Enhancing User Experience in Information Security E-Recruiting Platforms
Abstract
This study is justified by the growing importance of user-centered design in digital platforms where attracting and retaining talent is critical for organizational success. Specifically, it examines how personalization, search accuracy, attitude, subjective norms, and perceived behavioral control interact to affect these outcomes. Using Partial Least Squares Structural Equation Modeling(PLS-SEM), data was collected from 154 information security job seekers. The analysis reveals that personalization (β = 0.226, p = 0.004) and search accuracy (β = 0.233, p = 0.002) significantly enhance users' attitudes toward the platform. In turn, a positive attitude was found to increase both satisfaction (β = 0.294, p = 0.011) and WOM (β = 0.412, p < 0.001). Subjective norms positively influenced satisfaction but did not significantly impact WOM unless coupled with positive personal experiences. Perceived behavioral control was crucial for driving WOM but did not directly affect satisfaction.
초록
본 연구는 정보 보안 분야의 채용 플랫폼에서 사용자 만족도와 구전효과에 영향을 미치는 요인을 탐구한다. 이 연구는 이용자 맞춤화, 검색 정확도, 태도, 주관적 규범, 인지된 행동 통제를 반영하여 구전의도를 설명하는 것을 목적으로 한다. 연구 결과, 이용자 맞춤화 (β = 0.226, p = 0.004) 및 검색 정확도 (β = 0.233, p = 0.002)가 사용자의 태도를 크게 향상시킨다는 것과, 태도는 만족도(β = 0.294, p = 0.011)와 구전의도 (β = 0.412, p < 0.001)를 유의미하게 제고하는 것으로 나타났다. 주관적 규범은 만족도에 영향을 미쳤지만 (β = 0.234, p = 0.041), 구전효과에는 큰 영향을 미치지 않았다. 인지된 행동 통제는 구전효과를 높이는 데 유의했지만 (β = 0.389, p < 0.001), 만족도에는 직접적인 영향을 미치지는 않았다. 이 연구는 온라인 채용 플랫폼에서 사용자 경험에 영향을 미치는 여러 요소를 통합하는 포괄적인 모형을 제공함으로써 학술적 기여를 제시한다.
Keywords:
distributed graph database, recommender systems, collaborative filtering, cloud computingⅠ. Introduction
The digital transformation of recruitment has revolutionized how organizations find and hire talent, making e-recruiting platforms increasingly significant in today's job market[1]. These platforms offer numerous advantages, such as broadening the reach to potential candidates and streamlining the hiring process[2]. Personalization and accurate search results have become crucial features in these platforms, enhancing user satisfaction and engagement[3]. Studies have consistently shown that personalized experiences and precise job matching significantly impact users' perceptions and behaviors on e-recruiting platforms[4][5].
Despite the recognized importance of personalization and search accuracy, there is still a lack of comprehensive research that integrates these factors with other relevant constructs like attitude, subjective norms, and perceived behavioral control within a single model. Most existing studies have examined these factors in isolation, failing to capture the complex interplay between them[3][6]. This gap in the literature underscores the need for a more holistic approach to understanding user behavior on e-recruiting platforms.
The need for a more integrated perspective becomes even more pressing when considering the rapid advancements in information technology and the evolving nature of job search behaviors[2]. Users now expect highly personalized and efficient experiences, which means that e-recruiting platforms must continuously innovate to meet these expectations. However, the literature has yet to fully explore how these platforms can leverage personalization and search accuracy in conjunction with users' attitudes and social influences to enhance satisfaction and Word-of-Mouth(WOM) promotion. This study aims to address this gap by proposing a comprehensive model that integrates personalization, search accuracy, attitude, subjective norms, and perceived behavioral control. By examining these factors together, this research seeks to provide a deeper understanding of their combined effects on user satisfaction and WOM behavior. This integrated approach not only fills a critical gap in the literature but also offers practical insights for improving e-recruiting platforms.
Investigating the combined effects of these factors is crucial for several reasons. First, understanding how personalization and search accuracy interact with users' attitudes and social influences can help platform developers create more user-centric designs[7]. Second, by identifying the key drivers of user satisfaction and WOM, this study can inform the development of more effective marketing and engagement strategies. Finally, this research can provide valuable insights into how e-recruiting platforms can better support job seekers in the information security field, a rapidly growing sector with unique needs and challenges.
This study focuses on information security professionals as the target due to the critical and unique nature of the information security industry, which faces constant and evolving cyber threats that require highly specialized skills[8]. The demand for qualified professionals in this field is exceptionally high, making the recruitment process particularly challenging and competitive[9]. E-recruiting platforms play a crucial role in matching these specialized candidates with employers, and understanding the factors that influence satisfaction and word-of-mouth among information security professionals can provide valuable insights for improving these platforms. Moreover, the sensitive and high-stakes environment in which these professionals operate means that their expectations and requirements for job platforms may differ significantly from those in other industries, further emphasizing the need for targeted research in this area.
The course of action proposed by this study involves using Partial Least Squares Structural Equation Modeling(PLS-SEM) to analyze data collected from information security job seekers who have used e-recruiting platforms within the past two years. This method is particularly suited for exploring complex models with multiple constructs and indicators[10]. The findings from this analysis will offer a detailed understanding of how personalization, search accuracy, attitude, subjective norms, and perceived behavioral control collectively influence user satisfaction and WOM behavior.
The contributions of this study are manifold. Theoretically, it advances the literature by providing an integrated model that captures the interplay of multiple factors influencing user behavior on e-recruiting platforms. This model not only fills a significant gap in the existing research but also offers a robust framework for future studies. Practically, the insights gained from this research can help platform developers enhance user experience through targeted improvements in personalization and search accuracy. Additionally, the findings can inform marketers and recruiters about the importance of fostering positive user attitudes and leveraging social influences to drive engagement and advocacy.
In conclusion, this study addresses a critical need in the literature by integrating key factors that influence user behavior on e-recruiting platforms. By offering a comprehensive model and providing actionable insights, it contributes to both academic research and practical applications, ultimately helping to improve the effectiveness and user experience of e-recruiting platforms in the information security sector.
Ⅱ. Theoretical Development and Research Hypotheses
The theoretical framework for this research is grounded in the Technology Acceptance Model(TAM)[11] and the Theory of Planned Behavior(TPB)[12]. TAM suggests that perceived ease of use and perceived usefulness are key determinants of users' attitudes towards technology, which in turn influence their acceptance and use[11][13]-[15]. TPB extends this by incorporating the influence of subjective norms and perceived behavioral control on attitudes and behaviors[12]. The five selected factors-personalization, search accuracy, attitude, subjective norms, and perceived behavioral control-are derived from these foundational theories and are particularly relevant in the context of e-recruiting platforms. These factors are chosen because they comprehensively capture the elements that affect user satisfaction and Word-of-Mouth(WOM) behavior. The selection is justified as it balances the core constructs of TAM and TPB, providing a robust framework without overwhelming complexity.
2.1 Personalization
Personalization refers to tailoring services or content to individual users' preferences and needs[16]. In the context of recruitment platforms, personalization can enhance user experience by providing relevant job recommendations and custom content, thereby increasing user satisfaction and engagement[3,][. For job seekers in the information security field, personalized features such as job alerts, skill-matching algorithms, and customized career advice can significantly improve the perception of the platform's usefulness and reliability. Consequently, this increased relevance and perceived value can positively shape their attitude toward the platform. Therefore, this research proposes that personalization will have a positive effect on job seekers' attitudes toward the recruitment platform.
H1. Personalization positively influences attitude toward the e-recruiting platform.
2.2 Search acuracy
Search accuracy refers to the degree to which search results match a user's query and expectations[17]. High search accuracy in recruitment platforms ensures that job seekers find relevant and precise job listings, which can significantly enhance their user experience[6]. For instance, accurate search results can save time and reduce the frustration associated with irrelevant job postings, thereby fostering a more positive perception of the platform[18]. This improved user experience and satisfaction likely lead to a more favorable attitude towards the platform. Consequently, this study posits that higher search accuracy will positively influence job seekers' attitudes toward the recruitment platform.
H2. Search accuracy positively influences attitude toward the e-recruiting platform.
2.3 Attitude
Attitude refers to users' overall evaluative judgment of these platforms[12]. Positive attitudes towards e-recruiting platforms are likely to enhance satisfaction with these platforms, as a favorable view can lead to higher expectations and perceived value[19]. Users who appreciate the platform's ease of use, relevance of job matches, and innovative features are more likely to be satisfied with their overall experience[20]. Furthermore, a positive attitude toward e-recruiting platforms also influences WOM behaviors. Satisfied users are more inclined to share their positive experiences with others, thereby promoting the platform through personal recommendations. Therefore, this study proposes that positive attitudes toward e-recruiting platforms will lead to increased satisfaction and positive WOM.
H3a. Attitude positively influences satisfaction.
H3b. Attitude positively influences WOM.
2.4 Subjective nrms
Subjective norms refer to the perceived social pressure to perform or not perform a behavior[12]. In the context of e-recruiting platforms, if job seekers perceive that important people in their lives, such as family and friends, endorse the use of these platforms, they are more likely to feel satisfied with their own usage[21]. This social approval can enhance users' confidence and satisfaction, as they feel their actions are supported by their social circle[22]. Moreover, subjective norms can also influence WOM behavior[23]. When users experience positive reinforcement from their peers regarding the use of e-recruiting platforms, they are more likely to share their positive experiences and recommend the platform to others[24]. Thus, this study suggests that subjective norms positively impact both satisfaction and WOM regarding e-recruiting platforms.
H4a. Subjective norms positively influence satisfaction.
H4b. Subjective norms positively influence WOM.
2.5 Perceived bhavioral cntrol
Perceived behavioral control refers to an individual's perception of their ability to perform a given behavior[12]. In the context of e-recruiting platforms, users who feel confident in their ability to navigate and utilize the platform effectively are more likely to experience higher satisfaction. This sense of control can lead to a more positive user experience, as individuals are able to efficiently find relevant job opportunities and utilize platform features[25]. Additionally, perceived behavioral control can influence WOM behavior. Users who feel competent in using the platform are more likely to share their positive experiences with others, recommending the platform based on their ease of use and successful outcomes. Therefore, this study proposes that perceived behavioral control positively impacts both satisfaction and WOM regarding e-recruiting platforms.
H5a. Perceived behavioral control positively influences satisfaction.
H5b. Perceived behavioral control positively influences WOM.
Ⅲ. Empirical Methodology
3.1 Instrument development
The constructs in this study were derived from previously validated studies. A seven-point Likert scale was used for all questionnaire items, ranging from 1 (strongly disagree) to 7 (strongly agree). The questionnaire was designed in three parts: the first part gathered general experience information, such as timing of job changes, platform names, number of job changes, and number of successful job changes. The second part assessed users' perceptions of the main constructs, while the third part collected demographic information.
Table A1 lists the constructs and items used in this study, including personalization, search accuracy, attitude, subjective norms, perceived behavioral control, satisfaction, and WOM. Each construct is represented by three items, with corresponding questions designed to measure specific aspects of the construct. References are provided for each construct, including [26] for personalization, [27] for search accuracy, [12] for attitude, subjective norms, and perceived behavioral control, [28] for satisfaction, and [29] for WOM.
Content validity was established through a pre-test with experts from academia and industry, who reviewed the questionnaire for clarity and relevance. Additionally, a pilot test was conducted with voluntary participants from related fields to refine the instrument further.
3.2 Subjects and data collection
The survey targeted individuals who have used e-recruiting platforms to transition into the information security industry within the past two years. This target sample is justified because recent users provide the most relevant and current insights into the effectiveness and user experience of these platforms.
The survey was administered by a professional survey company, Drama & Company, known for their app Remember in South Korea. This company was chosen because they have access to a database of users' basic job information, making it easier to contact suitable participants for our study. The use of a professional survey company and a well-defined target sample enhances the validity and reliability of the study findings. This approach ensures that the data collected is representative of the target population and suitable for addressing the research questions. An online survey was distributed to this panel, explaining the purpose of the study, ensuring anonymity, and emphasizing the voluntary nature of participation. Informed consent was obtained from all participants. Data collection occurred over a two-week period in July 2024. Pre-processing procedures included data cleaning and filtering to remove incomplete or inconsistent responses, ensuring the reliability of the data, resulting in a final total of 154 data points.
To determine the appropriate sample size for this study, this study employed G*Power software for SEM analysis. Specifically, an a priori power analysis was conducted to compute the required sample size, ensuring that the study's results would be robust and reliable. The analysis was based on the following parameters: an anticipated effect size of 0.15, an alpha error probability of 0.05, and a desired statistical power (1-β) of 0.95, with three predictors included in the model. The results indicated a minimum required sample size of 119 participants. Our final sample size of 154 exceeds this minimum requirement, providing sufficient power to detect significant effects and supporting the validity of the findings[30][].
Table 1 shows the details of respondents, with 89.6% male and 10.4% female. Age distribution shows 8.4% not responding, 43.5% in their 20s or younger, 40.3% in their 30s, and 7.8% in their 40s. Educational levels include 1.3% with high school or less, 66.9% with a bachelor's degree, 28.6% with a master's degree, and 3.2% with a doctorate. Income levels are 0.6% earning 0-10,000, 13.6% earning 10,000-50,000, 59.1% earning 50,000-100,000, and 26.6% earning over 100,000.
Profile of the respondents
Ⅳ. Results
This study employed PLS-SEM due to its suitability for exploratory research and its ability to handle complex models with multiple constructs and indicators[10]. PLS-SEM is also effective for small to medium sample sizes and does not require normally distributed data[32].
4.1 Reliability and validity
The measurement model was evaluated using factor analysis and reliability assessments(Table 2). All constructs had factor loadings above 0.7, indicating good indicator reliability[10]. Personalization, search accuracy, attitude, subjective norms, perceived behavioral control, satisfaction, and WOM all demonstrated high internal consistency, with Cronbach's alpha values above 0.7 and AVE values above 0.5.
Factor analysis and reliability
Discriminant validity was confirmed using the correlation matrix and discriminant assessment(Table 3). The square root of the AVE for each construct exceeded the inter-construct correlations, ensuring discriminant validity[33]. This confirms the robustness and reliability of the measurement model.
Correlation matrix and discriminant assessment
4.2 Hypothesis test
SEM analysis was conducted to test and confirm the hypothesized relationships among the constructs of this study. A bootstrap resampling method with 5000 resamples was conducted to check the significance of the hypotheses within the research model. The analysis results are described in Table 4.
Significance testing results of the structural path coefficients
Ⅴ. Discussion
The analysis reveals that personalization significantly influences attitude towards e-recruiting platforms, with a positive beta coefficient of 0.226. This finding aligns with previous research indicating that personalized experiences enhance user engagement and satisfaction[3][4]. The tailored content and recommendations likely make job seekers feel more valued and catered to, which enhances their overall attitude towards the platform. These results suggest that e-recruiting platforms should prioritize personalization features to improve user attitudes and engagement.
Search accuracy was found to have a significant positive effect on attitude, with a beta coefficient of 0.233. This outcome corroborates earlier studies that emphasize the importance of relevant and precise search results in user satisfaction[6][18]. Accurate job listings reduce the time and effort users spend sifting through irrelevant information, which likely boosts their perception of the platform's efficiency and reliability. Thus, enhancing search algorithms to ensure high accuracy can be a critical strategy for improving user attitudes.
Attitude towards the e-recruiting platform significantly influences satisfaction, as evidenced by the beta coefficient of 0.294. This supports the notion that a positive overall evaluation of the platform leads to higher satisfaction levels[19]. When users find the platform easy to use, relevant, and innovative, they are more likely to be satisfied with their experience. This finding underscores the importance of fostering a positive user attitude through various means, such as user-friendly design and relevant job matches, to enhance overall satisfaction.
The significant positive effect of attitude on WOM, with a beta coefficient of 0.412, highlights the role of user attitude in promoting the platform through personal recommendations. Satisfied users are more inclined to share their positive experiences with others, which can help attract new users and enhance the platform's reputation. This suggests that e-recruiting platforms should focus on strategies that build positive user attitudes to leverage WOM as an effective marketing tool.
Subjective norms positively influence satisfaction, as indicated by the beta coefficient of 0.234. This finding aligns with the theory of planned behavior, which posits that social influences affect individual behaviors and attitudes[12][21]. When users perceive that important people in their lives support the use of the platform, they are more likely to feel satisfied. This highlights the importance of social proof and peer recommendations in shaping user satisfaction.
Interestingly, subjective norms did not significantly influence WOM, as shown by the non-significant beta coefficient of 0.081. This contrasts with some prior research suggesting that social influences can drive recommendation behaviors[23]. One possible explanation is that while social approval may enhance satisfaction, it does not necessarily translate into active promotion unless users have strong personal experiences to share. This suggests that subjective norms alone are insufficient to drive WOM and must be complemented by positive individual experiences.
Perceived behavioral control did not significantly influence satisfaction, with a beta coefficient of 0.075. This finding is somewhat surprising given the established link between self-efficacy and satisfaction in various contexts[25]. It may indicate that other factors, such as the actual usefulness and ease of use of the platform, play a more critical role in determining satisfaction than users' perceived control over the platform. This suggests that e-recruiting platforms should focus more on improving the practical aspects of their service.
Lastly, perceived behavioral control significantly influences WOM, with a beta coefficient of 0.389. This aligns with previous findings that users who feel confident and capable in using a platform are more likely to share their positive experiences[34]. This implies that enhancing users' perceived control through intuitive design and comprehensive support can encourage them to recommend the platform to others.
Ⅵ .Conclusion
6.1 Theoretical contribution
This study offers significant theoretical contributions by integrating personalization, search accuracy, attitude, subjective norms, and perceived behavioral control to explain user satisfaction and Word-of-Mouth(WOM) behavior on e-recruiting platforms. Unlike prior research that focused on these factors in isolation, this study uniquely examines their combined effects within a single model. The findings reveal that while subjective norms influence satisfaction, they do not significantly drive WOM unless paired with positive personal experiences, challenging previous assumptions[23]. This underscores the complex interplay between social and personal factors in shaping user behavior. Additionally, the role of perceived behavioral control in promoting WOM highlights the importance of self-efficacy and confidence in user advocacy, suggesting that future research should explore how platform design and support systems can enhance these elements to boost user engagement and recommendations[34].
6.2 Practical implication
In terms of practical implications, this study provides actionable insights for practitioners in the recruitment industry, particularly in the information security sector. For company executives, the emphasis on personalized user experiences is crucial for increasing job seeker engagement and satisfaction. Online recruitment platform operators should focus on refining search accuracy to ensure that job listings match user queries, thereby improving user attitudes toward the platform. Moreover, developers should design intuitive interfaces that enhance users' perceived control, which has been shown to positively influence WOM. Features such as skill-matching algorithms, user-friendly dashboards, and robust customer support are essential for making users feel confident and satisfied, leading to higher platform loyalty and positive recommendations. By implementing these strategies, practitioners can enhance the overall user experience and maintain a competitive edge in the recruitment industry.
6.3 Limitations and future research directions
While this study provides valuable insights, it is limited by its focus on e-recruiting platforms, which may not generalize to other types of digital platforms. Future research should explore these dynamics in different contexts, such as e-commerce or e-learning platforms, to validate and extend these findings. Additionally, examining longitudinal effects could offer deeper insights into how user attitudes and behaviors evolve over time. Finally, a limitation of this study is the focus on five key factors, which, while comprehensive, may exclude other relevant variables such as user experience design or platform trustworthiness. Future research should consider incorporating additional elements like these to further enrich the understanding of user satisfaction and word-of-mouth behavior on e-recruiting platforms.
6.4 Appendix
This section provides a detailed list of constructs and items used in the study. The construct of personalization is derived from [26]. The items used to measure personalization include: 1) PSN1: The purchase offer provided by the recruitment platform fully meets my requirements. 2) PSN2: I can be a very unique job seeker when using a recruitment platform to change jobs. 3) PSN3: The customizations I ask the recruitment platform to make meet my needs.
The construct of search accuracy is derived from [27]. The items for search accuracy include: 1) ACC1: The results of my search for 'information security' jobs on the recruitment platform were accurate. 2) ACC2: I was satisfied with the segmentation of 'Information Security' jobs on the recruitment platform. 3) ACC3: The information security job titles found on the job platform were accurate.
Attitude is derived from [12]. The items measuring attitude are: 1) ATT1: I believe it is desirable to use a recruitment platform. 2) ATT2: I think it is wise to use a recruitment platform. 3) ATT3: I think it is a good idea to use a recruitment platform.
The construct of subjective norms is also derived from [12], with the following items: 1) SNO1: Most people who are important to me (e.g., family, friends) understand that I use a recruitment platform site. 2) SNO2: Most people who are important to me (e.g., family, friends) agree that I use a recruitment platform site. 3) SNO3: Most people who are important to me (e.g., family, friends) support my use of the recruitment platform site.
The construct of perceived behavioral control is derived from [12]. Items include: 1) PBC1: I have the ability to use a recruitment platform. 2) PBC2: I am confident that I could use a recruitment platform if I wanted to. 3) PBC3: I have sufficient resources, time, and opportunity to conduct a job search on a recruitment platform.
The construct of satisfaction is derived from [28], with the following items: 1) SAT1: The recruitment platform I used met my expectations. 2) SAT2: Overall, I am satisfied with the recruitment platform. 3) SAT3: My experience with the recruitment platform met my needs well.
Finally, the construct of Word-of-Mouth(WOM) is derived from [29]. The items are: 1) WOM1: I would tell others positively about the recruitment platform if it launched a job matching service for information security jobs. 2) WOM2: I would recommend the recruitment platform if it launched a job matching service for jobs in the information security field. 3) WOM3: I would encourage others to purchase a job posting on a recruitment platform if it offered a job matching service for information security jobs.
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2005. 2. : BS Degree In industrial engineering SoongSil University
2013. 9. : MS Degree In Management Engineering, Korea University
2017. 2. : PhD Degree in Computer Science in Tech University of Korea
2022. 12. ~ Present : CEO, Secufind Co.,Ltd
Research interests : IT, Information Security Management, Generative AI, Human-computer Interaction