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Introduction to Data Analysis

Data analysis is the most significant stage of research since it allows the researcher to understand the collected data. This chapter involves data interpretation through logical and analytical reasoning to determine patterns, relationships, and trends as observed by the researcher. While many tools are available for data analysis, this study adopted two significant software: IBMs SPSS and Microsoft Excel. The study used SPSS version 20, and Microsoft Excel 2016 was used. The SPSS software allowed the researcher to construct tables and analyze the data using set variables. Meanwhile, Microsoft Excel helped coherently organize data and present it on graphs and other statistical data presentations. The researcher used the premium versions of the software to avoid data discrepancy and any unprecedented data misrepresentation that would occur through the software trial versions.

This chapter is dived into five major parts: data integrity and validity, quantitative analysis, qualitative analysis, results, and hypotheses. The first part of this chapter discusses how the researcher ensured that the data analyzed was valid. Data integrity and validity were ensured by subjecting every response to a validity formula. Data integrity and validity helped the researcher justify the findings and make this study credible. The second part analyses the quantitative data, including ages and other numerical data collected during the research. The qualitative data analysis part explores a thematic analysis that helped the researcher find meaning from the non-numerical data. Finally, the results, hypotheses, and research design interpret the data collected about this studys research design. The part allows the research to justify or dismiss the set hypotheses based on past studies on the overarching thematic area.

Data Analysis Model

Data Analysis Model.
Figure SEQ Figure * ARABIC 1. Data Analysis Model.

The data analysis model is significant in dividing the entire data analysis into simple steps to eliminate data bias and ensure data accuracy. Firstly, the researcher organized the collected data and prepared it for analysis. Organizing the data helped the researcher identify different variables that helped in the coding process (Castleberry and Nolen, 2018). The organized data was then subjected to a validity test to remove all form of bias and eliminate incomplete data collected, as shown in table 1. The valid data was then read and coded using IBM SPSS 20. and Microsoft Excel software. The latter software helped the researcher present the collected data on graphs and other statistical diagrams. After that, grounded theory was used to describe and interrelate the coded data with the three research hypotheses. Grounded theory was applied to interpret qualitative data through inductive reasoning and hypotheses interrelation. Figure 1. summarizes the data analysis model used in this study.

Research Model and Hypotheses

Research Model and Hypotheses.
Figure SEQ Figure * ARABIC 2. Research Model and Hypotheses.

This study investigates the impact and use of online platforms in the recruitment process. Consequently, this study adopted three hypotheses that the data analysis chapter seeks to prove and disapprove: online platforms are valuable in the recruitment process (‚1), online platforms are easy to use (‚2), and trusting the online platforms will be completely reasonable (‚3). The studys research model is shown in figure 2. The model consists of four constructs, with their item codes: perceived usefulness (PE_US), perceived ease of use (PE_EA), trust (TR_S), and intention to use (IN_US). Each of the four constructs was measured using various items.

Online Survey Data Analysis

The online survey involved a quantitative data analysis that transformed the collected data into various numerical codes. Coding the data allowed the researcher to enter the collected data into the SPSS software for interpretation and further analysis. The online survey involved thirty respondents whose data was determined valid upon conducting a simple validity test, as shown in table 1. The data were subjected to prove and disapprove the research hypotheses. The online survey involved thirty-nine questions that sought to understand the impact of online platforms on recruiting processes. The questions asked intended to help the researcher understand how online platforms usefulness, perceived ease to use, and trust influenced their intention to use the platforms.

Perceived Usefulness (PE_US)

Online platforms impact recruitment organizations in various dimensions. Their three main advantages influence the perceived usefulness of the online platforms. Online platforms such as social media help recruitment organizations reach many potential employees (Villeda et al., 2019). The platforms are also believed to provide an easier selection method since the recruiters can easily compare the potential employees instead of the traditional methods (Williams, McDonald, and Mayes, 2021). Moreover, online recruitment helps the recruiters save on material use since all the activities are digitized (Shapovalova and Pavlov, 2021).

Perceived Ease of Use (PE_EA)

Digitization and technological developments are believed to ease various processes. Online platforms have inbuilt features such as data analysis tools that help recruiters make informed decisions. The online platforms perceived ease to use is influenced by their capability to save time and effort (Mariani, Styven, and Teulon, 2021). The platforms inbuilt features make them an additional tool for the recruitment process. Therefore, online platforms can search for more information on potential employees (Williams, McDonald, and Mayes, 2021). Digital platforms have readily available materials like tutorials and e-books on how to use them (Shapovalova and Pavlov, 2021).

Trust (TR_S)

The increased use of online platforms has believed to increase trust among recruiters. The online platforms provide an outstanding step for a recruitment organization to move forward and improve its processes (Martins, Dominique-Ferreira, and Lopes, 2021). Consequently, organizations prefer using online platforms in the recruitment process (Martins, Dominique-Ferreira, and Lopes, 2021). Moreover, online platforms have proven valuable tools that set high recruitment standards for companies (Li et al., 2021).

Intention to Use (IN_US)

Organizations prefer effective processes and help save on various resources. Using online platforms helps the recruiters save on costs due to fewer materials and available learning resources (Shapovalova and Pavlov, 2021). The platforms have various inbuilt features like pictograms and tutorials, making them efficient (Villeda et al., 2019). Therefore, recruiters who use online platforms have a high standard process that leads to the most qualified and effective employees (Li et al., 2021). As summarized below, the researcher assigned each construct and unique item code for easy translation and transfer to SPSS.

Table 1. Research Models constructs and items codes

Item Code Item Description
PE_US Perceived Usefulness
PE_US1 Saves materials
PE_US2 Easier selection
PE_US3 Reach a large number of potential employees
PE_EA Perceived Ease of Use
PE_EA1 It saves effort and time
PE_EA2 Additional recruitment tool
PE_EA3 Readily available learning resources
TR_S Trust
TR_S1 Outstanding step forward
TR_S2 Most preferred method
TR_S3 Valuable tool
IN_US Intention to use
IN_US1 Most efficient tool
IN_US2 Cost-effective
IN_US3 High standard process

The online interview also involved a question on the gender and age of the participants. The researcher assigned each of the variables a code. The males were assigned code 1 while the females were assigned 2. The participants were of two age categories: 20-25 and 28-40. Age category 18-25 was assigned code 1 while age category 26-57was assigned code 2. The surveys responses were coded as summarized in table 3.

Table 2. Response codes

Response Strongly Disagree Disagree Neutral Agree Strongly Agree
Code 1 2 3 4 5

Results

Upon subjecting the collected data to IBM SPSS, the researcher noticed that the majority of the participants were females. Moreover, most participants were recruiters of ages 26 and above, as summarized below.

Table 3. Age and Gender Data Set

Statistics
Gender Age Category
N Valid 30 30
Missing 0 0

Frequency Table

Table 4. Gender frequency table

Gender
Frequency Percent Valid Percent Cumulative Percent
Valid Male 9 30.0 30.0 30.0
Female 21 70.0 70.0 100.0
Total 30 100.0 100.0

Table 5. Age-frequency Table

Age Category
Frequency Percent Valid Percent Cumulative Percent
Valid 20-25 3 10.0 10.0 10.0
28-40 27 90.0 90.0 100.0
Total 30 100.0 100.0

Correlation is crucial in determining the relationship between two variables, which is crucial in predicting the validity of the hypotheses. The study involved four contracts: perceived usefulness, trust, perceived ease of use, and intention to use online platforms for recruitment processes. Through correlation, the researcher measured the goodness of the constructs. Although an increase in the value of a correlation does not lead to an increase in the value of items, the formative constructs influence the value of the reflective construct. The studys correlations are summarized in table 7.

Correlations
Platforms are useful Online Platforms are easy to use Recruiters Trust Online platforms Recruiters intend to use online platforms
Spearmans rho Platforms are useful Correlation Coefficient 1.000 -.149 .135 .323*
Sig. (1-tailed) . .217 .238 .041
N 30 30 30 30
Online Platforms are easy to use Correlation Coefficient -.149 1.000 .084 -.293
Sig. (1-tailed) .217 . .329 .058
N 30 30 30 30
Recruiters Trust Online platforms Correlation Coefficient .135 .084 1.000 -.148
Sig. (1-tailed) .238 .329 . .218
N 30 30 30 30
Recruiters intend to use online platforms Correlation Coefficient .323* -.293 -.148 1.000
Sig. (1-tailed) .041 .058 .218 .
N 30 30 30 30
*. Correlation is significant at the 0.05 level (1-tailed).

Hypothesis Testing

Hypothesis testing helped the researcher to accept or reject the hypotheses based on statistical evidence. This studys model is based on three hypotheses: online platforms are valuable in the recruitment process (‚1), online platforms are easy to use (‚2), and trusting the online platforms will be completely reasonable (‚3), as shown in figure2. This study adopted regression analysis to explore the relationship between one dependent variable and two independent variables. The study involved thirty participants meeting the required population threshold for the regression formula.

Table 6. Regression model Summary

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .444a .197 .105 .53086
a. Predictors: (Constant), Recruiters Trust Online platforms, Platforms are useful, Online Platforms are easy to use

Table 7. Regression model variables and predictors

ANOVA a
Model Sum of Squares df Mean Square F Sig.
1 Regression 1.802 3 .601 2.132 .120b
Residual 7.327 26 .282
Total 9.130 29
a. Dependent Variable: Recruiters intend to use online platforms
b. Predictors: (Constant), Recruiters Trust Online platforms, Platforms are useful, Online Platforms are easy to use

Table 8. Dependent variables

Coefficients a
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.918 1.113 3.521 .002
Platforms are useful .238 .104 .408 2.274 .031
Online Platforms are easy to use -.104 .210 -.090 -.494 .625
Recruiters Trust Online platforms -.062 .163 -.068 -.382 .705
a. Dependent Variable: Recruiters intend to use online platforms

From the regression tables, the unstandardized B values helped determine the degree each of the predictors affects the outcome of the effects of all other predictors remain constant. A single unit of the online platforms usefulness increases the intention to use the platforms by 0.238 units. A single unit of the online platforms ease to use increases the intention to use the platforms by 0.104. Moreover, a single unit of recruiters trust in the online platforms increases their intention to use them by 0.062. Therefore, the online platforms usefulness is the largest coefficient, 0.408. The survey results on trust, ease of use, and usefulness are summarized in the frequency graphs below.

Trust frequency graph.
Figure SEQ Figure * ARABIC 3. Trust frequency graph.
 Ease to use frequency graph.
Figure SEQ Figure * ARABIC 4. Ease to use frequency graph.
Usefulness frequency graph.
Figure SEQ Figure * ARABIC 5. Usefulness frequency graph.

Online Interview Results

The study involved an online interview with one respondent from the UK. The respondent was asked a series of questions that he was supposed to answer with strongly disagree (1), disagree (2), neutral (3), agree (4), or strongly agree (5). The researcher adopted a thematic analysis in which relevant themes were identified and related to the study hypotheses. According to the interviewee, online methods help save time, but not materials. The interviewee agreed that modern technology has helped ease the recruitment process. However, traditional recruitment processes are not better than online platforms. He stated that both processes have merits and demerits. For instance, he stated that although face-face recruitment help companies understand candidates better, using data analytics on online platforms also helps employees understand the candidates.

The interviewee preferred both methods of recruitment: online and traditional. He stated that both methods complement each other and have varying advantages and disadvantages. With the developing technologies and social media platforms, the respondent stated that they use Facebook and Twitter, among other online platforms. However, LinkedIn is the most preferred online platform for the recruitment process. He confirmed that social media platforms have made the recruitment process easy. The companys major challenge when using online platforms is meeting the business goals since there is no personal touch between the recruiters and candidates.

Data Analysis Limitations

This studys inaccuracies are contributed by several factors that limited the collection and interpretation of the analyzed data. Firstly, the online survey involved thirty participants, of whom 70% were females, and 30% were males. Consequently, there was a biased opinion on responses due to gender imbalance. Secondly, the research was to involve two interviewees, but only one responded. Consequently, the interview results were biased and limited to one persons opinion. Moreover, this study was limited to the Philippines, leading to biased results since it lacked the perspective of other countries. Therefore, gender imbalance, limited interviewees, and limited jurisdiction led to inaccurate data collected for analysis.

Introduction to the Chapter

The discussion of finding chapter explores the studys findings and discusses whether the researcher achieved the objectives or not. This study involved three main hypotheses drawn from past studies on the same thematic area. The chapter relates the findings discussed in the data analysis chapter to the study hypotheses. This chapter examines how the literature review, quantitative and qualitative data approve or disapprove of the study hypotheses. The researcher starts this chapter with a findings summary, as observed in the data analysis chapter. The summaries include a short description of the online survey and online interviews results. Moreover, the chapter explores the various factors that limited the study results and those that contributed to the studys success. The researcher makes multiple recommendations that can help the recruitment agencies better their services based on the studys observations and suggestions from the participants.

Results Summary

Online Survey

The online survey involved thirty participants in the Philippines who are working with various recruitment organizations. The participants were subjected to structured questionnaires that were administered electronically. Digitization of the survey was significant since the researcher could save time and money. The questionnaires contained forty-five questions intended to understand their response on usefulness, ease of use, and trust in using the online platforms. Moreover, the questions explored the respondents age and gender, crucial demographics for social media use, and technology acceptance (Henzel and Håkansson, 2021). Of the thirty respondents, twenty-one were females, and seven were males.

A question on the ease of online platform use was asked to the respondents. Most of the respondents agreed that the platforms are easy to use (table 9., Appendix). In supporting the ease to use, the majority decided that the availability of reference materials influenced the use of online platforms. Furthermore, many online platforms have features that make work easier for recruiters. The majority of the respondents were neutral when asked whether online platforms boosted trust in the recruitment process (table 10., Appendix). Meanwhile, most of the participants agreed that online platforms are helpful in the recruitment process (table 11., Appendix).

Online Interview

The online interview involved one respondent subjected to a set of questions similar to those asked in the online survey. The respondents helped the researcher understand the responses since he gave in-depth answers to the questions asked. Unlike the majority of the survey respondents, the interviewee stated that online platforms do not save on materials used during the recruitment process. However, he agreed that digitized media make it easy to recruit candidates. The use of social media data analytics was a significant feature mentioned by the interviewee. Although many social media platforms exist, LinkedIn is the most preferred social media tool among the recruiters. Moreover, it was the interviewees averment that traditional and online recruitment platforms complement each other, and there is no preferable method between the two.

Impact of Online Platforms Use on Recruitment Process

Perceived Usefulness

Digitization has made work more accessible in various organizations since it allows fast and efficient processes. The recruitment organizations are the most affected organizations that utilize technology in their operations. The use of social media has proved significant among organizations since it allows the recruiters to reach a more substantial number of potential candidates (Koch, Gerber, and De Klerk, 2018). Moreover, the platforms have analytics tools to generate information about their target candidates (Meah and Sarwar, 2021). Unlike the traditional recruitment process, online platforms make it easier for the selection process. For instance, the recruiters focus on candidates qualifications rather than individuality, which is inevitable during face-face interviews.

LinkedIn is the most refereed online platform among the recruiters since it reflects a candidates professionalism. The social media platform has inbuilt features that allow the recruiters to track a particular candidates academic and professional journey (Koch, Gerber, and De Klerk, 2018). Moreover, the platform has embedded online-course that help improve the candidates experience and knowledge (Meah and Sarwar, 2021). Consequently, the media help the recruiters save on materials and costs spent training the candidates (Koch, Gerber, and De Klerk, 2018). The use of analytics, educational features, and the availability of many candidates make online platforms helpful to recruiters.

Perceived Ease of Use

Technological advancements have led to efficient social media and other digital platforms that are easy to use. The recruiters use candidates information to determine their effectiveness among different data types (Valanarasu, 2021). The traditional recruitment processes involve noting down the candidates strengths and weaknesses, and physically interpreting the collected information. Consequentlty, the recruitment processes were subject to human error. However, the digitized recruitment processes have inbuilt features that eliminate the complex candidates scrutiny and capability analysis. For instance, LinkedIn has a feature that generate the potential candidates bio data including former job descriptions and academic history. Moreover, the platform has rating features that describes the users capabilities in different areas of knowledge (Meah and Sarwar, 2021). Therefore, the online platforms help recruiters save on time and effort.

Online platforms help recruiters seek further information on a particular candidate who has been interviewed offline. Many recruitment organizations use social media and other platforms to confirm information as presented by candidates. Moreover, the platforms are used in tracking a candidates behavior that may affect the companys social responsibility and ethical values. While social media platforms allow freedom of expression and speech, some organizations abhor vices like hate speech and discriminatory opinions that are expressed on social media platforms (Clemons et al., 2021). Therefore, the online platforms are used as additional recruitment tools among the recruiters.

With highly digitized society, much information is readily available online for usage. The online platforms like Facebook and Twitter contain features that are use friendly. The features give directions on how to access the platform and how to conduct activities like password resetting. Moreover, social media platforms complement each other, making it easier to manage an account. For instance, YouTube contain videos that help Facebook, LinkedIn, Twitter, and other platform users navigate their profiles (Yang et al., 2021). Furthermore, search engines like Google help the social media account owners perform challenging tasks. The availability of learning resources makes social media platforms preferred by recruiters.

Perceived Trust

Various factors contribute to the dependability on online platforms by recruiters. The platforms provide crucial information on candidates to the recruiters. For instance, a candidates ethics and morals can be accessed through their social media handles and the their frequently engaged online conversations (Bhatia and Arora, 2022). The recruiters use such information to either accept or disqualify the candidates. Although, the online platforms are useful among the recruiters, the platforms are subject to profile impersonation and creation of pseudo accounts. Moreover, some platforms are vulnerable to hacking and other technical immoral activities (Rony and Ahmed, 2021). Therefore, many organizations use the platforms as an additional tool to the traditional recruitment processes.

Conclusions

Online platforms are valuable in the recruitment process (‚1)

Recruitment process is a tasking process that involves data collection, interviews, and suitability checks. According to this studys online survey many recruiters agree that the online survey is useful during the recruitment process. Value of the online platforms during recruitment process is three dimensional concept: saves on time, cost effective, and set high standards for companies (Guo, Zhu, and Chen, 2021). The online platforms may involve collection of data from the internet and use of such data in recruiting (Bhatia and Arora, 2022). Moreover, candidates participating in an online recruitment process may be sent a link that allow them to share the information with the recruiters. Meanwhile, the traditional recruitment process involves physical meeting with the candidate that is time consuming.

Traditional recruitment process may involve ability tests that require physical presence of the candidates. Moreover, the candidates may be subjected to training to gain specific knowledge as required by their employees (Tien et al., 2021). However, through online platforms, the candidates may not have to take the ability test and training since some platforms support such (Hangartner, Kopp, and Siegenthaler, 2021). For instance, LinkedIn has features that allow the users to perfect their skills. Consequently, the recruiters save on costs, and recruits competitive candidates. Although online platforms are valuable in terms of time, costs, and standards, the platforms are subject to system errors and high initial costs affecting their significance in influencing recruiters intention to use them. Therefore, online platforms value in the recruitment process does not sufficiently explain their adoption by recruiters.

Online platforms are easy to use (‚2)

The online platforms contain features that allow the recruiters access candidates traits and competencies. The platforms have supportive features that make it easy for the recruiting agencies to access and recruit a particular candidate. For instance, social media platforms like LinkedIn have a short tutorial that teaches the users on how to navigate the different buttons. Moreover, platforms like Facebook have inbuilt analytics features that easily generate user information (Senthil Raja and Arun Raj, 2021). The use of likes and dislikes buttons allows the recruiters to track the effects of a user social comment. Social media posts that abscond social values attract negative comments and dislikes from other social media users. Therefore, recruiters have easy time in analyzing candidate behaviors. Although, the majority of online platforms are easy to use, some platform contains complex steps requiring technical knowledge to u

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