Algorithms and Decision Making

Answer

1. • • • Algorithms and Decision Making

         The ongoing digital revolution has had a tremendous effect on a significant portion of transformations in the modern world, particularly in business-related operations. Software developers are consistently seeking value-driven solutions by creating and implementing convenient algorithms to enhance human practice (Agarwal, et al., 2018). Algorithms are commonly defined as step-by-step procedures that define the execution of processes to attain desired outputs. In the digital world, algorithms are used to instruct information machines such as computers, servers, and other devices on how they should cooperate to deliver anticipated results. The Google Search engine is a worthy example of a system which depends on internal web page crawling processes to showcase user’s search results (Cheng & Hackett, 2019). The past few years have seen the use of algorithms penetrate deep into human resource management. These innovative solutions are now playing pivotal roles in management processes involving the human resource (Agarwal, et al., 2018). Common areas of application includes hiring, promotion, restructuring, and layoffs. In the wake of such a realization, the present piece offers a detailed exploration of the use of algorithms in human resource management, and how this trend affects decision-making.

         Algorithms in the Selection Process

                     Innovativeness, productivity, and scalability are among the leading goals of organizations in the highly competitive business world, and they are largely dependent on an organization’s capacity to obtain and retain proficient workers. In an attempt to facilitate such growth, managers are seeking creative means of evaluating employees for selection processes (Agarwal, et al., 2018). Algorithms have been used to shed light on useful variables for effective decision-making including revenue generated by employees (individually), risks of turnover, stakeholder reviews, interpersonal networks, and other factors. These key performance indicators can be leveraged to help managers make right decisions when acquiring workers to fill sensitive positions.

                     It appears wise to clarify that the role of algorithms in the selection process is mainly consultative: this implies that the innovation should not replace human reasoning. Rather, it should be applied to enhance the amount of information available for decision-makers to improve the outcomes (Agarwal, et al., 2018). When placed into perspective, a leader seeking to fill a managerial position would be inclined to select a member of his/her specialized team leaders if analytical data on cross-department interactions affirm the potential recruit’s mastery of the organization’s processes. Absence of such a critical information would prevent the leader from noticing the talent in his/her team member. As a matter of fact, the leader might end up hiring an outsider who lacks a strong grasp of the firm’s operations, thus costing the business a lot of revenues.

         The preceding example shows how algorithms improve the decisions of hiring managers when they utilize data analytics to make decisions. Hiring managers who disregard data analytics software tend to make bias decisions (Cheng & Hackett, 2019). As implied in the preceding example, the manager might recruit someone based on his/her track record in another company regardless of the fact that such an individual knows nothing about the organization’s operations. Such a decision is bound to cause unwarranted losses since the recruit will need some time to implement potentially erroneous processes before he/she learns fundamental realities of the business/organization. Meanwhile, a veteran employee with equal qualifications would be in a better place to scale the business as early as possible due to familiarity with organizational procedures.

         The Need for Caution when Implementing Algorithms Internally

                     Since the emergence of intelligent systems, organizations have found means of gaining market insights. A significant portion of algorithms in the digital business sphere are designed to analyze customer trends. Some of these analytical tools focus on reviews, customer activity in online stores, social media interactions, and purchase patterns among others. Unlike in the case of external stakeholders such as customers, the use of algorithms internally can be plagued by bias. As controversial as the preceding postulation sounds, it is rightly grounded on the fact that some employees might offer bias information, thus resulting in misleading data analytics outcomes.

         For instance, employee reviews on the potential recruit for a promotional position might be compromised by factors such as patronage and favoritism: people are highly likely to favor an individual who they like when given the choice to cast votes between two equally qualified individuals. This scenario is quite evident in the assigned Harvard Business Review case study by Jeffery Polzer: noteworthy is Aliyah Jones’ inclination towards Molly Ashworh regardless of the fact that Ed Yu is equally qualified (Polzer, 2018). When viewed from such a lens, it is agreeable that the support Ed has from fellow employees might be largely attributable to his personal relations.

         Considering the scenario described in the preceding paragraph, it is fair to argue that data analytics algorithms should be applied with caution when it comes to human resource management since they lack the neutrality that is often evidenced in customers. Patronage and favoritism can result in bias data analytics outcomes, thus compromising the entire decision-making process.

         Algorithm Aversion

                     Algorithm aversion is a term coined from the skeptical nature of human beings when it comes to decision-making based on algorithms. According to Agarwal and colleagues (2018), people are highly likely to rely on logical forecast when making decisions, especially when an algorithmic systems records an error. Since little can be done to alter people’s perspectives on algorithms, it is fair to recommend the consultative use of this innovation. This implies that data analysis outcomes should be used to shed light on the decision-making process (Cheng & Hackett, 2019). For instance, when a company is recording high turnover rates, interview data regarding employee attitudes towards organizational aspects such as compensation, employment benefits, and workplace conditions can allow an analyst to determine the reason behind high turnover rates.

         Reflection on Aliyah Jones’ Dilemma

                     Now that both data analytics and logic-based decision-making have shown to have advantages as well as disadvantages, one cannot help but acknowledge the dilemma faced by Aliyah Jones: both Ed and Molly are competitive workers whose capacity to lead in the new managerial remain unquestionable. Apart from his competence, Ed has a strong relationship with leaders from various departments (Polzer, 2018). Such a scenario implies that he is in a better position to offer direction and guidance to members of different units since they are familiar with his style. Unfortunately, his rigidity might hinder innovative growth in the long-term. On the other hand, Molly is a composed innovative leader, whose relationship with the top management is quite commendable. This kind of openness is instrumental for a middle-level manager since she is bound to serve as a convenient bridge to enhance progress. Unfortunately, her interaction with employees from other departments is quite limited, so, she is highly likely to face friction when she takes charge of the higher position (Polzer, 2018). Another alarming issue is the fact that her promotion is bound to result in Ed’s resignation, yet he is a valuable leader of the Beauty department. Given both scenarios, it seems fair to recommend Ed for the promotion. Change in leadership often steers friction in the workplace, especially when the team is not familiar with the person in charge. Ed’s bonds with fellow employees across the departments will go a long way in boosting organizational performance.

         Recommended Steps for Aliyah

         Below is a brief outline of what Aliyah has to do:

         • Invite both Ed and Molly for a meeting
         • Explain each individual’s strengths and weaknesses
         • Explain why Ed has to take the position
         • Expand Molly’s sphere of influence in the organization by partnering her with Ed in leading cross-departmental projects

Answer

“Feed a cold, starve a fever”

 “Feed a cold, starve a fever” is a classical myth that inspired the people to focus on their eating habits. The saying suggests that when we tend to eat healthier we inevitably stay healthy (Fischetti, 2014). It is also arguable to suggest that adequate consumption of food increase body temperature, hence, activating the antibody. In this case scenario, flu like illnesses are combated easily by the body without the need for intensive medication. It is essential to eat hot food during the cold season especially chicken soup and herbal tea. This provides proteins and hydration during flu infested season.  

The concept of “feed a cold, starve a fever” is a positive motivation to the health of individual since it promotes healthy maintenance of body through nutritional consumption (Fischetti, 2014). In this case, if an individual increases the intake of calories it will help the body to fight fever. When increasing the intake of calories during cold seasons this increases the body temperature through metabolism (Fischetti, 2014). It is also common for people with fever to experience vomiting, and diarrhea. As a result the person experiences a decrease in body mass. This fact suggests that the body should be highly hydrated. In this case, healthy meals and water creates energy that is directed to the immune system. Providing the body with food rich in vitamin C and E is also essential since they improve the body’s immune system (Smith, 2015).

It is appropriate to treat a cold through following these protocols: drinking lots of warm fluids, eating healthy nutrition, avoid smoking and alcohol since they are dehydrates (Smith, 2015).

In conclusion healthy eating prevents fever and flu during the cold season. It is important to supply the body with adequate supply of water and antioxidants to promote immunity.

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