The application of AI in recruitment

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Method

The purpose of this chapter is to provide the method to the topic covering how data was collected for this study, the process of doing so and how it was analyzed in order to fulfill the purpose of this study.

Data collection

Data collection method

There are different methods one can apply when conducting research, many may seem good but not all is fitting to each research purpose. Some of the most common methods for a qualitative study, as this thesis is, include observations, focus groups or interviews (Gill, Stewart, Treasure & Chadwick, 2008). The reason interviews were chosen and not focus groups or observations is because interviews would allow for specific insights in the industry on a personal level where the opportunity to reach out to gain additional information would be possible. Observations would not have been efficient for this study as it only bases around spending longer time looking at and taking notes of a process, as for this study it would imply conducting observations of how AI is implemented and carried out within company’s recruitment process (Gill et al., 2008). Once qualitative data is gathered with sufficient time to carry out analysis and transcription in detail, it will give better insights to the topic of this thesis compared to that of quantitative data.

Data collection process

The data was firstly collected by choosing the type of companies that was desired, reaching out to them and setting up a date for an interview. Thereafter the planned structure for the interviews took place. The questions were determined based on three different aspects. The first aspect was based on general questions of the interviewed professionals and the other two aspects was based on aspects identified from the literature review. The two aspects include questions relating to application of AI in HRM and questions relating to challenges and benefits of AI in recruitment. The interview guide to this can be found in Appendix 1. The data collection took place roughly one and a half month into the started research and was conducted after a full frame of reference had been finished. This ensured that the ideas from previous researchers within the line of research had been covered and to establish what research could expand on. All interviews began by asking each interviewee if it was allowed to record the interview in order to capture what was being discussed. All the data was later transcribed and analyzed through the chosen method for data analysis, which completed the collection of the data.

Research process steps

Choice of companies

The companies used in this thesis were chosen based on their work within the area of AI in the recruitment process. Those who were reached out to was companies who either actively use AI software within their recruitment process, or companies who are developers of AI software for organizations to implement in their recruitment. In this way, a wide range of information was gathered from different perspectives. There was no delimitation locally to Jönköping or Sweden, as implementing AI within HRM is a rather new subject. This implies that there is limited amount of companies, especially within Sweden, who actually implement AI in recruitment.

Interviews

According to Collis and Hussey (2014) an interview is a primary data collection method where researches have chosen participants to answer questions about what the chosen participants do, feel and think. Interviews as a data collection method is suitable under an interpretivist paradigm and during the interviews the purpose is to probe data about individuals’ opinions, attitudes, feelings and understandings. Under semi-structured interviews, researchers have developed questions for the interviewees in advance (Collis & Hussey, 2014).
An online, asynchronous, in-depth interview can be conducted (Meho, 2006). Meho (2006) mention that an asynchronous interview that is in general conducted through email, is different from e-mail surveys, being more semi-structured. Meho (2006) have listed several qualitative studies that have been conducted through e-mail interviews. Persichitte, Young and Tharp conducted a study in 1997, where the researchers interviewed six education professionals by e-mail about the technology used at work. In 2004 Lehu conducted in-depth interviews with 53 top-level managers and executives about the brands age and how managers relate to them (Meho, 2006). In this thesis, the answers received by email were as comprehensive as the answers received during the telephone interviews.
Eight semi-structured interviews were conducted since the aim was to gather opinions and experience about the impact of AI in recruitment from professionals within the field of HR. Due to differences in locations, countries and time zones, seven interviews were conducted through either telephone or Skype. The overview of these interviews and their duration can be seen in Table 1. From the request of one participant, one of the interviews was conducted through email. The same questions that was asked during the telephone and skype interviews were sent out to this specific company by email. All interviews were conducted in English.

Method for Data analysis

The method that was chosen to analyze the data with was thematic analysis. This method was chosen based on the initial research paradigm of interpretivism, as thematic is a well fitted method for this paradigm (Peterson, 2017). Thematic approach is based on narrowing down the qualitative data, for an example by coding the interviews which has been conducted, and from that pick out the emerging themes. A thematic analysis is very flexible in its nature and is therefore applicable to many research areas (Maguire & Delahunt, 2017). The themes identified through the thematic analysis will be acting as the main points of data analysis and contrasted to the theoretical framework as well as the chosen model.

Coding & Themes

To apply the chosen thematic method for data analysis, the 6-steps thematic analysis process by Braun and Clarke (2006) was adhered. This model is described step by step in Table 2.
First the data gathered from the interviews was transcribed into a written form, listened and read several times. The second step include generating initial codes from the collected data. The pre-set codes were such as traditional recruitment, artificial intelligence, effectivity, time management, technology, automatization, talent acquisition and human resources. Additional codes which emerged during the analysis of the data was resources, personality traits, judgement, communication, screening and talents. In the third phase, the identified codes were closely analyzed and considered how to combine them in order to create a theme. In the fourth step, that is reviewing themes, authors started elaborating identified themes. In the fifth phase authors generated precise definitions and names for each identified theme that will be presented in analysis part of the thesis. Finally, the sixth phase of the model allows authors to conduct the final analysis and write the thesis with finalized themes (Braun & Clarke, 2006).
Table 3 below shows an overview of how the themes were found and their meaning in relation to the data that have been collected. It will name the themes and show how the themes was identified through the process described above.

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Research Trustworthiness

Validity and reliability

In qualitative research it is important to be able to convey the validity and reliability of the conducted research. The concept of validity can also be considered as the credibility of the research and it refers to the extent to which the arguments, interpretations and results that are presented in the research demonstrate the subject they are supposed to refer to. Several factors such as poor samples, faulty procedures and misleading measurement can deteriorate validity in research (Collis & Hussey, 2014). Reliability in turn refers to the fact that the data that is collected from the research can be used to describe the topic that has been explored. Reliability implies the repeatability of findings and the reliability of the data. Repeatability means that if someone were to conduct the same study again it should yield the same results (Collis & Hussey, 2014).
In each study, researcher’s own values, opinions, assumptions and understanding can have an impact on the reliability of the study. In this study, the subjective choices made by the authors have impacted the formation of the theoretical framework. It is also apparent that author’s own interpretations impact the results of the study. In order to have an appropriate balance with validity and reliability, it is important to have an organized description of the research process and hence this thesis process is described as accurately as possible. When it comes to reliability and the repeatability of the finding, it was noticed that with the sufficient number of interviews, the same message and points started repeating over and over again in the interviews.

Ethical considerations

For the research to be reliable, the trust of interviewees is important. According to Collis and Hussey (2014), when interviewees have the possibility to remain anonymous, their identity, insight and opinions will not be unveiled and the information received during the interview will be handled carefully. Therefore, in this research, all interview participants are referred as ’professional’ and no names of the interviewees or their companies are unveiled to protect their integrity.

Empirical Findings

The purpose of this chapter is to provide the empirical findings from the eight conducted interviews with HR professionals regarding the usage of AI within the recruitment process. It will be presented according to the themes identified during the data analysis.

Overview of empirical findings

The following section covers the empirical data found for this study. The empirical findings will be presented accordingly to the main themes which were identified through the thematic data analysis. The themes will be presented in the following order: effectivity in the recruitment process, application of AI in recruitment, benefits and challenges of using AI and lastly human error and bias.

Effectivity in the recruitment process

What traditional recruitment have to offer

All eight professionals agreed on a few things which traditional recruitment has to offer in terms of benefits. Some points in particular that every professional agreed on was that traditional recruitment has the value of the human touch. This implies that there is always a human who can interact with applicants and have a special connection with them. Some of the interviewees argued that by having human to human interactions as it is in recruitment nowadays, it is easier to communicate without misunderstandings. It also makes it possible to discuss ideas, both between recruiters but also between recruiter and job applicant.
“The human touch and feeling are something which can never be replaced…people are so comfortable with the things they know” (Professional 2)
Another point that six out of eight professionals agreed on is that traditional recruitment is an already tested measure. For an example, having a formal interview with candidates has been done the same way for several years within recruitment, as well as many other selection methods. This means that these practices have a long line of existing theories and research underlying to it, validating their results. Therefore, there is a lot of information recruiters can draw on and be confident in the results they get at the end of the recruitment. One interviewee said that as traditional recruitment has been mostly successful for organizations up to this point, not many feel comfortable changing from what is already proven to work.
There were not many opposing views that emerged about the traditional recruitment process. Many professionals choose to just bring up the human touch as one of the only key things with traditional recruitment. The only difference that could be seen between the professionals was how much emphasis they put on mentioning good things about traditional recruitment. There were three professionals who put more focus talking about the good things with having traditional recruitment. The rest of the interviewees just touched briefly upon benefits, but most chose to just quickly mention one or two things and then put more focus on the drawbacks. Therefore, it can be seen as a shift where some of the professionals think there are some good things with having traditional recruitment, whereas others are strongly drawn towards its negatives.

What traditional recruitment lacks

Many of the professionals had inputs and ideas about what stopped traditional recruitment from being the best that it could be. One of the major challenges that all eight professionals took up and talked in depth about was the length of the recruitment process. All agreed that the current recruitment process is very time consuming, both for candidates and the recruiter. It makes applicants go through lengthy and complicated processes which makes candidates wait for a long time to even get through the process.
‘’Traditional recruitment is time-consuming and, according to many applicants, old-fashioned.’’ (Professional 8)
“It’s a very traditional way, I think traditional way is also very time consuming, people still have to go through lots of steps, very complicated steps sometimes.” (Professional 1)
Due to the traditional recruitment being time-consuming, it also makes for less time for the recruiters to take the best decisions, which was mentioned by six interviewees. Furthermore, all professionals mentioned that recruiters often do not have the time to communicate with candidates during the ongoing recruitment. There is usually no time either to give applicants a heads up if they did not get the job they had applied for.
Another idea brought up by the professionals was that there is a lot of faking being done by applicants. The applicants write their CV’s way better than what they actually are. With faked CV’s it makes it hard for organizations to know that the candidate they hire actually have the experience they say in their CV. Two of the professionals also mentioned that one of the major problems in traditional recruitment is to find the right candidate among those who are not actively seeking for a job, as focus usually lies in finding new talent. Furthermore, it was said by one interviewee that candidates are easily forgotten after being rejected for a certain job while they still have their CV’s saved in the database. Instead of these candidates being re-discovered for a new position, recruiters look among other applicants.
Biases, racism and judgment from the recruiters are another area where traditional recruitment lacks. Some mentioned that there can be a biased selection based on the job applicant’s age, gender or heritage. Other biases were based on for an example if the recruiter preferred work experience from a specific company, such as Apple. Then that recruiter would only want to hire people who had worked there. All these biases were brought up as inconsistencies in the recruitment process, as when biases are involved the decisions are not fairly made.
“…maybe you’re just hiring a bunch of nice people, maybe your manager is hiring people just like that, then you decrease the diversity of the talent and that harm the performance of the team and the company overall” (Professional 7)

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The application of AI in recruitment

The following section will describe in what part of the organization AI software are implemented in the organizations interviewed. Overall, many of the interviewees agreed that even though AI is an interesting new technology, it has a long way to go before it could be perfectly implemented strategically.
‘’I do think we are at the very beginning and we are still learning.’’ (Professional 5)

Pre-screening and pre-selection

It was found that six out of eight professionals actively use AI software in the pre-screening and pre-selection process of their recruitment process. In the pre-screening part for the companies, AI technology bases itself on the job descriptions provided by the company and screens for possible job candidates. This is conducted by not just applying certain keywords, but also through language and other traits used in the applicant’s submissions. The pre-screening for four of the professionals was said to most often take place through social media channels such as LinkedIn or Facebook. The rest of the professionals said that the screening was conducted on applications sent in directly on a job posting. Some software also does pre-screening by applying personality tests. The personality tests are applied to see if the candidate has the right qualities and skills which the job requires.
In terms of pre-selection, it was discussed that AI helps give an organization all the necessary information to select candidates that seems the most fit. One professional described the process as the technology putting together a long list ranking the best fit candidates for a job vacancy. This then helps the company get a basic understanding of what the AI software feel, based on all data collected to evaluate who would actually be the best fit employee. From this, the organization itself gets to select the candidate which they wish to hire. They can either pick from the top ten candidates provided by the AI software or choose freely who of the candidates put forward they want to hire. An important focus was put here by one professional, who said that the AI software learns from the way you pick candidates. If you constantly would pick from the top 10, the AI software will learn that these are the type of people you want and therefore eliminate others who might be eligible for the job as well.

Communication with candidates

All eight professionals said that they actively used AI software to communicate with the candidates who apply to their open positions. Some of the professionals used similar type of AI software when communicating but for some it differed. Some companies’ software had chat bots which took the candidates information such as name, previous job experience and similar data. This was then converted into a CV and a job application which the AI software later would screen through. The chat bot has a continuous interaction with the job applicant, where the applicant can get updates and ask the chatbot questions. The AI software know itself how long time it has gone since the job applicant applied to the job and check in to see how the candidate is doing and if any questions has arisen regarding the position.
“What would it mean to a candidate to have someone, does not even matter who it is or it is even a person, to check on you, like: Hey, your interview is in a one week, do you have any questions? AI software can enable this” (Professional 7)
Many of the professionals said that the AI software can give each candidate an overview when a recruitment process has been finished of what qualities the candidate had or what it was lacking in relation to the job posting. It can in detail explain why the person did not get the job. It also allows for the job applicant to ask questions if they would still be unsure why they were not chosen.

Table of Contents
1. Introduction
1.1 Research Background
1.2 Problem
1.3 Purpose
1.4 Research Question(s)
1.5 Delimitations
2. Literature Review
2.1 Human Resource Management (HRM)
2.2 The traditional recruitment process
2.3 The concept of Artificial Intelligence (AI)
2.4 Online recruitment
2.5 The application of AI in recruitment
3. Methodology
3.1 Research Philosophy
3.2 Research Strategy
3.3 Research Approach
3.4 Conducting the Literature Review
4. Method
4.1 Data collection
4.2 Research process steps
4.2 Method for Data analysis
4.3 Research Trustworthiness
5. Empirical Findings
5.1 Overview of empirical findings
5.2 Effectivity in the recruitment process
5.3 The application of AI in recruitment
5.5 Benefits and challenges of using AI in recruitment
6. Analysis
6.1 AI in the traditional recruitment process model
6.2 Implications of using AI in recruitment for organizational effectiveness
7. Conclusion
8. Discussion
8.1 Contributions
8.2 Limitations
8.3 Suggestions for Future Research
9. Bibliography
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