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Recruiting for technology reliant positions: can common personality inventories improve success?

The recruitment process presents significant challenges for managers, given the competing issues of a weak economy, employee turnover, and the persistent shortage of highly skilled talent. The recruiting "industry" accounts for $124 billion in costs by US companies each year and an estimated $3,500 being spent per new hire (Bersin and Associates, 2011). With this significant investment, the desire to be more impactful with every dollar spent is increasing. The "one size fits all" formula for successful recruiting has eluded researchers and HR professionals alike; and yet, the pursuit continues. To that end, this study seeks to explore the linkages between general traits, such as personality and the specific trait of "affinity for technology," and how these linkages motivate prospective employees to pursue positions they deem high in technology. The affinity for technology (AFT) trait has been shown to be an important trait in both consumers and employees today. This paper attempts to empirically validate the links between currently administered personality inventories, the affinity for technology trait, and how this translates to employees' desire for technology reliant careers, as proposed by Jia and Fleming (2013). The goal of this work is to provide HR recruiters with tools and knowledge for how to best evaluate the potential fit of employment candidates, and how to select those with the intrinsic desire to work in technology professions.


In their conceptual work, Jia and Fleming (2013) note that the "Big Five" personality factors model (Costa and McCrae, 1992a, 1992b), which includes the traits of conscientiousness, agreeableness, neuroticism, extraversion, and openness to experience has emerged as the most agreed upon framework in the psychology literature (e.g., Goldberg, 1990, 1993; Judge et al., 2002; Zweig and Webster, 2004). This framework is also utilized frequently in the business literature (e.g., Viswesvaran and Ones, 2004; Harris and Fleming, 2005) and specifically in the human resources literature in the area of selection (see Rothstein and Goffm, 2006 for a comprehensive meta-analysis of the use of personality candidate selection). The five factors essentially capture the essence of one's personality (Digman, 1990), and they play a powerful role in explaining a multitude of individual attitudes and behaviors. In the meta-analyses by Barrick and Mount (1991) and Hurtz and Donovan (2000) the factors of conscientiousness, neuroticism (also known by its opposite, emotional stability), and agreeableness were shown to have an impact on job performance across contexts. Although traits impact performance, there remains debate in the literature as to the best way to model the linkages between personality and performance. For example, Harris and Lee (2004) used a hierarchical structure with intervening higher-level traits between personality and job performance which resulted in better explanatory power in salesperson performance. Examining the links between personality and affinity for technology and looking at how they impact employment related expectations and behaviors in a manner similar to Harris and Lee (2004) should allow organizations to hire individuals for jobs that best match their interests and to place individuals into jobs with duties that leverage their strengths. This, in turn, should lead to better performance by using common personality metrics that are currently in use in the hiring process. The AFT concept is discussed below.

The concept of Affinity for Technology (AFT) (Edison and Geissler, 2003) is a measure of an individual's overall positive feelings toward technology. The measure focuses on feelings rather than on adoption potential (Davis, 1989; Parasuraman, 2000) or on specific types of technologies (Heinssen et al, 1987). In academic research, AFT has been found to be a predictor of consumer behaviors such as market mavenism (Geissler and Edison, 2005) and employee behaviors such as self-directed learning project use (Fleming et al., 2014). This construct was selected as the starting point in the current investigation of the link between technology perceptions and career choice because it is similar to some of the higher order traits selected by Harris and Lee (2004) that mediate the relationship between personality and job performance, ft additionally focuses on technology in general to enhance its potential usefulness across industries and contexts as well as possessing face validity related to the topic of technology reliant jobs.

Personality and Affinity for Technology

Openness to Experience. Openness is a personality trait that is commonly linked with such descriptors as active imaginations, aesthetic sensitivity, variety-seeking, and intellectual curiosity (John and Srivastava, 1999). Additionally, those individuals with a strong degree of openness to experience seek autonomy and have a high desire to self-govern (Hmel and Pincus, 2002). Openness leads individuals to excel in innovative fields and professions that reward divergent thinking (Raja and Johns, 2010). Tuten and Bosnjak (2001) observed the adventure-seeking nature of those with strong tendencies toward openness gravitate to the online environment to satisfy their curiosity and to seek new and adventurous opportunities.

H1a. Openness to experience is positively related to AFT.

Conscientiousness. Conscientiousness, as a personality trait, describes an individual's socially prescribed impulse control that facilitates task and goal-oriented behavior. This yields individuals who are highly organized and who engage in advanced planning activities (John and Srivastava, 1999). Highly conscientious individuals are described as dependable, reliable and responsible (Judge and Zapata, 2015). According to Costa and McCrae (1992b), conscientious individuals are achievement-oriented and perform well under independent working conditions. They tend to be self-focused and self-governing and anxious to pursue and achieve their own goals, which, when pushed to extreme, can lead to workaholic behavior (Hmel and Pincus, 2002). In view of these collective research findings, individuals with a strong degree of conscientiousness are more likely to deliberately seek outcomes consistent with their goals and desires and would be more apt to seek technologically-oriented employment positions. In keeping with the action-oriented nature of conscientious individuals, when utilizing technology, such as the Internet, they tend to spend less time on leisurely indulgences. Rather, conscientious individuals utilize technology for more academic-minded pursuits (Landers and Lounsbury, 2006).

H1b. Conscientiousness is positively related to AFT.

Agreeableness. Individuals with a strong degree of agreeableness are described as warm, caring, cooperative, and communal (Costa and McCrae, 1992b). These are all traits that are consistent with prosocial work behaviors (Chiaburu et al., 2011) in that agreeable individuals are intrinsically motivated to foster and maintain positive work relationships. Similarly, those with a strong degree of agreeableness desire social acceptance (Malhotra and Galletta, 2005), and technological savvy is socially desirable. This relates to the context of AFT, in that these tendencies should lead agreeable employees to gravitate toward technology as a means of endearing themselves to others. Thus, agreeable individuals should be more likely than disagreeable individuals to exhibit AFT.

H1c. Agreeableness is positively related to AFT.

Extraversion. The personality trait of extraversion is a measure of social behavior or social attention (Ashton et al., 2002). Extraverts are naturally sociable, enthusiastic, and assertive (John and Srivastava, 1999). The social world is a natural draw for extraverts who excel in competitive, fast changing environments (Bentea and Anghelache, 2012). Similarly, extraverts utilize technology, and the Internet specifically, to connect socially with like-minded individuals and for information search purposes (McElroy et al, 2007).

H1d. Extraversion is positively related to AFT.

Neuroticism. Also known by its positive pole of emotional stability, neuroticism embodies feelings such as anxiousness, hostility, anger and despondence (Barrick and Mount, 1991; John and Srivastava, 1999) and those high in neuroticism are found to be indecisive, impulsive, and often engage in avoidance strategies to cope (Antonioni, 1998), which can result in negative relationships with others and poor overall interpersonal relationship quality (Lopes et al., 2003). Conversely, emotionally stable individuals are more resilient to stress, are more able to exercise emotional control, and are less susceptible to other's emotions (Gallagher, 1990). Those individuals with a strong degree of neuroticism tend to have a negative view of life and work, causing them to form negative beliefs about technology and its usefulness (Devaraj et al., 2008). Thus, relative to emotionally stable individuals, neurotics would be less likely to have high levels affinity for technology.

H1e. Neuroticism is negatively related to AFT.

Affinity for Technology and Career Choice

Jia and Fleming (2013) propose a congruity theory approach to explain the link between affinity for technology and a prospective employee's selection of a technology-oriented career. As discussed by Sirgy (1980, 1981, 1982) consumers are more likely to select products that possess traits which were consistent with positive aspects of their self-image. This area of research extends beyond product choice to include brand personality congruity (Lau and Phau, 2007), and the impact of personality congruity between providers and customers in service settings (Harris and Fleming, 2005; Ekinci and Riley, 2003) to show that congruity influences customer decisions and service outcomes. Research in this area has found that consumers show more loyalty to a company that they see as having an image that matches their own self-image (Sirgy and Samli, 1985) and are more likely to report a positive service experience with firms perceived as having personality traits aligned with their own (Harris and Fleming, 2005). The level of congruity influences how information is processed.

The logical extension of this literature is that employment candidates are predisposed to select jobs which are congruous with their self-image; in this case choosing a career that fits with their attitude about technology. Donohue (2006) found that people who perceive high congruence between their own personalities and the work environment are more likely to remain in their current career and people with low personality-work congruity are more likely to select new careers that better match their personalities. This is consistent with the notion of complementary person-environment congruence (Muchinsky and Monahan, 1987) which focuses on the alignment of what an individual is good at, or prefers, and the requirements of the job. Prospective employees with a high level of AFT are likely to actively process information on career opportunities that match their affinity while those with low levels of AFT will attend to those career paths with lesser levels of technology.

H2: AFT is positively related to the selection of a technology reliant career.



Sample. The sample used in Study One was made up of ROTC cadets at a mid-sized master's granting university in the Midwest. All cadets in the ROTC program participated which resulted in 58 completed surveys. The sample included students from a wide variety of majors and was a representative cross section of the university in terms of degrees being pursued. The sample was 75.4% white, 12.3% African American, 5.3% Hispanic, with the rest being of other ethnicities; it was 82% male and 18% female; and the average age was 20 with a range from 18 to 26 years old.

The selection of this sample is based on the fact that the military is one of the largest industries in the US that is continually seeking highly-skilled individuals with a desire to pursue technology reliant positions. In an effort to attract those individuals who would be most interested in this technology-laden industry, the military has considered video games as a means of recruitment. With 97% of American youths playing video games an average of 73 minutes a day (Media Literacy, 2010), the recruitment path became apparent to the military. According to the Bureau of Labor Statistics (2012), the complexity and technical requirements of military personnel continually increase, necessitating the selection of those most up to the challenge. This population was also proposed by Jia and Fleming (2013) in their conceptual work as an ideal one in which to test the impact of AFT on career choice due to its increasing reliance on technology.

Measures. The five-factor personality variables were each measured using a shortened scale that consists of four items for each factor. This shortened scale has been utilized in previous research (Harris and Fleming, 2005; Mowen, 2000). The measure of whether their career choice was technology related was determined by coding their response to the open ended question "What specific job/duty/position do you hope to be assigned when you graduate and complete your specialization training?" Coding was done by one researcher and a graduate assistant with any disagreements being decided by the other researcher. The inter-rater reliability was 95.4% (42/44) with 14 respondents not providing, or unsure, of their career path.

Analysis and Findings. The first step in the analysis process was to assess the multi-item scales for internal consistency. Reliability analyses were run on each scale as confirmatory factor analysis, however the sample size did not permit a CFA. The AFT scale performed well with a Cronbach's alpha of 0.97 and all item-to-total correlations at or above 0.70. The scales for conscientiousness, neuroticism, and extraversion performed as expected with Cronbach's alphas of 0.83, 0.82, and 0.76 respectively and all item-to-total correlations above 0.50. The remaining two scales for agreeableness and openness to experiences did not perform as well with alphas of 0.59 and 0.65 respectively. An examination of the item-to-total correlations for each scale revealed that one item on each scale was a poor indicator that did not fit with the others on each scale. For agreeableness the removal of the item, "Agreeable," which had an item to total correlation of 0.19, resulted in an increase of the alpha value from 0.59 to 0.62. While this alpha level is below the 0.70 level normally expected as evidence of reliability in research, it is above the 0.60 threshold that can be acceptable in exploratory research (Hair et al., 1998). For openness to experience the removal of the fourth item, "Novel," which had an item to total correlation of 0.14, resulted in an increase of the alpha value from 0.65 to 0.76. The removal of this item resulted in the scale meeting the minimum expectation for reliability.

The second step was to test the hypothesized paths in HI via a partial least squares structural equation model (PLS-SEM) using SmartPLS2 (Ringle et al., 2005). This technique was selected due to the fact that it can effectively handle non-normal data, which was the case with the scale items and the outcome measure; and it does not require a large sample size (Hair et al., 2011). The results of this analysis are presented in Table 1. The findings indicate that the latent variable constructs are of good quality with the average variance extracted from each construct being over the 0.50 threshold recommended by Hair et al. (1998), and acceptable composite reliabilities and Cronbach's alphas. In terms of model usefulness, the fact that the model accounted for over 50% of the variance in AFT (r-square = 0.505) provides support for the predictive ability of that part of the model. When it comes to hypothesis testing, the model indicates partial support for HI with only Neuroticism (Hie) and Openness (Hla) having a significant impact on AFT, in the anticipated directions. The other personality factors did not significantly influence AFT, and the magnitudes of the relationships between these three dimensions of personality and AFT were all less than 0.10 in absolute value.

The next step was to test Hypothesis 2. This was done using the coding of the jobs desired response (tech/nontech) as the outcome variable in a logistic regression with an averaged AFT summated scale score as the predictor variable. The results were not significant with a beta of -0.16 (p > 0.30) and a Cox and Snell R-square of 0.016. This suggests that, at least for this sample, AFT does not predict whether or not the student desires a technologically-oriented career. Additionally, the logistic regression was not able to differentiate between those who did and did not desire a technologically-oriented career with all cases being lumped into the "no" category when using AFT as a predictor. Finally, the data were checked for common method variance due to single-source self-reported data by a Harman's single factor test as described by Podsakoff and Organ (1986) which indicates common method variance if a single factor is found in an unrotated solution or if a first factor explains a majority of the variance in all the measured variables. The results showed that no single factor accounted for more than 21% of the variance and that items loaded together as expected on separate factors which mitigates the concern of CMV.


Study One partially supported the hypotheses, and as such, led to several questions. The first is why only two of the five factors of personality performed as expected. For the dimension of extraversion, while it was hypothesized that those who are more outgoing are likely to be goal driven and resource users (including technology), it could also be argued that higher levels of extraversion thrives on interpersonal interactions (Hall, 2005) and thus extraverts are less likely to use websites and other non-interactive technologies. The question of whether the link between extraversion and technology use/preference is dependent on the type of technology under consideration should be explored in future research. When it comes to agreeableness, the definition of the personality trait lends insight as to why the lack of a relationship may have occurred. The definition includes the idea of prosocial relationships which, like extraversion, may not be positively linked to the participant's affinity for technology depending on the nature of the technology. For conscientiousness, the focus on goal-oriented behavior may not link directly with affinity for technology due to the perception of the participants of the utilitarian vs. hedonic uses of technology. Given that the respondents are under the age of 30 and not yet in a career, it is possible that they view technology as more of a recreational tool than one that enables serious and persistent task focus. The question as to whether these findings are unique to those who are under 30 and not yet in a career, or are they unique to those who choose to go into the military, and specifically through the ROTC program remains.

When it comes to the failure of AFT to predict whether or not the participant hoped for technology-focused jobs (H2), the question is whether this is due to the nature of participants who are going into the military or does this hold across all types of potential employees. The lack of a relationship is surprising given the definition of AFT, but it may be that those going into the military select their desired position based on interactions with current military personnel and/or the service history/experience of the officers in the ROTC program. According to informal interviews with several of the participants, this latter reason may be most predominant as many stated that they chose their service path based on advice from their ROTC instructors who told the participants about their experience in a particular area. Thus, personal interactions with those working in an area may outweigh personality in determining career path selection. This should be explored to see if this is only a phenomenon for those going into lifestyle careers like the military or if it also applies to civilian career selection. If it holds for civilian jobs, it would highlight the importance of internships and mentoring programs as recruiting tools to attract desirable candidates.

These findings have significant implications for FIR practices in general, and in the military specifically. In fiscal 2014, according to the U. S. Department of Defense (2015), the military (active and reserve) hired nearly 40,000 individuals. This translates to a recruitment expense of $78 million in 2014, assuming an average cost per hire of $1,950, which is the average rate for large companies (Stevens-Huffman, 2012). Any means by which an organization can minimize this significant expenditure, especially through ensuring the hiring of good fitting employees, is welcomed.


In order to determine if the findings from Study One were unique to those who are going into the military, a second study was conducted. In addition to investigating the generalizability of Study One's results, this study also utilizes an explicit question of whether or not the participants expect to work in a job that is reliant on technology. In Study One the respondents' expectations of working in a technology reliant field were inferred from their choice of career area; but, in this study, the respondents are explicitly asked to state whether or not (yes/no) they expected their job to be reliant on technology.

Hypothesis Development

Technology Expectations. By the time people enter the workforce, they have had ample opportunities to examine their likes and dislikes, their talents and interests. This exploration serves to shape their preferences for employment. Individuals have expectations about the type of work environment they will be entering, which serve to guide their career choices. Personalities and preferences direct individuals toward certain professions and industries. For example, grocery stores have a variety of positions, servicing all types of personalities. Someone who would rather not interact with others may prefer to work as a stock person, whereas someone who enjoys socializing with customers may opt to be a cashier. Neither job choice is better or worse; rather, different people simply fit better in different kinds of work. Person-job fit is the concept that describes the matching of the characteristics of the individual with the characteristics of the job (Avens et al., 2010). This also fits with self-congruity theory (e.g., Sirgy, 1980, 1981), as discussed earlier, in that it would be expected that respondents select jobs that fit with their self-image including how they feel about technology. Given this, a recruit inherently interested in technology will seek out jobs they perceive to involve technology.

H3: AFT is positively related to the anticipated impact of technology on their chosen job.


Sample. This study utilized a sample of 178 students at two mid-sized Midwestern universities. The students were enrolled in a variety of business classes, including marketing management, sales, and introduction to business. The sample contained a wide variety of majors including business administration, marketing, accounting, finance, health administration, communications, public relations, management, management information systems, insurance, and biology. The average age in the sample was 20 years old, 43.28% were female, and 56.72% were male. The demographics matched the statistical profiles of the two business schools well.

Measures. The personality and AFT measures for this study were the same as in Study One. The measure of whether they were expecting to take a job reliant on technology to test Hypothesis 2 was assessed by a single yes/no question that read "Do you expect that your job will require you to be reliant on technology?" The measure of the extent to which they believe technology will impact their chosen career to test Hypothesis 3 was assessed by a single-item on an eight-point Likert-type scale reading "To what extent do you think technology will impact the way you do your chosen job?" There is debate in the literature regarding the use of single-item indicators, but in this case the measure meets the requirements for a single-item measure as put forth by Wanous et al., (1997).

Analysis and Findings. Consistent with Study One, the multi-item scales were subjected to reliability and item analyses. The AFT scale performed as expected with a Cronbach's alpha of 0.92. For the personality subscales, the measures for agreeableness and openness only contained the three items retained in the first study for consistency and to allow a comparison between the two studies. The alpha for agreeableness was 0.61 which was similar to the findings in Study One and below the desired level of 0.70 yet above the 0.60 level, conscientiousness had an alpha of 0.78, extraversion had an alpha of 0.78, neuroticism had an alpha of 0.74, and openness had an alpha of 0.76. In addition, the continuous variables were all tested for CMV in the same manner as Study One and the results were similar again indicating no issue with CMV.

To test Hypotheses la-e and 3, PLS-SEM was utilized to link the personality constructs to AFT and AFT to respondents rating of the extent to which they expected technology to impact their career. These results are presented in Table 2. The findings indicate that the latent variable constructs are of good quality with the average variance extracted from each construct being over the 0.50 threshold recommended by Hair et al. (1998), and acceptable composite reliabilities and Cronbach's alphas. Additionally, an examination of the AVE and latent variable correlations shows good discriminant validity for the multi-item constructs. The links between the various personality traits were different in this study versus the first with agreeableness (p < 0.05), conscientiousness (p < 0.10), and openness (p < 0.05) leading to AFT, while there was no relationship between neuroticism or extraversion and AFT. The personality factors explained 13.3% of the variance in AFT. As predicted by person-job fit theory, AFT significantly predicted the extent to which respondents expected technology to impact their future career. AFT accounted for 11.8% of the variance in this expectation outcome variable.

To test Hypothesis 2, logistic regression on the yes/no question related to whether the respondent expected their career to be technology reliant was calculated using AFT as the predictor. The results show again that there is no relationship between the two variables (beta = 0.072, s.e. = 0.217, p = 0.740) and there was nearly no explanatory power (Cox and Snell R-square = 0.001). This may be due to the fact that only 10 of the 168 respondents that answered this question said they did not expect their career to be reliant on technology.

The findings from this study partially corroborate the findings from Study One. The first being that AFT does not predict whether or not the respondent would select a technology reliant career as measured in the first study by the career selected and in the second study by their "yes/no" answer to their expectation of their career requiring them to be reliant on technology. While this seems to contradict the self-congruity literature that forms the basis of Hypothesis 2, it may be that the student respondents have determined, based on their exposure to the available careers that, regardless of personal preference about technology they will be reliant on technology regardless of the career area. This would explain why 94% of the respondents in this study said they expected their chosen career to require them to be reliant on technology.


When it comes to the personality antecedents of AFT, the results of this study differ from Study One. The only Big Five dimension that predicted AFT in both studies was openness, and this is logical because those who are receptive to new experiences would be expected to be receptive to new technologies. The fact that the extraversion dimension was not a significant predictor of AFT in either study is surprising given the definition of the extraversion aspect of personality; however as mentioned in Study One, this lack of a predictive relationship may be explained by the different types of technologies available. As previously mentioned, extraverts thrive on interpersonal interactions and may have a high affinity for technologies such as social media that enable those connections while introverts may have high levels of affinity for those technologies like online shopping or self-service technologies that enable them to avoid situations they find uncomfortable. The other dimensions of agreeableness and conscientiousness showed up as significant predictors of AFT in this study but were not significant in Study One. This result may be due to some aspect of the personality of those who choose military careers versus those who choose other career paths. In this case it may be that those who choose non-military careers see these traits as necessary to success in their chosen career field, while these aspects may not be seen as important to those who choose a military path and thus impacts how they responded to the survey for these items. In the same way, neuroticism/emotional instability showed up as a predictor in Study One but not in Study Two. In this case it may be that those who choose a military career, with its extraordinarily high level of stressors, view emotional stability as a necessary characteristic for their career field while those who choose to go into a nonmilitary career are less concerned about neuroticism, and this may shape their responses.

The link between AF I and the expected level of technology in their chosen career from Hypothesis 3 is a finding that holds potential for practitioners. This finding is in line with both congruity theory and person-job fit theory, and it shows that adding an assessment of affinity for technology to a current battery of pre-employment screenings may enable employers to select employees who expect to have a technology-reliant position. Additionally, the replication of a link between openness and AFT shows that the addition of another pre-employment screening metric may not be necessary, rather it may be that organizations desiring to hire technologically-oriented employees should focus on the openness metric to determine the fit of an applicant. Table 3 shows the results for each hypothesis by study.



These studies have several practical HR implications. Recruitment is a significant investment any company makes in its future. Having the "right" people in the "right" jobs yields multiple benefits for any organization and its employees. From the organization's perspective, successfully recruiting candidates that are a proper fit for the job and the organization results in financial gains, such as reduced training costs, diminished need for replacement recruiting expenditures, increased productivity, reduced absenteeism, and enhanced reputation. All of these costs of replacing an employee amount to at least 150% of that employee's salary (Bliss, 2004) which can become a significant drain on company profits if poor person-job fit decisions in the hiring process lead to higher than necessary turnover. This work indicates that companies can improve the match between their need to fill technology reliant positions and the people that they hire by examining the scores of applicants on the area of openness to experience which is already included in many pre-employment screenings. Benefits also extend to the employees through higher morale, increased work satisfaction, increased loyalty and commitment, as well as a decreased desire to leave the organization.

From an academic perspective, these two studies add to the HR literature as well as the marketing literature regarding AFT. When it comes to AFT, this manuscript extends the outcomes and antecedents of AFT thereby growing the nomological network around the construct. Specifically, this is the first study to examine the Big Five personality factors as antecedents to AFT and this is the first study to apply AFT in a hiring/HR context with outcomes related to improving selection. Previous empirical studies have looked at the role of AFT in consumer behaviors (Geissler and Edison, 2005) and in improving employee performance through self-directed learning (Fleming et al, 2014), but this is the first examination of this construct as a filtering variable in the employee recruiting process. These studies also contribute to the HR literature by expanding the discussion of factors that lead to career choice. Personality, interests, and values all play a role, which is supported by the findings. Person-job fit is well established in the literature, so subjecting the construct to a variety of different career choices and populations serves to substantiate the assertions of person-job fit across a wider contextual base. In addition, expanding the notion of person-job fit by bringing in the idea of congruity theory opens many avenues for future research in terms of the areas of fit that might be explored such as company or brand personality-employee fit.

Limitations and Future Research

As with any research this set of studies has limitations. Study One was conducted using only ROTC military personnel in the Midwest. This may or may not be generalizable to the entire population of the military. Similarly, participant behavior was self-reported, in a single session, leading to the potential for common-method bias. Future research should examine if the findings in Study One hold for larger samples, other areas of the military, and for military across the country. Another potential issue is that student samples were used, which can be problematic. In the present study, college students are heavy technology users and arguably serve as a representative sample of this population. Any bias resulting from using college students would be the samples' over-familiarity with technology. Additionally, as this research focuses on the current generation of career seekers, students make perhaps the best sample to study this because they are in or on the verge of this process. One specific question that bears future research is whether ROTC cadets are different from general enlisted personnel in the impact of personality on the links between personality factors, AFT, technology facilitated career research activities, and the desire for a technology-oriented career. Similarly, Study Two was conducted using a convenience sample of students in the Midwest, which can lead to generalizability questions when applied to a greater swath of the population. Another limitation to Study Two is the lack of respondents who indicated that they did not expect their desired career to require them to be reliant on technology. Future research should investigate whether AFT influences the type of jobs investigated or interviewed for, instead of using an expectation measure.

The findings also suggest avenues for future research. In addition to the antecedents explored in this paper, past studies have examined other factors that influence career choice, such as gender (Correll, 2001), self-efficacy (Bandura, 1977), and social class (Werts, 1966). Future research can explore a more comprehensive model of what factors lead to career choice, similar to the Social Cognitive Career Theory, developed by Lent et al. (1994). Also, past studies (i.e., Edison and Geissler, 2003) have noted that traits such as dispositional optimism, self-efficacy, and need for cognition are antecedents of AFT. This, combined with the findings in the current study related to personality factor antecedents of AFT, indicates that perhaps a hierarchical structure such as that proposed by Mowen (2000) would better model reality with the Big Five factors as elemental traits that lead to compound traits such as the three found by Edison and Geissler (2003). These would lead to situational traits (in this case AFT), which would lead to surface level traits like preference for a technology reliant career. This structure should be applied and examined in this HR context in a manner similar to what Harris and Lee (2004) did with personality and salesperson performance. This structural change to the model may also explain some of the issues as to why the links between personality and AFT were not behaving as expected.

David E. Fleming

Director, Sales & Negotiations Center and Associate Professor of Marketing

Indiana State University

Heather H. Jia

Assistant Professor

Illinois State University


Antonioni, D. 1998. "Relationship between the Big Five Personality Factors and Conflict Management Styles." International Journal of Conflict Management 9(4): 336-55.

Ashton, M. C., K. Lee, and S. V. Paunonen. 2002. "What is the Central Feature of Extraversion? Social Attention versus Reward Sensitivity." Journal of Personality and Social Psychology 83: 245-51.

Avens, J. B., F. Luthans, and C. M. Youssef. 2010. "The Additive Value of Positive Psychological Capital in Predicting Work Attitudes and Behaviors." Journal of Management 36(2): 430-52.

Bandura, A. 1977. "Self-efficacy: Toward a Unifying Theory of Behavioral Change." Psychological Review 84: 191-215.

Barrick, M. R., and M. K. Mount. 1991. "The Big Five Personality Dimensions and Job Performance: A Meta-analysis." Personnel Psychology 44: 1-26.

Bentea, C. C., and V. Anghelache. 2012. "Comparative Aspects Concerning the Effects of Extraversion on Performance in a Cognitive Task in Competitive and Cooperative Environments." Procedia: Social and Behavioral Sciences, 33: 558-62.

Bersin and Associates. 2011. The Talent Acquisition Factbook. 1: 7-8.

Bliss, W. G. 2004. "Cost of Employee Turnover." The Advisor. [Online]. Available:

Bureau of Labor Statistics. 2012. "Military Careers." Occupational Outlook Handbook. [Online],

Chiaburu, D. S., I. Oh, C. M. Berry, N. Li, and R. G. Gardner. 2011. The Five-Factor Model of Personality Traits and Organizational Citizenship Behaviors: A Metaanalysis. "Journal of Applied Psychology 96: 1140-66.

Correll, S. J. 2001. "Gender and Career Choice Process: The Role of Biased Self-Assessments." American Journal of Sociology 106(6): 1691-730.

Costa, P. T., and R. R. McCrae. 1992a. "Four Ways Five Factors are Basic." Personality and Individual Differences 13: 653-65.

--, and R. R. McCrae. 1992b. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-factor Inventory (NEO-FFI) Professional Manual. Odessa, FL: Psychological Assessment Resources.

Davis, F. D. 1989. "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology." MIS Quarterly 13: 319-40.

Devaraj, S., R. F. Easley, and J. M. Crant. 2008. "How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use." Information Systems Research 19(1): 93-105.

Digman, J. 1990. "Personality Structure: Emergence of the Five-Factor Model." Annual Review of Psychology 41: 417-40.

Donohue, R. 2006. "Person-Environment Congruence in Relation to Career Change and Career Persistence. "Journal of Vocational Behavior 68: 504-15.

Edison, S. W., and G. L. Geissler. 2003. "Measuring Attitudes towards General Technology: Antecedents, Hypotheses and Scale Development." Journal of Targeting, Measurement and Analysis for Marketing 12(2): 137-56.

Ekinci, Y., and M. Riley. 2003. "An Investigation of Self-Concept: Actual and Ideal Self-congruence Compared in the Context of Service Evaluation." Journal of Retailing and Consumer Services 10(4): 201-14.

Fleming, D. E., A. B. Artis, and J. M. Hawes. 2014. "Technology Perceptions in Employees' Use of Self-directed Learning ."Journal of Services Marketing 28(1): 509.

Gallagher, D. J. 1990. "Extraversion, Neuroticism and Appraisal of Stressful Academic Events." Personality and Individual Differences 11: 1053-57

Geissler, G. L., and S. W. Edison. 2005. "Market Mavens' Attitudes towards General Technology: Implications for Marketing Communications." Journal of Marketing Communications 11: 73-94.

Goldberg, L. R. 1993. "The Structure of Phenotypic Personality Traits." American Psychologist 48: 26-34.

--. 1990. "An Alternative "Description of Personality": The Big-Five Factor Structure. "Journal of Personality and Social Psychology 59: 1216-29.

Hair, J. F., R. E. Anderson, R. L. Tatham, and W. C. Black. 1998. Multivariate Data Analysis. Upper Saddle River, NJ: Prentice-Hall, Inc.

--, C. M. Ringle, and M. Sarstedt. 2011. "PLS-SEM: Indeed a Silver Bullet." Journal of Marketing Theory and Practice 19(2): 139-52.

Hall, A. 2005. "Audience Personality and the Selection of Media and Media Genres." Media Psychology 7(4): 377-98.

Harris, E. G., and D. E. Fleming. 2005. "Assessing the Human Element in Service Personality Formation: Personality Congruency and the Five Factor Model." Journal of Services Marketing 19(4): 187-198.

--, and J. M. Lee. 2004. "Illustrating a Hierarchical Approach for Selecting Personality Traits in Personnel Decisions: An Application of the 3M Model. "Journal of Business and Psychology 19(1): 53-67.

Heinssen, R. K., C. R. Glass, and L. A. Knight. 1987. "Assessing Computer Anxiety: Development and Validation of the Computer Anxiety Rating Scale." Computers in Human Behavior 3: 49-59.

Hmel, B. A., and A. L. Pincus. 2002. "The Meaning of Autonomy: On and Beyond the Interpersonal Circumplex." Journal of Personality 70: 277-310.

Hurtz, G. M., and J. J. Donovan. 2000. "Personality and Job Performance: The Big Five Revisited." Journal of Applied Psychology 85(6): 869-79.

Jia, H. H., and D. E. Fleming. 2013. "Conceptualizing the Use of Personality to Identify High Technology Career Preference in Military Recruits." Journal of the North American Management Society 7: 53-8.

John, O. P., and S. Srivastava. 1999. "The Big Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives." In Handbook of Personality: Theory and Research (2nd ed.). Eds. L. A. Pervin and O. P. John. New York, NY: Guilford Press.

Judge, T. A., D. Heller, and M. K. Mount. 2002. "Five-factor Model of Personality and Job Satisfaction: A Meta-analysis. "Journal of Applied Psychology 87: 530-41.

--, and C. P. Zapata. 2015. "The Person-Situation Debate Revisited: Effect of Situation Strength and Trait Activation on the Validity of the Big Five Personality Traits in Predicting Job Performance." Academy of Management Journal 58(4): 1149-79.

Landers, R. N., and J. W. Lounsbury. 2006. "An Investigation of Big Five and Narrow Personality Traits in Relation to Internet Usage." Computers in Human Behavior 22(2): 283-93.

Lau, K. C., and I. Phau. 2007. "Extending Symbolic Brands Using Their Personality: Examining Antecedents and Implications Towards Brand Image Fit and Brand Dilution." Psychology and Marketing 24(5): 421-444.

Lent, R., S. Brown, and G. Hackett. 1994. "Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance." journal of Vocational Behavior 45: 79-122.

Lopes, P. N., P. Salovey, and R. Straus. 2003. "Emotional Intelligence, Personality, and the Perceived Quality of Social Relationships." Personality and Individual Differences 35: 641-58.

Malhotra, Y., and D. F. Galletta. 2005. "A Multidimensional Commitment Model of Volitional Systems Adoption and Usage Behavior." Journal of Management Information Systems 22(1): 117-51.

McElroy, J. C., A. R. Hendrickson, A. M. Townsend, and S. M. DeMarie. 2007. "Dispositional Factors in Internet Use: Personality versus Cognitive Style." MIS Quarterly 31(4): 809-20.

Media Literacy. 2010. "Resources on Media Habits of Children." [Online].

Mowen, J. C. 2000. The 3M Model of Motivation and Personality: Theory and Empirical Applications to Consumer Behavior. New York, NY: Springer Science and Business Media.

Muchinsky, P. M., and C. J. Monahan. 1987. "What is Person-Environment Congruence? Supplementary versus Complementary Models of Fit." Journal of Vocational Behavior 31(3): 268-77.

Parasuraman, A. 2000. "Technology Readiness Index (TRI): A Multiple Item Scale to Measure Readiness to Embrace New Technologies." Journal of Service Research 2: 307-20.

Podsakoff, P. M., and D. W. Organ. 1986. "Self-Reports in Organizational Research: Problems and Prospects. "Journal of Management 12: 531-44.

Raja, U., and G. Johns. 2010. "The Joint Effects of Personality and Job Scope on InRole Performance, Citizenship Behavior and Creativity." Human Relations 20: 1-25.

Ringle, C. M" S. Wende, and A. Will. 2005. "SmartPLS 2.0." [Online],

Rothstein, M. G., and R. D. Goffin. 2006. "The Use of Personality Measures in Personnel Selection: What Does Current Research Support?" Human Resource Management Review 16(2): 155-80.

Sirgy, M. J. 1982. "Self-Image/Product-Image Congruity and Advertising Strategy." In Developments in Marketing Science (Vol. 5). Ed. Vinay Kothari. Marquette, MI: Academy of Marketing Science.

--. 1981. "Testing a Self-Concept Model Using a Tangible Product." Proceedings of the American Psychological Association - Consumer Psychology Division 89.

--. 1980. "Self-Concept in Relation to Product Preference and Purchase Intention." In Developments in Marketing Science (Vol. 3), Ed. V. V. Bellur, Marquette, MI: Academy of Marketing Science.

--, and A. C. Samli. 1985. A Path-analytic Model of Store Loyalty Involving Self-concept, Store Image, Geographic Loyalty, and Socio-economic Status ." Journal of the Academy of Marketing Science 13(3): 265-91.

Stevens-Huffman, L. 2012. "Cost Per Hire: How Do You Stack Up?" [Online].

Tuten, T., and M. Bosnjak. 2001. "Understanding Differences in Web Usage: The Role of Need for Cognition and the Five Factor Model of Personality." Social Behavior and Personality 29(4): 391-98.

U.S. Department of Defense. 2015. "DoD announces Recruiting and Retention Numbers for Fiscal 2015, Through November 2014." [online]:

Viswesvaran, C., and D. S. Ones. 2004. "Importance of Perceived Personnel Selection System Fairness Determinants: Relations with Demographic, Personality, and Job Characteristics." International Journal of Selection and Assessment 12: 172-86.

Wanous, J. P., A. E. Reichers, and M. J. Hudy. 1997. "Overall Job Satisfaction: How Good are Single-item Measures?" Journal of Applied Psychology 82: 247-52.

Werts, C. E. 1966. Social Class and Initial Career Choice of College Freshmen." Sociology of Education 39(1): 74-85.

Zweig, D., and J. Webster. 2004. "What Are We Measuring? An Examination of the Relationships between the Big-Five Personality Traits, Goal Orientation, and Performance Intentions." Personality and Individual Differences 36: 1693-708.
Table 1
Study One PLS-SEM Results

Latent Variable Quality      AVE    Composite   R-Square   Cronbach's
                                      Rel.                   Alpha

Agreeableness (Agre)*       0.510     0.747       N/A        0.625
Conscientiousness (Cons)    0.660     0.885       N/A        0.833
Extraversion (Extr)         0.578     0.845       N/A        0.765
Neuroticism (Neur)          0.648     0.880       N/A        0.822
Openness (Open)*            0.671     0.858       N/A        0.766
Affinity for Tech. (ATT)    0.786     0.962      0.505       0.954
Time Spent on Site (Time)   1.000     1.000      0.028        N/A

* Revised scale after reliability analysis

Latent          Agre     Cons     Extr     Neur

Agre           0.714
Cons           0.482    0.812
Extr           -0.174   -0.422   0.760
Neur           -0.051   -0.372   0.551    0.805
Open           0.553    0.629    -0.308   -0.287
AFT            0.300    0.442    -0.450   -0.606
Time**         -0.008   -0.037   0.020    -0.108

Latent          Open     AFT      Time

Open           0.819
AFT            0.514    0.887
Time **        0.111    0.167    1.000

** Single item scale

Note: Diagonal Elements = the square root of the AVE for the

Structural Paths                           Std. Path       Std.
                                           Coefficient     Error

H1c: Agreeableness [right arrow] AFT          0.096        0.083
H1b: Conscientiousness [right arrow] AFT     -0.006        0.078
H1d: Extraversion [right arrow] AFT          -0.083        0.087
H1e: Neuroticism [right arrow] AFT           -0.471        0.084
H1a: Openness [right arrow] AFT               0.305        0.093

Structural Paths                             t-value       Sig.

H1c: Agreeableness [right arrow] AFT          1.163        N.S.
H1b: Conscientiousness [right arrow] AFT      0.080        N.S.
H1d: Extraversion [right arrow] AFT           0.948        N.S.
H1e: Neuroticism [right arrow] AFT            5.617      p < 0.001
H1a: Openness [right arrow] AFT               3.279      p < 0.001

Table 2
Study 2 PLS-SEM Results

Latent Variable Quality    AVE     Composite   R-Square   Cronbach's
                                   Rel.                   Alpha

Agreeableness (Agre) *     0.551   0.786       N/A        0.596
Conscientiousness (Cons)   0.571   0.839       N/A        0.777
Extraversion (Intr)        0.591   0.847       N/A        0.778
Neuroticism (Neur)         0.546   0.824       N/A        0.739
Openness (Open) *          0.661   0.849       N/A        0.760
Affinity for Tech. (AFT)   0.672   0.935       0.133      0.918
Technology Expectations    1.000   1.000       0.118      1.000

* Revised scale after reliability analysis

Latent Variable   Agre    Cons     Extr      Neur

Agre              0.742
Cons              0.347   0.756
Extr              0.146   -0.097   0.768
Neur              0.145   0.030    0.355     0.739
Open              0.274   0.371    -0.208    0.101
AFf               0.246   0.252    -0.101    -0.090
Tech. Expect. **  0.241   0.247    -0.082    0.162

Latent Variable   Open    APT      Tech.
Correlations                       Expect.

Open              0.813
AFf               0.257   0.820
Tech. Expect. **  0.258   0.344    1.000

** Single item scale

Note: Diagonal Elements = the square root of the AVE for the

Structural Paths                           Std. Path     Std.
                                           Coefficient   Error

H1c: Agreeableness [right arrow] AFT       0.179         0.084
H1b: Conscientiousness [right arrow] AFT   0.128         0.087
H1d: Extraversion [right arrow] AFT        -0.037        0.125
H1e: Neuroticism [right arrow] AFT         -0.123        0.139
H1a: Openness [right arrow] AFT            0.165         0.087
H3: APT [right arrow] Technology impact    0.344         0.076
  on career

Structural Paths
                                           t-value       Sig.

H1c: Agreeableness [right arrow] AFT
H1b: Conscientiousness [right arrow] AFT   2.12          p < 0.018
H1d: Extraversion [right arrow] AFT        1.47          p < 0.071
H1e: Neuroticism [right arrow] AFT         0.30          N.S.
H1a: Openness [right arrow] AFT            0.88          N.S.
H3: APT [right arrow] Technology impact    1.90          p < 0.029
  on career                                4.51          p < 0.001

Table 3
Hypothesis Results

Hypothesis                                Study 1         Study 2

H1a Openness [right arrow] AFT            Supported       Supported
H1b Conscientiousness [right arrow] AFT   Not Supported   Supported
H1c Agreeableness [right arrow] AFT       Not Supported   Supported
H1d Extraversion [right arrow] AFT        Not Supported   Not Supported
H1e Neuroticism [right arrow] AFT         Supported       Not Supported
H2 AFT [right arrow] Technology reliant   Not Supported   Not Supported
  career choice
H3 AFT [right arrow] Anticipated          N/A             Supported
  technology impact on career
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Author:Fleming, David E.; Jia, Heather H.
Publication:Journal of Managerial Issues
Article Type:Abstract
Geographic Code:1USA
Date:Sep 22, 2016
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