to be, the single most important element of Amazon.com’s success” (that’s not just the most important HR function, but the most important business function). Jeff began making this recruiting priority clear in the company’s very first annual shareholder letter in 1998. Most other corporations don’t admit this reality. But, it’s simply not possible for a large corporation to innovate and grow rapidly without fully funded exceptional recruiting. 
• Yet with all this emphasis, recruiting remains their primary challenge – The CFO recently publicly revealed that even with its current high priority, recruiting maintains a primary challenge. When he noted, for example, in the package movement area, “The availability of workers is Amazon’s primary challenge .” Rather than resting on their laurels, they realize that they continuously need to get much better is a primary reason they continue to improve in recruiting. 
• Amazon’s size and growth are made possible by its excellence in recruiting – the prime limiting factor that restricts the company from maintaining its quantum growth rate is the ability to successfully recruit a huge volume of employees each year. And because Amazon employs about 1.4 million people globally , they have already done a high recruiting volume. The employee headcount makes them the US’s second-largest private employer (after Walmart). I predict that they will soon surpass Walmart for the #1 spot as the largest employer in the US. I would also note that Amazon has helped to reduce unemployment. Because of the 400,000 people they hired for their U.S. operations network, 45% were previously unemployed. Their new CEO, Andy Jassy, reinforced the importance of continuous growth through recruiting by announcing that he was planning to hire 55,000 people for corporate and technology roles globally during his first months. That’s close to all of Facebook’s current headcount and nearly 1/3 of Google’s headcount.
• Recruiting has made a major contribution to its stock value – businesswise, their recruiting and operational excellence have directly contributed to the corporation’s incredibly high stock valuation. Currently, Amazon is the fifth most valuable global company in market cap valuation, nearly 1.65 Trillion dollars. 
• Recruiting has made a major contribution toward having an extremely productive workforce – the average revenue generated by each employee last year was $353,000, which is an amazing ROI. HR helped maintain that productivity by increasing management prerogatives by remaining a 100% union-free workforce. 

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Pillar #2.  A proven capability for handling a huge volume of recruiting across a broad range of positions and locations

Amazon recruiting has proven over the years that it has the capability of recruiting a huge number of new hires across many different job families and locations.

• Recruiting volume and capability are second to none – the fact that during 2021 Amazon’s recruiting increased headcount by a whopping 63%  in a single year. The largest percentage increase in headcount ever accomplished by any large employer during peacetime! This is but one startling indication of recruiting’s agility and capability to ramp up their recruiting capability dramatically. Amazon, of course, must have an exceptional recruiting capability because it is America’s second-largest employer (and I predict that it will soon surpass Walmart). The workload handled by their recruiting function is unparalleled because it has as many as 30,000 openings at a single time.
• Powerful Employer Brand means that everyone considers them – it is clear that because of its HR work, Amazon is recognized as an excellent place to work. And its rankings, notoriety, and exposure are major contributors to its recruiting success. Some of their notable recognitions include:
•  This year, LinkedIn’s top US employer ranking – Amazon ranked by the prestigious professional network LinkedIn as the #1 company where Americans want to work and develop their careers. 
• A global best employer also – this year and a ranking of global employers, Amazon was ranked #2 on the “World’s Best Employers” list by Forbes. 
• Fortune’s world’s most admired companies – this year, Amazon was ranked #2 on Fortune’s prestigious “World’s Most Admired Companies” list for the fifth year in a row. (After Apple). 
• BCG’s most innovative firms – this year, the Boston Consulting Group rated Amazon #3 on their “most innovative firms” list (after Apple and Alphabet). 
• Amazon is the best at attracting a record-breaking volume of applicants – as previously noted. In 2020 Amazon received a record-shattering 30 million applications , an all-time record. But it is especially impressive because it occurred when almost every major corporation and business struggled to get even a few applications for each job. The attractiveness of Amazon is illustrated by the fact that they received a breathtaking “ 1 Million Job Applications (in 1 day) ” as part of their 2021 annual Career Day event.
• Amazon has the capability of recruiting over an amazing range of jobs – companies like Google and Facebook have an easy recruiting job because they recruit mostly engineers. In comparison, Amazon must have the capability of recruiting everything from AI experts, pilots, book specialists, entertainment specialists, and cloud experts down to package handlers. In fact, Amazon can recruit across five extremely diverse business units (Amazon.com, AWS, Alexa, Whole Foods Market, and Amazon Prime) and 32 distinct technical groups. Their new Project Kuiper will even require them to hire rocket scientists as they attempt to launch satellites into orbit to widen their broadband access. In my view, their recruiting leaders deserve major kudos for developing their recruiting capability in so many completely different skill areas. And because they are a technology company, they rely heavily on technology throughout their recruiting function. 
• Amazon’s recruiting capability is truly global – because it is a worldwide e-commerce company, Amazon operates and recruits in 13 countries. In the US alone, it operates more than 930 facilities (including two headquarters locations). And last year, it received job applications from 170 different countries.

Pillar # 3. Fanatical Insistence On Quality Hires

Their third and most important pillar of recruiting excellence is their fanatical insistence on only hiring quality candidates. In comparison, few corporations spend the time defining and measuring the quality of hire (i.e., top-performing new hire). And only 36% even attempt to measure the quality of hire . Amazon ensures that they will get those quality hires using seven unique recruiting approaches. They include:

• Their goal is to be the “Earth’s Best Employer” – yes, Jeff Bezos’ stated, and only a little bit outrageous, goal is to make Amazon “ the world’s best employer . However, in my experience, it is a goal that they have already met. Executives, managers, HR professionals, and recruiters work together to reach it. In their words, they reach that goal because  “Their leaders work every day to create a safer, more productive, higher-performing, more diverse, and more just work environment. They lead with empathy, have fun at work, and make it easy for others to have fun. Leaders ask themselves: “Are my fellow employees growing?” “Are they empowered?” “Are they ready for what’s next?” “Leaders have a vision for and commitment to their employees’ personal success, whether that be at Amazon or elsewhere.”
• The Bezos approach to hiring is laser-focused on quality – their hiring managers and the recruiting function’s insistence on quality has remained solid throughout the years. I find that this fanatical insistence on quality is in direct contrast to the approach taken by most hiring managers at other corporations. During this candidate shortage, managers have been allowed in desperation “to fill butts in chairs.”

Amazon’s #1 advocate of hiring only quality employees is Jeff Bezos. He has shown his expectations in many often-repeated quotes, statements, and expectations. Including: 

• “It would be impossible to produce results in an environment as dynamic as the Internet without extraordinary people… Setting the bar high in our approach to hiring has been and will continue to be the single most important element of Amazon.com’s success.”
•  “If you can’t hire quality, don’t hire at all.” “I’d rather interview 50 people and not hire anyone than hire the wrong person.”
• “Don’t “settle for second best” when hiring. Instead, “Do what it takes to find the best people available.”
• “Every time we hire someone, he or she should raise the bar for the next hire so that the overall talent pool was always improving.” Bezos “ doesn’t care about an efficient hiring process .” “And he certainly “Doesn’t believe in making a hire, simply for the sake of filling an open role.”
• At Amazon, raising the bar means answering three questions for each candidate. First, “Will this person raise the average level of effectiveness of the group they’re entering?” Next, it asks, “Will you admire this person?” And last, it asks, “In what important area might this person be a superstar?” (In cases where they should be placed in a different job than they applied for). 
• Amazon utilizes “bar raisers” as its primary way to ensure quality – a key Amazon expectation for leaders – “Is to raise the Amazon’s use of “ bar raisers .” They get that name because their sole role is to ensure that each new hire will “raise the bar over the last incumbent” in each open job. The work during the interview process is to provide outside and neutral candidate assessments. To prevent a candidate from focusing on these individuals, they are anonymous to the candidate. These quality control individuals are from outside the team that is doing the hiring. And as a result, they are more likely to be critical because they don’t face the same “pressures to immediately fill the job” that hiring managers and teammates do. With this volunteer role, they accept the responsibility to literally “veto” any candidate they feel will not be a good fit for Amazon. Amazon’s new hires are quality employees because Amazon promoted more than 68,000 employees globally during 2020.
• Hiring is a unanimous team decision – a second method for ensuring that they only hire a quality candidate requires a unanimous team decision. One prominent former Amazon executive noted that Bezos “ Believes hiring should not only be a team effort. It should be a team decision.” So in most cases, “After final interviews, each member of the hiring team meets in a room to share their opinions on each candidate. And after a discussion, a vote takes place, and the results have to be unanimous for the person to be hired.” A single “no” vote would mean that the team will have to go back and search again for the ideal employee. 
• Amazon’s “unregretted turnover metric” helps fix hiring errors – Amazon assigns an “unregretted turnover metric” to its managers. It serves as an imperfect post-hire check on weak performing employees that somehow made it through their hiring process. This after-hiring double-check mirrors the approach that General Electric had under Jack Welch. Under this “regrettable turnover metric,” Managers at Amazon have a target rate for annual employee turnover. This means they are expected to lose a specified number of employees that they “ wouldn’t regret losing ” (i.e., below-average performing employees). Although this practice may appear harsh on the surface, it forces hiring managers to reassess each new hire periodically. 
• Paying employees to quit – this “Pay Employees to Quit” approach is a second post-hiring check on quality under this program (borrowed from Zappos). Amazon proactively offers incentives to unhappy recent hires during their first five years. The goal is to force unhappy recent hires to take a minute once each year to decide if they “really want to stay.” Based on the premise that keeping workers unsure of their commitment to Amazon will harm both the customers and the team. So if a worker decides that they don’t want to be here, they can get between $1000 and $5000 for walking away.
• Finally, improve new-hire quality by assessing candidates on Amazon’s leadership principles – one of the primary ways Amazon maintains quality hiring and fit. By assessing every candidate on Amazon’s published “leadership principles.” So each candidate at Amazon is expected to know and commit to following them ( these principles are posted on their jobs website ). As a result, everyone involved in hiring is expected to assess every candidate’s knowledge and commitment to these principles. At least 3 of these 15 principles relate directly to recruiting. Those three principles are below:
• Hire and develop the best – leaders raise the performance bar with every hire and promotion. They recognize exceptional talent and willingly move them throughout the organization. Leaders develop leaders and take their role in coaching others seriously. We work on behalf of our people to invent mechanisms for development like Career Choice.
• Insist on the highest standards – leaders have relentlessly high standards. Many people may think these standards are unreasonably high. Leaders continually raise the bar and drive their teams to deliver high-quality products, services, and processes. Leaders ensure that defects do not get sent down the line and that problems are fixed, so they stay fixed.
• Deliver results – leaders focus on the key inputs for their business and deliver them with the right quality and timely fashion. Despite setbacks, they rise to the occasion and never settle. 

If you’re interested in the 12 remaining leadership principles, click here . The remainder mostly focuses on key workforce capabilities, including customer obsession, innovation, learning, and ownership of problems.

Pillar #4. A scientific data-driven approach is the foundation for their success

During my assessment, I found that a primary reason why Amazon recruiting excels in so many different areas is that it operates under the umbrella of one of the most strategic HR functions. Their HR function is guided by 7 HR tenets , which are the guidelines that every HR function follows to “Maintain a Culture of Builders and Innovators. In my experience, shifting to a data-driven approach is required to maintain a culture in a large organization. Fortunately, Amazon is one of only a handful of HR functions (along with Google, Sodexo, and Nestlé Purina) that already makes decisions based on data and results metrics. Find that HR tenet in the box below. 

Every strategic recruiting function should know and follow three additional Amazon HR and leadership tenets. They are:

• Recruiting must focus on directly impacting business results – because BCG research revealed that “ recruiting has the highest impact on business results .” Therefore, it makes sense to follow and adhere to their HR tenet “We manage HR as a business.” Acting like a business starts with, rather than simply “aligning with business goals,” recruiting leaders purposely set recruiting goals and manage recruiting actions and resources to produce the maximum direct and measurable impact on business results. The next step is to reduce recruiting approaches that can’t demonstrate their business impact. And the final step is to convert recruiting problems and results into their dollar impact on corporate revenue (e.g., our recruiting efforts on sales jobs allowed us to maintain $232.5 million in sales revenue). Reporting recruiting results in dollars of revenue impact allow executives to quickly compare your dollar impacts to those from other HR and business functions.
• You must assume continuous obsolescence along with rapid learning – you should also follow another of Amazon’s HR tenets. Which is “Learn and Be Curious.” Because in an unpredictable world, you simply can’t prepare for most things. The secret to thriving is rapid continuous learning immediately as new problems and opportunities arrive. So the first step in a recruiting world where everything changes should be operating under the assumption that every current thing in recruiting will soon become obsolete. And, of course, you won’t be able to detect that obsolescence without collecting and applying performance data. Next, you must also continually be looking for a replacement for every current recruiting approach and tool. And that can only be accomplished by continuously learning about evolving business and recruiting approaches at other advanced companies. To identify the ones that might be applied to your recruiting situation. And finally, you won’t be able to determine if your new solutions are superior without following the tenet hypothesis testing covered in the next bullet point. 
• The utilization of hypothesis testing and experimentation – perhaps the most prominent difference between traditional and scientific recruiting is an insistence on hypothesis testing to discover what works and what doesn’t. The HR tenet is “ We form hypotheses about the best talent acquisition, talent retention, and talent development techniques and then set out to prove or disprove them with experiments and careful data collection.” For example, a split-sample experiment could prove or disprove the hypothesis that “Diverse interviewers select more diverse candidates” (They don’t). Google HR has also long been a supporter of hypothesis testing. An outrageous example of Amazon’s hypothesis testing occurred when their AWS group experimented by placing a job ad on the Tinder dating site.

Amazon Recruiting – A Case Study Of A Giant Among Children (Part 2 of 2 parts)

Today, every manager needs to learn great recruiting… and to find it, they need only follow Amazon!

The title of this case study includes the phrase “A Giant Among Children.” That’s just how large I found the differential between Amazon’s recruiting and the recruiting practices at most corporations. And if you take the time to read this case study, I am sure that you will agree with the sharp assessment. Of course, many managers already justifiably study Amazon because of its excellence in well-known areas, including customer service, supply chain, and cloud computing. However, most don’t realize that Amazon can only excel in so many divergent business areas because it is “a recruiting machine .” It recruits effortlessly even during our current talent shortage when most others starved for applicants. This case study is designed to show you their best practices and what makes them “a recruiting giant among children.” 

Pillar #5. Amazon’s amazing array of targeted recruiting programs

In my view, the most surprising of all of Amazon’s 6 pillars of excellence is their willingness to develop and offer numerous individual recruiting and career transition programs that are “customized” to the needs of distinct groups of candidates and employees. Targeting subprograms is essential because different groups are attracted and motivated by different offerings. At Amazon, they specifically target a wide array of people, including diverse women, veterans, the elderly, and those that need internal movement or an upward push. Unfortunately, space limitations prevent me from highlighting all of the amazing, targeted programs in operation at Amazon. However, you will find a representative sample of 14 of their exceptional targeted recruiting programs below. The programs that likely have the largest impact appear first on the list.

• The Returnship program helps the unemployed reenter the workforce – The Returnship is a reentry program designed to help the underemployed and those who have been out of the workforce for at least a year (usually due to unemployment, children staying at home, or Covid concerns). This program aims to provide this target group with a rare opportunity to restart their careers by joining Amazon. At the beginning of the program, “returners” work on a specific project. And after four months, they have earned the possibility to move into full-time positions at Amazon. During those four months, participants work remotely from home. If they need it, they provide child and elder care assistance. So they can ease back into the workforce without making any major life changes while they are in this program. And when they accept a permanent role, Amazon will also pay for their relocation if needed. Since their Returnship pilot initiative in January of 2021, Amazon reports that the program has enrolled more than 60 people, and 95% of them received an offer for a full-time role at Amazon. In the future, Amazon has stated that they plan to hire 1,000 professionals into the program during the coming years in roles ranging from finance to engineering.
• The Best Fit Program makes it easier for software engineers to find their perfect job – this best fit program is an accelerated job identification program. Designed specifically to help software engineers that are applying find their perfect job fit among all relevant Amazon jobs. This program helps make their job search at Amazon quicker and more accurate. Those in the program can avoid putting in the traditional multiple hours of searching for their right job. It allows these software engineers to apply once and then be automatically considered for thousands of relevant jobs across the company. A combination of electronic and human matching approaches finds the jobs that fit their preferences during the first step. For their ideal kind of team and their desired working style. But the program will still recommend jobs in new areas in which Amazon thinks they would also be successful. During the last part of the process, applicants get to meet all of the hiring managers for each of the recommended jobs. And finally, they get to choose their first job at Amazon.
• The Career Choice Program supports employees who want a college degree – support for getting a college degree or GED is a major attraction factor. One of the goals of this Career Choice educational opportunity program is to help lower-level Amazon employees transition into more lucrative paying and high-demand fields (and perhaps even leaving Amazon). For eligible employees, Amazon will now pay 100% of its employee’s college tuition and fees for earning a diploma or certificate in a qualified field of study at eligible schools. Recently the program has been updated to allow more flexibility.
• The UX Apprenticeship – It encourages development in research and design – Amazon’s User Experience Design and Research Apprenticeship program provide a combination of instructor-led training and real-world experience in a one-year program. It offers employees the opportunity to learn and develop research and design skills on Amazon teams, including Prime Video, Alexa, AWS, and Amazon Fashion. Apprenticeship graduates can move into jobs that help improve the experience of Amazon customers, from making payments easier on Amazon sites to designing features that make devices more accessible.
• Surge2IT – Proactively encourages career advancement in IT – their Surge2IT program is another career transition program designed to help entry-level IT employees across Amazon’s operations network. It focuses on IT employees who don’t possess a software development degree. After completing this program, they can become software development engineers after about nine months. This program allows lower-level IT employees to pursue careers in higher-paying technical roles through this self-paced learning resource. The course helps employees develop the skills necessary to advance their careers in the information technology field. Participants who complete this course and move up at Amazon can make up to an additional $10,000 a year.
• The Amazon Technical Academy makes you a software developer in nine months – this career transition program requires nothing more than an interest in software development. It started as an experiment, and since then, it has successfully enrolled hundreds of employees. Amazon Technical Academy builds on their initial interest by training them in the essential skills needed to transition to an entry-level software developer engineer role at Amazon. The program is free for their employees. And it requires a high school diploma or GED. And the fortitude to get through a rigorous nine-month, full-time program that expert Amazon software engineers created.
• The Mechatronics program prepares employees for robot maintenance jobs – under this career transition program in robotic repair . It is designed for employees interested in learning engineering and mechanical skills necessary to repair and maintain the equipment and robots inside Amazon facilities. Those that are accepted get the opportunity to go back to school for a free 12-week course. After that, employees begin a year of on-the-job learning under a technical maintenance specialist. After completing this final step, employees who now have these highly sought-after skills are eligible for a full-time role as a mechatronics and robotics technician, which may increase their paycheck by up to 40%.
• Project Juno – aids in relocating current employees – this internal movement program helps out when a current employee must relocate. After they have decided that they must move, this Amazon job finding process electronically finds the relocating employee the same or a similar job available at the Amazon facility in their new city.
• CamperForce – This Program offers jobs to traveling seasonal workers – CamperForce offers jobs for those who travel in RVs and work along the way. They are known as Work Campers. And because Amazon especially needs people to work in its warehouses during the holidays. They now encourage and hire seasonal help that live in a trailer or RV. In addition to welcoming them, Amazon pays them a small monthly stipend to live in their own trailer at an RV facility close to an Amazon warehouse site where they will work.
• The Military Spouses Program –  provides jobs for military spouses – the goal is to find jobs for the spouses of Amazon’s 45,000 veteran and military employees. Designed to find military spouses an appropriate job at Amazon. Either for the first time or when he or she must relocate along with their military spouse. In addition, Amazon recently pledged to hire over 100,000 U.S. veterans and military spouses by 2024, further building on their commitment to military families. 
• Amazon Warriors – provides support for transitioning veterans – this veterans support program is designed to help recent veterans transition into Amazon’s workforce. It helps by offering a professional network of Amazon employees that are veterans. It also provides a mechanism for community outreach.
• People with disabilities – They have their own targeted website – Amazon offers a targeted site specifically to meet the needs of applicants with disabilities. The site also educates them on how to take the best advantage of what Amazon has to offer applicants and employees with disabilities.
• Amazon hires felons – Amazon has no blanket policy against hiring felons. In fact, they are open to hiring them into seasonal jobs. Depending on the type of felony, time since they fulfilled their sentence, and the corrective actions completed, however, after successfully completing that initial assignment and based on their performance. The felon may then be considered for a more permanent position. 
• Amazon employee referrals – like most large corporations, Amazon has a formal referral program. Unfortunately, I only rate it as a little better-than-average because only 11% of those interviewed are employee referrals . And they pay a range of bonuses up to $5000 for a referral that is hired .

Pillar #6. Unique elements in their “one-size-fits-one” agile hiring process

I have discovered 7 unique hiring process elements that contribute to making Amazon’s hiring process highly agile, flexible, and adaptable. These seldom found elsewhere elements make it possible for their hiring process to adapt to the recruiting needs of every Amazon business unit and location. Those unique elements include:

• By design, their hiring process flexes to fit every unique job – they hire in so many global locations and across so many jobs from pilot to janitor. Their candidate assessment process must be modifiable to fit the unique assessment requirements for each job family. We call this capability “one-size-fits-all one.” Of course, the hiring process includes the basic elements for all jobs, including the standard ATS/recruiter resume screen, a phone screen, and at least one structured remote or live behavioral interview. Some portion of that interview will be devoted to assessing the candidate’s understanding of Amazon’s culture through its leadership principles .  However, the interviews will likely last all day for most professional jobs. Often it will include an online test and a verbally presented work sample or problem to complete. The candidate may also be asked to write up an idea in a press release format (because that’s the way ideas are presented at Amazon). Or, developers may be required to participate in a virtual or in-person interactive whiteboard exercise for developer jobs where they have the candidate walk them through the steps they would take to solve a current software problem. In the end, the team will always make the final hiring decision, and the “bar raiser” gatekeeper will have the option of vetoing that choice.
• To increase innovation, Amazon specifically targets problem-solving skills – one thing that is common across all business units at Amazon is the need for innovation. And as a result, Amazon targets candidates that thrive at solving a never-ending queue of complex problems. They consider a spirit of innovation part of their DNA at Amazon. They clearly state upfront that they are looking for “analytical and critical thinkers with great judgment, who can both think big and roll up their sleeves to solve hard problems on behalf of our customers.” 
• Amazon increases its applications by removing the mystery from its hiring process – many firms talk about their “candidate experience.” However, I have found that applying for a job at most firms is a long way from being user-friendly. We know this because the number one complaint from applicants is almost always that the hiring process that they are about to face “is a complete mystery.” Amazon, instead, leads the way ( along with J&J ) in removing the mystery out of what the candidate can expect during their hiring process. They offer an extensive array of numerous free resources that guide applicants ( our hiring process website ) to meet this goal. It highlights what any candidate can expect from the day they apply until they begin work. In addition, they also offer suggestions on the best interviewing practices for its candidates to follow on its YouTube channel and its LinkedIn feed . They also make it clear that serious candidates must study the company’s leadership principles mentioned earlier. Finally, they help applicants understand the different teams they can work in. By providing them with a list of the 32 possible teams , a description of what they do, and how many open jobs are currently open in each team. They even have a “best-fit program” that uses artificial intelligence to help software engineers find their perfect job within Amazon.
• Amazon holds a national Career Day event like no other – many firms, including McDonald’s and Walmart, hold “national hiring days.” However, I find that they pale in comparison to Amazon’s. They call their unique Career Day “America’s biggest training and recruiting event.” It actually is unique because it goes well beyond the typical job fair. In addition to displaying open jobs, it offers remote personalized career coaching sessions and even some tactical training. It further provides candid advice on how job seekers can start, build, or transition their careers at Amazon. Last year, they received 1 million applications for their Career Day event.
• Amazon relies heavily on seasonal workers as a talent pipeline source – research has shown that often the new hire has the highest probability of success. Someone that has recently successfully served as a temp, intern, or contractor at the organization. Amazon takes advantage of this high-quality source by hiring well over 100k seasonal workers each year. In addition to filling their seasonal need, the seasonal workforce serves as an effective screening process for determining which seasonal workers should be offered a full-time job. It also gives the worker a chance to determine if they really want to work at Amazon.
• They use FC brand ambassadors to improve their brand proactively – I’ve never seen this done before. But, to counter the massive amounts of negative Twitter messaging found about working at their warehouses. Amazon has asked long-term employees at its fulfillment centers to act as brand ambassadors in an extraordinary move to improve their online employment branding. They don’t get extra pay, but they get $50 gift cards as a small reward for tweeting positive things about working in their warehouses.
• A shift in emphasis to remote and broader college recruiting – makes college recruiting more effective, diverse, and remote. Amazon is curtailing some campus visits and heavily emphasizing virtual student meetings. It has also broadened its reach to many more campuses to get added diversity to the point where for example, last year, it extended offers to students from 80 M.B.A. programs (instead of exclusively going to a few elite schools).

Amazon Utilizes Data To Identify The Most Powerful Attraction Factors

Rather than assuming that applicant attraction factors stay the same in a fast-changing world. A critical part of Amazon’s highly agile and adaptable recruiting process is continually gathering data to update “the most effective attraction factors” for their targeted potential applicants. Here are 8 examples of how they identify the attraction factors and the current ones.

• They start by using data to identify the most current attraction factors – most corporations simply guess at them or assume that they are the same as last year. In comparison, Amazon uses data to identify its current attraction factors. At Amazon, these attraction factors currently fit into four categories. Each of the four is emphasized on their main career website . The four primary attraction categories include benefits , career advancement , work/life balance, and culture . As part of their data-driven approach, they continually survey new hires to determine the general and the specific factors that actually attracted them to Amazon. And last year, 93% of their new hires cited Amazon’s Career Skills and Upskilling training program s as their top attraction factor. As a follow-up, Amazon is investing $700 million in upskilling 100,000 employees in the U.S. by 2025.
• They proactively encourage work/life balance – although some may argue about their level of success. Amazon boldly lists work/life balance as one of its four primary attraction categories. And on its work/life balance website , it describes how Amazon strives to help its employees reach that balance.
• Amazon is acting to reduce applicant health and injury concerns – during the pandemic. Amazon has focused on reducing Covid risks and workplace injuries as roadblocks that reduce potential warehouse applicants. So in that light, Amazon is currently developing a new automated staff schedule process. It reduces the risk of injury by utilizing computer algorithms to rotate employees between jobs when completed. A more frequent rotation is needed because their data reveals that roughly 40% of their work-related fulfillment center injuries are due to sprains and strains caused by repetitive motions. 
• Higher base pay – Amazon was one of the first companies to realize that they needed to raise employee pay and its hourly jobs in a tight U.S. job market. So Amazon’s average starting wage is now over $18 per hour, with an additional $3 depending on their shift.
• Sign-on bonuses – like many companies, Amazon has begun offering significant sign-on bonuses at some of their fulfillment centers (up to $4000).
• Being dog friendly is surprisingly an attraction factor – in work areas where it is safe. Amazon is one of the few companies that actively encourage dogs in the office. And because of their efforts, Amazon was listed as the #1 dog-friendly company in the US by Rover.com . Their leadership has noted that “Amazon has found that dogs in the office actually contribute to their collaborative company culture.” 
• They stopped testing applicants for cannabis –   in many states recreational or medical cannabis use is now legal. Amazon has been a leader in announcing that it will no longer screen finalist candidates for marijuana use. In part because this testing was unnecessarily reducing their candidate pool. But Amazon went one step further. It alerted its independent delivery service partners that if they too stopped testing for marijuana during their application process and prominently advertised that fact. They could boost their own business’s job applications by up to 400%.
• They offer anytime pay – this last attraction factor may not seem like much. However, it has proved to be an attraction factor for the many hourly workers that live paycheck to paycheck. Amazon’s free fast pay program offers the option, in some jobs, for eligible employees to receive 70% of their eligible earned pay whenever they choose (24×7).

Of course, Amazon is working on its weak points

Amazon is still far from perfect in areas other than recruiting despite all its efforts. Despite its ranking by LinkedIn as the #1 employer. They still receive relentless criticism because of their corporation’s size, speed of innovation, impact on small businesses, their percentage of diversity, and the waste they produce. Even some innovators criticize them for excessively keeping some innovative projects secret from other internal teams (just like Apple). 

In management, they have also received volumes of criticism, especially because of their anti-union stance and their common practice of continually replacing “human jobs” with robots. The media revealed that they once selected which workers to release using an algorithm, and they subsequently fired them via email. Its managers have been criticized for not telling their employees when placed under a performance management plan. They are also well-known for their fast-paced work environment that some argue can lead to excess injuries and employee burnout. And as a result of that work stress, in some cases, they have had to pay “show up bonuses” to reduce their sometimes-rampant warehouse absenteeism. Finally, as most great firms do, they have a relatively high employee turnover rate. This can be partly explained because they are constantly under attack by their competitor’s recruiters, who are logically targeting their exceptional talent. 

Final Thoughts

Today when I am asked by those beginning their career where they should work, I, without hesitation, say Amazon. It is primarily an innovation machine that dominates in so many different product areas and across so many industries. In the same light, if you are a recruiting leader, your goal is to lead your industry in recruiting and HR eventually. It’s time to realize that you must focus your best practice research exclusively on Amazon. You can learn so much so fast (Note: the previous recruiting leader, Google, has lost its luster since Laszlo left).

If you’re interested in past case studies by Dr. Sullivan 

The initial landing pages for Dr. Sullivan’s previous case studies on Google, Apple, and Facebook can be found on his www.drjohnsullivan.com website by clicking here . You can go directly to the introductory part of his four-part Apple case study by clicking here . The first part of his Google recruiting case study can be found here . Part 1 of his Facebook case study can be found here .

Author’s Note  

• Please share these best practices by sending this case study to your team and network or sharing it on social media. 
• Next, if you don’t already subscribe to Dr. Sullivan’s weekly Talent Newsletter, you can do that here .
• Also, join the well over 11,000 that have followed or connected with Dr. Sullivan’s community on LinkedIn . 

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What can Amazon’s AI recruiting tool teach us about reliance on automation?

Amazon, the technology giant that took its first steps back in 1995 as an online bookstore is now a global hub for anything and everything addressing human needs. Obviously, Amazon seems to have what can only be described as the Midas touch. The organization has seen immense success in it’s technology ventures. But that doesn’t mean that it has not seen failure. Let’s understand how and why Amazon failed in successfully delivering an AI backed recruitment tool that could have had the potential to change HR functions around the world.

What was Amazon’s AI recruiting tool?

It goes without saying that along with increased digital transformation efforts, the Covid 19 pandemic has significantly impacted and driven the recruitment process exponentially. This has consequently led to an obvious reliance on automation for supporting the mammoth task of hiring effectively and efficiently. But let’s back up a bit.

Amazon’s AI tool was launched in 2014. Its fundamental task was ranking job candidates from one to five stars in order to quickly identify top talent and stow away the rest. To achieve this objective, the tool created approximately 500 computer models that would crawl through past candidates’ resumés and pick up on about 50,000 key attributes or terms that showed up in the resumés. The model focused on specific job functions and locations with the engineers training the AI system by inputting the top performing employees’ resumés as models for what they sought after in new hires. This machine learning then matched all future resumes against the past inputted ones from the last ten years.

While on paper and in theory all this sounded great, the effort and tech machinery failed and that makes for an interesting case study.

Here are some key lessons highlighting the pitfalls from the Amazon AI recruitment tool:

Lacked reliance in screening candidates

While an AI enabled tool can be reliable in reading and screening resumes, any difference in pattern of resume formats, verbatim used and fonts, makes it difficult for the system to understand.

Additionally, the system exhibits a high probability of rejecting candidates because they don’t meet all criteria of the job description, even if their experience or skill set greatly overcompensates for the lack of a certain qualification. This leads to unoptimized resumes being ignored even if they come from great candidates. Case in point, in Amazon’s case, the tool was trained on resumes submitted over the course of ten years, which were predominantly from men. As a result, the system learned to favor resumes that contained the words and phrases commonly used by men only.

Inability to make future decisions based on past reads

The data output is as good as its input, which means that the problem was in the recency of the data. Amazon used the intelligence of the last 10 years to predict the future outcomes which in this case was a selection of candidates based on the prior understanding of high performing resumes, wherein there was a smaller pool of women candidates. The tool was unable to account for macro factors with respect to the changing times of the recruitment industry, for example policy changes, hiring trends, increasing emphasis on diversity and inclusion etc.

Owing to a smaller pool of women applicants in the past data, the algorithm failed to discriminate between acceptable and unacceptable women candidates. Given this, the algorithms ended up tagging more men candidates as acceptable.

Impersonal nature of AI

AI chatbots can answer most questions and do so quicker than human recruiters. However, often automation can bring with it the frustration of standardized responses, leading to a poor recruitment processing journey. Close to 80% of job seekers prefer human interaction to an exchange with a bot as machines have trouble processing human emotions and slang terms too.

While there is no doubt that automation and AI have transformed many aspects of the recruitment process, it is also true that human interactions remain imperative through the hiring process especially while shortlisting candidates based on their curriculum vitae. There are quite a few arguments justifying human interventions over bots through the recruitment process:

• Personalization : A human recruiter can cut through the biases and offer a more personalized and empathetic experience by customizing the approach differently for every candidate. Something extremely critical in a professional setup. The journey and window into the culture of an organization for a prospective candidate starts from their recruitment process, thereby making it critical for HR representatives to focus on making the experience seamless.
• Engagement : Human interactions are any day more engaging and interactive than a chatbot. For example, of the top issues quoted by prospective applicants while applying for a job on a company portal; 25% quoted the experience of filling an initial applicant form as a tedious exercise. Many employers now use applicant tracking systems to filter the influx of applications before a human sees it. Not only does it make it a lengthy process for the candidate but often comes at the cost and assumption of resumes not getting noticed, again a possible peril of AI.
• Trust : Candidates feel more comfortable knowing that a human is reviewing their application and making decisions based on their qualifications and experience.

Inability to measure intangibles in most cases

Artificial intelligence is great for processing measurable data like years of experience and education level. However, it struggles with the qualitative attributes of an applicant like personality, soft skills, and potential cultural fit within the organization. Unless there is access to data on things like cognitive aptitude and personality tests to inform the AI system, recruiters can lose the opportunity of finding great talent.

Lessons from Amazon’s case

Machine learning and human intelligence is a power packed combination. Machines are trained by humans, and the Amazon case study is a clear-cut representation of why many tech experts voice the flaw that rather than remove human biases from important decisions, artificial intelligence often simply automates them. Idea is to use the data with an additional lens of manual intervention to identify biases, action on various changes to drive diversity and inclusivity.

Over to you

While AI recruitment tools pose multiple advantages which include screening large volumes of resumes and job applications quickly, saving recruiters time and resources; with every day learning and feedback, AI, the developers, and the recruitment industry can get better at understanding the issues and explore the needs of the entire recruitment process. It is important to test, iterate and proof check every initiative that can act as a safety net, and avoid the chance of being unjust or discriminating.

Considering the substantial positive impact AI can have on our world when used right and just, it’s equally important that companies are supported in their development of it.

Sreevally Pasumarthy

Sree Pasumarthy's background in HR, marketing, and psychology gives her an authoritative edge in understanding the needs of the recruitment space. This knowledge powers her creation of AI-based HR Technology solutions. Her focus on streamlining recruitment processes, improving candidate experiences, and understanding the psychology behind hiring decisions makes her a trusted resource for organizations seeking to elevate their recruitment content.

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Hiring Bias Gone Wrong: Amazon Recruiting Case Study

• Kat Chia, PhD
• Research Scientist

Even though we fear artificial intelligence (AI) is sentient, it is far from it. 

A dystopian sci-fi universe in which computers rise up is quite removed from our present reality. AI is only as smart as the humans who program it, though it is quite efficient in its analytical processing efforts. 

This may seem hard to believe as we use AI daily:

•  In our professional writing: Grammarly
• While texting: smartphone sentence completion
• When driving: parking assistance

AI is less obvious in these respects. Consider the spam folder in your email inbox. How does Gmail know what is junk and what is not? AI.

Amazon’s Biased Artificial Intelligence

It only makes sense that AI’s reach should extend further. And Amazon acted upon this in 2015. The basis of their technology was simple and seemingly practical. 

It started with the question: “How do you identify high-fit candidates?” and their answer was, “You look at your existing thriving employees.” 

This approach is the basis for many machine learning problems in the hiring industry, so it seemed like a standard protocol.  Not quite. Amazon should have assessed whether they were falling prey to hiring trends in Silicon Valley (then dubbed “Brotopia” by Emily Change) and tech at large. 

In 2015, Amazon was amongst the tech titans whose workforce was disproportionately high in male employees. 

What happens when you feed a biased dataset to an algorithm? Scaled bias, mostly. The data they used (resumes of current employees) inadvertently suggested that male candidates were the better picks, instilling hiring bias in their talent acquisition process. 

This pipeline of bad data input resulting in bad data output is commonly referred to as Garbage In, Garbage Out . 

Can Artificial Intelligence Be Trusted?

In practice, this means that Amazon’s shiny new recruiting tool (read: biased AI) penalized resumes that mentioned “Women” or “Women’s.” It biased their hiring process.

Thus, a person on the “Women’s Rugby team” or who went to a “Women’s College” was penalized. 

It was more pronounced if the person had various affiliations with organizations or universities that included the word “Women’s” in it. Consequently, male candidates exceedingly benefitted from the AI’s flawed training set. 

Does that mean AI is biased and can’t be trusted? I mean, Amazon couldn’t get it right. Surely this is a lost cause. 

Not at all. This was almost 7 years ago. And a lot happens in 7 years, especially in a fast-paced field like AI. 

Bias-free AI is a possibility. Cangrade holds patent 11429859 for our innovative process of mitigating and removing bias from AI . 

Cangrade’s Bias-Free Artificial Intelligence

Our AI is not only ethical , but it is also recently ADA-compliant . And while most organizations cover the current list of EOCC-protected groups, we also protect against adverse impacts for two more groups: marriage status and whether applicants or candidates have children. 

Hiring bias and discrimination are rampant and aren’t necessarily intentional. There are countless measures we can take to mitigate them. Consider adopting responsible AI to build a more diverse and stronger workforce . When AI’s power is unchecked, it can scale hiring bias, disproportionately affecting minority groups. 

However, when ethical AI is designed, it can elevate voices that aren’t typically heard. Cangrade offers a patented and science-backed ethical AI solution. 

Contact us today for your demo.

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Amazon's sexist hiring algorithm could still be better than a human

Amazon decided to shut down its experimental artificial intelligence (AI) recruiting tool after discovering it discriminated against women. The company created the tool to trawl the web and spot potential candidates, rating them from one to five stars. But the algorithm learned to systematically downgrade women’s CV’s for technical jobs such as software developer.

Although Amazon is at the forefront of AI technology, the company couldn’t find a way to make its algorithm gender-neutral. But the company’s failure reminds us that AI develops bias from a variety of sources. While there’s a common belief that algorithms are supposed to be built without any of the bias or prejudices that colour human decision making, the truth is that an algorithm can unintentionally learn bias from a variety of different sources. Everything from the data used to train it, to the people who are using it, and even seemingly unrelated factors, can all contribute to AI bias.

AI algorithms are trained to observe patterns in large data sets to help predict outcomes. In Amazon’s case, its algorithm used all CVs submitted to the company over a ten-year period to learn how to spot the best candidates. Given the low proportion of women working in the company, as in most technology companies , the algorithm quickly spotted male dominance and thought it was a factor in success.

Because the algorithm used the results of its own predictions to improve its accuracy, it got stuck in a pattern of sexism against female candidates. And since the data used to train it was at some point created by humans, it means that the algorithm also inherited undesirable human traits, like bias and discrimination, which have also been a problem in recruitment for years.

Some algorithms are also designed to predict and deliver what users want to see. This is typically seen on social media or in online advertising, where users are shown content or advertisements that an algorithm believes they will interact with . Similar patterns have also been reported in the recruiting industry.

One recruiter reported that while using a professional social network to find candidates, the AI learned to give him results most similar to the profiles he initially engaged with. As a result, whole groups of potential candidates were systematically removed from the recruitment process entirely.

However, bias also appears for other unrelated reasons. A recent study into how an algorithm delivered ads promoting STEM jobs showed that men were more likely to be shown the ad, not because men were more likely to click on it, but because women are more expensive to advertise to. Since companies price ads targeting women at a higher rate (women drive 70% to 80% of all consumer purchases), the algorithm chose to deliver ads more to men than to women because it was designed to optimise ad delivery while keeping costs low.

But if an algorithm only reflects patterns in the data we give it, what its users like, and the economic behaviours that occur in its market, isn’t it unfair to blame it for perpetuating our worst attributes? We automatically expect an algorithm to make decisions without any discrimination when this is rarely the case with humans. Even if an algorithm is biased, it may be an improvement over the current status quo.

To fully benefit from using AI, it’s important to investigate what would happen if we allowed AI to make decisions without human intervention. A 2018 study explored this scenario with bail decisions using an algorithm trained on historical criminal data to predict the likelihood of criminals re-offending. In one projection, the authors were able to reduce crime rates by 25% while reducing instances of discrimination in jailed inmates.

Yet the gains highlighted in this research would only occur if the algorithm was actually making every decision. This would be unlikely to happen in the real world as judges would probably prefer to choose whether or not to follow the algorithm’s recommendations. Even if an algorithm is well designed, it becomes redundant if people choose not to rely on it.

Many of us already rely on algorithms for many of our daily decisions, from what to watch on Netflix or buy from Amazon. But research shows that people lose confidence in algorithms faster than humans when they see them make a mistake, even when the algorithm performs better overall.

For example, if your GPS suggests you use an alternative route to avoid traffic that ends up taking longer than predicted, you’re likely to stop relying on your GPS in the future. But if taking the alternate route was your decision, it’s unlikely you will stop trusting your own judgement. A follow-up study on overcoming algorithm aversion even showed that people were more likely to use an algorithm and accept its errors if given the opportunity to modify the algorithm themselves, even if it meant making it perform imperfectly.

While humans might quickly lose trust in flawed algorithms, many of us tend to trust machines more if they have human features. According to research on self-driving cars , humans were more likely to trust the car and believed it would perform better if the vehicle’s augmented system had a name, a specified gender, and a human-sounding voice. However, if machines become very human-like, but not quite, people often find them creepy , which could affect their trust in them.

Even though we don’t necessarily appreciate the image that algorithms may reflect of our society, it seems that we are still keen to live with them and make them look and act like us. And if that’s the case, surely algorithms can make mistakes too?

Maude Lavanchy is Research Associate at IMD.

This article was first published by The Conversation .

Research Information & Knowledge Hub  for additional information on IMD publications

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Why it's totally unsurprising that Amazon's recruitment AI was biased against women

• Amazon abandoned a project to build an AI recruitment tool, which engineers found was discriminating against female candidates.
•  Dr Sandra Wachter, an AI researcher at Oxford University, told Business Insider that the gender bias was hardly surprising.
• You feed an AI with garbage and it will spit garbage out, she said. In Amazon’s case, the machine may have reflected the fact that the historical data it was being fed was predominantly male résumés.
• Nonetheless, Wachter believes algorithms could become better decision-making tools than humans.

Amazon admitted this week that it experimented with using machine learning to build a recruitment tool. The trouble is, it didn't exactly produce fantastic results and it was later abandoned.

According to Reuters, Amazon engineers found that besides churning out totally unsuitable candidates, the so-called AI project showed a bias against women .

To Oxford University researcher Dr Sandra Wachter, the news that an artificially intelligent system had taught itself to discriminate against women was nothing new.

"From a technical perspective it's not very surprising, it's what we call 'garbage in and garbage out,'" she told Business Insider.

Garbage in, garbage out

The problem boils down to the data Amazon fed its algorithm, Wachter speculated.

"What you would do is you go back and look at historical data from the past and look at successful candidates and feed the algorithm with that data and try to find patterns or similarities," said Wachter.

"You ask the question who has been the most successful candidates in the past [...] and the common trait will be somebody that is more likely to be a man and white."

Reuters reported that the engineers building the program used résumés from a 10 year period, which were predominantly male. Amazon did not provide Business Insider with the gender split in its engineering department but sent us a link to its diversity pages . Its global gender balance is 60% men, with 74% of managerial roles being held by men.

"So if then somebody applies who doesn't fit that profile, it's likely that that person gets filtered out just because the algorithm learned from historical data," said Wachter. "That happens in recruitment, and that happens in basically everywhere where we use historical data and this data is biased."

Garbage in, garbage out (sometimes abbreviated to "GIGO") just means that bad input will result in bad output, and it's the same with bias. The problem is that it's incredibly difficult to filter out algorithmic bias, because the algorithms we build pick up on human prejudices.

"What is the algorithm supposed to do? It can only learn from our semantics and our data and how we interact with humans, and the moment there is no gender parity yet, unfortunately," said Wachter.

Machine learning can produce self-fulfilling prophecies

This is far from the first time a computer program has displayed human bias. "It's just yet another example of how algorithmic decision-making and AI in general can actually reinforce existing stereotypes that we have in our society," said Wachter.

In 2016, a ProPublica investigation found that a computer program called COMPAS, designed to assess the risk of criminals re-offending, was discriminating against black people. As an example, the program deemed an 18-year old black girl who briefly stole a child's scooter to be more likely to re-offend than a 41-year old white man with two prior convictions for shoplifting power tools.

Wachter points out that COMPAS's software asked questions which led to individuals being judged by their social environment, such as "Was one of your parents ever sent to jail or prison?" or "How many of your friends/acquaintances are taking drugs illegally?"

"This is not about the individual anymore, that is about your social environment, and being judged based on other people," said Wachter. "If you apply that to every single person, that's a self-fulfilling prophecy."

Scanning for bias

That isn't to say there's no use in perfecting our algorithms in the meantime. The first thing we can do is come up with effective methods for spotting bias inside them.

"There's been a lot of discussion in the field about trying to come up with standards and testing periods before we deploy those systems," Wachter said. "If you have a very easy to understand algorithm detecting bias will be easier but when it comes to machine learning, a very opaque system, testing for bias and discrimination, or even understanding what's going on in that system, will become more and more difficult."

Wachter has worked closely devising ways to check for bias in machine learning models, and her work has been cited by Google in its "What If" tool , which lets users analyse machine learning models without writing extra code. She believes that before companies can deploy a system, they should be able to pass a standardised test that demonstrates it's not biased.

"Especially when it comes to employment, you should have some statistical evidence that your system isn't biased. And if you can't provide that, maybe you shouldn't use [the system] for making important decisions," she continued.

Amazon said in a statement that its hiring tool "was never used by Amazon recruiters to evaluate candidates." A source told Reuters that Amazon recruiters looked its recommendations, but they never solely relied on it for actual decision-making.

'An algorithm doesn't get grumpy'

Although rooting out algorithmic bias poses a technical challenge, Wachter is confident that using AI properly could actually improve fair decision-making in our society.

"If you look at it from the other perspective, if we play this right and if we work on data providence [...] I actually think algorithms could be a better decision-making tool than humans," she said. "An algorithm cannot lie to you, you cannot force an algorithm, you cannot entice or bribe an algorithm."

She also thinks that algorithmic decision-making could help cancel out a profoundly human quality — moodiness. "Algorithms are more consistent as well. If I sit on an employment panel for eight hours, my mood will swing from time to time. I might get angry, or grumpy, or hungry, so that could influence my judgement," she said. "An algorithm doesn't get grumpy or moody or hungry."

Wachter's not in favour of removing human oversight altogether, rather she believes that humans and AI play to each other's strengths. "I think ideally they would be complementary and cancel out each other's blind spots," she said.

• Main content

Why Amazon’s Automated Hiring Tool Discriminated Against Women

In 2014, a team of engineers at Amazon began working on a project to automate hiring at their company. Their task was to build an algorithm that could review resumes and determine which applicants Amazon should bring on board. But, according to a Reuters report this week, the project was canned just a year later, when it became clear that the tool systematically discriminated against women applying for technical jobs, such as software engineer positions.

It shouldn’t surprise us at all that the tool developed this kind of bias. The existing pool of Amazon software engineers is overwhelmingly male, and the new software was fed data about those engineers’ resumes. If you simply ask software to discover other resumes that look like the resumes in a “training” data set, reproducing the demographics of the existing workforce is virtually guaranteed.

In the case of the Amazon project, there were a few ways this happened. For example, the tool disadvantaged candidates who went to certain women’s colleges presumably not attended by many existing Amazon engineers. It similarly downgraded resumes that included the word “women’s” — as in “women’s rugby team.” And it privileged resumes with the kinds of verbs that men tend to use, like “executed” and “captured.”

Fortunately, Amazon stopped using the software program when it became clear the problem wasn’t going to go away despite programmers’ efforts to fix it. But recruiting tools that are likely similarly flawed are being used by hundreds of companies large and small, and their use is spreading.

HOW ARTIFICIAL INTELLIGENCE IS CHANGING THE WORKPLACE

There are many different models out there. Some machine learning programs — which learn how to complete a task based on the data they’re fed — scan resume text, while others analyze video interviews or performance on a game of some kind. Regardless, all such tools used for hiring measure success by looking for candidates who are in some way like a group of people (usually, current employees) designated as qualified or desirable by a human . As a result, these tools are not eliminating human bias — they are merely laundering it through software.

And it’s not just gender discrimination we should be concerned about. Think about all the ways in which looking at resume features might similarly cluster candidates by race: zip code, membership in a Black student union or a Latino professional association, or languages spoken. With video analysis, patterns of speech and eye contact have cultural components that can similarly lead to the exclusion of people from particular ethnic or racial groups. The same goes for certain physical or psychological disabilities.

We’ve seen these types of problems with artificial intelligence in many other contexts . For example, when we used Amazon’s facial recognition tool to compare members of Congress against a database of mugshots, we got 28 incorrect matches — and the rate for false matches was higher for members of color. This is due, in part, to the fact that the mugshot database itself had a disproportionately high number of people of color because of racial biases in the criminal justice system.

These tools are not eliminating human bias — they are merely laundering it through software.

Algorithms that disproportionately weed out job candidates of a particular gender, race, or religion are illegal under Title VII, the federal law prohibiting discrimination in employment. And that’s true regardless of whether employers or toolmakers intended to discriminate — “disparate impact discrimination” is enough to make such practices illegal.

But it can be difficult to sue over disparate impact, particularly in “failure-to-hire” cases. Such lawsuits are very rare because it’s so hard for someone who never got an interview to identify the policy or practice that led to her rejection.

That’s why transparency around recruiting programs and other algorithms used by both companies and the government is so crucial. Many vendors who market these hiring tools claim that they test for bias and in fact are less biased than humans. But their software is proprietary, and there’s currently no way to verify their claims. In some cases, careful work by outside auditors may be able to uncover bias, but their research is thwarted by various obstacles. We’re challenging one such obstacle — a federal law that can criminalize testing of employment websites for discrimination.

But even this kind of outside research can’t give us the full picture. We need regulators to examine not only the software itself but also applicant pools and hiring outcomes for companies that deploy the software. The Equal Employment Opportunity Commission, the federal agency that enforces laws against job discrimination, has begun to explore the implications of algorithms for fair employment, and we urge the agency to do more. EEOC should issue guidance for employers considering using these tools, detailing their potential liability for biased outcomes and steps they can take to test for and prevent bias. It should also include questions about data-driven bias in all of its investigations.

Big-data algorithms will replicate and even magnify the biases that exist in society at large — unless they are designed and monitored very, very carefully. The right kind of oversight is required to make sure that happens.

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Automation has been key to Amazon's e-commerce dominance, be it inside warehouses or driving pricing decisions. The company's experimental hiring tool used artificial intelligence to give job candidates scores ranging from one to five stars - much like shoppers rate products on Amazon. In effect, Amazon's system taught itself that male candidates were preferable. It penalized resumes that included the word “women's,” as in “women's chess club captain.” And it downgraded graduates of two all-women's colleges, according to people familiar with the matter. Amazon's experiment began at a pivotal moment for the world's largest online retailer. Machine learning was gaining traction in the technology world. The American civil liberties union is currently challenging a law that allows criminal prosecution of researchers and journalists who test hiring websites' algorithms for discrimination.

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Amazon ditched AI recruitment software because it was biased against women

• Erin Winick archive page

The data on which the artificial-intelligence algorithm was trained created a preference for male candidates.

The news: According to a report by Reuters , Amazon began developing an automated system in 2014 to rank job seekers with one to five stars. But last year, the company scrapped the project after seeing it had developed a preference for male candidates in technical roles.

Why? The AI tool was trained on 10 years’ worth of résumés the company had received. Because tech is a male-dominated industry, the majority of those résumés came from men.

The result: The system was unintentionally trained to choose male candidates over female candidates. It would reportedly penalize résumés containing the word “women’s” or the names of certain all-women colleges. Although Amazon made changes to make these terms neutral, the company lost confidence that the program was indeed gender neutral in all other areas.

Why it matters: We can’t treat artificial intelligence as inherently unbiased. Training the systems on biased data means the algorithms also become biased . If unfair AI hiring programs like this aren’t uncovered before being implemented , they will perpetuate long-standing diversity issues in business rather than solve them.

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How we hire and develop the best talent at Amazon

• Recruiting from diverse colleges and universities (including Historically Black Colleges and Universities (HBCUs), Hispanic Serving Institutions (HSIs), women’s colleges, and tribal colleges) in the U.S.
• Hosting hiring fairs and career enrichment summits like Represent the Future, Success is Inclusive, to partner with underrepresented communities around the world.
• Bringing college students to Amazon’s campus for programs like the Amazon Finance Diversity Leadership Summit to learn from our finance and accounting leaders, and to interview for finance internships at Amazon.
• Partnering with organizations like Grace Hopper Celebration, GEM Consortium Fellows, AfroTech, AnitaB.Org, Lesbians Who Tech, Girls in Tech, AISES, and others.
• Creating opportunities for people with disabilities to find success at Amazon, like our silent delivery station in Mumbai, and our partnership with the Northwest Center in Seattle.
• Exploring new ways to engage populations of employees, like our all women delivery stations in India , which opened employment and leadership opportunities to women in an area where they were not applying for traditional roles.

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The AI Recruitment Evolution - from Amazon’s Biased Algorithm to Contextual Understanding

Ever since companies started using AI for screening and sourcing candidates, the technology behind it never stopped evolving. There were a couple of major learning moments in the brief history of AI that lead the technology to develop to where it is today. And even though the technology is not as near to where it’s set out to be in the next couple of years, the advancements and the accuracy of the software have been increasing significantly.

When thinking about all the readers of the article, we’ve realized that there will probably be two entirely different groups - the ones completely new to the idea of artificial intelligence in recruiting, and the experts who are already leveraging their AI usage. However, regardless of which group you belong to, we invite you to keep reading the blog and get to know the evolution process of AI  in recruitment on some real life examples. 

AI recruitment pioneers 

It’s been a long way since 2014 and the infamous Amazon case in which the company mistakenly created a biased recruiting software that would end up repeating human mistakes by discriminating against women. Luckily, the company has abandoned the project and it became one of the greatest learning lessons for using AI in recruitment. So, what really happened at Amazon?

While the company tried to be the pioneer in automating the recruitment process, the way they collected data for making the decisions was flawed. The idea behind AI-powered technology is that for it to make future decisions, it needs to be fed with large amounts of data. Therefore, this leads to a logical conclusion that the quality of data is a crucial part of the process. However, Amazon made a mistake here. The way they provided data for their software was by  feeding it with recruitment information of the company from the previous 10 years of hiring . The outcome was biased against women and showed that just like a human makes mistakes, the software can do the same. For many who didn’t take a close look at the issue, this caused mistrust in using new technologies for such important tasks. 

However, big attention to this case was drawn by the entire industry, and here are a couple of good takeaways  that helped prevent the same mistakes in the future: 

M achines are not the one producing the bias: Machines are trained by humans. If the data that the machine consumes is based on biased decisions of humans, the machine has no other way to perform the tasks than to repeat the same mistakes. This mistake created a new challenge; how do we create the machine as immune to human mistakes as possible?

The importance of data: In the Amazon case, the company was using only the data from its previous hirings. Even though Amazon is an enormous company, this data still isn’t enough to feed the software for the sake of diversity. 

Making future decisions based on past events: Finally, one of the most important mistakes in the Amazon case was basing future decisions on the past 10 years of recruitment data at the company. Within those years, the policies, hiring trends, and procedures changed drastically. The use of such data for building the recruitment software of the future represented only a setback for the company. 

Always own your mistakes: While developing a complex piece of technology that will make future decisions for your company, it is a necessity to analyze every step of the way and to be transparent with your mistakes. Mistakes will happen, but only truly owning them can be a good learning lesson for the future. In the Amazon case this can mean deleting the elements of the CV that are subject to biases and training software for the new conditions.

The second wave and the era of keywords

Learning from Amazon’s mistakes gave a great deal of confidence for others who attempted to create recruitment software of the future. This created an entirely new way of screening CVs - by using keywords. 

This means that now the software was able to screen through a large amount of CVs, searching to find certain keywords that were previously set. At the first glance, this way of conducting CV parsing was amazing. However, it didn’t take long for recruiters to realize that some candidates found a way to cheat. By simply disguising a bunch of relevant keywords in their CVs and writing them in a white font, candidates were now able to position themselves way up high in the recruiter’s list regardless of their actual competencies. 

Besides people finding loopholes and ways to trick the software, another big problem arose. There are countless ways in which people with similar skill sets can name and describe their education, previous work experience, or soft skills. 

For example, three different people from Free University Berlin can use different ways to name it in their CV; the first one using the full name in English, the second one using a short name - FUB, and the third one using the original name in German - Freie Universität Berlin. And this is just the name of the University, try imagining what happens with the software when people start naming their soft skills. Ultimately, the software wasn’t yet capable of understanding the similarities and differences between synonyms, homonyms, or difficult concepts. 

In order to address these issues softwares began using something called Ranked Retrieval  systems that had already been available for many years but used for different purposes (such as for Google search). In this context, the system works in a way that it analyses a plain text query a user writes into a query box (instead of making the user learn how to code information into booleans) and attempts to construct a query automatically. The results are then ranked in order of relevance to the query depending on the context and number of times in which terms appear in documents.

While this technology simplifies the process for the recruiters by not requiring the knowledge of complex Boolean syntax, the end result becomes less accurate and loses the power that Boolean queries provide. This can result in losing relevant candidates that end up not being discovered at all or being too low in recruiters list.

Semantic similarity is the way to go

Thanks to all the mistakes along the way, AI has been developing tremendously which paved the way for deep machine learning techniques to start overtaking the AI recruitment processes. The newest semantic-based AI can eliminate all the previous mistakes and help find the best candidates faster. When software uses semantic similarity for natural language processing as a way to read through a high volume of CVs here is what happens. 

Based on the enormous amount of CVs that the software reads through, it starts learning the patterns and most importantly understanding the content of those CVs. This goes way beyond simply recognizing certain keywords. Most importantly, thanks to semantic similarity, the language gap between the recruiter's job descriptions and candidate's CVs can be overcome. 

However, it is important to mention that it is not a single tool that leads to a revolution in AI, but that it’s the combination of multiple technologies which aim to minimize previously mentioned issues. For instance, AI technology uses models that are generally trained for understanding a certain language (often used English). By taking such model and training it to understand a certain domain (such as the domain of human resources) the technology learns the HR related vocabulary and is then capable of extracting certain words, terms and expressions and matching them with a specific level of expertise - leading to a final act of matching CVs with the job requirements. The model is trained based on predetermined characteristics that would lead to the most accurate way of matching. 

This way the task of imputing all relevant synonyms manually or based on keywords becomes redundant since the software learns its own patterns and ways of attaching some words to another and the opposite. When put simply, the three different people from the previous paragraph who are listing their education at  Free University Berlin by using different expressions can now be understood by the software.

Furthermore, a technology called Contextual Validation is used to understand the context and frequently used terms to accurately place a candidate with a specific job requirement. This way resume parser analyzes which terms the candidates are using to refer to their skills, and which terms are merely referring to incidental context, which ultimately increases the accuracy.

Finally, for the benefit of ranking candidates, the technology called linguistic analysis/resume parsing and ranking can be used. This technology is capable of ranking the candidates based on not only a particular skill of the candidate but also on the level of experience of using that skill. One of the technology's key features enables recruiters to have their candidates ranked and in order so that they are always sure that the highest rank belongs to the most relevant candidates and opposite. 

(a preview of TalentLyft software ) 

AI needs us to keep learning

Finally, it’s been a long way for AI since its beginnings. However, there is no sign of slowing down. With every day and every feedback, AI, the developers, and the recruitment industry are getting better at understanding the issues and looking into the needs of the entire recruitment process.

Instead of sitting back and enjoying the ride, it’s time for all of us to embrace the learning mentality and taking part in creating the technology of tomorrow!

Frequently asked questions

What was the significant flaw in Amazon's early AI recruitment system?  

Amazon's AI system showed bias against women due to flawed data from past recruitments, reflecting past human biases in its decision-making.

How has AI recruitment evolved since the initial issues?  

AI recruitment has evolved from basic keyword-based screening to advanced methods using semantic similarity and contextual understanding for more accurate candidate evaluation.

What is the importance of data quality in AI recruitment?  

High-quality, unbiased data is crucial for training AI systems to avoid perpetuating historical biases and to make fair, effective hiring decisions.

How do modern AI systems enhance recruitment accuracy?  

Modern AI uses deep learning and natural language processing to understand the context and content of CVs, going beyond mere keyword matching.

What future advancements are expected in AI recruitment?  

Future AI recruitment is expected to continually learn and improve, utilizing a combination of technologies for increasingly accurate and fair candidate matching.

2024 Guide to Buying a Recruiting Tool

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TalentLyft is an intuitive recruitment app made for successful hiring.

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Supercharging Amazon’s Recruitment in EMEA

‘Who’s Amazon?’ — said no one, ever. Surely, this tech giant doesn’t need an introduction?

Turns out, Amazon wasn’t as well known as you might expect.

In emerging European markets like Hungary, Poland and the Czech Republic, Amazon wasn’t a household name in the same way it is in the USA or UK.

When Amazon launched a large-scale recruitment programme across these new markets, it discovered this lack of familiarity presented a unique challenge.

Amazon was seeking to hire an ambitious 85,000 employees within the year.

85,000 employees in the EMEA region within the year to work in the 80+ Fulfillment Centers that were currently underway.

First was to create a recruitment strategy

To design a new talent acquisition platform specifically targeted at emerging markets in the EMEA region (Europe, Middle East and Africa) to help streamline and improve the recruitment process from application, interview to onboarding. As well as provide strategic and design support for recruitment campaigns.

“For each new Fulfillment Center that was planned, Amazon needed to hire about 1500 people.

Essentially, people were being asked to apply to work for a company they’d never heard of , in a building that didn’t exist , doing jobs they had never trained for. “.

– Josh Chesney, Chief Digital Officer at Dauntless

The Challenge

Amazon discovered it was only managing to hire 5-10% of candidates required..

This low uptake was partly due to low application rates, which in turn were due to a lack of understanding amongst potential candidates of Amazon, its culture and its employee experience.

The challenge was to design a recruitment portal that effectively communicated all these things and more whilst also streamlining the recruitment process to make it easier and more efficient for candidates.

Amazon’s most successful European recruitment campaign  to date.

The final solution included multiple facets, from a new website that acted as a window to the inner workings of Amazon, to a marketing strategy targeting more specific candidates, all the way to improving the candidate experience by streamlining the hiring process. We also built a comprehensive reporting dashboard, which published, responded to and tracked individual roles.

Recruitment website

A window to the inner world of amazon.

A brand new website was designed and built to support Amazon in achieving its goals. Its purpose was primarily to tell the Amazon story — who they are, what it’s like to work at Amazon, and how the application process works.

To further boost engagement, we created 360º videos and turned the warehouse tour into a complete VR experience. In addition, cinemagraphs (high-performance looping videos) were created to provide a unique experience in the headers.

The website also doubled as a content management system (CMS) for job promotion and recruitment. The site also required optimisation and hosting support to equip it for high-volume traffic.

Marketing Strategy

Internal and external marketing strategy with tactical support.

A new marketing strategy was created for the new target audiences.

To support Amazon’s creative agencies across Europe, we provided the tactical creative output they would need, making adjustments for each country based on its exposure to the Amazon brand.

We partnered with multiple internal teams to ensure Amazon was attracting the best candidates for the designated roles. Additionally, we capitalised on geo-fencing to target ads to specific audiences near Amazon’s Fulfillment Centers to further support the recruitment drive.

HR – Operations – Campus – Pathways – Brand

User Experience

An improved candidate experience.

Telling the right story and compelling a candidate is only the beginning. Helping them find the roles they are qualified for, and how to apply for them, is another battle entirely.

By partnering with the Amazon EU team, we defined the application process, found the friction points along the way, and helped to streamline and explain how it worked.

We also worked over several months updating job descriptions, for improved clarity, consistency, and tone of voice.

Hire high-quality people, better and faster.

 From improving the interview experience, streamlining the hiring process, or simply a more targeted marketing campaign, we can help.

We can give you a digital strategy, improved processes and a new digital experience. We’ll work alongside you to map the path to reach your goals.

We do Recruitment at scale. 

Hacking the Case Interview

If you’re interviewing for a business role at Amazon, there is a good chance that you’ll receive at least one case study interview, also known as an Amazon case interview. Amazon roles that include case study interviews as part of the interview process include:

• Business Analyst : Candidates are often given mini case interviews
• Business Development : Candidates are often given M&A case interviews
• Corporate Strategy : Candidates are often given strategy case interviews
• Product Manager : All candidates are given product manager case study interviews
• P roduct Marketing : Candidates are often often given product manager case study interviews
• Marketing : Candidates are often given marketing case interviews

To land an Amazon job offer, you’ll need to crush every single one of your case interviews. While Amazon case study interviews may seem ambiguous and challenging, know that they can be mastered with proper preparation.

If you are preparing for an upcoming Amazon case interview, we have you covered. In this comprehensive Amazon case interview guide, we’ll cover:

• What is an Amazon case study interview
• Why Amazon uses case study interviews
• The 6 steps to ace any Amazon case interview
• Amazon case interview tips
• Recommended Amazon case study interview resources

If you’re looking for a step-by-step shortcut to learn case interviews quickly, enroll in our case interview course . These insider strategies from a former Bain interviewer helped 30,000+ land tech and consulting offers while saving hundreds of hours of prep time.

What is an Amazon Case Study Interview?

Amazon case study interviews, also known as Amazon case interviews, are 20- to 30-minute exercises in which you are placed in a hypothetical business situation and are asked to find a solution or make a recommendation.

First, you’ll create a framework that shows the approach you would take to solve the case. Then, you’ll collaborate with the interviewer, answering a mix of quantitative and qualitative questions that will give you the information and data needed to develop an answer. Finally, you’ll deliver your recommendation at the end of the case.

Case interviews have traditionally been used by consulting firms to assess a candidate’s potential to become a successful consultant. However, now a days, many companies with ex-consultants use case studies to assess a candidate’s capabilities. Since Amazon has so many former consultants in its business roles, you’ll likely encounter at least one case study interview.

The business problems that you’ll be given in an Amazon case study interview will likely be real challenges that Amazon faces today:

• How can Amazon improve customer retention for their Amazon Prime subscription service?
• How can Amazon improve its digital streaming service?
• How can Amazon increase ad revenues from merchant sellers?
• How should Amazon deal with fake products among its product listings?
• How can Amazon Web Services outcompete Microsoft Azure?

Depending on what team at Amazon you are interviewing for, you may be given a business problem that is relevant to that specific team.

Although there is a wide range of business problems you could possibly be given in your Amazon case interview, the fundamental case interview strategies to solve each problem is the same. If you learn the right strategies and get enough practice, you’ll be able to solve any Amazon case study interview.

Why does Amazon Use Case Study Interviews?

Amazon uses case study interviews because your performance in a case study interview is a measure of how well you would do on the job. Amazon case interviews assess a variety of different capabilities and qualities needed to successfully complete job duties and responsibilities.

Amazon’s case study interviews primarily assess five things:

• Logical, structured thinking : Can you structure complex problems in a clear, simple way?
• Analytical problem solving : Can you read, interpret, and analyze data well?
• Business acumen : Do you have sound business judgment and intuition?
• Communication skills : Can you communicate clearly, concisely, and articulately?
• Personality and cultural fit : Are you coachable and easy to work with?

Since all of these qualities can be assessed in just a 20- to 30-minute case, Amazon case study interviews are an effective way to assess a candidate’s capabilities.

In order to do well on the personality and cultural fit portion, you should familiarize yourself with  Amazon’s Leadership Principles before your interview. At a high level, these principles include:

• Customer obsession : Leaders start with the customer and work backwards
• Ownership : Leaders are owners and act on behalf of the entire company
• Invent and simplify : Leaders expect and require innovation and invention from their teams and always find ways to simplify
• Learn and be curious : Leaders are never done learning and always seek to improve themselves
• Insist on the highest standards : Leaders have relentlessly high standards
• Think big : Leaders create and communicate a bold direction that inspires results
• Frugality : Accomplish more with less
• Earn trust : Leaders listen attentively, speak candidly, and treat others respectfully
• Dive deep : Leaders operate at all levels and stay connected to the details
• Deliver results : Leaders focus on key inputs for their business and deliver them with the right quality and in a timely fashion

The 6 Steps to Solve Any Amazon Case Interview

In general, there are six steps to solve any Amazon case study interview.

1. Understand the case

Your Amazon case interview will begin with the interviewer giving you the case background information. While the interviewer is speaking, make sure that you are taking meticulous notes on the most important pieces of information. Focus on understanding the context of the situation and the objective of the case.

Don’t be afraid to ask clarifying questions if you do not understand something. You may want to summarize the case background information back to the interviewer to confirm your understanding of the case.

The most important part of this step is to verify the objective of the case. Not answering the right business question is the quickest way to fail a case interview.

2. Structure the problem

The next step is to develop a framework to help you solve the case. A framework is a tool that helps you structure and break down complex problems into smaller, more manageable components. Another way to think about frameworks is brainstorming different ideas and organizing them into different categories.

For a complete guide on how to create tailored and unique frameworks for each case, check out our article on case interview frameworks .

Before you start developing your framework, it is completely acceptable to ask the interviewer for a few minutes so that you can collect your thoughts and think about the problem.

Once you have identified the major issues or areas that you need to explore, walk the interviewer through your framework. They may ask a few questions or provide some feedback.

3. Kick off the case

Once you have finished presenting your framework, you’ll start diving into different areas of your framework to begin solving the case. How this process will start depends on whether the case interview is candidate-led or interviewer-led.

If the case interview is a candidate-led case, you’ll be expected to propose what area of your framework to start investigating. So, propose an area and provide a reason for why you want to start with that area. There is generally no right or wrong area of your framework to pick first.

If the case interview is interviewer-led, the interviewer will tell you what area of the framework to start in or directly give you a question to answer.

4. Solve quantitative problems

Amazon case study interviews may have some quantitative aspect to them. For example, you may be asked to calculate a certain profitability or financial metric. You could also be asked to estimate the size of a particular market or to estimate a particular figure.

The key to solving quantitative problems is to lay out a structure or approach upfront with the interviewer before doing any math calculations. If you lay out and present your structure to solve the quantitative problem and the interviewer approves of it, the rest of the problem is just simple execution of math.

5. Answer qualitative questions

Amazon case study interviews may also have qualitative aspects to them. You may be asked to brainstorm a list of potential ideas. You could also be asked to provide your opinion on a business issue or situation.

The key to answering qualitative questions is to structure your answer. When brainstorming a list of ideas, develop a structure to help you neatly categorize all of your ideas. When giving your opinion on a business issue or situation, provide a summary of your stance or position and then enumerate the reasons that support it.

6. Deliver a recommendation

In the last step of the Amazon case interview, you’ll present your recommendation and provide the major reasons that support it. You do not need to recap everything that you have done in the case, so focus on only summarizing the facts that are most important.

It is also good practice to include potential next steps that you would take if you had more time or data. These can be areas of your framework that you did not have time to explore or lingering questions that you do not have great answers for.

Amazon Case Interview Tips

Below are eight of our best tips to help you perform your best during your Amazon case study interview.

1. Familiarize yourself with Amazon’s business model

If you don’t understand Amazon’s business model, it will be challenging for you to do well in their case interviews. If you are interviewing for the Amazon Web Services team, you should know how Amazon makes money as a cloud service provider. If you are interviewing for the Amazon Prime team, you should be familiar with how their subscription service works.

2. Read recent news articles on Amazon

A lot of the times, the cases you’ll see in an Amazon case study interview are real business issues that the company faces. Reading up on the latest Amazon news will give you a sense of what Amazon’s biggest challenges are and what major business decisions they face today. There is a good chance that your case study interview will be similar to something that you have read in the news.

3. Verify the objective of the case 

Answering the wrong business problem will waste a lot of time during your Amazon case study interview. Therefore, the most critical step of the case interview is to verify the objective of the case with the interviewer. Make sure that you understand what the primary business issue is and what overall question you are expected to answer at the end of the case.

4. Ask clarifying questions

Do not be afraid to ask questions. You will not be penalized for asking questions that are important and relevant to the case. 

Great questions to ask include asking for the definition of an unfamiliar term, asking questions that clarify the objective of the case, and asking questions to strengthen your understanding of the business situation.

5. Do not use memorized frameworks

Interviewers can tell when you are using memorized frameworks from popular case interview prep books. Amazon values creativity and intellect. Therefore, make every effort to create a custom, tailored framework for each case that you get.

6. Always connect your answers to the case objective

Throughout the case, make sure you are connecting each of your answers back to the overall business problem or question. What implications does your answer have on the overall business problem?

Many candidates make the mistake of answering case questions correctly, but they don’t take the initiative to tie their answer back to the case objective.

7. Communicate clearly and concisely

In an Amazon case study interview, it can be tempting to answer the interviewer’s question and then continue talking about related topics or ideas. However, you have a limited amount of time to solve an Amazon case, so it is best to keep your answers concise and to the point.

Answer the interviewer’s question, summarize how it impacts the case objective, and then move onto the next important issue or question.

8. Be enthusiastic

Amazon wants to hire candidates that love their job and will work hard. Displaying enthusiasm shows that you are passionate about working at Amazon. Having a high level of enthusiasm and energy also makes the interview more enjoyable for the interviewer. They will be more likely to have a positive impression of you.

Recommended Amazon Interview Resources

Here are the resources we recommend to land an Amazon job offer:

For help landing interviews

• Resume Review & Editing : Transform your resume into one that will get you multiple interviews

For help passing case interviews

• Comprehensive Case Interview Course (our #1 recommendation): The only resource you need. Whether you have no business background, rusty math skills, or are short on time, this step-by-step course will transform you into a top 1% caser that lands multiple consulting offers.
• Case Interview Coaching : Personalized, one-on-one coaching with a former Bain interviewer.
• Hacking the Case Interview Book   (available on Amazon): Perfect for beginners that are short on time. Transform yourself from a stressed-out case interview newbie to a confident intermediate in under a week. Some readers finish this book in a day and can already tackle tough cases.
• The Ultimate Case Interview Workbook (available on Amazon): Perfect for intermediates struggling with frameworks, case math, or generating business insights. No need to find a case partner – these drills, practice problems, and full-length cases can all be done by yourself.

For help passing behavioral & fit interviews

• Behavioral & Fit Interview Course : Be prepared for 98% of behavioral and fit questions in just a few hours. We'll teach you exactly how to draft answers that will impress your interviewer.

Land Multiple Tech and Consulting Offers

Complete, step-by-step case interview course. 30,000+ happy customers.

How Amazon uses Seenit to bring their employer value proposition to life

Pieces of content captured using Seenit.

Different creators across the global business

Uploads from over 240 locations across the globe.

Meet Amazon

Amazon needs little introduction. They are now the world’s largest company by employees and the fifth largest by market capitalisation.

Seenit was brought into Amazon in 2018 by Amazon’s Employer Brand team in the Retail division. The main goal of Seenit was to produce videos captured by their employees to build trust with their audiences and tell more authentic stories.

“It's really been a pleasure to work with the Seenit team and think about how we can constantly raise the bar to be more fun and be more creative”

Sadie Nachtigal, Senior Campaign Manager - Employer Brand

The challenge

The State of Employer Brand is evolving. Companies’ employer brand has never been more visible to future and existing employees. Amazon’s Employer Brand and Recruitment teams have the added challenge of being the fastest growing team in the world. They need to hire at volume while also crucially competing against the likes of Apple and Google for the best talent in the tech space. On top of this, Amazon needed to increase trust with the brand and demonstrate their commitments to diversity and inclusion and climate change to appeal to younger generations of talent.

“It’s incredible, and it’s been happening for a while this whole UGC thing. I feel very fortunate that we’ve been working with Seenit to create content with our employees.” Matt Sharp, Employer Branding Lead

The solution

Amazon has been using Seenit since 2018. They use the platform to co-create video content with their most engaged employees, giving their employees the platform to share their experiences and stories. They’re able to provide a more authentic look into what it’s really like working at one of the world’s fastest-growing businesses.

These stories highlight the career opportunities and the critical areas of Amazon’s employer value proposition (EVP). They also showcase the company’s commitment to social good and the opportunities they provide their employees to find purpose in their work.

Since partnering with Seenit , Amazon has seen increased engagement with their Employer Brand campaigns across the board. ‘Business as usual is very much about empowering employees – helping people to tell their story and share their experiences’, explains Matt Sharp in a webinar with Seenit .

In 2019 Amazon released the video, ‘Brand Specialists, what do they do?’ which aimed to raise awareness of the Brand Specialist role and fill 300 open position s.

The video was embedded throughout the candidate journey, from the careers page, social channels and used as an advertising tool. It has been watched over 45,000 times on their careers landing page and took their click-through rate on the jobs page up by a factor of 2x. They even won a coveted Seenit award for the video.

Not only this, but Amazon has scaled content creation across seven Employer Brand teams and has collected nearly 4,000 uploads from 500 employees across 25 countries . They’ve produced upwards of 100 videos with the Seenit platform . Check out more from Amazon below!

Some of Amazon's best videos

Amazon Security Celebrates Hispanic Heritage Month

Example from Amazon

Student Programs SQL Interview Tips: Data and Business Intelligence Engineer

Meet Trisha

Unlock the power of employee-generated video

Amazon Firm’s Personnel Recruitment and Selection Practices Research Paper

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Literature review.

• Methods, Justification, and Explanation

Results and Analysis

Conclusion, recommendation, and implementation, reference list.

Amazon is currently one of the leading online retailing companies in the United States. The firm faces stiff competition from other major retailers, making it necessary for it to remain innovative in its activities. However, the high employee turnover rates compromise its ability to remain innovative as some of its talented employees are lost to rival firms. The firm needs an effective recruitment strategy to enable it replace such talents.

In this paper, the aim was to detect any drawbacks in personnel recruitment and selection and offer up-to-date solutions and recommendations. Data in this study was obtained from both primary and secondary sources. Findings of the investigation show that the use of technology, especially the AI and data analytics, can help simplify the process and eliminate most of the weaknesses associated with the use of human resources during recruitment and selection.

Introduction to the Research Topic

The ability of a firm to achieve sustainable success in the current competitive business environment largely depends on the skills, competence, experience, innovativeness, and commitment of its employees. According to Lepistö and Ihantola (2018), the top management unit is responsible for developing policies that need to define the path a firm takes in the market.

However, it is the responsibility of the junior workers to take specific actions, in line with the policies set by the superiors, meant to facilitate the success of the firm. As such, firms are always keen on creating a pool of highly skilled and committed employees to facilitate successful operations. It all starts with the recruitment of the right candidates based on job specifications and the environmental forces that a firm has to face.

Staff recruitment currently presents a major concern to human resource managers irrespective of the industry in which a firm operates. Aimee (2018) explains that the process of hiring a competent employee takes a considerable amount of time. It starts with advertising the positions available in the company, receiving job applications, and selecting candidates considered most qualified based on their credentials.

The recruitment team then has to invite the candidates for a face-to-face interview to determine if they can deliver on the firm’s expectations. The selected candidates have to be screened further, by making phone calls to specific institutions to verify their credentials. It is at this stage, through this traditional recruitment method, that a firm can offer a candidate a given position. The cost may be higher and the period longer when the recruitment team encounters unforeseen challenges.

Some firms are currently considering the use of human resource recruitment agencies when hiring new employees. Instead of spending time to do the job internally, these organizations opt to pay for the services of agencies that have specialized in these tasks. The approach is considered less costly and less time-consuming (Bilan et al ., 2020). These agencies have large databases from which they can easily select individuals who meet the criteria set by the firm. They are better placed to do the job matching, which is critical when hiring workers.

Even with the help of such an external agency, the HR unit will still need to assess employee turnover and organizational performance. As such, a firm needs to develop efficient and innovative employee recruitment and management strategies to remain competitive, sustainable, and prosperous in the market.

The researcher seeks to detect any drawbacks in personnel recruitment and selection and offer up-to-date solutions and recommendations. The researcher appreciates that currently, firms face the problem of high turnover rates irrespective of their size (Nikolaou, 2021). Highly skilled employees are in high demand as firms compete to recruit and retain the best talents in the labor market.

It is rational for HR to appreciate the fact that they can easily lose some of their best employees to their rival firms. They have to find ways of replacing such workers with equally skilled workers within the shortest period possible. The selected firm for the case study was Amazon. The investigation will focus on identifying issues that it faces in personnel recruitment and offer research-based solutions.

Organization’s Background

Amazon, Inc. is an American conglomerate that offers e-commerce services, cloud computing, artificial intelligence, and digital streaming. Founded in 1994 by Jeff Bezos, the company has experienced massive growth to become the world’s largest online marketplace (Derous and De Fruyt, 2016). It started as an online bookstore and currently, it offers a variety of products in the retail industry.

When the COVID-19 pandemic struck, Amazon gained massive revenue growth as many people were limited to online shopping (Nikolaou, 2021). Its ability to process customers’ orders and make deliveries efficiently is unparalleled in the United States. Its operations in the global market have also been relatively successful. The financial reports indicate that in 2020, the firm made $404.4 billion in e-commerce sales and $ 21.3 billion in net income (Nikolaou, 2021). Its performance in the other sectors has also been impressive over the past few years. Despite this impressive performance, the company is facing a unique challenge of high employee turnover.

Amazon Inc., which is an online marketing platform, heavily relies on human resources to manage warehouses, process orders, make deliveries, and communicate with customers regularly. It is estimated that the company currently has 1,468,000 employees, 810,000 of which are in the United States (Abbasi et al ., 2020). The firm has gained a reputation in the market as one that processes and delivers products promptly based on its promise. As such, the company invests a significant amount of its resources in employee recruitment and training. They have to understand how to respond to customers’ needs and expectations in the market. They are also expected to have the capacity to respond to their complaints, especially when products delivered fail to meet what they ordered.

Labor unions in the United States have accused the company of overworking its employees. Others have reported that the workplace environment is not suitable as it fails to observe occupational safety and health standards set by OSHA (Albert, 2019). It explains why a significant number of employees at this firm have left to look for better opportunities at other companies. The nature of business of this firm makes it difficult to address some of the complaints its employees have raised.

Having an employee turnover rate that is as high as Amazon’s 150 percent is undesirable, as Donohue (2019) observes. It takes time to replace such workers and resources to equip them with relevant knowledge that makes them efficient in their actions. The HR at the firm should find a way of addressing issues that workers have raised. It may take some time for the firm to address all the issues that its employees have raised. As such, the company needs an excellent staff recruitment system that will enable it to replace workers lost to other companies.

Problem Statement

The online retail industry has been growing rapidly over the past two decades. According to Balan et al . (2020), for a long time, many people preferred visiting brick-and-mortar stores to make their purchases. They believed it offered them the opportunity to physically assess products that they are purchasing to ascertain their quality. However, various factors have led to the popularity of online platforms.

Donohue (2019) explains that people tend to spend most of their daytime at work, hence they barely have time to visit physical stores. Many buyers have also come to trust online market platforms to deliver exactly what they promise. As the market size continues to grow, new firms are making entry into it. In the United States, Amazon has to compete against eBay, Bestbuy.com, and Etsy among many other small e-commerce platforms. Traditional chain stores that have operated brick-and-mortar stores for decades such as Walmart and Target are also offering online retail services. It means that competition is stiff, causing immense pressure on the firm to deliver on its promise to customers.

The unique problem created by the growing market is that employees can easily move from one company to another. When that happens, the HR at this firm has to make sure that they are replaced immediately. This is so because the services of these workers, especially those doing the sorting and delivery of these products to customers, are essential for the company’s success. The high rate of employee turnover at this firm is an indication that there is a problem in the recruitment process (Maamari and Alameh, 2016). It may be a sign that the firm has failed to select an employee who shares in the firm’s value and mission to customers. As such, they are constantly looking for better opportunities at other firms.

The researcher believes that redefining the recruitment strategy can help in ensuring that those who are hired can stay within the firm for a long period. The investigation will focus on understanding the current issues and challenges in the current recruitment strategy. Addressing these problems may help in ensuring that once recruited, these employees stay longer in the firm.

Importance of the Study to the Company

This report focuses on Amazon’s personnel recruitment and selection practices to detect drawbacks in the process and recommend possible solutions. This report is specifically important to this company because it seeks to address an issue that is currently threatening its sustainability.

As the market leader, Amazon is constantly under the focus of regulators and employee unions (Okolie and Irabor, 2017). Hiring workers who do not understand and embrace values of the firm means that it will continue to have a group of dissatisfied customers constantly complaining and looking for better opportunities.

The report seeks to identify specific weaknesses in the current recruitment strategies used by the firm. It will then propose a solution, based on research, which the company can use to overcome the identified challenges. The company is spending resources to recruit employees every year (Hunter et al ., 2017). It also has to spend more resources to ensure that these employees are trained to equip them with skills relevant to their specific tasks. Losing even a single employee means that the firm is losing money that can be spent on other developmental activities.

When the problem of employee turnover is addressed at the initial stage of recruitment, it means that such losses associated with employee turnover will be eliminated or significantly reduced at this firm. Retaining these skilled and innovative employees will also enhance its competitiveness in the market.

The document is also important to firms in the same or other industries, which are struggling with the problem of high employee turnover. They can use the proposed solutions to address their problem. Scholars who are interested in conducting further studies in this field may find this document useful in providing background information or a method that can be used to collect and process data.

Contribution to Research and Theory

Findings in this report will help address gaps in the current body of knowledge. AI and analytics are emerging as a major tool that firms, both large and small, are using to make critical decisions and to undertake major activities that are time-consuming when human capital is involved. This study will assess how this new tools can be utilized in the recruitment and selection of employees.

Moreover, this study contributes to the firm-level and market-level challenges that can affect selection and recruitment. This knowledge adds to the HR theory on both internal and external factors of influence on recruitment and selection. Information provided in this document will form a basis for further studies among future scholars. They can also find the method and strategies used to collect and process data in this study useful in their own research.

Aim and Objectives

The study aims to, through the research, detect any drawbacks in personnel recruitment and selection and offer up-to-date solutions and recommendations. The following are the specific objectives that the study seeks to achieve by collecting and analyzing both primary and secondary data:

• To understand the impact of new technologies such as AI and data analytics on selection and recruitment;
• To identify the firm-level challenges existing in making effective selection and recruitment;
• To examine the market-level challenges existing in making effective selection and recruitment;
• To examine the key changes required to make the selection and recruitment processes more effective in Amazon.

The researcher will rely on both primary and secondary data to achieve the above aim and objectives.

Scope of the Study

The scope of this study will be limited to the assessment of any drawbacks in personnel recruitment and selection and offer up-to-date solutions and recommendations. The researcher selected Amazon as the primary focus when collecting primary data. The firm was selected because of its large size and high rate of employee turnover. Narrowing the investigation to a specific firm was appropriate in understanding challenges that are related with recruitment and selection of employees. A mixed method research was used to process primary data collected from the participants.

• Applicant/candidate – a potential employee who is still in the process of screening and selection before their confirmation.
• Applicant tracking system (ATS) – a software that helps in managing applications.
• Benefits – incentives and programs, including salaries, offered to employees.
• Recruitment timeline – the period that it takes for the HR department or the recruitment agency to complete the hiring process.
• Sourcing – networking and the use of professional streams to recruit candidates.
• Social recruitment – the strategy of using social media platforms to recruit potential candidates.
• Talent pool – a team of highly-skilled and competent individuals capable of meeting organizational goals.
• Talent acquisition – the process of hiring and retaining a team of skilled and competent employees.

The previous chapter has provided detailed background information about the study. In this chapter, the researcher seeks to review relevant literature on this topic. As Hamza et al . (2021) observe, the field of human resources has attracted the attention of many scholars over the past several decades. In the past, large corporations could easily dictate terms of engagement with employees because of the limited alternative employment opportunities. However, that is no longer the case as skilled and talented workers can easily move from one employer to the other (Brown et al ., 2019).

Firms, irrespective of their size, market coverage, and financial power, have realized that they must have effective HR strategies to ensure that they create a pool of highly skilled and talented employees. Balan et al . (2020) explain that the process starts with developing an effective recruitment strategy.

A firm needs to employ a unique strategy that will ensure that its employees have skills and experience that match job requirements. These employees should also believe in the mission, vision, and values of the firm to avoid high turnover rates. Different companies have developed unique recruitment strategies that they believe meet their expectations. It was necessary to review what other scholars have found out in this field.

The chapter discusses the impact of emerging technologies, such as artificial intelligence and data analytics, on selection and recruitment process. It then focuses on firm-level and market-level challenges in making effective selection and recruitment of candidates. Key changes that firms need to embrace to make the selection and recruitment process effective are also discussed. The chapter then analyzes relevant theories that can help explain and address these challenges.

Impact of New Technologies on Selection and Recruitment

The challenge that companies face when recruiting employees has forced them to turn to emerging technologies. According to James et al . (2019), a survey they conducted revealed that the most challenging activity in talent acquisition is the screening of candidates. When a firm makes a job offer, numerous people will make an application for the job. It is the responsibility of HR to develop a shortlist of the numerous candidates to select those who are most qualified. The process must be done meticulously to ensure that those with the best qualifications are selected for further screening.

Traditionally, the shortlisting would be based on the academic background of the applicant, experience, talents, and sometimes age and gender (Lepistö and Ihantola, 2018). However, the world is evolving and sometimes these factors may not be the best way of selecting uniquely talented individuals.

Hunter et al . (2017) argue that some of the most creative and talented individuals in this century, such as Steve Jobs, Bill Gates, and Mark Zuckerberg dropped out of college. However, their unique ability to lead others and develop new products enabled them to develop some of the most successful companies in the world. If their academic qualifications were to be used as the basis for assessing their skills, they would probably be considered unqualified for such top jobs.

Artificial intelligence is emerging as a tool that can now be used to facilitate the recruitment of employees. Balan et al . (2020) explain that AI for recruitment involves the use of artificial intelligence in the process of acquiring talents. It involves automating the process of screening the candidates to help in selecting those who are most qualified for the job. It eliminates the need for HR officers to go through individual resumes for the candidates to make selections. Using machine learning, the machine ranks candidates based on specific criteria (Maslow, 2019).

First, the job requirement is defined and the expectations of candidates are stated for every job. The machine then assesses each candidate based on traditional factors such as academic background, experience, and age. It also uses unique traits of the applicants such as their creative works, innovativeness, commitment, and the likelihood of leaving the firm after being recruited. Using data analytics, the machine can then rank the applicants based on their scores for each job.

Benefits of AI and Data Analytics in Employee Recruitment

The use of AI and data analytics in selecting employees for a firm presents many benefits. According to Hunter et al . (2017), one of the main benefits of using technology is that it eliminates personal bias in the recruitment process. In many cases, the recruitment panel would eliminate highly qualified candidates for a given job because of personal bias. It may be their gender, age, race, or physical appearance that makes them appear less attractive to the panel. However, they may be highly qualified for the job advertised. The AI eliminates the challenge by specifically focusing on the set criteria without any form of bias. It means that candidates are selected based on their qualification as opposed to their physical look or the class in which they belong based on societal standards.

The use of artificial intelligence and data analytics helps to significantly minimize the time it takes to recruit employees. Lepistö and Ihantola (2018) note that it takes about 23 hours for a single hire, and as long as 42 days to complete the process when a firm intends to replace a few workers. Most of the time is spent screening the candidates to ascertain if they have the qualifications needed. The use of machine learning eliminates the complex process of screening these individuals. Once candidates make online applications for specific jobs, it takes a few minutes for it to screen large volumes of data to determine those who are qualified for the job based on the set criteria.

As companies continue to share relevant data, it becomes easy for the machine to verify the credentials of the candidates such as whether they attended a specific school, if they worked for a specific firm, reasons why they left their previous employments, and if they have any criminal record. Such detailed screening procedures would take weeks or even months if done in the traditional way (Balan et al ., 2020). However, the use of big data means that the process only takes a few minutes. The HR can focus on more important activities within the firm as the bulk of the recruitment activities are done by the machine.

The use of data analytics and AI helps in improving the quality of the recruitment process by standardizing job matching. Hunter et al . (2017) believe that one of the reasons for employee turnover rates in some companies is a mismatch of jobs with the skills of workers. An individual who is highly skilled and interested in marketing would be assigned to a finance department because they also did a unit of finance and accounting. The problem is that such individuals will struggle to work in this department because of limited skills, experience, and interests (Balan et al ., 2020). AI eliminates such challenges by ensuring that workers are assigned tasks based on their qualifications.

The process enhances transparency and improves the confidence of employees in their employer. The process does not involve any form of bias or personal preferences as opposed to the opinionated views of the managers. At this early stage of engaging with employees, the firm will be instilling integrity among its workers (Hunter et al ., 2017). They will understand that their race, religion, gender, age, or any other democratic factor does not define their performance in the firm. They will learn to be committed to the vision and values of the firm in the market.

Challenges Existing in Making Effective Selection and Recruitment

Making effective selection and recruitment of employees presents numerous challenges to HR officers. One of the major challenges that HR faces in the recruitment and selection process is the falsification of credentials among applicants, as Balan et al . (2020) observe. Many people tend to present fake academic credentials or use the documents of their friends and relatives to secure an employment opportunity.

In the traditional system where data is saved in physical files, it is almost impossible to authenticate some of these documents. HR officers are forced to make calls to institutions of higher learning to verify the credentials presented by their applicants. Such verification processes are not only tedious and time-consuming but also inefficient. In many cases, firms are forced to trust the papers that candidates present.

Employers are always keen on determining why a given employee is moving from one firm to another. As Lepistö and Ihantola (2018) state, companies often want to avoid individuals who are dismissed from their previous employment because of factors such as fraud, underperformance, negative attitude, physical or verbal abuse, racism, and other related factors.

It is a common practice for HR to contact the previous employer to seek further information about the employee. The problem is that it is always not guaranteed that the former employer will provide accurate information. Some of them may lie to help get rid of their underperforming employee while others may provide misleading facts because of the desire to punish a hardworking employee who decides to seek alternative employment.

There is the problem of losing talented applicants to competitors. There are cases where some candidates make job applications to two or more companies. When they are invited for the interview, they would not reveal that they had already made similar applications to other institutions. They will wait for the job offer to be made by each of these employers.

When the other company offers better remunerations and terms of service, they will quit their job immediately (Balan et al ., 2020). In such cases, HR will have to initiate another process of recruitment to replace such workers. Such challenges not only waste time for the firm but also resources as the tedious process of advertising, screening, and interviewing has to be repeated.

Attracting talents is another major challenge, which some firms face when trying to attract the right candidates for a specific job. The problem affects both large well-established firms and small companies. For small companies with invisible brands, they rarely attract some of the top talents in the job market. They lack the financial muscle to pay their employees’ high salaries (Abbasi, et al ., 2020). On the other hand, some of the large corporations have failed to attract top talents because of their perceived unwillingness or inability to offer conducive workplace environment.

For instance, Amazon has been accused of forcing their employees to spend a lot of time standing or walking in the warehouses, as they sort out and prepare customers’ orders (Lepistö and Ihantola, 2018). Such negative publicity makes top talents in the industry uncomfortable to work for the firm. They have the perception that the firm is exploitative and unwilling to create a favorable workplace environment for its employees.

Key Changes Required to Make the Selection and Recruitment Processes Effective

Companies around the world are keen on redefining their selection and recruitment processes to ensure that they can address some of the challenges discussed above. According to Balan et al . (2020), one of the best ways of addressing these challenges is the introduction of emerging technologies in the recruitment process. Companies should change from the traditional time-consuming and expensive process of hiring employees to a new one that is based on emerging technologies. They need to use big data to ensure that they not only match the skills of employees with job requirements easily but also lower the cost and time it takes to replace a worker.

Data sharing is another change that firms may need to consider as they try to improve the recruitment process. Many firms are always keen on avoiding any attempts to share data with their rivals. However, they can no longer ignore the need to share data as a way of enhancing their cooperation in the market. Once a firm has a shared database with other firms and institutions of higher learning, it becomes easy to verify the credentials of their candidates. The HR can easily understand the performance of a given candidate in their previous companies and possible reasons why they opted to move from their former employer.

Theoretical Framework

Personnel recruitment and selection practices that firms embrace define their ability to develop a pool of competent and loyal employees. Some of the challenges that Amazon is currently going through may be attributed to the strategy that the firm uses to recruit its workers. Using theoretical models, it is possible to assess these problems and to propose solutions that can be used to achieve desired goals.

Theory X and Y

McGregor’s theories X and Y have widely been used by human resource managers to help in defining the right approach to supervising and instructing employees. Theory X assumes that people tend to be lazy, and as such, require constant supervision to ensure that they complete their assignments. It limits the ability of workers to operate unsupervised and to make independent decisions. They have to follow guidelines provided by the management (Abbasi, et al ., 2020).

When recruiting workers, a firm should understand the need to assess the perception and views of workers towards this approach to human resource management. A candidate who states that they work under minimal supervision and can deliver their work on time may not perform well in an environment where theory X is used to govern subordinates. When this management approach is common in a firm, then a firm should recruit semi-skilled workers as much as possible, including immigrants, who can withstand the constant supervision.

Theory Y takes an opposite approach to human resource management to that of theory X. It holds the view that when provided with the right environment and support, people tend to be self-motivated and can undertake their duties with minimal supervision if any (Lepistö and Ihantola, 2018). This governance approach is highly effective when handling highly-skilled workers who know what they should to do to enhance the success of their departments and the organization.

When recruiting, such individuals will be identified by their academic credentials, their experience, and performance in other firms. In many cases, they would explain that their main reason for switching from their previous employer to the new one is lack of space to explore their skills and repressive governance that limits creativity and independent thoughts (Abbasi, et al ., 2020). The new firm should be willing and ready to provide them with an environment where they can be innovative in their assignments.

Hierarchy of Needs

Maslow’s hierarchy of needs is a model that has been used to define human behavior. It identifies five basic classes of needs that often motivate people. This model can be specifically useful when a firm is recruiting its employees. At the bottom of the pyramid, shown in figure 2.1 below, are physiological needs of food, water, warmth and rest (Balan et al ., 2020). Those who are motivated by these factors only need means of survival. In the United States, such individuals are recent immigrants from developing economies who come to the country to find any form of employment. They are less demanding and often accept minimum wage as long as it meets their basic needs. Many firms are often tempted to hire such workers because they are less demanding.

Above it are safety needs, where an individual is concerned about their security and safety at work. The assumption is that such individuals have met their primary needs of survival and now need some form of protection (Hunter et al ., 2017). A significant number of such individuals are immigrants and unskilled or semi-skilled American citizens (Abbasi, et al ., 2020). Although they may not be demanding, they want a workplace environment where their safety and security are guaranteed. When these needs are met, they are less likely to consider moving to other companies once recruited.

The third tier has belongingness and love needs, often defined as psychological needs. Such individuals have already met their basic needs and are now concerned about creating useful relationships at work. They include skilled American workers and immigrants coming from different parts of the country (Aimee, 2018). When hiring such individuals, the management should ensure that issues such as racism, bigotry, and any form of discrimination against a section of employees are fully addressed. When the environment does not support such psychological needs, these employees are likely to consider leaving the firm soon after the recruitment.

The next tear, which is still addressing psychological needs, focuses on esteem needs. Such individuals value the feeling of accomplishment and prestige (Balan et al ., 2020). In most cases, they are highly learned and skilled workers holding managerial positions. They come to the company to make a difference and they value being in an environment where they can make decisions. When conducting a recruitment process, such individuals will be known based on their current positions with their past employers, and possibly, reasons why they are leaving their employer for this firm (Waxin et al., 2018). Before hiring such a candidate, the management must be ready to offer them an environment where their views can be heard and respected. They should also be granted the opportunity to be creative in their assigned tasks.

At the apex of the pyramid are the self-fulfillment needs, often referred to as self-actualization. At this level, one is interested in achieving their full potential, which includes creative activities (Nikolaou, 2021). The scholar explains that the number of those who reach this level is significantly few. However, they tend to be highly creative workers interested in transforming their organizations. They hold senior managerial positions in their past and present firms and believe that they can continue creating positive changes to make the world a better place (Hunter et al ., 2017). They tend to be highly demanding individuals who can only accept specific standards in their terms of employment.

Research Gap

The review of the literature reveals that many scholars have explored challenges related to recruitment and selection of workers in organizations of different sizes. However, it was evident that information about the use of technology, especially the AI and data analytics, in employee recruitment and selection is limited. The field is relatively new and scholars are yet to provide detailed evidence-based reports about it. The researcher seeks to address this major knowledge gap to help firms and HR professionals to understand how to use this technology to improve the process of screening candidates,

Methods, Justification and Explanation

The previous chapter provided a review of the literature to understand what other scholars have found out in related studies. It helped in identifying the research gaps and emerging trends that require further investigation. In this chapter, the goal is to discuss the method that was used to collect and process data. As Tan (2018) observes, once a review of literature is complete, a researcher will have a clear picture of what needs to be investigated. Primary data collection and analysis helps in addressing the identified knowledge gaps based on the experience and knowledge of a sample of the entire population.

This chapter discusses how the researcher gained access to the organization, the sampling method that was used, and the primary data collection instrument that facilitated the process of collecting data. Also discussed in this chapter is the data collection administration (how primary data was collected using the instrument), data analysis approach, and ethical issues that had to be observed when conducting the study.

The researcher had to gain access to the organization and get the relevant consent from the management before reaching out to the employees. Breakwell, Wright and Barnett (2020) reiterate the importance of contacting the management of the targeted organization and explaining the significance of the study and the reasons why it was selected to take part in it. The researcher contacted the management of Amazon at their local branch explaining the focus of the study and reasons why employees of the firm were needed to take part in it. Doing so was important to eliminate any suspicion or concerns that the firm may have when it realizes that its employees are taking part in a study. Employees of the firm were only contacted when the consent from the management was obtained.

Sampling Method and Sample Size

Amazon currently has 1,468,000 employees spread across the world, 810,000 of which are in the United States, as was noted in the background information. The time and resources available for this study cannot allow the study to engage the entire population of this firm’s workers. For that reason, it was necessary to identify a small sample size. A local fulfillment center was identified to facilitate the data collection process. Upon getting consent from the management, the researcher had to select the sample. Convenience (non-probability) sampling technique was considered appropriate for the study. The sampling method was used because of the need to include specific individuals in the study.

The sample had to include those in the top management, mid-level management, supervisory roles, and non-management employees. It was also important to ensure that there was an effective representation in the sample in terms of gender, age, race, and religious affiliation. As discussed in the literature review, these demographical factors sometimes influence the recruitment of workers, especially when the recruiters are biased.

A sample size of 50 individuals was considered sufficient for the study. 12 HR staff were involved in a face-to-face interview, specifically to collect qualitative data. The other 38 participants participated in the study through an online survey. They represented the entire population of almost 1.5 million employees of this company, especially those in the United States. The inclusion criteria were that all the participants must be working in the HR department of the firm, most preferably the recruitment and selection unit.

Primary Data Collection Instrument Used

The researcher used a questionnaire to facilitate the collection of primary data from the sampled employees of Amazon. The document had three main sections. Section A focused on general information about the participants focusing on the demographics and experience that they have. It was meant to determine the authority of the respondents to provide credible information needed for the study. Section B of the document had two parts, focusing on various issues. The first part was interested in capturing the recruitment process at the company.

The second part was concerned with the process of selection that the firm has been using. Section C of the document was specifically interested in determining challenges in the recruitment and selection process. There was also a list of interview questions that were meant for HR. The researcher used both structured (closed-ended) and unstructured (open-ended) questions to collect data. A mix of both structured and unstructured questions was important to facilitate both qualitative and quantitative analysis of data.

Data Collection Administration

When consent was given and the instrument was ready, the next step was to collect data from the sampled population. Primary data was collected from the participants using two strategies. The first approach involved collecting data through a face-to-face interview. The researcher called the 12 participants, requested them to take part in the interview within the premises of their company.

The researcher visited these employees at their preferred time, especially during lunch break or in the evening just before they leave the premises of the firm. Each interview lasted 10-15 minutes as had been requested by the participants because of their busy schedules. 2 or 3 participants were interviewed each day, which means that the process took about a week to complete.

All the 50 participants were given a link which directs them to the questionnaire. They were requested to go through each of the questions and respond accordingly based on their knowledge and experiences. They were given one week to read and answer all the questions. Filled questionnaires from the online survey and those from the face-to-face interviews were collected, ready for coding and analysis.

Data Analysis Approach

The final stage in this process was the analysis of data collected from the participants. The method that one chooses to analyze primary data, as Thanem and Knights (2019) observe, often depends on the aim and objectives of the study. The style should enable the researcher to effectively achieve the aim and respond to all the research questions.

The investigation focused on understanding the current issues and challenges in the current recruitment strategy. To achieve this aim, the researcher considered it appropriate to conduct both qualitative and qualitative analysis of data. Quantitative analysis made it possible to use mathematical methods to understand the severity of the problem. It allowed the researcher to present findings in charts and graphs that make it possible for the reader to understand the severity of the problem.

Quantitative analysis of data was conducted using an Excel spreadsheet. Detailed explanations of each output were presented to make it clear for the readers to understand facts based on primary data. Qualitative data was also considered essential in this study. Respondents were asked to explain in detail some of the challenges that they go through when recruiting and selecting employees. Using open-ended questions, they were allowed to go into details in their explanations. Information obtained from these unstructured questions was analyzed thematically. The mixed-method analysis made it possible to have a thorough analysis of the problem, making it possible to present credible solutions to the firm.

Ethical Considerations

When conducting research, one should take into consideration ethical issues that may affect the credibility and success of the study. In this project, the researcher had to observe various ethical requirements at various stages of the work.

According to Hennink, Hutter and Bailey (2020), when collecting data from employees of a given institution, it is required that consent be obtained from the management before approaching the employees. It helps in addressing any concern that the firm may have when it realizes that its workers are taking part in a given study. In this case, consent had to be obtained from Amazon. As explained in chapter 1, this company is large and with a global presence. Reaching out directly to the top managers of the firm at its headquarters in Seattle was not possible. As such, consent had to be obtained through their regional branches.

The manager was contacted and requested to allow their employees to be part of the investigation. They were explained the primary goal of the study, the reasons why Amazon was selected, the role that the employees were to play, and an assurance that sensitive information about the firm will not be made public. The researcher had to get consent from the manager before contacting the employees of the firm.

When the consent was obtained, the next task was to engage specific employees of the firm. Peters and Fontain (2020) emphasize the need to ensure that identity of these participants is protected. Cases, where people are harassed or discriminated against because their views are different from that of the majority of those in power, are always common. It explains why it is essential to ensure that their identity remains anonymous. In this study, the researcher used codes instead of the actual names of the respondents. They were assigned numerical identities (Respondent 1, Respondent 2, Respondent 3 …) as a way of ensuring that no one can determine their identity.

The researcher explained to each respondent the aim of the study and the specific role that they needed to play in it. All their questions and concerns were addressed and only those who stated that they are comfortable enough to take part in the study were included. The researcher reminded them that participation was voluntary and that they were at liberty to withdraw from it when they felt it was necessary to do so. They were also reminded that consent had been obtained from the relevant authority in the institution. The researcher allowed them to choose whether they were willing to take part in a face-to-face interview or an online one, taking into consideration their tight schedule and the fear of the spread of the COVID-19 virus which is still common in the country.

The project had to observe school rules and regulations from the initial stage of proposal development to the final stage of delivering the completed paper. The school has clear regulations against academic malpractices, one of which is plagiarism. The researcher made sure that the paper was written from scratch, and that information that was obtained from secondary sources was properly cited using the Harvard referencing style. A list of all the sources used to inform the study was provided on a separate page as required by that style. The researcher maintained adequate communication with the adviser whenever necessary to address issues that arose in the project. The paper was completed within the timeframe that the school had set via the portal that was provided.

The previous chapter explained the method that was used to collect and analyze primary data that was collected from a sample of participants. In this chapter, the focus is to present findings, made from the analysis, and a discussion that integrates information from primary data and the review of the literature. The goal is to, through the research, detect any drawbacks in personnel recruitment and selection and offer up-to-date solutions and recommendations. The organization of focus was Amazon, the world’s largest online retail shop. To achieve the primary aim of the study, the researcher had to address every objective that had been set in the first chapter of the paper-based on primary data collected.

Recruitment Strategy at Amazon

In this study, one of the primary issues of interest was to investigate the recruitment strategy that is used at the company. As Hamza et al . (2021) suggest, when trying to identify a recruitment challenge at a firm, it is advisable to start by assessing the strategies used by the company. Table 4.1 below provides the demographical data of the participants in the study.

When collecting data from the participants, the following was one of the questions presented to the participants:

How do you recruit employees?

The participants were asked to state the most common recruitment method in the firm based on two options provided, which were internal or external recruitment. Data obtained from the participants were fed into the Excel spreadsheet and figure 4.1 below shows the outcome. 70% of the participants (35 out of 50) relies on external recruitment. Only 30% uses internal recruitment. The data shows that Amazon uses both internal and external recruitment strategies. However, external recruitment is almost twice as common as internal recruitment. It means that the employee turnover rate at the firm is significantly high.

As some of the current employees leave to work in other companies, the HR has to find a way of replacing them. The process of replacing such employees requires external recruitment. Respondent 4 also noted that “internal recruitment is used at Amazon to fill some vacancies, especially senior positions.” This strategy allows current employees of a firm to get promoted based on their skills, experience, performance, and commitment to the firm.

The outcome of the analysis shows that external recruitment is more than twice the internal one. Maamari and Alameh (2016) note that although it is normal for external recruitment methods to be more common than internal strategies, the disparity should not be significantly large.

A firm that has an effective employee retention strategy will regularly use an internal recruitment strategy to promote current employees and an external recruitment strategy to replace those who have been promoted or those retiring from the firm. The current state at Amazon shows that those who are leaving the firm before their retirement is a significant number, and they have to be replaced through external recruitment.

Market-Level Challenges Existing in Making Effective Selection and Recruitment

The respondents noted that Amazon faces some level of challenge when it comes to the selection and recruitment of employees. It explains why it is often forced to go back to the labor market to replace those who have resigned from their jobs. It was necessary to classify these challenges as internal or industry-specific. In this section, the focus was to identify and discuss industry-specific challenges that affect almost every firm and are not unique to Amazon. Table 4.2 below identifies the most common challenges within the retailing industry.

Table 4.2: Market-Specific Challenges in Recruitment and Selection of Employees

The information on the table above was plotted in the Excel spreadsheet for statistical analysis. Findings from the analysis were presented in the chart shown in figure 4.2 below.

The figure and table above identify major challenges in the recruitment and selection of employees in the retail market, especially among the firms in the online retailing sector. The industry faces numerous challenges. The data analyzed above show that the biggest challenge that firms in this industry face is competitors’ terms of employment. The data shows that 80% of the respondents either strongly agree (38%) or agree (42%) with the statement.

Competitors in this industry are using every means possible to attract and retain highly-skilled employees. Only 16% of the respondents had a contrary opinion, with 14% disagreeing and 2% strongly disagreeing. 4% of the respondents stated that they were unsure about the truthfulness of the statement. It means that within the industry, there is a struggle among companies to attract and retain talented employees. Some firms are setting attractive terms for top talents, which creates a form of competition among firms.

The analysis identified the company’s image as the next major challenge that firms face during the recruitment and selection process. The data above shows that 76% of the respondents- 36% strongly agree and 40% agree- believe that this is a major issue to the company. These respondents explained that once a company image is tainted in the country, it becomes highly challenging to attract top talents in the local job market.

According to Respondent 2, “the complaints about long working hours and poor conditions at Amazon’s fulfillment centers have created a negative image of the firm in the job market.” Potential employees feel that they may suffer when hired by the firm. It is not easy to fight a negative image of a firm once it is created, as Donohue (2019) observes. 16% of the participants felt that this is not a major problem, while the other 8% stated that they are not sure about the issue. Statistically, the overwhelming majority of the respondents feel the image of the company can be a major impediment to the successful recruitment and selection of talented workers.

Presenting fake documents and false information by candidates has remained a major challenge for decades. Respondent 16 noted that “at Amazon, the problem is common and it affects the ability to recruit employees capable of meeting the expectations of their employer.” 34% of the respondents strongly agree with the statement, while the other 26% agree. It means that a significant majority of the respondents (60%) believe that fake documentation and false information are major issues that the firm faces in its recruitment and selection process. 30% of the respondents had a contrary opinion, with 20% disagreeing and another 10% strongly disagreeing. 14% of the participants neither agreed nor disagreed with the statement.

Government policies and regulations were identified as another concern during the recruitment and selection. 60% of those who were interviewed believe that this issue is a major challenge. These regulations include the minimum wage and workplace environment. Another emerging issue is related to the level of fines firms are charged when it is proven that an employee has been discriminated against in any way.

These regulatory policies change regularly, which means that it is the responsibility of the employer to keep updating its employment policies in line with the changes introduced by the government. 24% of those who participated in the study felt that it was not a major concern for the firm. Weak educational backgrounds for candidates, high fees for e-recruitment, and the unemployment rate were identified as minor issues in the recruitment and selection of employees at this company.

Firm-Level Challenges Existing in Making Effective Selection and Recruitment

After identifying industry-specific challenges to successful recruitment and selection, it was necessary to identify those that are company-specific. Some firms are better when it comes to hiring top talents than others because of various factors. Donohue (2019) advises that the best way of overcoming these challenges is to start by identifying them so that an effective solution can be developed. Table 4.3 below identifies some of the major internal factors that may affect employee recruitment and selection.

Table 4.3: Firm-Level Challenges

The analysis revealed that Amazon has made an effort to address most of the internal weaknesses that may affect its ability to recruit highly-skilled employees. However, there are still some issues, which need to be addressed as the company seeks to attract and retain a pool of talented workers. The most concerning issue that they identified was the budget limitation.

60% of the respondents identified it as a major issue, with 24% strongly agreeing and 36% agreeing that budget limitation sometimes makes it difficult to recruit top talents for the firm. HR is expected to use a specific amount of money to recruit and select employees. Some challenges may emerge, making it necessary to exceed the initial target. The need to stay within budget provisions sometimes affects the process. 36% of the participants had a contrary opinion over the issue while 4% noted that they were not sure.

Recruitment and selection policies at Amazon were identified as another factor that sometimes hinders the firm’s ability to recruit talents. Diversity and inclusion has become a worldwide policy in most of the large multinational firms. This made firms such as Amazon develop new regulations that strictly define what people should be hired at the firm.

Some of these policies make the hiring process long and complex as HR has to go beyond traditional screening of one’s academic credentials and experience to their nationality and status of residence. 58% of those who took part in the study noted that this is a concern that delays the recruitment and selection process. 40% had a contrary opinion, noting that these policies are not a major concern. 2% of the respondents noted that they were not sure about the effect of these policies on a firm’s recruitment and selection process.

Company culture is often another major issue when hiring employees. At Amazon, it was identified as another significant issue in the recruitment and selection of employees. For a long time, the company has embraced a culture of employing relatively young employees at the fulfillment centers and as drivers because of the belief that they are agile and energetic enough to undertake physically demanding tasks. The trend has led to cases where equally-capable employees of advanced ages are ignored.

54% of the respondents noted that this is an issue that the firm needs to address as it limits its ability to recruit workers based on their skills and capabilities. 6% of the participants noted that they are not sure about the effects of company culture while the remaining 40% noted that they do not think company culture at Amazon has a major impact on its recruitment and selection strategies.

Effective HR planning was another factor that was identified to have an impact on the recruitment and selection process. According to Okolie and Irabor (2017), some firms tend to lack an effective plan for recruiting and selecting employees. They use reactionary strategies, struggling to replace workers who have been lost from the company. However, it is necessary to note that 56% of the respondents felt that the company has been able to overcome this challenge. They explained that the firm has been keen to have effective plans that facilitate timely recruitment and selection of employees when necessary. However, 32% of those interviewed felt that the company is yet to fully address the problem. They believe that effective recruitment and selection of employees at this firm are affected by ineffective HR planning.

Professional training and development of HR staff is a minor issue at the firm, but it still needs the attention of the management. Amazon has a large pool of employees responsible for various activities.

As such, it should ensure that its HR staff is effectively trained to not only manage talents but also facilitate their recruitment. They need to screen and identify candidates that have the best capacity to undertake specific responsibilities. Doing so requires some level of professional training and development among the HR staff. 36% of those who took part in this study identified this as a problem that the company still needs to give adequate attention to. 44% of them believe that this is no longer an issue at the firm because of the measures that the management has introduced.

Compensation and benefits package has a major impact on the recruitment and selection of employees. In the interview that was conducted and online research that followed, it was established that Amazon is one of the best-paying companies in the industry. However, that was not the case before as the firm struggled to achieve growth. Over a decade ago, it was considered a low-paying company because of the number of hours workers had to spend at its fulfillment centers undertaking physically-demanding jobs.

Although the workplace environment at the company and the pay have improved significantly, there is still the perception that the remuneration at the firm does not reflect the effort that employees put in, especially among the elderly and more experienced workers (Okolie and Irabor, 2017). 36% of the respondents noted that it is still an issue that can easily be addressed through awareness campaigns.

The analysis shows that 30% of the participants felt that this is no longer a concern as the firm is currently one of the best-paying companies in the industry. 34% of those interviewed noted that they are not sure if remuneration is still a major concern at the firm. Lack of HR staff was identified as another issue that requires the attention of the firm’s management. Figure 4.2 below summarizes the findings made when conducting the statistical analysis.

The Impact of New Technologies, Such as AI and Data Analytics, on Selection and Recruitment

The use of emerging technologies is simplifying the selection and recruitment process in both large and small corporations. Data analytics and artificial intelligence have specifically made it easy for firms to screen candidates’ credentials and to select the most qualified candidates. It eliminates human bias and significantly reduces the time that it takes to process the data of the applicants.

Respondent 1 noted that “Amazon is one of the companies that have perfected the use of big data to make important decisions in the market.” It has embraced artificial intelligence as a tool that can help make critical decisions purely based on facts and data instead of opinions and perceptions of individuals. In this study, it was necessary to determine how this company has been using these emerging technologies to improve recruitment and selection processes. The researcher started by asking the respondents to state whether they are aware that the company is using AI in the recruitment of the employees.

Do you use artificial intelligence in the recruitment process?

Technology promises to solve most of the recruitment challenges that Amazon faces. It was necessary to determine if the company is already using the technology to recruit and select candidates in the hiring process. As shown in figure 4.3 below, there was a near-unanimous view (96%) among the respondents that the company is using data analytics to process data from applicants. They explained that it is common for the firm to process employees’ data using emerging technologies to help in the ranking process.

The HR staff who participated in the study explained that Amazon receives thousands of job applications every year. Most of these applications are currently done online through a system that the firm developed. Sorting the information using human resources can take several months. As such, the company uses data analytics to determine the academic qualifications of the applicants, the job they are applying for, and their suitability. It makes it easy to select the most qualified candidates to go through the final stages of screening before they are selected to be part of the human resource team at the firm.

The information obtained from the participants about the use of AI was contrary to the expectations of the researcher. Amazon is one of the firms that have advanced the technology of AI when making decisions. Alexa is Amazon’s AI tool that is meant to assist its users to perform specific tasks. As such, it would be expected that this software would be widely used in the recruitment and selection of employees. However, the data in figure 4.3 above shows that Amazon is slow when it comes to the use of AI to recruit employees. 76% of those who participated in the study, especially those who are in managerial positions of the HR department, noted that the company has not replaced humans with AI when making final decisions on employee selection.

Further research revealed that when Amazon tested the use of AI in making employee recruitment decisions, it emerged that the tool was biased against women, often preferring male candidates. As such, its application was suspended to help in addressing such fundamental issues. Only 24% of the participants noted that the firm is using this technology to recruit and select employees even if it is not being public about the same. Based on the information above, it is evident that although this company is yet to fully embrace automation of the recruitment process, it has made significant progress towards the use of technology in hiring employees. Based on the findings, the researcher concludes that emerging technologies holds key to this firm’s success in the selection and recruitment process.

Key Changes Required to Make the Selection and Recruitment Processes More Effective in Amazon

The individuals involved in this study have encountered these challenges and are in the best position to propose effective ways of addressing them. Amazon is currently one of the largest employers in the United States and its success significantly depends on its ability to recruit highly skilled employees. Table 4.4 identifies some of the most effective ways through which it can address the challenges. Asking for employee referrals was identified as one of the most effective strategies, with 98% of the participants saying it is effective. Only 2% of the respondents doubted its effectiveness, holding that the current strategies are sustainable. The majority who supported this strategy argued that it helps in identifying highly qualified candidates at the least cost possible and within a short period.

Investing in recruitment and selection technologies was identified as another major action that the firm can take to address the challenges discussed above. The outcome of the analysis shows that 96% of the participants support the strategy (46% strongly agree and 50% agree). They believe that emerging technologies can help this company to simplify the process and eliminate human errors.

There was also overwhelming support for changing the benefits package to attract top talents in the industry, with 80% of the participants supporting the idea. They explained that candidates tend to be motivated when they know that they will be offered attractive remuneration packages. The 20% of the participants who rejected this strategy argue that Amazon is already one of the best-paying employers in the industry, which should be adequate motivation for applicants.

Conducting professional training for all job interviewers is another way of improving the recruitment and selection process at the firm. 80% of those who took part in the study believe that it can help in eliminating some of the problems identified. It helps in empowering the interviewers so that they can understand what to look for in candidates for specific jobs. It can also help them overcome the challenge of bias and discrimination. Surveying candidates about the hiring process was also supported by a majority of the participants, at 60%. They argued that the process helps in identifying weaknesses in the current system so that the firm can improve as may be necessary.

The analysis shows that 28% were in disapproval of the strategy while 12% remain non-committal about the effectiveness of the strategy. 66% of the respondents supported the need to improve candidates’ assessment procedure at the firm as a way of enhancing the process and another 48% encouraged the need to consider additional candidate sources. Despite the effort that the company has made, the researcher concludes, based on the data analysis, which Amazon still needs to take into consideration various recommendations to enable it to achieve success in the recruitment and selection process.

Amazon is currently the dominant player in the online retail market. Its ability to retain its current prestigious position depends on the strategies it uses to attract and retain talented employees. As such in the previous chapters, the online retail industry is highly competitive and firms are under pressure to remain creative when meeting customers’ needs.

The analysis conducted above has identified possible ways through which this company can attract and retain a pool of loyal customers. Findings of the investigation show that the firm not only needs to invest in emerging technologies but empowering its HR officers as well. The company should also protect its image to help attract top talents in the industry.

Theoretical implications

Findings made in this study have made major contributions to the field of human resource management. Findings strongly suggests that the use of technology can no longer be ignored in the recruitment and selection process. It has highlighted an emerging trend where humans are being replaced by machines to recruit and select most qualified candidates for specific jobs. The study has also explained the practicality of various theoretical concepts when making decision. It shows that Maslow’s hierarchy of needs can be used when making a decision of who to employ. It has also explained how HR can use McGregor’s theory X and Y to manage workers depending on the environmental forces that it faces.

Recommendations

The analysis conducted above shows that Amazon faces a major problem when recruiting and selecting candidates to be employed. These challenges should be addressed to ensure that the firm is able to hire some of the best talents in the industry despite the existence of industry-level challenges. The following are the recommendations based on the analysis conducted above based on the priority set by the participants:

• Asking for employee referrals when recruiting new workers to help reduce the cost and time;
• Investing in recruitment and selection technology to help in automating the process and eliminating human bias;
• Changing the benefits package to help attract some of the top talents in the job market within the online retail industry;
• Conducting a professional training for all job interviewers at the firm;
• Improving the firm’s candidate assessment procedures by eliminating unnecessary bureaucracies;
• Surveying candidates about the hiring process to get their opinion about areas of weakness in the system that the firm should address;
• Considering additional candidate sources beyond the conventional sources that it has been using.

Practical Implications: Implementation of the Strategies

The company will need to implement most of these strategies to enable it to address the identified challenges. In this study, the researcher proposes that the HR department of this firm could consider implementing the strategies, because they have received massive support from the participants. Asking for employees’ referrals when recruiting new ones is one of the easiest strategies that the firm can use. To implement this strategy, HR will need to introduce a new policy where job vacancies are advertised only internally.

Employees will be informed that they are at liberty to propose friends working in other companies who strictly meet the set criteria. For instance, they should be holding the same or a higher position in another company. Once they are recommended, the firm will take them through the standard screening process. This strategy does not require any significant financial investment. People will be motivated to help their friends work at this company.

It will only take regular communication from HR to the employees about the vacancy and expectations. When a talented employee is recruited through this strategy, HR can offer the proposer recognition as a way of appreciating them.

Investing in recruitment and selection technology is another recommendation that the company should consider. When implementing this strategy, the company will need to invest in data analytic technologies. The HR department will need to purchase the relevant hardware and software that can help facilitate the screening and processing of the applications. At the fulfillment center where this data was collected, it may cost the company about $250,000 to purchase the hardware and software, install the system, train HR staff on its usage, and ensure that it is operational.

When funds are available, purchasing and installing the system can take one month. However, the training of employees may take a longer period. Within the first month, they will have the basics needed to operate the new system. However, they will need an additional three months to fully understand the new system. Regular training will also be needed whenever new concepts about the system emerge.

Changing the benefits package to help attract some of the top talents in the job market within the online retail industry is another recommendation that the HR at Amazon should not ignore. As discussed in chapters 2 and 4, Amazon is currently among the top ten best-paying companies in the United States. However, it is not holding the top position, which means that it is not attracting the topmost talents.

The HR should negotiate with the finance department and the company’s board of directors to find ways in which the remunerations can be adjusted upwards. This strategy may be costly as it will require the firm to have an upward adjustment of the salaries of all its current employees. It may also take 6-12 months to implement because of the consultation and approvals needed before it can be approved. As such, it should be classified as a long-term project that the firm should consider only when it is ascertained that the company is losing talented employees to rivals and is unable to replace them.

Conducting a professional training for all job interviewers at the firm was another recommendation that was made based on the analysis of data conducted. It is one of the oldest companies in the industry and as such, it understands the expectations of the market. It has created a team of skilled HR specialists to enable it to recruit and retain some of the best talents. However, that does not mean the team does not need any form of improvement. Respondents felt that this department can improve its activities if the staff at HR are empowered. They need to learn more about emerging trends and practices in the industry. As was discussed in chapter 2, talented employees are empowered, so they can easily move from one company to another. HR needs to develop unique ways of retaining these skilled workers long enough at the firm.

Some of the participants felt that the recruitment process is affected by bureaucratic policies that need to be eliminated. As such, they suggested improving candidate assessment procedures by eliminating unnecessary bureaucracies. This goal can be achieved by reviewing the current recruitment procedures. To implement this concept, HR will need to engage its employees, especially those that recently went through the recruitment system, to get their views about procedures they believe are redundant and unnecessary. The HR can then evaluate the relevance of such procedures and find a way of either improving or eliminating them. The process should take about 3 months to complete and it should be done as part of normal functions of HR, so it will not require a significant amount of funding.

Limitations of the Report

When conducting research, it is common to face challenges that may affect the outcome of the study. Balan et al . (2020) advise that it is important to ensure that such challenges are identified and managed to protect the credibility of the study. In this study, the researcher identified challenges that had to be addressed. One of the major challenges was the reluctance of the sampled participants to engage in face-to-face interviews. Although the country has vaccinated a significant proportion of its population and the pandemic is largely under control in the country.

To overcome this challenge, the researcher selected a few participants (12 individuals) who were willing to participate in the face-to-face interview. The rest of the respondents were allowed to participate in an online interview. The study was narrowed down to a single company, which was Amazon. Other firms likely face unique challenges not similar to those of Amazon. As such, future work should focus on other companies in the same or different industries. New challenges and different solutions can be identified through such further studies.

Overall Summary

The study above shows that Amazon success depends on its ability to recruit and retain a team of highly talented and committed employees. The traditional recruitment and selection strategies have major weaknesses. As such, the firm need to embrace the emerging technologies to help address the identified challenges. The use of AI and data analytics has been proven to be an effective way of solving the challenges that were identified in the study. The firm also need to train its HR officers to understand how to use the emerging technologies in the recruitment and selection of employees.

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Airlines grounded flights. Operators of 911 lines could not respond to emergencies. Hospitals canceled surgeries. Retailers closed for the day. And the actions all traced back to a batch of bad computer code.

A flawed software update sent out by a little-known cybersecurity company caused chaos and disruption around the world on Friday. The company, CrowdStrike , based in Austin, Texas, makes software used by multinational corporations, government agencies and scores of other organizations to protect against hackers and online intruders.

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The fallout, which was immediate and inescapable, highlighted the brittleness of global technology infrastructure. The world has become reliant on Microsoft and a handful of cybersecurity firms like CrowdStrike. So when a single flawed piece of software is released over the internet, it can almost instantly damage countless companies and organizations that depend on the technology as part of everyday business.

“This is a very, very uncomfortable illustration of the fragility of the world’s core internet infrastructure,” said Ciaran Martin, the former chief executive of Britain’s National Cyber Security Center and a professor at the Blavatnik School of Government at Oxford University.

A cyberattack did not cause the widespread outage, but the effects on Friday showed how devastating the damage can be when a main artery of the global technology system is disrupted. It raised broader questions about CrowdStrike’s testing processes and what repercussions such software firms should face when flaws in their code cause major disruptions.

How a Software Update Crashed Computers Around the World

Here’s a visual explanation for how a faulty software update crippled machines.

How the airline cancellations rippled around the world (and across time zones)

Share of canceled flights at 25 airports on Friday

50% of flights

Ai r po r t

Bengalu r u K empeg o wda

Dhaka Shahjalal

Minneapolis-Saint P aul

Stuttga r t

Melbou r ne

Be r lin B r anden b urg

London City

Amsterdam Schiphol

Chicago O'Hare

Raleigh−Durham

B r adl e y

Cha r lotte

Reagan National

Philadelphia

1:20 a.m. ET

CrowdStrike’s stock price so far this year

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