Katz_Jonathan-IS (3)

The College of Wooster

All is Fair in Love and Work:

A Comparison of Job Search and Dating Websites

by

Jonathan Katz

Presented in Partial Fulfillment of the

Requirements of Independent Study Thesis Research

Supervised by

Dr. Barbara Thelamour

Department of Psychology

2015-2016

Katz 2 A COMPARISON OF JOB SEARCH AND DATING WEBSITES

Table of Contents

Acknowledgments …………………………………………………………………………….......5

Abstract……………………………………………………………………………………………6

Introduction………………………………………………………………………………………..7

Social Media………………………………………………………..……..........................8

Uses of social media………………………………………………………………8

Traits of users……………………………………………………………………...9

Learning from companies’ branding on social media……………………………10

Attraction………………………………………………………………………………...13

Attraction and Arousal…………………………………………………………………...18

Measure of arousal……………………………………………………………….18

Attraction and eye tracking………………………………………………………20

Factors that influence arousal and attraction online……………………………………..22

Gender....................................................................................................................23

Race………………………………………………………………………………24

Picture professionalism…………………………………………………………..26

Current Study…………………………………………………………………………….27

Hypotheses……………………………………………………………………………….27

Method…………………………………………………………………………………………..30

Participants……………………………………………………………………………….30

Materials…………………………………………………………………………………30

Procedure...………………………………………………………………………………31


Results……………………………………………………………………………………………33

LinkedIn……………………………….............................................................................33

Professionalism of target profile……………………………………………...….33

Race of target profile……………………………….……………………………34

Gender of target profile…………………………………………………………..35

Hiring of job seekers……………………………………………………………..36

Match.com…………………………………………………………..…………………...37

Professionalism of target profile……………………………………………...….37

Race of target profile……………………………….……………………………43

Gender of target profile…………………………………………………………..47

Comments on profiles…………………………………..………………………..50

Discussion…………………………………………………………….………………………….64

LinkedIn…………………..……………………………………………………………...65

Match.com…………………………………………………………………………….…70

Limitations and Future Research …………………………………...………….………..73

Sample…………..…………………………………………...…………………...73

Materials……………………………………………………………….………..74

Missing Data Cell……………………………………….…………….…………75

Future Research………………………………….………………………………75

Conclusion……………………………………………………………..………………...77

References………………………………………………………………………………………..78

Appendix A: Demographic Information Form…………………………...……………………...92

Appendix B: Recruiter Survey………………………………………………..………………….93


Appendix C: Relationship Seeker Survey…………………………..……………………………94

Appendix D: Example of Match.com Profile………………………………...………………….95

Appendix E: Example of LinkedIn Profile………………………..……………………………..96


Acknowledgments

I would like to thank my advisor, Dr. Barbara Thelamour, for her invaluable help during the

process of creating this study. I would also like to thank Dr. Bryan Karazsia of the College of

Wooster’s Psychology Department, as well as Dr. Gary Katz of the California State University of

Northridge Psychology Department for their significant contributions to the project. Lastly, I

would like to thank my family and friends for their support during the process.


Abstract

This study analyzes what attracts/influences recruiters and relationship seekers when viewing

LinkedIn.com and Match.com profiles respectively. It will also look to see if the gender, race, or

professionalism of the person in the profile has an effect on how much time is spent looking at

the profile and how attractive the participants find the individuals in the profile. Visual behavior

(e.g., what participants look at most) was investigated through the use of MH100 Smart Glasses.

Additionally, participants were asked to complete a survey based upon the strengths/weaknesses

of 15 Match.com profiles and the 10 LinkedIn profiles. Attractiveness to the profiles for

Match.com and the willingness to hire the profiles for LinkedIn was also measured. Participants

had 20 minutes to look through 10 LinkedIn profiles or through 15 Match.com profiles. The

specific numbers of profiles were chosen based upon the need for variety in race, gender, and

professionalism capacities. A series of 2 X 2 Analyses of Variance (ANOVAs) was used to

analyze any differences between the viewing of different LinkedIn profiles and Match.com

profiles. A series of Chi-squared test of independence were tested to see if there was a

relationship between the LinkedIn target profiles and the gender of the recruiter. Participants

(recruiters) spent significantly more time viewing the professionally styled profile pictures for

LinkedIn in comparison to the unprofessional LinkedIn profile pictures. In addition,

unprofessional minority male and unprofessional minority females were more likely to be hired

by female recruiters in comparison to the male recruiters. Lastly. the professional profiles for

Match.com were rated as more attractive than the unprofessional profiles for Match.com.


Introduction

So much of today’s world happens in a digital space. Whether it is job searching, online

dating, or news scanning, the extent to which information is accessible in a matter of seconds is

endless. Images and videos stream online at rates higher than ever in history. The sheer amount

of content that is available to skim, read, or summarize is constantly growing at significant rates

every day and the search engines such as Google, Yahoo, or Bing are shifting through this data.

Rayner, Smith, Malcolm, and Henderson (2009) found that viewers can distinguish and extract

the main points of a visual scene within a mere 40 to 100 milliseconds of exposure. That means

that job recruiters or relationship seekers usually only take less than a second to let the content

on the screen grab their attention. Every website designer is attempting to grab the attention of

visitors. The more frequently a website is visited and the longer the interaction, the more likely

the page will appear in the top links on search engines as well. Getting viewers to “click” on a

website is the job of website designers or marketers, but every individual who has a LinkedIn

profile or Match.com are their own marketing team for themselves.

Based on these uses of the internet to attract people, a few questions arise: Can the image

of an attractive individual take the attention away from the rest of a web page? Do gender or

racial biases affect hiring or dating trends? In this empirical study, recruiters and relationship

seekers will use an eye-tracker to predict whether there are similarities or differences between

what they most often view on a LinkedIn.com and Match.com profile page. This study will

additionally analyze whether professional attire, gender, or race influences desirability by a

recruiter or relationship seeker as well as the how an image on a profile influences the time spent


on an “about me” section of a profile. Lastly, the study will examine whether there are

similarities between a professional profile and a casual dating profile.

Social Media

Uses of social media. Research has identified that the increase in Internet usage has

made everything people look to do (e.g., relationship seeking, product buying and selling,

television shows) accessible via online resources. Jones and Fox (2009) found that social

networking sites and instant messaging are being utilized for communication purposes and more

than a third of all Internet users are using these resources. The current study’s definition of social

media use is “the discrete intake of digital media or Internet that focuses on areas outside

traditional media use” (Buettner, 2016, p. 1). It provides a mechanism for society or an

individual to connect and communicate via social networking or instant messaging sites. In one

study, college undergraduates developed a diary-like response to their daily usage on the popular

social media site Facebook (Pempek, Yermolayeva & Calvert, 2009). Users in Pempek’s study

spent an average of 30 minutes on Facebook per day. The authors found that during these 30

minutes, users spent more time observing content on Facebook compared to posting content

while also expressing their personal identity through posts or articles.

Online social networking is not being used exclusively by the millennial generation.

There has been a significant increase in adult social media usage over the past several years and

this type of internet use has spread across generations (Lenhart, 2009). Over a third of adult

internet users report having a social networking site (Lenhart, 2009). Research indicates

similarities in online (e.g., texting, posting, tweeting) and offline (e.g. everyday vocabulary)

communication for emerging adults with their peers and family members (Subrahmanyam,

Reich, Waechter, & Espinoza, 2008). Use of these online media helped emerging adults


reconnect with relationships that may have splintered since going off to separate colleges after

high school graduation. Many of the older students in the study stated that they would use social

networking sites like Facebook to stay in touch with friends who go separate ways after college

graduation. After understanding which age groups are currently utilizing social media, it is

important to analyze the type of social media user.

Traits of users. Social media outlets such as Facebook, LinkedIn, or YouTube have

expanded to reach hundreds of millions of users. Psychological research behind the effects of

social media on current users is a new topic and has the potential to evolve into a separate field

of study. Extraversion has been found to be positively correlated with social media usage, but a

more emotionally unstable male was more likely to be a regular user (Correa, Hinsley, & Zuniga,

2009). Another study found that extraversion, neuroticism, and openness to experience were the

big three personality traits the majority of social media users embodied. Later research by Correa

et al. (2009) examined not only the gender and age of these users but also investigated factors

such as life satisfaction and socio-economic background.

Studies have also analyzed the amount of time that Generation Z (i.e. “millennials,” born

after the year 1994) have interacted online. Results of these studies indicate that more than one-

third of millennial users check their profile page daily and almost another 25% visit every few

days (Correa et al. 2009). In addition to constantly checking their social networking sites, these

young adults are also more open to experiences (Ross et al., 2009) which correlates with higher

curiosity levels. Having higher curiosity and novelty levels will allow them to stay online even

longer and search for a greater range of information as their curiosity continues to grow with

each click of the mouse.


Overall emotional well-being greatly influences social media as well. Research indicates

that participants who were high in positive collective self-esteem were more likely to utilize

social media to communicate with their groups (Barker, 2009). In addition, individuals who

identified with negative collective self-esteem communicated with their social groups as an

alternative form to communication compared with the uses of the high collective self-esteem

group. Gratification and social compensation are two other emotional rewards associated with

social media use (Barker, 2009). Online usage and the connection of these online resources to

social well-being has significantly impacted perceived gratification levels by users. Previous

literature indicates that individuals are devoted, engaged, and highly motivated to spend effort

and time in uploading content to social media sites (Boyd and Heer, 2006).

Learning from companies’ branding on social media. Social media has been found to

be beneficial for businesses, provided that they use it strategically. By avoiding negative

comments, questions (which cause forced answers), and entertainment/call to action messages on

brand fan pages, a company can increase their visibility and popularity on social networking sites

(Vries et al. 2012). Having positive news about the company and strategic content placement as

well as sharing positive customer feedback are all ways to increase attractiveness of the

company’s website (Vries et al. 2012). This in turn is a direct reflection of the company itself.

One could suggest that the more attractive a company’s website is to a user, the more likely

customers will purchase products from that company.

Social media sites are categorized by different functionalities and capabilities. For

instance, Friendster, Sixdegrees, and Hi5, all pre-Facebook social media sites, focused their

business platforms on creating friend lists and profiles while also having the ability to connect


with friends-of-friends (Boyd and Ellison, 2008). Myspace, YouTube, and Flickr focused on

photos and shared music. Integrated marketing communications, a term developed by Boone and

Kurtz, (2007), describes how individuals are able to advertise through their own personal public

relations outlet and sell themselves through direct marketing strategies which potentially could

lead to employment promotions. A few functions that major social media sites offer for users

when presenting themselves include wide-ranged “word-of-mouth” blogs, discussion boards, and

chat rooms that can be hosted by companies/organizations, product/service ratings, as well as

social networking sites (Mangold & Faulds, 2009). Companies have now utilized social

networking sites during business to business (B2B) branding in order to connect with other

organizations. Thanks to the advent of sites such as Web 2.0, there are new and more efficient

ways to share, upload, and communicate content online (Enders, Hungenberg, Denker, & Mauch,

2008). Social networking sites such as Facebook, who has the most unique visitors globally, as

well as Twitter and LinkedIn connect individuals, groups, and organizations through

contemporary commercial online interaction (NielsenWire, 2010). According to Michaelidou,

Siamagka, and Christodoulides (2011), over a quarter of business to business interactions were

utilizing social networking sites to achieve their brand objectives with attracting new customers

as their number one priority. Increased market spending on this specific channel also illustrates

the importance of utilizing social networking sites for businesses on a non-business to consumer

platform. (Michaelidou, Siamagka, & Christodoulides, 2011). This modern form of business-to-

business interaction translates to a person-to-person online platform as well.

Individuals also have the ability to promote both their professional and personal selves.

Vries, et al. (2012) studied how specific page content influences website’s popularity. By

observing how a company brands itself on social media, a person can learn how to “brand”


themselves via social networking sites. Results from Vries’ study specifically looked at location

of brand posts on pages as well as the type of content that should be posted, but one could

suggest that these type of results could relate to an individual’s own personal fan page (e.g.,

Facebook, LinkedIn, Match.com). Yet, the most important finding by Subrahmanyam et al.

(2008) which was later replicated by Pempek et al. (2009) is the fact that individuals would

express their personal identity on these social networking platforms. By having the ability to

self-promote on the internet, there are possibilities for increased engagement of content and

information that is out there for people to research.

The most common use of social networking sites such as Facebook is uploading and

sharing of photos. Many individuals utilize photo sharing for both intrinsic and extrinsic needs

(Nov, Naaman, Ye, 2010). Yet individual differences have been found for reasons behind social

media usage. Depending on an individual’s perceived identity, users will either depict

themselves as “self-revealing” or “responding to others” (Chang, 2015). Self-revealers will be

more likely to disclose personal information such as political opinions, day-to-day activities, or

personal feelings. When an individual self-reveals on social media, they are projecting their

thoughts, ideas, passions, and opinions on a world-wide platform. From there, viewers of profiles

are able to get a sense of who a person is and what he/she represents.

When viewing online profiles, the different types of content being viewed have an impact

on the viewers of the profiles. One study conducted by Burkell and Saginur (2015) compared

how rural vs. urban girls and young woman viewed online social networking sites differently.

There findings were consistent with earlier research conducted by Fischer (1982) which

suggested that individuals living in urban settings had fewer relatives in their social network and

less densely connected networks. This is contrasted with the smaller, but highly dense social


networks of individuals living in rural areas. Fischer (1982) also pointed out that individuals

from urban backgrounds are more tolerant of deviations from traditional norms such as religion

or sexuality compared to people with rural backgrounds.

Outside of an individual’s setting and where people live, factors such as race and gender

also impact how individuals view online profiles. Research suggests that whites are least open to

dating outside their race (i.e. out-dating) (Robnett & Feliciano, 2011). Yet an interesting

discovery by Robnett and Feliciano (2011) illustrated how Asians and Latinos are adopting this

type of racial exclusion that is similar to whites. Early research suggests that in addition to the

economic and structural organization of society, racial boundaries are created due to intimacy

preferences (Blumer, 1958). Racial boundaries between whites and minorities has been

extensively studied (Robnett and Feliciano, 2011) and research has shown that most people

prefer to date within their own racial group (Gordon, 1964; Kalmijn, 1998). In the present study,

attractiveness is being measured alongside strengths/weaknesses of online dating profiles

(specifically Match.com). Whether racial/gender boundaries are portrayed through the survey

responses and how these potential boundaries may influence attractiveness level of these online

profiles has yet to be researched.

Attraction

Physical appearance and sexual identity are the most accessible characteristics to others

in social interaction (Dion, Berscheid, & Walster, 1972). Attractiveness has been a key influence

on recognition and memory as well. Social psychology defines attraction as the natural feeling of

being drawn to other individuals and desiring their company (Psychology Dictionary, n.d.).

Beauty or attractiveness levels are two common characteristics used when describing a person’s

physical attributes. Whether arousal and attractiveness are related may depend on how


memorable one individual is to another individual based upon attraction and arousal levels.

According to Singer (1984), men and women differ in how they recognize personal sexual

arousal with men being better able to identify their sexual arousal than women. Sexual arousal

recognition is how individuals are able to comprehend and come to the realization that they have

become sexually aroused. In addition, research suggests that “neutral” faces, not negative or

“unattractive” faces are the least memorable (Cross, Cross, & Daly, 1971).

First impressions are powerful in that having a positive primacy effect on another

individual can greatly increase your chances of corresponding with that individual again. First

impressions and attractiveness tend to have a corresponding impact. In connection with primacy

effects (i.e. reasons such as being a juror on a court case), people tend to stand true to primacy

effects and hold to their original impressions of an individual (Tetlock, 1983). This is why it is

essential to make a strong, positive first impression when meeting with someone online or

offline. This research dates back to Asch (1964) and the findings that suggest that a multitude of

characteristics are weighed into how a person perceives another individual. In addition, when

characteristics are disassociated, the disassociated perception tends to hinder our ability to form

an accurate depiction and representation of an individual.

Forming an attractive first impression that is recognizable is crucial when developing

one’s personal brand. Commonality and similarity are more easily forgotten in facial recognition

(Hunt, 1995) but distinctiveness of objects do create more significant memories. Displaying

characteristics that separate oneself from other people (e.g., competition in the job market) in a

positive manner will make that individual more memorable. Furthermore, distinctiveness

positively correlates with actual and predicted recognition of faces (Sommer, Leuthold, Matt, &

Schweinberger, 1995). This finding further reiterates the importance of creating a unique


external image of oneself. Attractiveness has been attributed as a distinctive feature when

observing and commenting on people’s faces. For instance, Cross, Cross, and Daly (1971) found

that after a surprise test on facial recognition, the faces that were rated as more attractive were

more likely to be remembered. Later research found that not only attractive, but unattractive

faces were the most memorable (Fleishman, Buckley, Klosinky, Smith, & Tuck, 1976). Both of

these findings suggest that attractiveness or unattractiveness are distinct features of an individual

and these distinct features, whether positive or negative, will be memorable.

Commonality, or the state of sharing features or attributes, has an effect on distinguishing

between images since the more common a type of image is, the less likely it will be to

distinguish between common images. A more gender-specific study suggested that attractive

females and unattractive males were the most easily recognized (Yarmey, 1979). A meta-analytic

review was performed concerning the relationship between physical attractiveness and

intellectual competence (Jackson, Hunter, & Hodge, 1995). Two major findings by Jackson et al.

(1995) concluded that attractiveness effects were stronger for males than for females and even

stronger when explicit information about competence was absent than when it was present.

These findings coincide with the theory that direct measures of competence, compared with

indirect measures of competence were influenced strongly by attractiveness (Jackson et al., 1995.

Feingold’s (1992) earlier findings suggest more attractive participants are not distinguishable

from less attractive participants when analyzing personal characteristics, skills, or attributes.

Studies have found that more attractive individuals are perceived to have more socially

desirable characteristics, regardless of sex (Dion, Berscheid, & Walster 1972). Socially desirable

characteristics range from a perceived sense of humor to intelligence. Aside from the

professional benefits of being attractive, physically attractive individuals may have advantages


outside on regular dating benefits. The material benefits and happiness that are associated with

being beautiful attract individuals toward attractive marriage partners. Yet, the professional

benefits that are associated with beautiful people could directly affect people in the job market.

This past research shows that more attractive people perceived to be happier, live happier lives,

and be more successful than unattractive people. Additionally, teachers favor better looking

students (Clifford & Walster, 1973), voter preferences in political elections lean toward more

beautiful people (Efran and Patterson, 1974), and jury judgements favor the more attractive in

pseudo trials (Efran, 1974). All of these professional, judicial, and educational benefits relate

with a person’s attractiveness level. Job interviewing is another area where attractiveness has

affected screening decisions (Watkins & Johnston, 2000). These advantages may greatly

influence whether an individual receives a job opportunity.

A number of studies have found that attractiveness affects the hiring process with job

occupations with decreased exposure and person-to-person interaction (Dipboye, Arvey &

Terpstra, 1977; Dipboye, Fromkin & Wiback, 1975). Some even have found that hiring based

upon attractiveness level negatively impacts overall organizational performance (Shahani-

Denning, Andreoli, Snyder, Tevet, and Fox, 2011). On the contrary to Feingold (1992) findings,

studies on attraction have found that similarity (Byrne and Nelson, 1965), proximity (Festinger,

Schachter, and Back, 1950), and misattribution of arousal (Dutton and Aron, 1974) are all

significant reasons behind attraction between people. More attractive people were also rated as

being more socially pleasing (e.g., kind, outgoing, modest) as well as more successful

professionally. The biggest influence that Dion, Berscheid, and Walster (1972) found on

attraction was physical attractiveness.


Could external attractiveness, such as beauty, relate with internal personality

characteristics? Research findings have found that more attractive individuals are perceived to

have more socially desirable characteristics. Research has also indicated that the cause of

attraction is a major influencer. Arousal and attraction are two concepts that have been

interconnected through past research findings. In this study, attractiveness level will be measured

through behavioral responses to the viewing of online profiles. Attraction is an essential portion

of viewing online profiles as people make behavioral responses (i.e. messaging the person they

are viewing, liking the photo) based upon attraction level.

Personality traits of potential job candidates in the recruiting world are also assessed by

traits outside of attractiveness level (Cole, Feild, Giles, & Harris, 2009). Company recognition,

previous experience, and education are other areas that are receiving attention by recruiters,

especially when reviewing LinkedIn.com profiles (Benjamin, 2015). Based upon an applicant’s

personality traits, besides extraversion and openness, the Big 5 personality traits usually lacked

validity when the personality traits and Big 5 were correlated together (Cole et al., 2009). This

finding illustrates that recruiters should not assume the personality traits of applicants based

solely off of the written resume. Personality trait evaluations should be judged solely from in-

person interviews instead of the “dispositional characteristics” formed from the resumes (Cole et

al., 2009, pg. 5; Cole, Field, & Giles, 2003).

For online dating sites, the attractiveness of the profile picture has a large influence on

the overall quality of an individual’s online profile (10 things women look for in dating profiles,

2014). Yet, there are other factors and sections of an online dating profile that have been found

to have an impact on a relationship seeker viewing dating profiles. Dating websites state that

although the profile picture is the essential piece of an online profile, dating headlines (first


words on a profile, usually an “about me” section) and then biography and background (usually

demographics of the person) are other pieces that influence whether a relationship seeker pursues

another individual online (Oasis, 2012). With increases in online dating from age groups under

25 (due to increases in mobile dating apps) and individuals over the ages of 50/60, understanding

what aspects of an online dating profile is crucial in the quest to find love (Anderson & Smith,

2016).

Attraction and Arousal

Research indicates that arousal, (i.e. sexual, physical, physiological arousal), has been

linked with attraction. Although studies have defined arousal in terms of: “the external incentive

and the state of arousability of the nervous system” (Toates, 1986, p. 97) or as alternatives for

“sexual excitation” (Kinsey, Pomeroy, & Martin, 1948) and “sexual arousability” (Whalen,

1966), this study will define arousal similarly to Sachs and Barfield (1976): the level of

excitation relative to a threshold. Arousal will be analyzed to see if this type of emotional

excitation will influence an individual’s behavioral actions.

When analyzing aspects of visual behavior, myriads of studies have published

information pertaining to people’s perspectives and biases as influenced by a person’s level of

arousal. Once aroused, the behaviors that follow the arousal can be measured as a way to

understand how that specific action affected that individual. Research has measured the various

types of arousal ranging from physical to cognitive arousal.

Measure of arousal. Past research has shown that glances, fixations, or avoidances are

forms of nonverbal communication, which are often related to emotional reactions perceived

from visual interactions. For instance, Gibson and Pick (1963) concluded that individuals are

able to sense when another individual is looking at him/her fully in the face, resulting in the


notion that “signs” or nonverbal communication can be perceived. There are emotional responses

to looking people fully in the face (Exline et al., 1965). These emotional responses have been

measured by the amount of avoidant or fixated visual tendencies participants performed when

discussing potentially arousing or embarrassing topics. Results indicated that participants were

more likely to become fixated in the conversation when they were the “listener” compared with

the “speaker” in the interview setting. In addition, others have found that due to a female’s

tendency to portray more affection and inclusive relationships, females would be more willing to

fixate their gaze during an uncomfortable or arousing situation (Parsons, 1955; Exline et al.,

1965).

Embarrassment and concealment were the main measurements of Exline et al. (1965) and

these two emotions have often been associated with feelings of physical attraction and arousal.

Results of participant’s concealment during embarrassing situations concluded that although the

uncomfortableness created avoidant tendencies by the participants, but Exline et al. (1965) could

not confidently state that this was solely due to the desire to conceal information. Although

factors such as fatigue or distractions may have affected the results, men did look at female

instructors more frequently when they were uninstructed. In contrast, females did not look at

male instructors as frequently when uninstructed compared to instructed.

Selective attention is also affected by arousal. According to Portas, Rees, Howseman,

Josephs, Turner, and Frith (1998) factors such as consciousness are directly affected by circadian

changes in arousal, which selective attention influences. This may result in the reduction of the

impact of specific stimuli. In effect, when an individual experiences changes in arousal due to

selectively focusing on a specific object or stimuli, other stimuli may be excluded from their

vision. Selective attention, defined as focusing attention to a single source of information for a


significant period of time, is affected by level of arousal (Portas et al., 1998; Das, Naglieri,

Kirby, 1994). The other type of attention focused on in previous research is sustained attention,

which is defined as excluding other information while focusing on one source of information

also is affected by level of arousal. Further research indicates that although moderate levels of

arousal do not deter attention performance, high levels of arousal excitement significantly

impacts performance (Easterbrook, 1959). Later research by La Berge (1995) identified the

connection between the pulvinar and the mediodorsal nuclei of the thalamus and the effect that

these two systems have on prefrontal voluntary control of attention. In other words, levels of

arousal may be regulated by the thalamus and the specific level of arousal elicited directly affects

the attention systems. When an individual views online profiles, selective attention has a

significant effect on both arousal level and behavioral responses. For the present study,

participants will be viewing profiles of people’s faces. The fact that the majority of online

profiles have individuals viewing other individual’s faces may influence how individuals react to

each profile. In addition, the present study features images of profiles of both sexes regardless of

the participant’s gender or sexual orientation. According to past research (Exline et al. 1965),

this may influence the behavioral responses of the participants. Lastly, where an individual

focuses their attention was recorded via an eye-tracking camera and measurements of arousal

will be analyzed by the responses to the 3 question survey.

Attraction and eye-tracking. One way of measuring arousal is via eye movements.

Overall, eye movements can be separated into four different types of movement: pursuit,

vergence, vestibular, and saccadic (Rayner & Pollatsek, 1992). Each of these eye movements

have a different effect on perceiving visual stimuli. Pursuit eye movements are used to track

something that is moving and vergence eye movements are used when an individual is looking


from a distant object to a near object (or vice-versa). Vestibular eye movements are used when a

person is tracking an object and the viewer is in motion while saccadic eye movements are

largely seen as voluntary eye movements towards stimuli that garner one’s attention.

This study will focus on vestibular and saccadic eye movements. When the head or body moves,

vestibular eye movements help compensate this type of action (head/body movement). Saccadic

eye movements facilitate rapid movements that serve to position the eye for visual processing

(Rayner & Pollatsek, 1992; Rayner & Pollatsek, 1994). For infants, saccadic eye movements

have been measured when analyzing preferential looking. Preferential looking, defined as the

discrimination between two stimuli, has been found to imply early knowledge of physics,

memory, visual attention, numbering in infants (Spelke, Katz, Purcell, Ehrlich, & Breinlinger

1994; Hirsh-Pasek & Golinkoff, 1996). Arousal has also been indicated as having a significant

effect on eye movement activities. Previous research has indicated that decreases in arousal

result in the reduction of microsaccade (type of fixation eye movement) rate and pupil

fluctuation (how often a pupil enlarges or minimizes) during continuous gaze of a fixated target

(Honda, Kohama, Tanaka, & Yoshida, 2013). These findings suggest that monitoring eye

movements can create precise evaluations of arousal levels.

Monitoring eye movements and pupil fluctuations may also become more significant

when restricted to initial slow eye movements. While other studies have analyzed pupil

fluctuations and pupil size in response to increases in blood pulsation (Koprowski, Szmigiel,

Kasprzak, Wrobel, & Wilczynski, 2015), studies on eye movement have also been associated

with increased blood pulsation caused by arousal. Arousal levels have been measured during

slow eye movement activities such as the wake-sleep transition while driving a vehicle. Although

auditory alarms would awaken a drowsy driver, eye movements would not be fully enabled by


the auditory signals and reaction times were still poor. (i.e. the individual would be cognitively

awake, but vision was still not optimal) (Sakai, Shin, Uchiyama, Terashima, & Wakita, 2011).

This would infer that auditory arousal (e.g., an alarm), does not fully arouse an individual to

make advanced behavior movements, such as driving a car.

Factors that Influence Arousal and Attraction Online

As social media continues to evolve, different facets of everyday life will make the

transition to the digital world. Dating websites are one area where self-presentation is key in

grabbing the attention of a potential suitor. Presenting oneself as being liked, perceiving

competence or high status, varies among the type of audience (Guadagno, Okdie, & Kruse,

2012). For a particular situation, such as a job interview, one would want to focus on the

perception of competence over being liked. On the other hand, a person on a date would value

likability over competence. Factors such as deception may also have an underlying impact on

online self-presentations due to the lack in face-to-face interaction.

However, this lack of face-to-face interaction does not seem to deter individuals from

evaluating attractiveness online. As stated earlier in Jackson et al. (1995) findings, high levels of

attractiveness have been found to elicit perceived levels of high competence. This perception of

competence stems back to the notion that high levels of attractiveness in an individual lead to

assumed intelligence levels. Past research indicates that attractive profile photos have not only

been found to be more favorable overall (Fiore, Lindsay, Mendelsohn, & Hearst, 2008), but the

notion “what is beautiful is good” (Dion et al., 1972, p. 1108) has also been attributed to

possessing higher qualities in social skills and intelligence. Originating from Dion et al. (1972)

study, the physical attractiveness stereotype states that more attractive people are more popular


and socially skilled, while also being perceived as more intelligent (Langlois, Kalakanis,

Rubenstein, Larson, Hallam, & Smoot (2000).

A job candidate’s characteristics can influence his or her attractiveness. Much research

has highlighted racial and gender discrimination, especially for hiring purposes, but less has

focused on discrimination according to these characteristics for dating. Because the present study

will be conducted with social media in mind, the image the job-seeker portrays may also impact

his/her attractiveness to a job recruiter.

Gender. In the recruiting world, there is pressure to fulfill certain types of employees in

specific roles in order to move the business forward and create profit. The people who work for

the company are the ones directly responsible for the bottom-line profit and without a solid core

of employees, the business cannot succeed. Smith (1978) studied overseers or gatekeepers,

people who set the standards and values of the field and directly influence who is admitted into a

system. Essentially, Smith looked to see how past hiring managers went about either bringing in

certain employees or keeping certain employees out. Yoder, Crumpton, and Zipp (1989)

expanded upon Smith’s (1978) initial research and explored whether gatekeepers would favor

same-sex candidates and their contributions to the field. Both studies concluded that same-sex

candidates were favored by the gatekeepers. Bielby and Baron (1986) argued that the best

practices focusing on deterring sex discrimination in the workplace settings involved firms who

published examples of how to establish minimal levels of sex discrimination.

How attractiveness effects hiring with gender as a moderator of the relationship between

attractiveness level and hiring tendencies has also been closely researched. When a male-

dominated employment opportunity was undergoing hiring, attractive applicants were seen as

more qualified than unattractive applicants (Cash, Gillen, and Burns, 1977). The same type of


hiring decision was found for female-dominated job industries employment. Early research

supports the hypothesis that recruiters will be more likely to hire attractive applicants as Heilman

and Saruwatari (1979) found that college students when asked to rate resumes with an attached

picture favored the more attractive male for a masculine job. On the contrary, Saruwatari

suggested that attractive females were less likely to be hired for a job with masculine

characteristics due to their perceived increased in femininity levels. Yet, more attractive female

applicants were also perceived to be more qualified for jobs overall (Cash, Gillen, and Burns,

1977), which coincides with this studies hypothesis on attractive female applicants being more

likely to be hired by recruiters.

Race. Online dating preferences have been analyzed to see what type of attributes,

characteristics, or values draw individuals to connect with a potential “mate”. As seen in prior

research, similarities play a detrimental role in attractiveness levels (Hunt, 1995), but similarities

have influenced online dating preferences (Hitsch, Hortacsu, & Ariely, 2010). Although strategic

behavior has not been found to influence dating preferences, race and income have had a

significant influence (Hitsch et al., 2010). Similarities between education levels in online dating

has indicated that higher education levels for a woman result in the woman searching for a mate

with a similar education level (Kalmijn, 1998). This suggests that perceived intelligence is

related to attractiveness level (as in Dion et al., 1972), resulting in more attractive individuals

attracting highly educated “mates.” Across the United States, interracial relationships are rare,

potentially due to same race preferences (Kalmijn, 1998) and later research has discovered that

same-race preferences are significant for both males and females (Hitsch et al., 2010).

Race has also had a significant impact on hiring preferences for recruiters. Fix and Turner

(1998) found that minority auditors were less likely to be given a call back after an in person


interview, performed worse (potentially due to the social psychological stereotype threat), and if

they are called back, they are less likely to receive an offer. In addition, even before the

interview process takes place, the minority candidates were less likely to offered an interview

based upon their resume. Even with the black and white participants in their study being matched

in exact characteristics and resume content, the white participants were more likely to be hired.

An interesting finding about paper resumes that were utilized in Bertrand and Mullainathan

(2004) suggests that an additional year of work experience on a resume increases the chances of

a callback by 0.4%. When combining this statistic with the percent call back between a white and

minority candidate, an extra year of experience on a white named candidate actually equals over

eight years of additional experience compared with a minority candidate.

What Bertrand and Mullainathan (2004) lack in their study is the addition of the online

resume. The present study examined not only the “white” or “black” names on a recruiter’s racial

bias, but the addition of an online profile picture as seen on LinkedIn.com. Quality of paper

resumes have been found to be higher quality with characteristics of work experience, minimal

holes in employment history, work at school, some military experience (Bertrand &

Mullainathan, 2004). Even with equal resume quality, the chances of receiving a callback

between white-named applicants and black-named applicants was significant with white-named

candidates receiving a higher chance of a callback. White-named applicants with a higher quality

resume received over a 2% increase in callbacks compared with a 0.5% increase in callback for a

higher quality resume with a black-name. This finding is alarming. Could a black-name hinder a

recruiter from even looking down the resume at the potential candidate’s experience? This study

was all based on a written name and written information without an image.


Race and sex composition are vital for creating a healthy and positive work environment

if composed justly (Reskin et al. 1999). It is also very important to have a diverse employee

composition overall in order to interact with a wide range of different races and sexes that

encompass today’s society. According to Kalleberg, Knoke, Marsden, and Spaeth (1996), an

average company averages one-fifth of their workforce as racial/ethnic minorities. This number

becomes even more significant when over one-fourth of companies do not have a single minority

employed (Reskin et al. 1999). These findings coincide with this studies hypothesis on how

racial biases will effect hiring and dating trends. For sexual biases, the results of Yoder et al.

(1989) suggest that men were more likely to favor male applicants over female applicants in

nonacademic professions. With an overwhelming amount of males representing the work force,

especially in hiring positions, Yoder et al. (1989) findings may illustrate why minorities were

being underrepresented in United States companies during that time. Industries that have been

found to be more male dominated for a significant part of history have even started to even out

between men and women. For instance, the field of psychology was largely occupied by male

psychologists only, but by as far back as 1986, over fifty percent of doctoral degrees were

granted to women (Howard et al. 1986). At the turn of the 1980's to the 1990's, affirmative action

policies were pressuring organizations with low levels of minorities (women especially) to hire

qualified female candidates in order to even out their representation in the areas lacking diversity

(Yoders et al., 1989). The reduction of negative stereotypes against women and other minorities

assisted in progressing businesses to open their eyes to potential candidates outside of male

dominated industries.

Picture professionalism. According to a study conducted by eHarmony.com, profiles

with poor quality images, narrow portraits that may suggest that a prior mate was cut out of the


photo, and wide, far away images received the least amount of communication (eharmony.com).

In addition, bussinessinsider.com found that word choice greatly influences whether your online

dating profile is more attractive, while selfies for men and women differed greatly in

effectiveness (45% for women who were rated as “hot” and 13% for men). In the present study,

recruiters and relationship seekers will be studied by an eye-tracker to predict whether there are

similarities or differences between what they most often view on a LinkedIn.com and Match.com

profile page. In addition, the study will be analyzing whether professional attire or race

influences attractiveness by a recruiter or relationship seeker as well as the how an image on a

profile influences the time spent on an “about me” section of a profile. Lastly, this study measure

the similarities between a professional profile and a casual dating profile.

Current Study

As demonstrated, the world has shifted from paper to the internet and written resumes,

although still collected by a majority of employers, is not the first line of defense in hiring

screening. Facebook, Twitter, Instagram etc. are the online social place to be as a millennial and

LinkedIn.com is the new professional online resume where employers are searching through

millions of potential candidates. Similarly, online dating sites have become a commonplace way

for people to meet prospective partners. The present study measured how changes in arousal

levels caused by viewing different online dating and job search profiles effects visual tendencies.

The levels of arousal were measured through a survey which will elicit cognitive and behavioral

feelings toward the specific profile. The eye-tracker recorded visual tendencies during the

viewing of the profiles.

My hypotheses for LinkedIn are as follows:


1. It is predicted that the participants (recruiters) who view the professional LinkedIn

profiles with professional profile pictures will spend more time looking at the pictures and the

skills section compared to the LinkedIn profiles with unprofessional profile pictures. It is also

predicted that there will be gender group differences for how long the participants look at

professional versus unprofessional profiles.

2. It is predicted that the participants (recruiters) will spend more time looking at the

white profiles compared to the minority profiles. It is also predicted that there will be gender

group differences for how long the participants look at white versus male profiles.

3. It is predicted that there will not be a significant difference in how long the participants

(recruiters) will view the male versus female profiles. It is predicted that there will be gender

group differences for how long the participants look at male versus female profiles.

4. It is predicted that the participants (recruiters) who view the professional LinkedIn

profiles with professional profile pictures will be more likely to hire the professional profiles

compared to the LinkedIn profiles with unprofessional profile pictures. It is also predicted that

there will not be gender group differences for the participants on whether or not they would hire

professional versus unprofessional profiles.

My hypotheses for Match.com are as follows:

1. It is predicted that the participants (relationship seekers) who view the professional

Match.com profiles with professional profile pictures will spend more time looking at the

pictures compared to the Match.com profiles with unprofessional profile pictures. It is also

predicted that there will be not any gender or racial group differences for how long the

participants look at professional versus unprofessional profiles.


2. It is predicted that there will not be a significant different for the time spent viewing

the white and minority profiles by the participants (relationship seekers). It is predicted that there

will not be any differences with regard to the race or gender of the participants when viewing the

white vs. minority profiles.

3. It is predicted that participants (relationship seekers) will spend more time viewing

the female profiles compared to the male profiles. It is predicted that white participants will view

the white profile pictures longer than the minority profile pictures and the minority participants

will view the minority profile pictures longer than the white profile pictures. In addition, it is

predicted that the male participants will view the female profile pictures longer than the male

profile pictures.

4. Attractiveness hypotheses differing according to professionalism, race and gender of

target profile. It is predicted that participants (relationship seekers) will rate the professional

profiles higher compared to the unprofessional profiles. It is predicted that white participants will

rate the minority target profiles lower in comparison to white target profiles and that minority

participants will rate minority target profiles higher in comparison to white target profiles. It is

also predicted that the female target profiles will be rated as higher than male target profiles.

This study will also provide descriptive analyses of the comments participants made about both

LinkedIn and Match.com profiles. Specifically, the strengths and weaknesses of profiles

according to race, gender, and professionalism will be highlighted.


Method

Participants

Twelve recruiters (i.e. employers with hiring responsibilities) were recruited by email and

22 College of Wooster students were recruited through the research participation system at the

College of Wooster called SONA. Students received academic credit for participating. The

students were current first- or second-year undergraduates and the ages for the recruiters ranged

from 24-60 (M = 40). Two of the recruiters declined to give their age. There were a total of 3

male recruiters and 9 female recruiters. Students were recruited from a small, Midwestern, liberal

arts college in Northeastern Ohio and recruiters were employees who have hiring experience

within the career center at the College of Wooster. The recruiters’ racial backgrounds were all

Caucasian/white. From the student sample, there were 12 female participants and 10 male

participants. The student samples were divided according to white participants and minority

participants. Minority students had a large range of racial backgrounds: black, Indian, Asian,

African American, Asian American, Nigerian American, Hispanic, and bi-racial. The students

did not give their actual age, just the year of undergraduate study in which they were engaged.

Materials

In this study, relationship seekers (i.e. student participants) were asked to view 15

Match.com profiles. Recruiters were asked to view 10 LinkedIn.com profiles. Every relationship

seeker viewed the same 15 Match.com profiles and every recruiter viewed the same 10 LinkedIn

profiles. The professional profiles were profiles that had the individual in the profile wearing

professional-styled clothing. For men, that entailed a suit, jacket, and tie with dress pants if

visible in the image. For females, this entailed either a professional dress or suit in the image.

Unprofessional profiles had individuals in the profile picture wearing casual clothing. All


profiles were real profiles. The Match.com profiles were selected by creating fake Match.com

profiles and having them available to select as a potential match. The profiles were then captured

as an image via screen-shot and projected on a projector. The LinkedIn profiles were also real

profiles that were shown on a Google website via a URL link which connected the participant to

the target profiles. All LinkedIn profiles were clicked on beforehand so all the participant had to

do was click on the profiles listed across the top of the web page as they completed each target

profile.

M100 Smart Glasses were used to track what visual information the participants were

looking at most frequently. The Smart Glasses recorded what the relationship seekers viewed on

a projected screen and recorded what the recruiters viewed on a laptop. After the participants

viewed each of their respected profiles individually, they filled out a survey pertaining to the

individual profile for either the LinkedIn.com or Match.com. Survey questions can be found in

the appendix.

Procedure

The relationship seekers met the researcher in an academic classroom on campus, and the

recruiters met the researcher in an academic office or classroom at a specific time in order to

complete the study. The participants took a seat and were asked to place the eye-tracking device

on their head. After the eye-tracking device was fitted to their head, the instructions were read to

them by the researcher. Participants were told they would have approximately 20 minutes to look

through their respected profiles (LinkedIn.com for recruiters and Match.com for relationship

seekers) and respond to each profile with the individual survey pertaining to the specific profile.

The approximate time was told to the participants due to the battery life and storage capacity on

the Smart Glasses. The participants were not told to stop once the 20 minutes were completed.


Individual participants and their data were identified by a code number that was known only to

the experimenter. The participants identified themselves on the survey with a subject number that

the experimenter gave to them before the study began.


Results

LinkedIn

Professionalism of target profile. A 2 (gender of recruiter) x 2 (professionalism of

target profile) mixed model ANOVA was run to see if there were differences in time spent

viewing the profile pictures. There was a main effect for gender of the recruiter F(1,10) = 7.107,

p = .024, and there was a main effect for the professionalism of the target profiles F(1,10) =

44.957, p < .001. Participants looked longer at profiles with professional pictures, M = 23.611

seconds, compared to unprofessional pictures, M = 19.208 seconds. The interaction between the

participant’s gender and the target’s professionalism was significant, F(1,10) = 58.304, p < .001.

Female participants looked longer at unprofessional photos, M = 15.417 seconds, compared to

professional photos, M = 14.806. Male participants looked longer at professional photos, M =

32.417, compared to unprofessional photos, M = 23.000 seconds.

The amount of time viewing the profile pictures did differ significantly between the

target gender and target professionalism F(1,10) = 8.021, p = .018. Unprofessional male profile

pictures were viewed longer, M = 21.500 seconds, compared to professional male profile

pictures, M = 20.472 seconds. Professional female profile pictures were viewed longer, M =

26.750 seconds, compared to unprofessional female profile pictures, M = 16.917 seconds. The

interaction between the target race and professional profile picture with the gender of the

participant was significant, F(1,10) = 7.914, p = .018. Female participants looked longer at

unprofessional minority profile pictures, M = 14.833 seconds, compared to professional minority

profile pictures, M = 13.111 seconds. Female participants looked longer at professional white

profile pictures, M = 16.500 seconds, compared to unprofessional white profile pictures, M =

16.000 seconds. Male participants looked longer at professional minority profile pictures, M =


21.833 seconds, compared to unprofessional minority profile pictures, M = 20.667 seconds. Male

participants looked longer at professional white profile pictures, M = 43.000 seconds, compared

to unprofessional white profile pictures, M = 25.333 seconds. All other interactions did not have

a significant effect.

A 2 (gender of recruiter) x 2 (professionalism of target profile) mixed model ANOVA

was run to see if there were differences in time spent viewing the skills section. There was a

main effect for gender of the recruiter F(1,10) = 5.477, p = .041, but there was not a main effect

for the professionalism of the target profiles F(1,10) = .475, p > .05. Female participants viewed

the skill sections longer, M = 61.083 seconds, in comparison with the male participants, M =

46.500 seconds. The interaction between the target’s race and the target’s professionalism did

differ significantly, F(1,10) = 6.20, p = .032. Minority professional targets were viewed longer,

M = 52.972 seconds, compared to the minority unprofessional targets, M = 49.139 seconds.

White unprofessional targets were viewed longer, M = 60.583 seconds, compared to the white

professional targets, M = 52.472 seconds. All other interactions did not have a significant effect.

Race of target profile. A 2 (gender of recruiter) x 2 (race of target profile) mixed model

ANOVA was run to see if there were differences for time spent looking at the profile picture.

There was a main effect for gender of the recruiter F(1,10) = 7.107, p = .024, and there was a

main effect for the race of the target profiles F(1,10) = 8.389, p = .016. Participants looked

longer at white profiles, M = 25.208 seconds, compared to minority profiles, M = 17.611

seconds. The interaction between the target’s race and gender with the participant’s gender was

significant, F(1,10) = 6.922, p < .025. Female participants looked longer at white male targets, M

= 18.556 seconds, compared to minority male targets, M = 15.389 seconds. Female participants

looked longer at white female targets, M = 13.944 seconds, compared to minority female targets,


M = 12.556 seconds. Male participants looked longer at white male targets, M = 26.833 seconds,

compared to minority male targets, M = 23.167 seconds. Male participants looked longer at

white female participant’s M = 41.500 seconds, compared to minority female participants, M =

19.333 seconds.

Profile picture looking times did differ significantly between target race and target

professionalism, F(1,10) = 13.608, p = .004. Unprofessional minority profile pictures were

looked at longer, M = 17.750 seconds, compared to professional minority profile pictures, M =

17.472 seconds. Professional white profile pictures were looked at longer, M = 29.750 seconds,

compared to unprofessional white profile pictures, M = 20.667 seconds. All other interactions did

not have a significant effect.

A 2 (gender of recruiter) x 2 (race of target profile) mixed model ANOVA was run to see

if there were differences for time spent looking at the skills section. There was a main effect for

gender of the recruiter F(1,10) = 5.477, p = .016, but there was not a main effect for the race of

the target profiles F(1,10) = 3.092, p > .05. The interaction between the target’s race and the

target’s gender was significant, F(1,10) = 6.200, p = .032. Minority professional targets were

viewed longer, M = 52.972 seconds, compared to the minority unprofessional targets, M =

49.139 seconds. White unprofessional targets were viewed longer, M = 60.583 seconds,

compared to the white professional targets, M = 52.472 seconds. All other interactions did not

have a significant effect.

Gender of target profile. A 2 (gender of recruiter) x 2 (gender of target profile) mixed

model ANOVA was run to see if there were differences for time spent looking at the profile

picture. There were a main effect for gender of the recruiter F(1,10) = 7.107, p = .024, but there

was not a main effect for the gender of the target profile F(1,10) = .186, p > .05. Male


participants viewed the profiles pictures longer, M = 27.71 seconds, compared to female

participants, M = 15.12 seconds. The interaction between the participant’s gender and the gender

of the target picture profiles was significant F(1,10) = 5.420, p < .05. Female participants looked

longer at female pictures, M = 16.250 seconds, compared to male pictures, M = 13.250 seconds

and male participants looked longer at female pictures, M = 30.417 seconds, compared to male

pictures, M = 25.000 seconds. All other interactions did not have a significant effect.

Hiring of job seekers. Several chi-squared tests of independence were run to see whether

recruiters would hire the job-seekers based on their profiles. There were not gender differences

in willingness to hire a professional white female, and results revealed that there were no

differences χ2 (1, N = 12) = .364, p > .05. 3 cells had expected count less than 5. The minimum

expected count is .25. The relation between being hired for a professional white male and the

gender of the participant was tested. The relation between these variables was not significant, χ2

(2, N = 12) = 2.000, p > .05. 5 cells had expected count less than 5. The minimum expected

count is .25. The relation between being hired for an unprofessional minority female and the

gender of the participant was tested. The relation between these variables was significant, χ2 (2,

N = 12) = 12.000, p = .002. Female participants were more likely to hire the unprofessional

minority female, M = 9, compared to male participants, M = 0 (for Yes Hire). 5 cells had

expected count less than 5. The minimum expected count was .25.

The relation between being hired for an unprofessional white male and the gender of the

participant was tested. The relation between these variables was not significant, χ2 (1, N = 12) =

.000, p > .05. 3 cells had expected count less than 5. The minimum expected count was .25. The

relation between being hired for a professional minority male and the gender of the participant

was tested. The relation between these variables was not significant, χ2 (2, N = 12) = 1.333, p >


.05. 5 cells had expected count less than 5. The minimum expected count was .25. The relation

between being hired for an unprofessional white female and the gender of the participant was

tested. The relation between these variables was not significant, χ2 (2, N = 12) = 1.333, p > .05. 5

cells had expected count less than 5. The minimum expected count was .25. The relation between

being hired for an unprofessional minority female and the gender of the participant was tested.

The relation between these variables was not significant, χ2 (2, N = 12) = 1.333, p > .05. Five

cells had expected count less than 5. The minimum expected count was .25. The relation between

being hired for an unprofessional minority male and the gender of the participant was tested. The

relation between these variables was significant, χ2 (2, N = 12) = 8.000, p = .018. Female

participants were more likely to hire the unprofessional minority male target, M = 6 (for Yes

Hire), compared to male participants, M = 0 (for Yes Hire). 3 cells had expected count less than

5. The minimum expected count was .25.

Match.com

Professionalism of target profile. A 2 (gender of relationship seeker) x 2 (gender of

target profile) mixed model ANOVA was run to see if there were differences for time spent

looking at the profile picture. There was not a main effect for gender of the recruiter F(1,16) =

2.808, p > .05, and the main effect for profile picture looking times for the male target’s

professionalism was approaching a level of significant, F(1,16) = 3.292, p = .088, but was not

significant. Participants viewed the professional male targets longer, M = 10.913 seconds,

compared to the unprofessional male targets, M = 7.259 seconds. Profile picture looking times

for the minority target profiles did not differ significantly between the target’s professionalism,

F(1,16) = 2.761, p > .05. The interactions for the profile picture looking times between the

minority target’s professionalism, F(1,16) = .260, p > .05. The interaction for the profile picture


looking time for the minority targets between the minority target’s professionalism and the

participant’s race did not differ significantly, F(1,16) = 1.203, p > .05. All other interactions did

not have a significant effect.

A 2 (race of relationship seeker) x 2 (gender of target profile) mixed model ANOVA was

run to see if there were differences for time spent looking at the profile picture. There was not a

main effect for race of the recruiter F(1,20) = 8.098, p > .05, and the main effect for profile

picture looking times for the male target’s professionalism was approaching a level of

significant, F(1,16) = 3.292, p = .088, but was not significant. The interaction for the profile

picture looking times between the male target’s professionalism and the participant’s race was

not significant, F(1,16) = 1.489, p > .05. The interaction for the profile picture looking times

between the male target’s race and professionalism with the participant’s race was not

significant, F(1,16) = 1.709, p > .05. All other interactions did not have a significant effect.

A 2 (gender of relationship seeker) x 2 (professionalism of target profile) mixed model

ANOVA was run to see if there were differences in time spent viewing the demographic

sections. There was not a main effect for gender of the recruiter F(1,16) = 2.009, p > .05, but

there was a main effect for the professionalism of the male target profiles F(1,16) = 5.473, p =

.033. Professional male targets demographic looking times were looked at longer, M = 13.213

seconds, compared to the unprofessional male targets demographic looking times, M = 9.841

seconds. The interaction between the professionalism of the male targets and the gender of the

participant was not significant, F(1,16) = .719, p > .05. Demographic looking times for the

minority target’s professionalism was approaching levels of significance, F(1,16) = 3.752, p =

.071, but was not significant. Professional minority targets demographic looking times were

looked at longer, M = 15.306 seconds, compared to unprofessional minority demographic


looking times, M = 12.185 seconds. The interaction between professionalism of the minority

target’s demographic looking times and the gender of the participant did not differ statistically,

F(1,16) = .196, p > .05. All other interactions did not have significant effects.

A 2 (race of relationship seeker) x 2 (professionalism of target profile) mixed model

ANOVA was run to see if there were differences in time spent viewing the demographic

sections. There was a main effect for the race of the recruiter F(1,16) = 6.633, p = .020, but

there was a main effect for the professionalism of the male target profiles F(1,16) = 5.473, p =

.033. White participants viewed the demographic sections longer, M = 16.773 seconds, compared

to the minority participants, M = 9.839 seconds. The interaction for the time spent viewing the

demographic information between the male target’s professionalism and the participant’s race

was not significant, F(1,16) = 2.353, p > .05. The interaction for the time spent viewing the

demographic information between the male target’s race and professionalism with the

participant’s race was not significant, F(1,16) = .414, p > .05. The interaction for viewing the

demographic information for the minority profiles between the minority target’s professionalism

and the participant’s race was not significant, F(1,16) = 1.307, p > .05. The interaction for

viewing the demographic information for the minority profiles between the minority target’s

gender and professionalism with the participant’s race was not significant, F(1,16) = .213, p >

.05. All other interactions did not have significant effects.


ANOVA was run to see if there were differences in time spent viewing the about me sections.

There was not a main effect for the gender of the recruiter F(1,16) = 2.136, p > .05, but there

was a main effect for the professionalism of the minority target profiles F(1,16) = 2.136, p =

.033. The about me looking times for the professional minority targets were looked at longer, M


= 44.978 seconds, compared to the unprofessional minority targets, M = 30.711 seconds. The

interaction between the minority target’s professionalism and the gender of the participant did

differ significantly, F(1,16) = 5.672, p < .030. Female participants viewed the about me sections

of the professional minority targets longer, M = 33.656 seconds, compared to the unprofessional

minority targets, M = 29.240 seconds. Male participants viewed the about me sections of the

professional minority targets longer, M = 56.300 seconds, compared to the unprofessional

minority targets, M = 32.183 seconds. The about me looking times for the male targets did differ

significantly between the professionalism of the profiles, F(1,16) = 12.720, p = .003. The about

me sections for the professional male targets were looked at longer, M = 45.559 seconds,

compared to the unprofessional male targets, M = 30.513 seconds. The interaction for the

looking times for the about me sections between the professionalism of the target and gender of

the participant was approaching significance, F(1,16) = 3.334, p = .087, but was not significant.

Female participants looked longer at the about me sections of the professional targets, M =

35.469 seconds, compared to the about me sections of the unprofessional targets, M = 28.125

seconds. Male participants looked longer at the about me sections of the professional targets, M

= 55.650 seconds, compared to the about me sections of the unprofessional targets, M = 32.900

seconds. The interaction for the looking times for the about me sections between the race and the

professionalism of the targets with the gender of the participant was approaching significance,

F(1,16) = 3.284, p = .089, but was not significant. Female participants viewed the about me

sections of the professional minority male targets longer, M = 29.375 seconds, compared to the

about me sections of the unprofessional minority male targets, M = 22.313 seconds. Female

participants viewed the about me sections of the professional white male targets longer, M =

41.563 seconds, compared to the about me sections of the unprofessional white male targets, M =


33.938. Male participants viewed the about me sections of the professional minority male targets

longer, M = 61.100 seconds, compared to the about me sections of the unprofessional minority

male targets, M = 26.000 seconds. Male participants viewed the about me sections of the

professional white male targets longer, M = 50.200 seconds, compared to the about me sections

of the unprofessional white male targets, M = 39.800 seconds. All other interactions did not have

significant results.


ANOVA was run to see if there were differences in time spent viewing the about me sections.

There was not a main effect for the race of the recruiter F(1,14) = 1.387, p > .05, but there was a

main effect for the professionalism of the male target profiles F(1,16) = 11.325, p = .004. The

about me section for the professional minority target profiles were viewed longer, M = 45.011

seconds, compared to the unprofessional minority target profiles, M = 31.443 seconds. The

interaction for the viewing of the about me section of the male target’s between the male target’s

race and professionalism with the participant’s race was not significant, F(1,16) = 1.286, p > .05.

The interaction for the viewing of the about me section of the male target’s between the male

targets’ professionalism and the participant’s race was significant, F(1,16) = 5.191, p < .05. The

minority participants’ viewed the about me section of the professional male profiles longer, M =

39.536 seconds, compared to the about me section of the unprofessional male profiles, M =

35.071 seconds. The white participants’ viewed the about me section of the professional male

profiles longer, M = 51.227 seconds, compared to the about me section of the unprofessional

male profiles, M = 28.045 seconds. The interaction for the viewing of the About Me section for

the minority target profiles between minority target’s gender and professionalism with the


participant’s race was not significant, F(1,16) = 2.722, p > .05. All other interactions did not

have significant effects.


ANOVA was run to see if there were differences in attractiveness levels. There was not a main

effect for the race of the recruiter F(1,14) = .106, p > .05, but there was a main effect for the

professionalism of the male target profiles F(1,20) = 27.065, p = .000. The attractiveness levels

of the professional male target profiles were higher, M = 5.823, compared to the attractiveness

levels of the unprofessional male target profiles, M = 4.485. All other interactions did not have

significant effects.


ANOVA was run to see if there were differences in attractiveness levels. There was not a main

effect for the race of the recruiter F(1,20) = 2.181, p > .05, but there was a main effect for the

professionalism of the male target profiles F(1,20) = 30.907, p = 000. The professional male

target’s had higher attractiveness levels, M = 5.927, compared to the unprofessional male target

profiles, M = 4.525. The interaction for the attractiveness levels for the minority target profiles

between the minority target’s professionalism and the participant’s race did not differ

significantly, F(1,20) = .588, p > .05. The interaction for the attractiveness levels for the minority

target profiles between the minority target’s professionalism and gender with the participant’s

race did not differ significantly, F(1,20) = .067, p > .05. The interaction of the attractiveness

levels for the male targets between the male target’s professionalism and the participant’s race

were not significant, F(1,20) = 1.473, p > .05. The interaction of the attractiveness levels for the

male targets between the male target’s race and professionalism with the participant’s race was

not significant, F(1,20) = .520, p > .05. The attractiveness levels of the minority target’s did


differ significantly between professionalism, F(1,20) = 5.377, p = .031. The professional profiles

were rated higher, M = 5.827, compared to the unprofessional profiles, M = 5.306. All other

interactions did not have a significant effect.

Race of target profile. A 2 (gender of relationship seeker) x 2 (race of target profile)

mixed model ANOVA was run to see if there were differences in viewing times for the profile

pictures. There was a main effect for the gender of the relationship seeker F(1,14) = 18.909, p =

.001, but there was not a main effect for the race of the male target profiles F(1,16) = .645, p >

.05. The male participants viewed the profile pictures longer, M = 10.708 seconds, compared to

the female participants, M = 3.994 seconds. The interaction between the race of the male targets

and the gender of the participant was not significant, F(1,16) = .046, p > .05. The interaction

between the target gender and target race did not differ statistically for the profile picture looking

times, F(1,14) = .778, p > .05.

A 2 (race of relationship seeker) x 2 (race of target profile) mixed model ANOVA was

run to see if there were differences in viewing times for the profile pictures. There was not a

main effect for the gender of the relationship seeker F(1,14) = 1.735, p > .05, and there was not a

main effect for the race of the male target profiles F(1,14) = .003, p > .05. The interaction for the

profile picture looking times between the unprofessional target’s race and the participant’s race

was significant, F(1,14) = 5.559, p < .033. Minority participants viewed the unprofessional

minority profile pictures longer, M = 7.929 seconds, compared to the unprofessional white

profile pictures, M = 6.357 seconds. White participants viewed the unprofessional white profile

pictures longer, M = 9.083 seconds, compared to the unprofessional minority profile pictures, M

= 7.435 seconds. The interaction for the profile picture looking times between the male target’s


race and the participant’s race was not significant, F(1,16) = .006, p > .05. All other interactions

did not have significant effects.

A 2 (gender of relationship seeker) x 2 (race of target profile) mixed model ANOVA was

run to see if there were differences in viewing times for the demographic sections. There was a

main effect for the gender of the relationship seeker F(1,16) = 4.625, p = .047, and there was a

main effect for the race of the male target profiles F(1,16) = 9.566, p = .007. Minority male

target’s demographic looking times were looked at longer, M = 13.625 seconds, compared to the

white male targets, M = 9.428 seconds. The interaction between the race of the male targets and

the gender of the participant was not significant, F(1,16) = .395, p > .05. All other interactions



run to see if there were differences in viewing times for the demographic sections. There was a

main effect for the race of the relationship seeker F(1,16) = 7.572, p = .014, and there was a

main effect for the race of the male target profiles F(1,16) = 9.540, p = .007. White participants

viewed the demographic sections longer, M = 14.000 seconds, in comparison to the minority

demographic sections, M = 8.125 seconds. The minority target demographic sections were

viewed longer, M = 13.224 seconds, in comparison with the white target demographic sections,

M = 8.901 seconds. The interaction for viewing the demographic information on the

unprofessional profiles between the target’s race and gender with the participant’s race was not

significant, F(1,14) = .453, p > .05. All other interactions did not have significant effects.


run to see if there were differences in viewing times for the About Me sections. There was not a

main effect for the race of the relationship seeker F(1,16) = 2.136, p > .05, and there was not a


main effect for the race of the male target profiles F(1,16) = 2.245, p > .05. The interaction for

the looking times for the about me section between the race of the target and the gender of the

applicant did not differ significantly, F(1,16) = 1.381, p > .05. The interaction for the viewing of

the about me section for the minority target profiles between minority target’s professionalism

and the participant’s race was not significant, F(1,16) = 3.254, p > .05. The interaction for the

viewing of the unprofessional target profiles between the target gender and the target race was

significant, F(1,14) = 8.796, p = .010. Unprofessional white male About Me sections were

viewed longer, M = 33.798 seconds, compared to the unprofessional minority male about me

sections, M = 23.071 seconds. Unprofessional minority female about me sections were viewed

longer, M = 35.717 seconds, compared to the unprofessional white female About Me sections, M

= 34.187 seconds.


run to see if there were differences in viewing times for the about me sections. There was not a

main effect for the race of the relationship seeker F(1,14) = 1.387, p > .05, and there was not a

main effect for the race of the male target profiles F(1,14) = 3.217, p > .05. The interaction for

the viewing of the unprofessional target profiles between the target gender and the target race

was significant, F(1,14) = 8.626, p = .011. Unprofessional white male about me sections were

viewed longer, M = 35.171 seconds, compared to the unprofessional minority male about me

sections, M = 23.817 seconds. Unprofessional minority female about me sections were viewed

longer, M = 36.294 seconds, compared to the unprofessional white female about me sections, M

= 35.544 seconds. The interaction for the viewing of the about me section of the male target’s

between the male target’s race and the participant’s race was not significant, F(1,16) = .720, p >

.05. The interaction for the viewing of the about me section between the unprofessional target’s


race and the participant’s race was not significant, F(1,14) = .316, p > .05. The interaction for the

viewing of the about me section between the unprofessional target’s race and gender with the

participant’s race was not significant, F(1,14) = .081, p > .05. All other interactions did not have



run to see if there were differences in attractiveness levels. There was not a main effect for the

race of the relationship seeker F(1,20) = 4.036, p > .05, and there was not a main effect for the

race of the male target profiles F(1,20) = .106, p > .05. The attractiveness level of the male target

profiles was approaching significance between the race of the target profiles, F(1,20) = 4.036, p

= .058, but was not significant. The minority male target profiles had a higher attractiveness

level, M = 5.379 seconds, compared to the attractiveness levels of the white male target profiles,

M = 4.929 seconds. The interaction between the race of the male target profiles and the gender of

the participant was not significant, F(1,20) = .377, p > .05. All other interactions did not have




race of the relationship seeker F(1,20) = 2.181, p > .05, and there was not a main effect for the

race of the male target profiles F(1,20) = 3.386, p > .05. The interaction of the attractiveness

levels for the male targets between the male target’s race and the participant’s race was not

significant, F(1,20) = .192, p > .05. The interaction of the attractiveness levels of the

unprofessional targets between the target’s race and the participant’s race was not significant,

F(1,20) = .005, p > .05. The interaction of the attractiveness levels of the unprofessional targets


between the target’s gender and race with the participant’s race was not significant, F(1,20) =

.355, p > .05. All other interactions did not have significant effects.

Gender of target profile. A 2 (gender of relationship seeker) x 2 (gender of target

profile) mixed model ANOVA was run to see if there were differences in time spent viewing the

profile pictures. There was a main effect for the gender of the relationship seeker F(1,14) =

18.909, p = .001, but there was not a main effect for the gender of the male target profiles

F(1,14) = 2.926, p > .05. The male relationship seekers spent more time, M = 10.708 seconds

looking at the profile pictures than the female relationship seekers, M = 3.994 seconds. The

interaction between the profile picture looking time of the gender of the participant and the

gender of the target did not differ statistically, F(1,14) = .355, p > .05. The interaction between

the profile picture looking times of the target’s gender’s race and the gender of the participant

did not differ statistically, F(1,14) = .543, p > .05. Profile picture looking times for the minority

target profiles did not differ significantly between the target’s professionalism, F(1,16) = 2.761,

p > .05, or the target’s gender F(1,16) = .001, p > .05. The interactions for the profile picture

looking times between the minority target’s professionalism, F(1,16) = .260, p > .05, and the

minority target’s gender, F(1,16) = .668, p > .05, were both not significant. All other interactions



run to see if there were differences in time spent viewing the profile pictures. There was not a

main effect for the race of the relationship seeker F(1,14) = .226, p > .05, and there was not a

main effect for the gender of the male target profiles F(1,14) = 2.978, p > .05. The interaction for

the profile picture looking time for minority targets between the minority target’s gender and the

participant’s race did not differ significantly, F(1,16) = .182, p > .05. The interaction for the


profile picture looking time for minority targets between the minority target’s professionalism

and gender with the participant’s race did not differ significantly, F(1,16) = 1.815, p > .05. The

interaction for the profile picture looking times between the unprofessional target’s gender and

the participant’s race was not significant, F(1,14) = 2.978, p > .05.

A 2 (gender of relationship seeker) x 2 (gender of target profile) mixed model ANOVA

was run to see if there were differences in time spent viewing the demographic sections. There

was a main effect for the gender of the relationship seeker F(1,16) = 4.625, p = .047, but there

was not a main effect for the gender of the male target profiles F(1,16) = .027, p > .05. Male

relationship seekers viewed the demographic section longer, M = 16.721 seconds, compared to

the female relationship seekers, M = 10.771 seconds. The interaction between the gender of the

minority targets and the gender of the participant did not differ significantly, F(1,16) = 1.343, p

> .05. All other interactions did not have significant effects.


run to see if there were differences in time spent viewing the demographic sections. There was a

main effect for the race of the relationship seeker F(1,16) = 6.633, p = .020, but there was not a

main effect for the gender of the male target profiles F(1,16) = 4.358, p > .05. White relationship

seekers viewed the demographic sections longer, M = 16.773 seconds, compared to the minority

relationship seekers, M = 9.839 seconds. The interaction for viewing the demographic

information on the unprofessional profiles between the target’s gender and the participant’s race

was not significant, F(1,14) = .036, p > .05. The interaction for viewing the demographic

information for the minority profiles between the minority target’s gender and the participant’s

race was not significant, F(1,16) = .617, p > .05. All other interactions did not have significant

results.



was run to see if there were differences in time spent viewing the About Me sections. There was

not a main effect for the gender of the relationship seeker F(1,14) = .642, p > .05, but although

the main effect for the gender of the male target profiles was approaching significance, F(1,14) =

3.763, p = .073, it was not significant as well. The About Me sections of the unprofessional

female targets were viewed longer, M = 34.952 seconds, compared to the About Me sections of

the unprofessional male targets, M = 28.435 seconds. The interaction for the About Me looking

times between the target’s professionalism and the target’s gender was approaching significance,

F(1,16) = 3.991, p = .063, but was not significant. The About Me sections of the professional

minority male targets were viewed longer, M = 45.283 seconds, compared to the professional

minority female targets, M = 44.719 seconds. The About Me sections of the unprofessional

minority female profiles were viewed longer, M = 37.267 seconds, compared to the

unprofessional male profiles, M = 24.156 seconds. All other interactions did not have significant

effects.


run to see if there were differences in time spent viewing the About Me sections. There was not a

main effect for the race of the relationship seeker F(1,14) = 1.387, p > .05, there was not a main

effect for the gender of the target profile, F(1,14) = 3.700, p > .05. The interaction for the

viewing of the About Me section for the minority target profiles between minority target’s

gender and the participant’s race was not significant, F(1,16) = .899, p > .05. The interaction for

the viewing of the About Me section between the unprofessional target’s gender and the

participant’s gender was not significant, F(1,14) = .230, p > .05. All other interactions did not

have significant results.



was run to see if there were differences in attractiveness levels. There was not a main effect for

the gender of the relationship seeker F(1,20) = .106, p > .05, and there was not a main effect for

the gender of the target profile, F(1,20) = .377, p > .05. All other interactions did not have




race of the relationship seeker F(1,20) = 2.181, p > .05, but there was a main effect for the

gender of the target profile, F(1,20) =46.487, p = .000. The unprofessional female targets had

higher attractiveness levels, M = 6.099, compared to the unprofessional male targets, M = 4.525.

The interaction of the attractiveness level for the unprofessional target profiles between the

target’s race and the target’s gender was significant, F(1,20) = 26.183, p < .05. Unprofessional

minority female targets had higher attractiveness levels, M = 5.618, compared to the

unprofessional minority male targets, M = 4.994. Unprofessional white female targets had higher

attractiveness levels, M = 6.581, compared to the unprofessional white male’s targets, M = 4.057.

The interaction of the attractiveness levels of the unprofessional targets between the target’s

gender and the participant’s race was not significant, F(1,20) = .290, p > .05. The interaction for

the attractiveness levels for the minority target profiles between the minority target’s gender and

the participant’s race did not differ significantly, F(1,20) = .351, p > .05. All other interactions


Comments on profiles. The comments on the profiles came from the participant’s

survey responses. The relationship seekers would fill out individual surveys for all 15

Match.com profiles while the recruiters would fill out individual surveys for all 10 LinkedIn


profiles. The categories were chosen after the experimenter analyzed the common responses

within the strengths and weaknesses for both Match.com and LinkedIn. Each graph is

representing the strengths and weaknesses according to the gender, race, and professionalism of

the individual profiles for both Match.com and LinkedIn.

Some of the interesting differences within the comments from the survey are illustrated

when comparing between the strengths and weaknesses for relationship seekers. The majority of

the strengths listed for the Match.com profiles focused around the personality of the individual.

With at least 55% of all strength comments for Match.com focusing on personality, it was

important for the target profiles to portray a positive personality within their About Me sections.

Experience across the board for the LinkedIn profiles was a commonality between all of the

recruiters. A person’s hands-on experience or lack thereof would be either a benefit or major

detriment on all of the recruiters’ eyes. When comparing the strengths of the professional

Match.com and LinkedIn profiles, 7% of the strengths focused around the profile picture

LinkedIn, while 14% of the strengths for professional Match.com profiles focused around the

“looks” of the individual. Although not on the same scale as personality for Match.com or

experience for LinkedIn, the image of the individual on the screen did draw benefits for the

professional profiles. On the other hand, the weaknesses for the unprofessional profiles for

Match.com focused 9% of the weaknesses on the “looks” of the individual, while 28% of the

weakness comments for LinkedIn unprofessional profiles focused around the quality of the

picture. These findings show that profile pictures for both dating purposes and job search

applications have an impact on how people view the profiles.


Survey Responses for LinkedIn

Figure 1. Comments Made about White Profiles’ Strengths

Figure 2. Comments Made about White Profiles’ Weaknesses

33%

11%12%

4%

13%

11%

11%5%

1 2 3 4 5 6 7 8

4% 6%

13%

19%38%

18%0%2%

1 2 3 4 5 6 7 8

Legend

1. Experience

2. Recommendations

3. Summary

4. Quality of Picture

5. Descriptions/Titles

6. Advancement/Job

Hopping

7. Connections/Followers

8. Education

Legend

1. Experience

2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


Figure 3. Comments Made about Minority Profiles’ Strengths

Figure 4. Comments made About Minority Profiles’ Weaknesses

40%

8%19%

5%

13%

2%6% 7%

1 2 3 4 5 6 7 8

19%

7%

5%

9%

33%

24%0%3%

1 2 3 4 5 6 7 8

Legend 1. Experience

2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


Figure 5. Comments made About Male Profiles’ Strengths

Figure 6. Comments Made about Male Profiles’ Weaknesses

38%

10%14%

6%

10%

7%

8%7%

1 2 3 4 5 6 7 8

14%

4%7%

9%

38%

25%

0%3%

1 2 3 4 5 6 7 8


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


Figure 7. Comments Made about Female Profiles’ Strengths

Figure 8. Comments Made about Female Profile’s Weaknesses

35%

10%18%

3%

17%

4%9% 4%

1 2 3 4 5 6 7 8

7%9%

12%

21%33%

16% 0%2%

1 2 3 4 5 6 7 8

Legend

1. Experience

2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


Figure 9. Comments Made about Professional Profiles’ Strengths

Figure 10. Comments Made about Professional Profiles’ Weaknesses

34%

7%18%

7%

11%

7%

8%8%

1 2 3 4 5 6 7 8

13%

9%

9%

2%

35%

30%

0%2%

1 2 3 4 5 6 7 8


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


Figure 11. Comments Made about Unprofessional Profiles’ Strengths

Figure 12. Comments Made about Unprofessional Profiles’ Weaknesses

40%

15%11%

2%

18%

3%8% 3%

1 2 3 4 5 6 7 8

8% 2%12%

28%33%

14% 0%3%

1 2 3 4 5 6 7 8


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


2. Recommendations

3. Summary



6. Advancement/Job

Hopping


8. Education


Survey Responses for Match.com

Figure 13. Comments Made about White Profiles’ Strengths

Figure 14. Comments Made about White Profiles’ Weaknesses

9% 1%

68%

2%2%2%6%

4%2% 4%0%

1 2 3 4 5 6 7 8 9 10 11

13%6%

26%

10%4%1%

18%

7%5%

8% 2%

1 2 3 4 5 6 7 8 9 10 11

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views

6. Religions/Spirituality

7. Quality of Profile

8. Alcohol and Drugs

9. Past Marriage

Status/Kids

10. Financial/Occupation

11. Sexuality

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality


Figure 15. Comments Made about Minority Profiles’ Strengths

Figure 16. Comments Made about Minority Profiles’ Weaknesses

15%1%

54%

5%1%3%

3%7%

4% 7% 0%

1 2 3 4 5 6 7 8 9 10 11

11%4%

19%

9%7%3%

21%

4%

14%7% 1%

1 2 3 4 5 6 7 8 9 10 11

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage Status/Kids


11. Sexuality

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality


Figure 17. Comments Made about Professional Profiles’ Strengths

Figure 18. Comments Made about Professional Profiles’ Weaknesses

14%0%

60%

5%2%2%3%

6% 4% 4%0%

1 2 3 4 5 6 7 8 9 10 11

8%5%

25%

11%7%3%

15%

3%

12%

9% 2%

1 2 3 4 5 6 7 8 9 10 11

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality


Figure 19. Comments Made about Unprofessional Profiles’ Strengths

Figure 20. Comments Made about Unprofessional Profiles’ Weaknesses

11%1%

61%

2%1%3%6%

5% 3% 7% 0%

1 2 3 4 5 6 7 8 9 10 11

14%5%

20%

9%4%1%

23%

6%

10%6% 2%

1 2 3 4 5 6 7 8 9 10 11

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality


Figure 21. Comments Made about Male Profiles’ Strengths

Figure 22. Comments Made about Male Profiles’ Weaknesses

12%0%

61%

5%1%2%

4%5% 3% 7% 0%

1 2 3 4 5 6 7 8 9 10 11

14%5%

21%

8%2%2%20%

7%

11%8% 2%

1 2 3 4 5 6 7 8 9 10 11

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality


Figure 23. Comments Made about Female Profiles’ Strengths

Figure 24. Comments Made about Female Profiles’ Weaknesses

13%1%

60%

2%2%3%5%

7% 3% 4%0%

1 2 3 4 5 6 7 8 9 10 11

8%5%

23%

12%10%2%

21%

2%9%

6% 2%

1 2 3 4 5 6 7 8 9 10 11

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality

Legend

1. Looks

2. Age

3. Personality

4. Education

5. Political Views




9. Past Marriage

Status/Kids


11. Sexuality


Discussion

With today’s online platforms offering users endless amounts of options for uses, the job

market and dating scene have made their way to the top of user’s opportunities. With over 40

million Americans using online dating services (Broussard, 2011) and over 414 million people

utilizing LinkedIn worldwide, what people are viewing on these sites becomes an intriguing

question (Number of LinkedIn members 2009-2015, 2016). Capturing the attention of a recruiter

searching through LinkedIn profiles and a potential relationship seeker skimming through

Match.com profiles is crucial for people in the job or dating market. Brasel and Gips’ (2008)

research findings on how brand recognition is weakened when commercials are fast-forwarded

through may simulate findings on how recruiters and relationship seekers view job or dating

websites respectfully. Recruiters sifting through LinkedIn profiles will be “actively” sifting

through the profiles and although this skimming may be considered “fast-forwarding”, they are

still closely paying attention to the digital content.

It is important to understand what people are looking at when viewing online profiles, but

in addition, being able to see if there are certain racial or gender biases that may influence what

people view. Secondly, professionalism is a trait that is discussed at length in regards to people’s

occupation. What has not been analyzed is the impact that a professional or even more

importantly, unprofessional attire has on people being hired or being sought after in a romantic

way (e.g. online dating websites). This study’s goal is to analyze whether race, gender, or

professionalism causes the viewers of the profiles to either positively or negatively react to the

profiles through the behavioral responses to a survey pertaining to each individual profile. In

addition, race, gender, and professionalism were analyzed to see if these attributes ultimately

effected whether or not an applicant would be hired based upon their LinkedIn profile or the

attractiveness level of Match.com profiles.


LinkedIn

In this study, it was predicted that the LinkedIn profiles with professional pictures will be

viewed for a longer amount of time than the LinkedIn profiles with unprofessional pictures. This

study concluded that although female participants viewed the unprofessional profiles longer

compared to the professional profiles, male participants looked longer at the professional profiles

compared to the unprofessional profiles. This could suggest that male recruiters, but not female

recruiters, favor professionalism over unprofessionalism. In contrast, when taking gender of the

participant out of the equation and strictly focusing on the total time spent on profile, the total

time spent on unprofessional male targets was longer compared to professional male targets and

the total time spent on professional female targets was longer compared to unprofessional female

targets. This finding may suggest that the gender of the applicant on the LinkedIn profile has a

greater impact on professionalism compared to the gender of the recruiter. This study’s findings

coincide with Hirsh-Pasek and Golinkoff’s (1996) conclusions on eye movements and arousal,

while also suggesting that arousal levels may be influenced by selective attention (Portas et al.,

1998; Das, Naglieri, Kirby, 1994).

In addition, it was predicted that the LinkedIn profiles with the professional photos would

more likely be hired compared to the LinkedIn profiles with the unprofessional photos. After

running chi-squared tests for the “hire/no hire” dependent variable in relation to the gender of the

applicant, there was not a significant relationship between any of the professional profiles and

the gender of the participant. When analyzing the survey responses, for the unprofessional

profiles, over 25% of the weaknesses stated by the recruiters focused around the quality of the

profile picture. This is contrasted with 7% of the recruiter’s statements on the surveys about the

LinkedIn profiles with professional profile pictures focusing on the quality of the profile picture


as a strength. Although there was not a significant finding for the chi-squared tests, 11 out of the

12 recruiters (91.666%), regardless of the recruiter’s gender stated that they would hire the

professional white female. In addition, 2/3 of the recruiters, regardless of the recruiter’s gender,

stated that they would hire the professional white male, 75% of the recruiters would hire the

professional minority male, and 2/3 of the recruiters stated that they would hire the professional

minority female. Altogether, 75% of the recruiters stated that they would hire an individual

regardless of the target profile’s race or gender (36 out of 48 total responses). This can be

compared with the fact that only 16.667% of the recruiters, regardless of the recruiter’s gender,

stated that they would hire the unprofessional white female. All of these findings illustrate that

white females in the job market will be more likely to be given a second interview or even hired

in comparison to minority females. Yet the important take away from these results show that by

dressing professionally in a LinkedIn profile picture, you have a better chance at being

considered for employment in contrast to wearing unprofessional clothing.

There were two significant relations between the two variables. The relation between

being hired for unprofessional minority females and unprofessional minority males with the

gender of the participant was found to be significant. This finding contrasted the prediction that

the white profiles would be hired more than the minority profiles. There was not a significant

result for any of the white profiles in relation to the gender of the recruiter. For the

unprofessional minority females, nine female participants stated they would hire the

unprofessional minority female compared to none of the male participants. This finding in

regards to the male participants in this study correlates with past research findings stating that

minority applicants are less likely to be given an interview compared with white applicants (Fix

& Turner, 1998). In contrast, the female participants differed with past research findings and all


nine of the female participants stated that they would hire the unprofessional minority female.

This may be caused by gender biases male participants in hiring positions are more likely to hire

male applicants. The same has been found for female participants in hiring positions being more

likely to hire female applicants (Yoder et al., 1989). A major limitation of the study is the lack of

male participants, which may be an additional reason for the lack of “hiring” by the male

participants for the unprofessional minority female applicants. In addition, the lack of

professionalism in the unprofessional minority female profiles may have also factored into the

male participant’s choosing to not hire the target. None of the male participants chose to hire the

unprofessional male minority in comparison to the female participants. These findings could

open the conversation to whether male recruiters vs. female recruiters differ in how they view the

importance of professionalism in hiring decisions.

Professionalism has been linked to increased attractiveness levels (McElroy, Morrow, &

Eroglu, 1990). In short, McElroy, Morrow, and Eroglu (1990) argued that attractiveness of the

salesman may arouse the customer causing them to elicit positive or negative emotional labeling

of the salesperson. This in turn may impact the customer’s reactions to the salesperson or product

they are selling. These past research findings by McElroy, Morrow, and Eroglu (1990),

specifically the significant interaction between professionalism and distance resulting in

increased donations are consistent. It was also predicted that male viewers would spend longer

amounts of time viewing the LinkedIn profiles. The interaction between the gender of the

participant and the gender of the target was not significant, but male participants viewed female

targets longer, compared to male targets. In addition, the interaction between the participant’s

gender and the gender of the target picture profiles was significant. Male participants looked

longer at female pictures, compared to male pictures. With the increased time spent on the


female pictures, this may influence whether or not the male recruiters higher female applicants

more than male applicants. It may also be suggested that the longer a recruiter spends on an

individual profile, the higher the likelihood the recruiter will be interested in the applicant.

Furthermore, if a recruiter is spending more time on one particular profile, this will be taking

away from potential time spent on other profiles. An intriguing finding pertaining to race was

that looking times for the skills/experience sections did differ significantly between the race of

the target. White target’s skills section was looked at longer, compared to minority target’s skills

section. Having only white recruiters participate in the study (potentially due to a lack of

diversity in higher education and one of the major limitations of the study) may have produced

the racial biases that have been discussed at length in hiring discrimination research (Stoll et al.,

2004). Hiring discrimination occurs at all levels in the hiring process. From recruiting to

interview selection, hiring biases based on race often lead to discrimination in the hiring process.

For a hiring manager, their first outlet when beginning the hiring process is to reach out to their

personal network and coworkers for recommendations. The best hires for a company usually

come from referred candidates from people who work at the firm hiring as they are up-to-date on

the company's culture and work environment. An individual’s social network will be influenced

by their race (Stoll et al., 2004). They will prefer to hire within their social network, emphasizing

the “it’s not what you know, it’s who you know” statement, and black candidates for

employment are more likely to stay in touch with their personal social network. This will allow

for more black candidates to feel as a priority in the hiring process and be more likely to apply

toward firms with black hiring managers (Holzer, 2000). The societal and professional pressures

to hire candidates that match with the business owner’s ideals for the hiring managers is

constant, causing the likelihood of a black-owned business to hire black candidates over white


candidates. With black candidates more likely to apply to black-owned businesses (Holzer,

2000), these findings deter from the notion that reverse discrimination is a potential effect of

these hiring practices.

Prior research suggests that black hiring managers are more likely to higher black job

candidates over white job candidates (Stoll, Raphael, and Holzer, 2004). Even into the early

2000’s there continues to be a significant difference between the amount of African-American

workers compared with white workers, especially in large firms (Stoll, Raphael, & Holzer,

2004). This disparity is nullified when a black hiring manager is in charge of the hiring process,

whether it be in suburban or central firms (Raphael, 1998). Stoll et al. (2004) studied the

reasoning behind the greater likelihood of black hiring managers hiring black candidates. Their

results suggested that more black candidates applied to firms with black hiring managers and

when hiring between a black candidate and a white candidate, the black hiring manager is more

likely to hire the black candidate (Stoll et al., 2004). Other research suggested that blacks are

disproportionately sorted into firms, companies, and organizations (Carrington and Troske,

1998) as well as the percentage of blacks hired in minority and non-minority areas that are black-

owned firms (Bates, 1993).

When analyzing the results between the Match.com and LinkedIn groups, there are some

interesting findings to discuss. For instance, although only the professional LinkedIn profiles had

a significant difference in looking times in comparison to the unprofessional LinkedIn profiles,

the Match.com profiles was approaching significance in regards to professionalism. The missing

data cell in Match.com (white professional female) negatively impacted the ability to truly

analyze group differences between professional vs. unprofessional profiles for Match.com. With

the missing data cell added into the data, it is fair to assume that a significant difference between


looking times for professional profile pictures vs. unprofessional profile pictures for Match.com

may occur.

Match.com

When analyzing the Match.com data, it was predicted that the looking times for the

Match.com profiles with professional profile pictures in the profile would be looked at longer

compared to the profiles with unprofessional profile pictures. Unfortunately, due to the missing

data cell (professional white females), this analysis was not able to be tested. For the male targets

specifically, the profile picture looking times did not differ significantly between the race of the

male targets, but the profile picture looking times for the male target’s professionalism were

approaching a level of significant, but were not significant. Participants viewed the professional

male targets longer, compared to the unprofessional male targets. These statistics illustrate two

interesting concepts. First, no matter what the race of the target was, the race was not a

significant factor in either drawing a participant away from the profile picture or drawing a

participant toward the profile picture. Racial biases in dating, as seen in studies such as Robnett

& Feliciano (2011), state that people are less likely to date outside of their race. Although

viewing a profile picture does not directly insinuate dating preferences, the lack of significant

data could open the argument that millennials have adopted different outlooks on dating that are

not impacted by race. On the other hand, college students tend to have a more open-minded

outlook on life compared to older aged people, which may be the cause for this sense of

openness in the relationship seekers. When looking at the survey responses, even with having 3

additional unprofessional profiles, the unprofessional profiles only outgained the professional

profiles by 88 total strength comments (unprofessional = 420 total strength comments,

professional = 332). One could make the argument that if the two missing professional white


female profiles were added and the additional unprofessional minority female profile was taken

away, there could have been a significant result between professionalism and profile picture

looking time.

In this study, it was also predicted that professional profiles would be rated as more

attractive than the unprofessional profiles. Although the missing data cell hindered the analysis

of all professional vs. unprofessional profiles, the attractiveness level of the male target profiles

did differ statistically between the professionalism of the target profiles. The attractiveness levels

of the professional male target profiles were higher, compared to the attractiveness levels of the

unprofessional male target profiles. This suggests that professional male targets on average were

rated almost one and a half points higher than the unprofessional male targets. This finding backs

research conducted by Ronkainen and Reingen (1979) and more recent work completed by

Caballero and Solomon (1984) which concluded that physical attractiveness has an effect on

personal selling; however, the present study view this past research from a different angle. The

professionalism of the male profile target increased the attractiveness level of the targets. In

addition, the unprofessionalism of the male profiles had a negative impact on the overall

attractiveness of the unprofessional male targets. Comments such as, “he looks like a want to be

soulja boy” which was directed at one of the unprofessional minority male profiles or “not very

good looking” which was directed at an unprofessional white male profile are just two of the

assortment of poor comments made about the unprofessional pictures. Altogether, 14% of the

weaknesses for the unprofessional profile focused around the people’s looks.

Race has been found to have a major influence on who people decide to date as well as

who they find attractive (Robnett & Feliciano, 2011; Gordon, 1964; Kalmijn, 1998). Survey

responses such as “African descent” listed as a weakness on many of the minority profiles


occurred in this study. In addition, minority profiles (393 weakness comments) received over

140 more “Weaknesses” comments in comparison to the white profiles (249 weakness

comments). Unfortunately, the missing data cell will have an impact on the total number of

weaknesses for the white profiles (2 missing professional white female profiles) as well as the

additional unprofessional minority female profile, but the amount of negative comments about

the minority profiles in a study where 13 out of the 22 participants are white could suggest some

racial bias occurring in the present study. This study predicted that the white participants would

rate the white target profiles as more attractive when compared with the minority profiles. The

interaction of the attractiveness levels for the male targets between the male target’s race and the

participant’s race was not significant. As stated earlier, the missing data cell restricted this

analysis, but racial biases between the male targets was not found in this study. This study’s

findings contradict findings of Robnett and Feliciano (2011), Gordon (1964), and Kalmijn

(1998), but the missing cell may have had a significant impact on the data. Judging solely from

the survey responses, it is possible to make the argument that with the missing cells, racial biases

may have been concluded in this study.

Regardless of race, gender, or professionalism, and interesting finding of this study is that

the majority of strengths across the all profiles focused around the personality of the individual’s

profile. For white target profiles, 68% of the survey responses revolved around the personality of

the individual. The lowest percentage for the personality percentages was the minority target

profiles which still had over 50% of the comments focused around the personality of the

individual. Could this be another reflection of what millennial relationship seeker’s value when

searching for a relationship? Do people look past race or potentially even gender when assessing


an individual’s attractiveness level? All of these questions are important factors when assessing

the future of dating websites.

Limitations and Future Research

Sample. The sample size of this study greatly impacts the results. Due to time restraints

and unforeseen technological setbacks, this current studies sample size is too small. With a total

of 12 recruiters, 3 males and 9 females, the sample size directly impacted the data for the

LinkedIn profiles. In addition, the MH100 Smart Glasses’ storage capacity and battery were both

weaker than expected when purchased. Instead of a 20 minute, in-and-out experiment, one

individual who took the full 20 minutes to complete the 10 LinkedIn profiles or the 15

Match.com profiles would require an additional 15-20 minutes to download the recorded video

off of the glasses onto another hard drive in order to complete another participant. With the

deadline for completing the study fast approaching and fitting into recruiter’s schedules, it was

difficult to obtain a large sample size of recruiters. A problem that may also spark future research

into higher education system is the lack of diversity within the career center of a private liberal

arts school. All of the recruiters tested in this study were white and 9 out of the 12 participants

were white women. This could pose the argument for greater diversity within career centers at

higher education universities.

For this study, the relationship seekers sample size was also very small, 22 total

participants (13 white and 9 minority participants). Even with this small sample size, there was a

greater amount of diversity in this sample size. For the first-year and sophomores who

participated in this survey, there were a range of minority students from bi-racial to Native

American. Stemming back to the lack of diversity within the career center’s staff, the question

can be asked about how are these minority student’s needs being met with the absence of a


career advisor that grew up in a similar background as them. The technological issues that

hindered the LinkedIn studies also impacted the Match.com studies with the relationship seekers.

The battery would often die out after 25 minutes of use, which would result in lost video

recording data. In two incidents, the battery died out or the storage became too full with only 2

megabytes of storage available and two participants video recordings were lost completely. This

unforeseen technological issues affected the amount of participants that could complete the

experiment within a realistic timeframe.

Materials. As stated earlier, even though the MH100 Smart Glasses recorded the

necessary footage needed to collect data, there were multiple issues with the glasses. Often

times, the participants were wearing glasses and a limitation that was not foreseen personally

was the effect the glasses would have on an individual who could not see without their

prescription glasses. This required the participant to fit the Smart Glasses over their prescription

glasses which was uncomfortable and awkward for the participant. On the other hand, the glasses

would either slide up or down the glasses and resulted in the video footage being difficult to

analyze. Lastly, the battery life and storage capabilities of the glasses were below average

resulting in unforeseen loss of time between participants and lost video footage.

The recruiters’ viewed the LinkedIn profiles on a PC. Many of the recruiters were either

accustomed to a Mac computer or were not accustomed to using a PC. This required additional

technical difficulties in viewing the LinkedIn profiles while responding to the survey questions

for each profile. The uncomfortableness of the Smart Glasses (heating up, sliding up or down, or

becoming heavy) also resulted in the participant discomfort during the experiment. These

technological issues were completely unforeseen and had to be dealt with during the entirety of

data collection.


Missing Data Cell. Due to personal lack in awareness, the data cell for the white

professional females was missing from the Match.com profiles. Until the data collection ended

and analysis of the data began, the knowledge of the missing data cell was unknown to the

researcher. Due to the time restraints of the research project, any chance of redoing the data

collection was not possible and analysis of the data continued with the professional white female

profile missing. Unfortunately, this missing data cell negatively impacted the ability of the

researcher to fully analyze the professional or the gender data for Match.com.

Future Research. The magnitude of the data collected from this experiment leads to an

assortment of potential future research. The full analysis of the nested variables within the data

would require a multi-level modeling analysis, something that was hindered by the time

restraints and experimenter expertise levels with this type of high level analysis. Having the

ability to analyze each individual participant without collapsing the data may show even greater

impacts of racial, gender, or professional biases for the profiles. Time restraints and the necessity

for data analysis required the profiles for Match.com to be collapsed. The data collapsing did

deter any type of individual profile biases that may have increased or decreased the time spent on

the profiles different features or the attractiveness level for one subject group (e.g.

unprofessional female minority).

With a more diverse sample size for the recruiters, racial differences in hiring could also

be analyzed. Having only white recruiters viewing the LinkedIn profiles limited the type of

analysis that could be used to assess the potential racial biases that are occurring when viewing

online profiles. Earlier research suggested that “task competence” should be the sole factor when

choosing between candidates (Bayles, 1973). Task competence pertains to an individual's ability

to perform the job responsibilities (tasks) that are relevant to the job description. Some may


argue that reverse discrimination plays a part in the hiring process when skin color, race,

ethnicity, or religious affiliation are weighed into the hiring decision (Bayles, 1973). There are

two premises that Bayles (1973) discusses which pertain to the situations that may involve

reverse discrimination. First and foremost, the equality of opportunity requires that every

individual is given the same opportunity to receive rewards (benefits) or penalties (burdens). In

addition, the integration process states that the workforce should replicate the total population in

society without favoring one part of society over another in regards to sex, race, religion, ethnic

background, or national alliance. Names have also had a significant influence in the hiring

process. Bertrand and Mullainathan (2004) concluded that white-sounding names were 50%

more likely to be given a callback for an interview compared to African-American sounding

names. Is this caused by racial similarities or racial biases? The Council of Economic Advisers

(1998) concluded that in addition to later findings on unequal interview callbacks, when African

Americans did secure employment, they were paid 25% less than their white counterparts. All of

this literature suggests that as the hiring process moves to a more digital culture (e.g. Skype

interview, Go-To-Meeting applications, and LinkedIn “resumes”), it will be very interesting to

research how past racial biases may be seen in today’s hiring process.

Lastly, properly analyzing how sexual orientation or age is an area where future research

can improve upon this current study’s findings. Age discrimination is a major issue occurring in

today’s workforce. This experiment has the age of all of the participant’s but due to time

restraints, this data was not fully analyzed. There were an additional two profiles that were

viewed for LinkedIn that were not discussed in the results. One of the profile pictures featured a

cartoon and the other profile picture did not have a picture. Further research could look into the

effect that “no picture” or “cartoon pictures” could have on a person’s chances of being hired.


Depending on a person’s industry or even facial abnormalities, there is a possibility of having

one of the two “alternative” pictures and it would be interesting to see the impact. In addition, as

sexual orientation and sexuality in general continues to become a headline topic in society,

understanding how sexual bias impacts hiring decisions would be very intriguing.

Conclusion

In spite of these limitations, this study looks at some important issues pertaining to

racial/gender biases in dating and hiring. In addition, by adding in the concept of professionalism

into both the dating and job search industry, it is pertinent for individuals to understand the

impact that appearing professional vs. unprofessional has on their love life or professional career.

Within a flash of a second, an individual could have connected with the love of their life or the

next stepping stone in their career. Dressing professionally and understanding the potential

racial/gender biases may also assist people searching for relationships or exploring new career

opportunities on LinkedIn. It is fair to say that all is fair in love and work.


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Appendix A

Demographic Information Form

Demographic Information

Age:

Gender: Male Female Trans-Male Transgender-Female Other ____________

Sexual Orientation: Male Female Bisexual Other________ Prefer not to disclose

Race:


Appendix B

Recruiter Survey

LinkedIn.com – Adam

1. What are the strengths of this individual?

2. What are the weaknesses of this individual?

3. Would you hire this individual? (Yes or No)


Appendix C

Relationship Seeker Survey

Match.com – Lance

1. What are the strengths of this individual?

2. What are the weaknesses of this individual?

3. How would you rate this individual’s attractiveness level on a scale of 1-10

(“1” being unattractive and “10” being attractive)


Appendix D

Example of Match.com Profile


Appendix E

Example of LinkedIn Profile

Documents

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