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Harnessing Algorithms for Decision-Making- Trusting Data or Intuition in Promoting Candidates

Harnessing Algorithms for Decision-Making- Trusting Data or Intuition in Promoting Candidates

Algorithms in the Selection Process

Selecting new employees in an organization can be a consuming task due to the sheer number of resumes presented. Organizations that are patient enough to go through every application may encounter psychological biases and other limitations. For instance, racism may result from selection processes that entirely rely on human input. Also, bracketing, the tendency to select the latest interviewed and outstanding employee instead of the overall best employee, is common among human-guided selection processes (Mann & O’Neil, 2016). However, the adoption of machine learning and enhanced data access means that organizations can improve hiring processes with the help of algorithms.

Algorithms are a set of instructions fed to a computer program to assess new employee applications. The instructions review an employee’s application content to select the most qualified employee based on the organization’s hiring premises (Mann & O’Neil, 2016). Traditionally, algorithms have been used to recruit university graduates, which has revealed some weaknesses in using the algorithms. Most organizations typically prefer university graduates from ‘best’ universities, a factor that eliminates even better-qualified graduates from universities that are perceived to be ‘small.’

One of the key differences between using data-driven algorithms and human input is that the latter is exposed to psychological bias, while the former is based solely on organizational decisions. Another crucial difference between the two is that algorithm-based tools monitor contextual performance apart from task-related performance (Leicht et al., 2019). Non-task-related performance includes an applicant’s health, internet activity, mood, fitness, and other metrics.

Another difference is that algorithm-based hiring integrates extra data that is not part of traditional non-data-driven tools. These metrics include customer relationships, manufacturing, finance, and supply chain management (Leicht et al., 2019). Such data can be obtained through software like IBM, SAP, and Oracle. By capturing such data, human resource managers can recruit based on less obstructive data than the traditional framework.

Finally, the technical capability to analyze algorithm-based data has expanded with time. That means that algorithms can offer descriptive, predictive, and prescriptive data. For instance, descriptive algorithms provide human resource managers with essential data that aid the recruitment process, bringing better results than previously used tools. An example of a descriptive algorithm is the balanced scorecard, which offers crucial employee information, including turnover, absenteeism, and supervisor feedback (Leicht et al., 2019). These algorithms provide managers easy to easy-to-handle HR instruments that go a long way in tracking employees’ performance in the long term. Employers can also measure the employees’ performance, and motivation and eventually create comprehensive employee profiles. These capabilities are not possible if organizations resort to using traditional non-data-driven human resource systems.

Differences between Customer Algorithms and Employee Algorithms

Whereas algorithms are used for employee selection, organizations use algorithms for customer clustering. Clustering is a segmentation process used by businesses to classify similar customers together (Laursen, 2011). It helps companies discover both static demographics and behaviors, which assist organizations in tailoring their products and services according to the needs of each segment.

Unlike employee algorithms, customer algorithms are not supervised. Although it is not supervised, it helps discover data sets that are closely linked with each other. Unsupervised data used in customer clustering helps to identify hidden patterns, due to the unavailability of a defined output (Laursen, 2011). To put it in simple words, companies have a predetermined preference whenever they search for new employees. For instance, such preference could be a college degree or a working experience. On the other hand, businesses search for customers regardless of their demographics or behaviors. Essentially, the system groups customers with similar characteristics together, helping them design products and services according to their needs.

Another key difference between customer and employee algorithms is the nature of data used in the analysis process. Whereas employee-based algorithms rely on already available data, customer algorithms delve into social media, website analytics, and other e-commerce platforms. Besides, customer-based analytics rely on both online and offline worlds to determine customer characteristics. Notably, offline platforms provide most of the data used by customer clustering, and a company that misses incorporating such data is likely to lag competitively. Physical analytics providing customer data include point of sale, WIFI analytics, and location analytics (Laursen, 2011). Physical data provides the business with an opportunity to capture data that could be missed if the business only employed digital data.

Reliance on Data-based Selection Approach over People-driven Selection

Although manual human selection sometimes provides the most reliable employee selection criterion, there are situations where only a data-based approach will help the organization achieve underlying expectations. Incorporating the data-driven approach in these situations will help the organization above and beyond current customer expectations (Marr, 2018). Data-driven recruitment will go a long way in attracting and keeping employees who will help the organization achieve its expectations, besides recruiting them at the right price.

One such instance is when the organization seeks to embed diversity in the hiring exercise. Traditional recruiting techniques make it hard to identify whether the organization will achieve diversity goals and maintain equity during the hiring exercise. Instead of just guessing, data-driven methods help the organization monitor the hiring funnel to incorporate diversity metrics (Marr, 2018). Some of the demographic ratios used to achieve diversity in the organization include veteran status, gender, and ethnic inclusion. Traditional recruitment techniques, as mentioned earlier, are subject to psychological bias that may prevent the organization from achieving predetermined targets. By using data-driven algorithms, the organization is likely to track diversity and implement corrective evidence-based programs that will ensure diversity throughout an organization’s HR funnel.

Aliyah Jones Case Decision

Aliyah Jones faces a dilemma between hiring ED and Molly as both candidates seem qualified to her. However, Aliyah is biased towards Molly. Such could be probably due to the trust the two have established, having worked together for some time. On the other hand, data-driven metrics show that ED Yu is better positioned to deliver organizational goals. Ed Yu scored 96% based on data-driven analysis, while Molly scored 83%. Although instincts are critical in getting good employees, Ed is a clear favorite, having outscored Molly on several metrics. It is not fair for Aliyah to use ED’s perceived lack of composure to lock him out. Aliyah should ignore her attachment to Molly and hire ED for the position.

Apart from task-based performance in which Aliyah surpasses Molly, the former also has established networks that can help the business in the future. Networking skills are not easily teachable. Therefore, Aliyah should hire Ed to eliminate such unnecessary costs since ED’s emails prove that he has established networking experience.

References

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics160(2), 377-392.

Mann, G., & O’Neil, C. (2016, December 9). Hiring Algorithms Are Not Neutral. Harvard Business Review. https://hbr.org/2016/12/hiring-algorithms-are-not-neutral

Laursen, G. H. (2011). Business analytics for sales and marketing managers: How to compete in the information age (Vol. 41). John Wiley & Sons.

Marr, B. (2018, April 13). Why Data Is HR’s Most Important Asset. Forbes. https://www.forbes.com/sites/bernardmarr/2018/04/13/why-data-is-hrs-most-important-asset/?sh=

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Question 


Algorithms can aid in decision-making. In the Harvard Business Review case Trust the Algorithm or Your Gut?, company VP Aliyah Jones reviews an algorithm to help make a decision on which candidate to promote.

To complete this Assignment, review the Learning Resources for this week and other resources you have found online, then respond to the following bullet points in a 4- to 6-page paper:

Harnessing Algorithms for Decision-Making- Trusting Data or Intuition in Promoting Candidates

Harnessing Algorithms for Decision-Making- Trusting Data or Intuition in Promoting Candidates

**Introduce the topic of algorithms in the selection process. How might the recommendations an algorithm makes differ from those of a hiring manager who is not using data analytics?
**How might using algorithms to analyze customers differ from using them on employees? Should companies be more cautious in implementing these methodologies internally?
**Studies have revealed a phenomenon called “algorithm aversion.” Even when data-driven predictions yield higher success rates than human forecasts, people often prefer to rely on the latter. And if they learn an algorithm is imperfect, they simply won’t use it. Describe a situation where you would base a decision on data analysis.
**Should Aliyah Jones choose Molly or Ed? Analyze each alternative solution. Consider the short-term and long-term implications. What are the advantages and disadvantages of each decision? Support your decision with two additional scholarly articles.

Note: You should make a firm case for one of the two candidates with the information in the case. Don’t suggest a committee or new selection tools or a new candidate pool.

**Outline the next steps of Aliyah Jones. What information should she give the candidates?

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