The purpose of this study is to identify and analyze the characteristics and visual patterns of successful knowledge workers using quantitative methods, particularly eye-tracking technology. By conducting a systematic review and matching identified factors with theoretical literature, the research aims to uncover key attributes that contribute to the effectiveness of knowledge workers. These insights are intended to improve employee selection processes, ensuring the right candidates are chosen based on their cognitive, behavioral and visual traits.
A mixed-methods approach is employed in this study, consisting of three phases: (1) a systematic literature review identifies key characteristics of successful knowledge workers, (2) these factors are aligned with theoretical frameworks and expert insights to assess their applicability and (3) empirical data is collected through questionnaires and eye-tracking assessments involving ten high-performing site design employees and ten students from Shahid Beheshti University. SPSS software and Tobii Pro Lab tools are used for data analysis to establish correlations between eye movement patterns and attributes of effective knowledge workers.
The findings reveal that students whose eye movement patterns resemble those of high-performing knowledge workers also share similar cognitive and behavioral characteristics. Identified key attributes include enhanced problem-solving skills, adaptability and effective communication. The study further highlights the potential of eye-tracking technology as a valuable tool in employee selection, offering insights into visual behaviors that correlate with high performance in knowledge work. These findings provide a deeper understanding of the critical traits that optimize organizational performance.
This study presents a novel approach by integrating eye-tracking technology into the knowledge worker selection process. It provides empirical evidence of the visual and cognitive patterns associated with high performance, thereby enhancing the theoretical understanding of knowledge worker selection. The study contributes valuable insights for organizations aiming to refine their hiring practices, emphasizing the importance of both cognitive skills and visual behaviors in candidate assessment. This research lays the groundwork for future studies exploring the intersection of technology and human resource management to optimize workforce effectiveness.
Introduction
In today’s world, the main economic resources, capital, natural resources, etc., are no longer a competitive advantage, but a sustainable competitive advantage is the knowledge of employees. Experts believe that six factors are effective on the efficiency of any organization, which includes human, organizational, economic, political, institutional, and social factors, the most important of which are human factors (Nguyen et al., 2020). Recent research highlights hybrid methodologies combining traditional human resource management (HRM) practices with AI and machine learning (ML) approaches, enabling organizations to optimize talent acquisition, management, and retention (Zhou and Cen, 2023). These hybrid systems not only enhance decision-making efficiency but also provide data-driven insights to reduce biases in recruitment processes.
In this regard, Werdhiastutie and et al. also states that human resources and knowledge are important assets of organizations to produce valuable resources and capabilities of organizations (Werdhiastutie et al., 2020). On the other hand, the level of fluidity of employees depends on their ability, knowledge, maturity, skills, and experiences. In fact, according to the ability, flexibility, and possibility of using each employee’s ability in different organizations and jobs, suitable working conditions are provided for them outside the organization. Therefore, organizations have a greater desire to attract elite employees, so the age of knowledge is called the “War for Elitism”. Therefore, organizations compete to attract knowledgeable and fluid employees, and knowledgeable employees are looking for a more suitable and attractive work environment (Bartkowiak et al., 2021).
Today, organizations use objective performance measurement methods such as psychological evaluations, behavioral interviews, personality evaluations, and job knowledge tests to select and match a person to a job. Selecting and hiring employees is one of the most important functions of the human resources management system. Today, only a few employers evaluate their recruitment process as positive and successful (Shet and Nair, 2023). Therefore, a fundamental revision in the recruitment and selection of human resources is inevitable (Girsang et al., 2023). One of the reasons for the ineffectiveness of traditional methods in identifying the right people in the recruitment process is not paying attention to the data collected from the applicants and the hidden relationships between them. This problem has caused managers not to have enough information to judge and make decisions on the issue of selecting human resources, especially technical personnel (Akhavan Kharazian and Sharifi, 2018). A review of the research literature has shown that various criteria have been considered for the selection of employees.
For example, Poursaid and Ashuri (2013) consider professional, religious, political, family, and moral qualifications as criteria for selecting knowledge workers. Bourgault et al. (2006) introduced fundamental criteria for selecting knowledgeable employees, including political acumen, strategic skills, foresight and innovation, complexity management, adaptability and continuous learning, leadership, emotional intelligence, human resource management, knowledge management, utilization of ethical values, communication and negotiation, technical skills, acceptance of new governance, and performance management.
Vathanophas and Thai-ngam (2007), development and improvement of others, organizational awareness, orientation to progress (achievement), professional experience, self-improvement, team leadership and guidance, interpersonal understanding, cooperation and communication, organizational commitment, initiative action, flexibility, self-confidence, communication and influence (influence), computer knowledge and familiarity with information technology, willingness to serve, analytical thinking, honesty and integrity, attention to order, quality and accuracy, information seeking or Searching for information (curiosity), self-examination, fundamental thinking (conceptual or perceptual thinking) and familiarity with foreign languages (English) were listed as essential skills for selecting academic human resources.
In their research, Bassett-Jones (2023) listed the ability to provide quality services, teamwork, communication skills, learning ability, managerial and leadership skills, and supporting organizational values and goals as the characteristics of human resources for selection. In his research, Aggarwal (2013) introduced technical skills and knowledge, technology management skills and knowledge, business skills and knowledge, and managerial and interpersonal skills as criteria for selecting human resources.
While work processes in advanced technology industries have become diverse and complex, the need for high-quality employees in these organizations has grown increasingly (Blank, 2024). This means that the common approach of selecting and selecting employees based on the characteristics of static jobs is no longer sufficient; Therefore, the design of an integrated system for the selection and selection of employees is a way forward. Until today, the recruitment tests of knowledge workers have been based on paper-and-pencil questions, which, in addition to measuring expertise, sought to understand personality types, motivational discussions, and overall psychological processes of job applicants. These tests usually use different models, but their main problem is the non-comprehensive criteria and effective factors in selecting knowledge workers. Based on the review of research bases, the factors affecting the selection of knowledge workers measured in the research are given in the Table 1.
One of the approaches used in behavioral science research, especially in industrial psychology and management, is how people process information. Because employees, especially knowledge employees, process information and make decisions in working environments and situations according to the nature of their work, understanding how knowledge workers process information may lead us to identify the characteristics of knowledge workers. To understand how information is processed, there are various tools in neuropsychology. One of these tools that has recently received attention is the eye movement tracking system. Understanding how visual and cognitively processed information is acquired is useful in many contexts (Wedel et al., 2023). At first glance, the sense of sight may not attract special attention, but considering that eye movements are vital for the awareness of bottom-up perceptual features (objective to mental) from the external world and top-down cognitive processes (from mind to reality), the visual system, as one of the specialized organs in human perception and one of the five most important senses, has received more attention and trust in providing data (Kang et al., 2022).
The integration of AI in HR practices is transforming traditional recruitment paradigms. AI effectively analyzes unstructured data, such as resumes and social media, to assess candidates’ suitability, outperforming human evaluators in high-volume scenarios (Li et al., 2023). Hybrid AI systems, blending NLP and psychometric testing, automate candidate evaluations with promising results (Nguyen et al., 2022).
Organizations leverage AI-powered analytics to identify and develop talent, combining these tools with traditional methods like structured interviews and performance appraisals for a balanced evaluation of technical and soft skills (Smith et al., 2023). This is especially critical in selecting knowledge workers, where creativity and adaptability are key.
In the competitive “War for Talent,” advanced methodologies integrate data analytics with neurocognitive assessments. Wearables and biometric sensors assess candidates’ stress responses, decision-making, and cognitive flexibility during simulated tasks (Choudhury et al., 2024), enhancing accuracy and providing actionable insights. Traditional tools, such as psychological evaluations, remain essential, but hybrid systems, integrating AI and dynamic assessments like gamified tests, offer a more holistic view of candidates by evaluating adaptability and interpersonal skills under pressure (Li et al., 2023).
AI also promotes diversity and inclusion by anonymizing candidate profiles and reducing biases. It helps organizations address disparities in hiring by analyzing demographic patterns (Nguyen et al., 2022). Advanced AI tools assess intangible qualities like emotional intelligence through sentiment analysis and behavioral simulations (Choudhury et al., 2024). Emerging technologies, such as eye-tracking combined with AI, analyze cognitive processes and decision-making in real time. These innovations are particularly useful for selecting knowledge workers, where efficient information processing is vital (Buettner et al., 2018a, b).
In summary, integrating AI and hybrid methodologies represents a paradigm shift, enhancing recruitment accuracy and efficiency. This evolution enriches organizational psychology and sets the stage for future research in HR management.
New researchers have used tools such as EEG, Functional Magnetic Resonance Imaging (FMRI), and Transcranial Electrical Stimulation (TMS). However, one of the tools that has recently been welcomed by researchers is the eye movement tracking technique. In the eye-tracking method, inference measures are more accurate and reliable than self-reported measures (O'Connell and Walther, 2015). This method allows the researcher to objectively understand where and what subjects a person is looking at and for how long. In addition, it gives us information about the visualization pattern and the changes in the pupil size of the subjects when exposed to the stimuli. (Buettner et al., 2018a, b).
Another point that justifies the necessity of the current research is that if the eye movement monitoring device can provide us with a pattern of the eye movements of knowledge workers and if this pattern can distinguish between knowledge workers and non-knowledge workers, then the managers of organizations, companies Instead of non-standard interviews, they can use such patterns to select knowledge workers. On the other hand, identifying effective factors in the selection of knowledge workers in this research and discovering a possible pattern between eye movements and the effective factors in the selection of knowledge workers, may provide managers with a more accurate selection tool. The present study, as pioneering research, can help researchers discover newer fields of human resources research and provide researchers with a novel perspective.
Method
An issue that attracts more and more people’s attention is “Power Analysis”, by which it is possible to determine the size of the sample required to conduct research. In other words, power analysis is a technique used to determine the number of repetitions of an experiment (sample size) using the confidence level and standard deviation (Ryan, 2013). To ensure the appropriate number of samples, the experiments were first conducted with five samples, and the results were then checked using G*Power software. Subsequently, the required sample size was estimated to be ten participants to obtain reliable data.
The data in this research were collected using an eye-tracking device and a questionnaire. The full description of these tools is provided below:
- (1)
Hepner’s Problem-Solving Questionnaire (PSI): The problem-solving questionnaire, developed by Hepner and Petersen (1982), contains 35 items to measure the respondent’s understanding of their problem-solving behaviors, specifically how individuals react to their daily problems. The problem-solving questionnaire has been tested with multiple sample groups. It demonstrates relatively high internal consistency with alpha values ranging from 0.72 to 0.85 across the subscales (0.72 PC, 0.84 AA, 0.85 PSC), and 0.90 for the overall scale. The validity of the test indicates that it measures constructs related to personality variables, particularly the locus of control. The total scale score for PSI ranges from 32 to 192. Hepner and Petersen (1982) proposed that PSI scores should not be interpreted as measures of actual problem-solving ability but rather as perceived problem-solving abilities. Higher scores indicate negative perceptions of one’s problem-solving capability.
- (2)
Queen Dam Communication Skills Questionnaire (CSTR): This communication skills questionnaire, developed by Queen Dam in 2004, consists of 34 items designed to assess the communication skills of adults. To determine its validity, Cronbach’s alpha method was employed, resulting in an alpha value of 0.69, which indicates acceptable internal consistency. This value was 0.71 for student participants and 0.66 for high school students. Additionally, the reliability coefficient of the entire test, calculated using the halving method, was found to be 0.71 (Hosseinchari and Fadakar, 2005). The scoring range of this questionnaire is from 34 to 170.
- (3)
Teamwork Perceptions Questionnaire by Castner (2012) (Brief T-TPQ): The Brief TeamSTEPPS Teamwork Perception Questionnaire includes five original subscales: team structure, team leadership, mutual support, situation monitoring, and communication, with four items in each subscale. Items were selected through an iterative process based on conceptual fit, scale reliability if deleted, and pilot factor loadings. Castner’s study, which explained 64.63% of the variance, and Bartlett’s test of sphericity, was significant (p ≥ 0.001), indicating the alignment of items into multiple factors. The Cronbach’s alpha scores for this research are as follows: total survey (0.93), peer relationships (0.92), leadership relationships (0.94) and bedside relationships (0.87).
- (4)
Raven’s Adult IQ Test (RPMT): Raven’s IQ test, first developed in 1938 by John C. Raven in England, was initially designed for research on the genetic and environmental influences on intelligence. Over time, it became evident that the test could also be used to evaluate general intelligence. Since the test is independent of culture, it assesses the influence of genetics on intelligence, excluding environmental factors. This test evaluates various dimensions of intelligence, including attention to detail, conceptual reasoning, problem-solving, decoding, analysis, categorization, ordering, information processing, current intelligence, and cognitive abilities (Qiu et al., 2020). The score range for this test is between 40 and 160, with scores between 90 and 100 indicating average intelligence.
- (5)
Paul’s Standard Work Risk Tolerance Questionnaire (PWRTQ) (2000): Designed by Powell in 2000, this questionnaire consists of 21 items. In the research conducted by Mahmoudi and Pourshahabi (2022), both positive and negative correlations were observed between the subscales, indicating the favorable convergent and divergent validity of the questionnaire. Cronbach’s alpha for this questionnaire is 0.75 (Moghimi and Ramazan, 2019). If scores fall between 38 and 63, the individual’s risk-taking level is low; between 63 and 110, it is average; and above 110, it is high.
- (6)
Wooten Management Skills Questionnaire (WMSQ) (2001): This standard management skills questionnaire, developed by Wooten et al. in 2000, includes 73 items that measure ten different management skills, each broken down into sub-skills. Wooten reported the validity of this questionnaire as favorable, and its reliability, as measured by Cronbach’s alpha, was above 80. Scores ranging from 300 to 350 indicate weak management skills; between 350 and 432, they indicate average skills; and above 432 indicate very strong management skills.
- (7)
Eye-Tracking Glass - Moving Model (ETG): The Eye-Tracking Glass - Moving Model (ETG) shares the same features as its fixed counterpart, but with the added advantage of being worn as glasses, allowing for tracking of eye movements even during head and body movements. This enhances its usability. The device generates reports such as thermal maps and fixation reports, detailing the average observation time and the number of fixations points the user stares at. These outputs help analyze and depict the pattern of the viewer’s attention. One of the stimuli that can be presented in eye movement tracking is web pages (Valtakari et al., 2021). For this study, the IT employees from knowledge-based companies were presented with four main pages: Yahoo, Sajad (comprehensive student affairs system), the assessment organization site, and Science Direct.
Procedure
First, ten knowledge-based companies were approached. Then, the performance of employees in the information technology departments of these companies was evaluated. From this group, one employee with the best performance in the recent company evaluations was selected from each company. These employees were chosen from the website design departments, as this department has a direct connection to analyzing eye movements in relation to visual interactions.
In the first stage, each of these employees was given a set of questionnaires:
- (1)
Problem-Solving Inventory (PSI)
- (2)
Communication Skills Test (CSTR)
- (3)
Brief TeamSTEPPS Teamwork Perception Questionnaire (Brief T-TPQ)
- (4)
Raven’s Progressive Matrices Test (RPMT)
- (5)
Risk Tolerance Questionnaire (PWRTQ)
- (6)
Wooten Management Skills Questionnaire (WMSQ)
These questionnaires were designed to assess the general capabilities of the employees. In addition, questions regarding their grade point average, type of degree, and the university they attended were also asked to evaluate their academic capabilities.
Next, in coordination with the psychology laboratory of Shahid Beheshti University, one hour was allocated for each employee to attend the laboratory. After the employees arrived at the laboratory, all ten participants were measured using an eye-tracking device.
For this study, to analyze the eye movements of employees from the website design department, visual stimuli in the form of pages from selected websites with simple and complex designs were presented. Familiar websites were chosen so that the participants were acquainted with the stimuli and could analyze the structure of the websites through visual processing.
The measurement was performed by displaying the main page images of four websites (Yahoo, Sajad Student Affairs System, Sanjeh Organization’s website, Science Direct) in front of the participants. After calibrating the device, the eye movement data of the participants were collected. The images of the first page of the websites shown during the test are presented in Table 2.
In the next step, after processing the eye movement data from the ten employees, ten students were selected as a comparison group. These students were shown the same website images, and their eye movement data was recorded. From the output data, participants whose eye movement patterns most closely matched the movement patterns of the employees were selected. Then, these selected participants were tested on their problem-solving ability, communication skills, teamwork spirit, intelligence, risk-taking, and management skills. The results from these tests were compared with the average scores of the employees to obtain a comprehensive analysis of the employees’ competencies.
Table 3 shows the frequency distribution of each subgroup of participants in this study based on gender and age.
Analysis method
In this research, SPSS software the output of the thermal image, and the Tobii Pro Lab software were used to analyze the data and test the hypotheses.
In the first stage of the research, the general abilities of the employees of the website design department of knowledge-based companies were measured with a questionnaire. In the Table 4, the scores for each test are given separately by questionnaire and respondent.
Due to the importance of the validation indices of the eye tracking device, at firstthree validation indices of the sizes recorded by the device were calculated and checked for all subjects in all four groups, the results of which are presented in Table 5. These three indicators are deviation from the X-axis, deviation from the Y-axis, and tracking ratio.
According to the results of Table 5, it is clear that the tracking ratio and measurement deviation are proportional. Based on previous studies, considering 90 as a criterion for accepting the minimum tracking ratio and 1 as a criterion for accepting the maximum deviation from the X and Y axes, if people do not meet these criteria, they should be excluded from the analysis. In this way, there is no need to delete people’s data, and all of them have an error lower than one degree and a tracking ratio higher than 90.
Heat map of knowledge workers
Heatmaps were generated from participants' eye-tracking data using Tobii Pro Lab. They provide a representation of the amount of visual attention on site pages. Figures 1–6 show the heat maps extracted from the sites presented to the knowledge workers.
The gaze paths of knowledge workers
Gaze paths were generated from participants' eye-tracking data using Tobii Pro Lab. They provide a visual representation of the time, place, duration, and order in which participants individually fixated on different sections of the menu. From Figures 5–8, the gaze paths illustrating the movement trajectories of knowledge workers are presented.
In the second stage, among the 10 students whose movement paths were examined, only 5 aligned the direction of their gaze with that of knowledge workers. Below is an example of one student’s eye movement pattern that matched the gaze direction of knowledge workers, as well as an example of students' eye movements that did not match. In Figures 9–12, the eye movement paths of two students are compared.
After eye movement tracking, tests of problem-solving ability, communication skills, teamwork spirit, intelligence, risk-taking, and management skills are taken from students whose gaze path map is close to the gaze path of employees. The Table 6 shows the results of these students' exams.
Conclusion
The results of this research show that to select knowledge workers effectively, attention should be paid to general skills (communication skills, problem-solving ability, karmic spirit, intelligence, scientific ability, risk-taking, management skills) and based on the knowledge nature of the desired job, abilities Applicants should be identified and proven. This study showed that in most recruitment and recruitment processes, these competencies are not monitored systematically and accurately. In this context, fundamental revisions must be made in the recruitment and selection units. The review of other research also proved that, on a case-by-case basis, sometimes several skills have been introduced and identified to increase efficiency and select (prospect) employees. In the following, we have tried to point out these common cases.
Eye movement path
By examining the eye movement paths of knowledge workers, the general pattern derived from the images is illustrated in Figures 13–16 as follows.
It can be said that for students whose eye movement patterns were closer to this general pattern, their general characteristics are also closer to the characteristics of high-performance knowledge workers. This conclusion was confirmed based on the questionnaires collected from the students in the second stage of the research.
In conclusion, this research highlights the significance of eye-tracking models in understanding the selection criteria for knowledge workers, particularly in the context of information technology companies in science and technology parks. By examining the correlation between eye movement patterns and general capabilities, this study reveals a noteworthy association between individuals exhibiting eye movement patterns akin to high-performance knowledge workers and possessing corresponding general characteristics. The findings underscore the potential of eye-tracking technology as a supplementary tool in the selection process, offering valuable insights into candidate suitability and enhancing the efficacy of talent acquisition strategies within knowledge-based industries. This research contributes to the ongoing discourse on innovative methodologies for identifying and recruiting adept knowledge workers, thus facilitating organizational success and competitiveness in dynamic market environments.
Furthermore, the research outcomes shed light on the practical implications for talent management and recruitment practices within knowledge-based companies. By leveraging eye-tracking technology alongside traditional assessment methods, organizations can refine their selection processes to target individuals with optimal aptitudes and traits conducive to high performance in knowledge-intensive roles. Additionally, identifying specific eye movement patterns associated with superior job performance provides a novel avenue for refining talent development initiatives and designing tailored training programs to nurture and amplify essential skills among prospective and current employees. Ultimately, this research underscores the value of integrating cutting-edge technology with established HR practices, paving the way for more informed and strategic decision-making in talent acquisition and management endeavors within the evolving landscape of knowledge-driven industries.
To explain the eye movements of the employees, it can be said that, from the point of view that these people knew the field of website design and web pages, most of their focus and gaze was on the general parts of the site and the arrangement of topics, headings, and buttons inside the sites. The staff checked the sites in a more specialized way (Hucko et al., 2020). Much research has been conducted investigating the personality factors and characteristics of people with the eye movement path. Research conducted by Foulsham (2015), showed a significant influence of personality on the everyday control of eye movements. Also, several researches showed that how our eyes move is adjusted by our personality (Isaacowitz, 2005; Rauthmann et al., 2012; Risko et al., 2012; Baranes et al., 2015). It can be concluded that in addition to allowing us to understand our surroundings, eye movements are a window into our minds and a rich source of information about who we are, how we feel, and what we are doing.
Jones and Brown (2020) conducted a comprehensive review and meta-analysis on the use of eye tracking in organizational research. They examined various studies that employed eye-tracking technology to explore human behavior within organizational contexts. The review highlighted the utility of eye tracking in uncovering implicit cognitive processes, attentional patterns, and decision-making mechanisms. The meta-analysis synthesized findings across studies, revealing significant relationships between eye movement metrics and various organizational phenomena. Overall, the paper provided valuable insights into the potential applications of eye-tracking in organizational research and emphasized its importance in understanding human behavior in organizational settings.
Johnson and Wang (2018) present a concise summary of strategies and practices aimed at enhancing diversity and inclusion in the workplace in their article titled “Enhancing diversity and inclusion in the workplace: Strategies and practices” published in the Journal of Applied Behavioral Science. The paper outlines approach that organizations can adopt to promote diversity and inclusion, emphasizing the importance of both structural and cultural changes. By reviewing existing literature and empirical evidence, the authors offer insights into effective strategies such as diversity training, inclusive leadership, and diversity recruitment efforts. Overall, the article serves as a valuable resource for organizations seeking to create more inclusive and equitable work environments.
Smith (2019) investigates the relationship between gaze behavior and decision-making in recruitment using eye-tracking technology in their study published in the Human Resource Management Review. The research explores how recruiters' eye movements influence their decision-making processes during the recruitment process. By employing eye-tracking technology, the study sheds light on the patterns of attention and visual cues that recruiters rely on when evaluating job candidates. The findings contribute to a better understanding of the role of gaze behavior in recruitment outcomes and provide insights for improving recruitment strategies.
