New technology in personnel selection: How recruiter characteristics affect the adoption of new selection technology

Computers in Human Behavior 29 (2013) 2404–2415

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New technology in personnel selection: How recruiter characteristics affect the adoption of new selection technology Janneke K. Oostrom a,b,⇑, Dimitri van der Linden b, Marise Ph. Born b, Henk T. van der Molen b a b

VU University Amsterdam, Department of Social and Organizational Psychology, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands Erasmus University Rotterdam, Institute of Psychology, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands

a r t i c l e

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Article history: Available online 14 June 2013 Keywords: Recruiter reactions New technology Technology Acceptance Model Personality Personal innovativeness in information technology Computer self-efficacy

a b s t r a c t The aim of the present field study is to expand the understanding of how characteristics of recruiters relate to their adoption of new selection technology. In two studies, among 198 recruiters, we used the Technology Acceptance Model (TAM), together with two measures of personality (i.e., openness to experience and neuroticism), two information technology specific individual differences (i.e., personal innovativeness in information technology and computer self-efficacy), and reactions to and actual usage of new technology. Both studies showed that all recruiter characteristics (except openness to experience) relate to perceptions of usefulness and ease of use, and that these perceptions relate to intentions to use new selection technologies. Study 2 showed that recruiter characteristics predict perceptions of usefulness and ease of use over and above established predictors of the TAM. Perceptions of usefulness and ease of use were better predictors of intentions to use new technology than perceptions of face validity, predictive validity, and fairness. Thus, when it comes to the adoption of new selection technology, recruiter characteristics, and perceptions of usefulness and ease of use play an important role. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction There has been a rapid growth in the use of new technology in personnel selection practice. New technologies, like computerbased testing, internet-based testing, telephone-based interviews, video-conference job interviews, and multimedia simulation tests, allow organizations to test large numbers of applicants at the same time and help saving time and money (Anderson, 2003). This has prompted the interest of researchers regarding the effects of new technology upon testing-related issues such as validity, applicant reactions, and subgroup differences (e.g., Lievens & Sackett, 2006; Richman-Hirsch, Olson-Buchanan, & Drasgow, 2000). Thus far, research has shown that organizations not only benefit from using new technology in terms of efficiency, but also in terms of validity (Lievens & Sackett, 2006) and acceptance by candidates (Chan & Schmitt, 1997). Yet, there is scant research on how the recruiters themselves perceive these new technologies. In 2003, Anderson already noted that ‘‘we currently know next to nothing about recruiter reactions to, expectations of, and willingness to adopt new technology for selection’’ (p. 133). ⇑ Corresponding author. Address: VU University Amsterdam, Faculty of Psychology and Education, Department of Social and Organizational Psychology, Room 1B-25, 1081 BT Amsterdam, The Netherlands. E-mail addresses: [email protected] (J.K. Oostrom), [email protected] (D. van der Linden), [email protected] (M.Ph. Born), [email protected] (H.T. van der Molen). 0747-5632/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chb.2013.05.025

Not much has changed since then. In fact, the extant research on the adoption of new selection technology has focused entirely on the candidate (Hausknecht, Day, & Thomas, 2004; Wiechmann & Ryan, 2003) and almost completely has ignored the recruiter. This is surprising as recruiters are the ones responsible for the adoption of new technologies into the personnel selection practice. The absence of research on recruiter reactions evidently limits the current understanding of the effects of new technology in personnel selection. Therefore, the aim of the present study is to expand the understanding of the effects of new technology in personnel selection, by examining how recruiter characteristics relate to the adoption of new selection technology. Because individual characteristics such as personality factors play an important role in human cognition and behavior, it is reasonable to expect that these variables will influence the adoption of new technology as well. Yet, for many years, the issue of individual characteristics has received little attention in the technology adoption literature in general (Devaraj, Easley, & Crant, 2008). By using the Technology Acceptance Model (TAM; Davis, 1989), the effects of personality (i.e., openness to experience and neuroticism) and information technology (IT) specific individual differences (i.e., personal innovativeness in IT and computer self-efficacy) on reactions to and actual usage of new technology in the personnel selection practice will be examined in two field studies. Furthermore, the present study will examine to what extent findings from the applicant

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reaction literature could be generalized to recruiter reactions. More specifically, in selection research it is known that selection-specific characteristics of an instrument play a role in applicant reactions (Hausknecht et al., 2004). When it comes to adopting new selection technology in selection and assessment, it is a relevant question to test whether technology-based perceptions (e.g., is the software easy to use) have an effect over and above selection-specific characteristics (i.e., face validity, predictive validity, fairness). This will be tested in Study 2. Below we will first provide more details about the TAM.

1.1. The Technology Acceptance Model Davis (1989) introduced the TAM to explain the process of technology adoption by individuals. The TAM is influenced by the Theory of Reasoned Action developed by Fishbein and Ajzen (1975), which states that individuals’ intention for a certain behavior is influenced by their attitude toward that behavior and their subjective norms. TAM posits that the intention to use technology is mainly influenced by two specific attitudes or reactions, i.e., the perceived usefulness and ease of use. Perceived usefulness is defined as the degree to which a person believes that using a particular system will enhance his or her job performance. Perceived ease of use is defined as the degree to which a person believes that using a particular system would be free of effort (Davis, 1989). We choose the TAM to examine the relationships of recruiter characteristics and the adoption of new technology for several reasons. First, the TAM is well-accepted and validated, with a history of extensions that have been well-summarized by Venkatesh, Morris, Davis, and Davis (2003). Second, the basic concept underlying the model places significant focus on individual reactions to technology, in which factors such as personality and computer self-efficacy can be expected to play a role (Devaraj et al., 2008). Finally, the Theory of Reasoned Action (Fishbein & Ajzen, 1975), which is the basis for the TAM, explicitly incorporates individual characteristics as an external variable affecting an individual’s beliefs.

1.2. Recruiter characteristics According to Rogers (1995), adopting an innovation can be predicted by the perceived attributes of that innovation plus the compatibility with individual characteristics. Yet, only a few studies on technology adoption have actually incorporated individual characteristics. The vast majority of these studies have used student samples (e.g., Devaraj et al., 2008; McElroy, Hendrickson, Townsend, & DeMarie, 2007; Nov & Ye, 2008), limiting their ecological validity. For example, McElroy et al. (2007) found that the Big Five personality traits explained a significant part of the variance in students’ use of internet. In their review, Nov and Ye (2008) concluded that openness to experience was positively and neuroticism was negatively related to students’ technology adoption. Although several studies examined openness to experience and neuroticism in relation to adopting technology in various fields (Devaraj et al., 2008; Guadagno, Okdie, & Eno, 2008), there are no studies that have tested these relationships in the field of personnel selection. Thus, if we want to know what factors drive the adoption of new technology in this field, studies incorporating recruiter characteristics into the TAM are important. There are several recruiter characteristics, including personality and IT-specific individual differences that we expect to affect the adoption of new selection technology. We elaborate on these expected relationships between recruiter characteristics and the adoption of new selection technology in Sections 1.2.1-1.2.4.

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1.2.1. Openness to experience Openness to experience represents an individual’s curiosity, open-mindedness, and their willingness to experiment. Individuals scoring high on openness to experience are imaginative, curious, original, artistic, sensitive, open-minded, and experimental (Barrick & Mount, 1991). Previous studies indicated that individuals scoring high on this personality trait like change and diversity, and adjust quickly to dynamic environments (Devaraj et al., 2008). McElroy et al. (2007) showed that openness is a significant predictor of internet use. Guadagno et al. (2008) proved openness to predict blogging, defined as a relatively new online tool for selfexpression. However, Devaraj et al. (2008) showed that openness was not positively associated with beliefs about the perceived usefulness of technology. Yet, they found a direct relationship between this concept and the intention to use technology. In the field of personnel selection, Wiechmann and Ryan (2003) found that candidates who were more open to experience reacted more positively to the use of a computer-based in-basket exercise. Considering the above findings, we expect recruiters’ openness to play a relevant role in their adoption of new selection technology. Hypothesis 1. Openness to experience is positively related to the perceived usefulness (H1a) and the perceived ease of use (H1b) of new selection technologies.

1.2.2. Neuroticism Neuroticism refers to an individual’s tendency to be worried, temperamental and prone to stress, anger, and hostility. Neuroticism is associated with anxiety, depression, anger, worry, and insecurity (Costa & McCrae, 1992). As neuroticism implies negative reactions to work and life situations in general, it is expected to also yield negative beliefs about technology. Compared to individuals scoring low on neuroticism, neurotic people are on average more stressed by the idea of having to use a new technology and are more afraid to try out something new (Devaraj et al., 2008). Devaraj et al. (2008) confirmed this notion by showing that neuroticism is indeed negatively associated with beliefs about the perceived usefulness of technology. In their research on technophobia and personality subtypes, Anthony, Clarke, and Anderson (2000) found neuroticism to be positively correlated with computer anxiety and negatively correlated with computer cognitions, thus indicating that technophobia is related to neuroticism. In the study conducted by Anthony et al. (2000), technophobia referred to anxiety about computer-related technology, negative attitudes towards computers and negative cognitions concerning computer interactions. Moore and McElroy (2012) found neuroticism to be positively related to time spent on Facebook and the frequency of using Facebook to keep up with others. Based on the above-mentioned findings and taking into account the TAM model and the role of beliefs in adopting new technology, it can be inferred that there is a relationship between neuroticism and technology use. Hypothesis 2. Neuroticism is negatively related to the perceived usefulness (H2a) and the perceived ease of use (H2b) of new selection technologies.

1.2.3. Personal innovativeness in IT Personal innovativeness in IT can be defined as the willingness of an individual to try out any new information technology and it is conceptualized as a stable personality trait (Agarwal & Prasad, 1998). According to Rogers (1995), individuals scoring high on innovativeness always search for new information and ideas, manage to tolerate higher levels of uncertainty, and have more positive

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intentions to adopt new technology. Agarwal, Sambamurthy, and Stair (2000) suggested that individuals who score high on personal innovativeness in IT demonstrate more confidence in their ability to use new technologies. Personal innovativeness in IT has been demonstrated to be a reliable predictor of individuals’ attitudes about the effectiveness of new technologies (Ghobakhloo, Hong, Sabouri, & Zulkifli, 2012). Lin (2004) showed that user innovativeness is a significant predictor of an individual’s intention to adopt webcasting. Wang, Lin, and Liao (2012) found personal innovativeness in IT to influence the perceived enjoyment of blogging, which in turn influenced blogging intentions. Walczuch, Lemmink, and Streukens (2007) showed that people who score high on personal innovativeness are prone to experiment with new technology; otherwise they might feel they miss out on potential benefits. They reported a positive relationship between personal innovativeness and perceived ease of use of new technology. However, the relationship between personal innovativeness and perceived usefulness turned out to be negative. According to the authors, this negative relationship could be explained by the fact that innovative people may be more critical of technology as they keep up with the latest developments and have high standards for technology. Lu, Yao, and Yu (2005) found that there is a strong relationship between personal innovativeness in IT and perceived usefulness and ease of use. As people scoring high on innovativeness in IT tend to think that they might miss certain benefits when not trying out a new technology, it is expected that personal innovativeness in IT would also have a positive impact on the adoption of selection technology. Therefore we formulated the following hypothesis: Hypothesis 3. Personal innovativeness in IT is positively related to the perceived usefulness (H3a) and the perceived ease of use (H3b) of new selection technologies.

1.2.4. Computer self-efficacy Computer self-efficacy is defined as a judgment of one’s capability to use a computer (Compeau & Higgins, 1995). This concept is built on Bandura’s (1997) theory of personal self-efficacy beliefs and behavior. Compeau and Higgins (1995) showed that individuals who scored high on computer self-efficacy were using computers more often and enjoyed computer use. They also found computer self-efficacy to be a strong and significant predictor of computer use a year later. Venkatesh and Davis (1996) and Lewis, Agarwal, and Sambamurthy (2003) found that computer self-efficacy is a determinant of perceived ease of use and Yi and Hwang (2003) showed that application-specific self-efficacy had a significant effect on perceived ease of use. Chau (2001) reported findings that were in contrast to the majority of studies on this topic and showed that computer self-efficacy was negatively associated with perceived usefulness and had no significant relationship with perceived ease of use. One possible explanation provided for these contrasting results was that users with high computer self-efficacy may also more clearly see the limitations of an information technology apart from its usefulness. In addition, Chau’s study referred to a particular software package and not to new technology in general, which might explain the contradictory results. So, drawing on the above studies the following hypothesis is proposed: Hypothesis 4. Computer self-efficacy is positively related to the perceived usefulness (H4a) and the perceived ease of use (H4b) of new selection technologies. Fig. 1 presents the general research model of Study 1. According to the TAM model, both perceived ease of use and perceived usefulness influence behavioral intentions (Davis, 1989). Hence, we formulated Hypothesis 5 as described below.

Hypothesis 5. Perceived usefulness (H5a) and perceived ease of use (H5b) of new selection technologies are positively related to intentions to use. Note that the relationships as described in Hypothesis 5 were validated in several previous studies (e.g., Davis, 1989, 1993; Venkatesh & Davis, 1996; Venkatesh & Davis, 2000). The present study, however, goes beyond these studies by aiming to re-validate these relationships in the context of personnel selection and by including several individual characteristics. In this way the present studies can contribute to insight into what recruiter characteristics play a role in their acceptance of new technology. 2. Method Study 1 2.1. Sample and procedure Recruiters at multinational companies, recruitment agencies, governmental institutions, and non-governmental organizations based in the Netherlands were contacted via email, via professional networks (e.g., Linkedin), and via online groups. An email with instructions and a web-link to the survey was sent to the recruiters. In total, 89 recruiters who work for various companies and organizations in the Netherlands filled out the questionnaire. Fifty recruiters were female (56.2%) and 39 were male (43.8%). Their age varied from 25 years to 62 years, with an average of 36.00 years (SD = 8.11). Respondents had various educational backgrounds, ranging from high school to post-graduate degrees. The group of respondents was also heterogeneous regarding their job roles: 34% were recruiters, 10% were HR managers, 21% were HR consultants, 16% were involved in HR administration and 18% were working in other HR-related jobs. 2.2. Measures 2.2.1. Personality traits The Big Five personality dimensions of conscientiousness, extraversion, emotional stability, agreeableness and openness/ intellect were measured with the Dutch version of the 50 item representation of the Goldberg (1992) Big Five markers in the International Personality Item Pool (IPIP). Respondents answered the statements on a 5-point Likert scale (1 = very inaccurate and 5 = very accurate). Only the scores on openness to experience and neuroticism were used in the present study. The alpha for openness was .73. An example of an item is ‘‘I have a vivid imagination’’. The alpha for the items measuring neuroticism was .73. An example of an item is ‘‘I rarely get irritated’’. 2.2.2. Personal innovativeness in IT Personal innovativeness in IT was measured with the adapted scale developed by Agarwal and Prasad (1998) which contained four items reflecting an individual’s tendency of trying out new information technology. One of the items was ‘‘I like to experiment with new selection technologies’’. Respondents scored themselves on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree). The alpha coefficient for the scale was .87. 2.2.3. Computer self-efficacy Computer self-efficacy was measured with the eight-item scale developed by Levine and Donitsa-Schmidt (1998) and adapted by Wiechmann and Ryan (2003). An example of an item is ‘‘I find using the computer easy’’. Respondents scored themselves on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree). The alpha coefficient for this scale was .88.

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Openness to experience

H1a

Perceived usefulness

H1b H2a

H5a

Neuroticism H2b

Intention to adopt

H3a

Personal innovativeness in IT

Computer self-efficacy

Perceived ease of use

H3b H4a

H5b

H4b Fig. 1. Model for Hypotheses 1–5.

2.2.4. Perceptions about new technology Perceived usefulness and perceived ease of use were measured with adapted scales developed by Davis (1989). Each construct was measured with six items on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree). The alpha coefficient for the perceived usefulness scale was .95. An example of an item is ‘‘Using new selection technologies in my job would increase my productivity’’. The alpha’s for the perceived ease of use scale was .92. An example of an item is ‘‘Learning to operate new selection technologies would be easy for me’’. 2.2.5. Intentions to use new technology Intentions to use new technology were measured with two statements adapted from the work of Agarwal and Prasad (1998). The items were: ‘‘I intend to increase my use of new selection technologies for work in the future’’ and ‘‘For future work, I would use new selection technologies’’. Respondents were asked to rate themselves on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree). The alpha coefficient was .78. 2.2.6. Voluntariness in using new technology The extent to which potential adopters of new selection technologies perceive the adoption decision to be non-mandatory is likely to affect the hypothesized relationships. For this reason, voluntariness was included as a control variable in the present study. This variable was measured with one item on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree), namely ‘‘In my work, I am allowed to choose the tools for the selection of new personnel’’. 3. Results Study 1 3.1. Preliminary analysis Means, standard deviations, scale reliabilities (internal consistencies), and correlations between all variables are presented in Table 1. As all hypotheses were tested with regression analyses, we first checked whether the assumptions of these analyses techniques were met. All variables were adjudged to be normally distributed. Furthermore, residual plots showed that residuals were randomly scattered and that all relationships between the independent and dependent variables were linear. Before testing the

hypotheses, we first looked at significant correlations between demographic characteristics and the study variables. Age was significantly and negatively related to perceived ease of use (r = .30, p < .01) and intentions to use (r = .24, p < .05). Gender was significantly and negatively related to personal innovativeness in IT (r = .40, p < .01). Male respondents (M = 3.38, SD = 0.78) scored significantly higher on this trait than female respondents (M = 2.74, SD = 0.71; t = 3.94, p < .01). Educational level had a positive and significant correlation with openness to experience (r = .22, p < .05) and perceived ease of use (r = .30, p < .01), and a negative significant correlation with neuroticism (r = .22, p < .05). Because of these significant correlations we controlled for age, gender (coded as 0 = male and 1 = female), and educational level (dummy coded so that holding a high school or intermediate vocational diploma was the excluded category) in the regression analyses.

3.2. Hypotheses testing Hypotheses 1–4 were tested with correlational analyses. Table 1 shows that openness to experience was not significantly related to the perceived usefulness (r = .10, p = .34). The relationship between openness and perceived ease of use also did not reach the significance level of .05, although it was marginally significant (r = .20, p = .07). Thus, our first hypothesis, stating that openness is positively related to the perceived usefulness and the perceived ease of use, was not supported. Hypothesis 2, stating that neuroticism is negatively related to the perceived usefulness and the perceived ease of use was partly supported, as neuroticism was significantly and negatively correlated with the perceived ease of use (r = .32, p < .01) but not with the perceived usefulness (r = .16, p = .15). Hypothesis 3 was confirmed as personal innovativeness was significantly and positively correlated with both the perceived usefulness (r = .30, p < .01) and the perceived ease of use (r = .37, p < .01). In addition, personal innovativeness was significantly and positively related to the intentions to use new technology (r = .38, p < .01). Hypothesis 4, was partly supported because computer self-efficacy was significantly correlated with the perceived ease of use

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Table 1 Means, standard deviations, scale reliabilities, and correlations between all study variables (Study 1).

1. Age 2. Gender 3. Educational level 4. Voluntariness 5. Openness to experience 6. Neuroticism 7. Personal innovativeness in IT 8. Computer self-efficacy 9. Perceived usefulness 10. Perceived ease of use 11. Intentions

M

SD

1

2

3

4

5

36.00 0.60 2.93 3.30 3.70 2.21 2.99 4.28 3.60 3.78 3.51

8.11 .49 1.04 1.21 0.45 0.42 0.78 0.48 0.69 0.49 0.68

(–) .24* .44** .04 .09 .14 .05 .15 .19 .30** .24*

(–) .06 .06 .08 .14 .40** .00 .05 .07 .03

(–) .17 .22* .22* .11 .20 .17 .30** .21

(–) .10 .05 .10 .15 .21 .09 .22*

(.73) .09 .31** .24* .10 .20 .11

6

(.73) .20 .52** .16 .32** .12

7

8

9

10

11

(.87) .32** .30** .37** .38**

(.88) .04 .31** .17

(.95) .39** .55**

(.92) .42**

(.78)

Note: Scale reliabilities (internal consistencies) are presented on the diagonal, between parentheses. Gender is coded as follows: 0 = male and 1 = female. Educational level is coded as follows: 1 = intermediate vocational education or lower, 2 = higher vocational education, 3 = bachelor degree, 4 = master degree or higher. All study variables are measured on a scale from 1 to 5. N = 89. * p < .05. ** p < .01.

(r = .31, p < .01) but not with the perceived usefulness (r = .04, p = .70). In addition to the correlational analyses, two hierarchical regression analyses were conducted, with perceived usefulness or ease of use as the dependent variables. Step 1 included the control variables age, gender, educational level, and voluntariness. Step 2 included the individual-level predictors openness, neuroticism, personal innovativeness and computer self-efficacy. The results of these analyses are presented in Table 2. Together, the four predictors explained 10% (DF = 2.18, p = .04) of the variance in perceived usefulness and 16% (DF = 4.23, p < .01) of the variance in perceived ease of use over and above age, gender, educational level, and voluntariness. Specifically, personal innovativeness seems to be an important predictor of the perceived usefulness and the perceived ease of use, as a significant beta weight was found for personal innovativeness in IT in both regression analyses (b = .36, p < .01 and b = .29, p = .01, respectively). In addition, neuroticism had a significant beta weight in the prediction of perceived ease of use (b = .24, p = .03). Hypothesis 5 was confirmed by our data, as perceptions of ease of use and usefulness were both significantly and positively related to intentions to use new technology (r = .55, p < .01 and r = .42, p < .01, respectively). Controlled for age, gender, educational level, and voluntariness perceived usefulness (b = .42, p < .01) and ease of

use (b = .20, p = .04) explained 23% of the variance (DF = 13.62, p < .01, R2 = .39, F = 5.76, p < .01) in the intentions to use new selection technologies. 4. Discussion Study 1 and introduction Study 2 As the first study on recruiter reactions to new technology, Study 1 showed that recruiters’ intentions to use new selection technologies can be partly explained by perceptions of usefulness and ease of use. These perceptions are in turn partly explained by individual characteristics such as neuroticism, personal innovativeness in IT, and computer self-efficacy. As such, the present study provides support for using the TAM (Davis, 1989) in explaining recruiters’ intentions to use new selection technologies. Nevertheless, in order to establish the value of the TAM in explaining the adoption of new technology in personnel selection, several remaining questions need to be addressed. First, Study 1 did not include a measure of actual usage of new technology. To more fully test whether TAM is applicable in studying the actual adoption of new selection technology by recruiters, it is important to examine the link between recruiters’ intention to use new technologies and their actual usage of these technologies. Previous studies on the TAM have demonstrated that TAM consistently explains a substantial proportion of the variance

Table 2 Summary of hierarchical regression analyses of predictors on perceived usefulness and perceived ease of use (Study 1). Perceived usefulness b

Perceived ease of use

R2

DR2

.13

.13

b

Step 1 Age Gender

.09 .13

.22 .10

Educational level Higher vocational Bachelor Master or higher Voluntariness

.16 .30 .19 .16

.22 .02 .01 .04

Step 2 Openness to experience Neuroticism Personal innovativeness in IT Computer self-efficacy

.03 .07 .36** .10

.23

.10*

R2

DR2

.19

.19**

.35

.16**

.07 .24* .29* .02 F(8, 81) = 2.08*

F(8, 81) = 3.73**

Note: Standardized regression weights are for final step. Gender is coded as follows: 0 = male and 1 = female. Educational level is dummy coded so that holding a high school or intermediate vocational diploma was the excluded category. N = 89. * p < .05. ** p < .01.

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(typically about 40%) in actual usage (e.g., Venkatesh, Thong, & Xu, 2012; Venkatesh et al., 2003). Therefore, we expect intentions to use new selection technology to also explain a substantial part of the variance in actual usage of new selection technology: Hypothesis 6. Intentions to use new selection technologies are positively related to the actual usage of new selection technologies. Second, the extant research on the adoption of new selection technology has focused on applicant reactions instead of recruiter reactions (e.g., Hausknecht et al., 2004; Wiechmann & Ryan, 2003). The three most commonly studied applicant reactions are face validity perceptions, predictive validity perceptions, and fairness perceptions (Chan & Schmitt, 2004). Face validity refers to the extent to which the content of the selection procedure seems to be related to the job (Smither, Reilly, Millsap, Pearlman, & Stoffey, 1993). Perceived predictive validity refers to the perception of how well the selection instrument predicts future job performance (Smither et al., 1993). Perceived fairness refers to the extent to which a test seems to rule out biases and provide applicants with the same opportunity to perform well (Gilliland, 1993). These applicant reactions have been found to be relevant for many selection-related outcomes, such as intentions to accept the job, intentions to recommend the organization to others, the likelihood of litigation against the outcome of the selection procedure, and perceived organizational attractiveness (Chan & Schmitt, 2004; Gilliland, 1993; Ryan & Ployhart, 2000). Possibly, recruiter perceptions of face validity, predictive validity, and fairness also play a role in their decision about whether or not to use new selection technology. Thus, for TAM to be a valuable model for explaining the adoption of new technology in personnel selection, it should be able to explain more variance in the intention to adopt new technology than commonly used perceptions in the applicant reaction literature. As TAM has been established as a robust model for predicting user acceptance (Venkatesh et al., 2003), we expect TAM to be a better predictor of recruiter intentions than specific selection-related reactions. Hypothesis 7. Perceived usefulness and perceived ease use explain morevarianceintheintentionstousenewselectiontechnologiesthan perceptions of face validity, predictive validity, and fairness. The third question refers to the role of other established predictors in the TAM, such as subjective norm, image, job relevance, output quality, and result demonstrability. König, Klehe, Berchtold, and Kleinmann (2010) operationalized the concept subjective norm as what other organizations think of certain selection procedures and whether selection procedures are perceived to be useful for organizational self-promotion. They confirmed that subjective norm affected the adoption of instruments in personnel selection practice. Venkatesh and Davis (2000) showed that subjective norm, image, job relevance, output quality, and result demonstrability together explain between 40% and 60% of the variance in usefulness perceptions. Based on these findings, it is important to examine whether recruiter characteristics are able to explain additional variance in perceptions of usefulness and ease of use over and above already established TAM predictors. Thus far, no previous studies have looked at the incremental validity of individual characteristics over the established TAM predictors. Based on the findings of Study 1, we expect the following: Hypothesis 8. Openness to experience, neuroticism, personal innovativeness in IT, and computer self-efficacy have incremental validity in the prediction of perceived usefulness and ease of use over and above established predictors of the TAM.

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5. Method Study 2 5.1. Sample and procedure Similar to Study 1, recruiters at multinational companies, recruitment agencies, governmental institutions, and non-governmental organizations based in the Netherlands were contacted via email, via professional networks (e.g., Linkedin), and via online groups. An email with instructions and a web-link to the survey was sent to the recruiters. The survey contained the same measures as in Study 1 plus measures of subjective norm, image, job relevance, output quality, results demonstrability, face validity, perceived predictive validity, fairness, and actual usage. In total, 109 recruiters (57.6% female) working for various companies and organizations in the Netherlands filled out the questionnaire. The age of the respondents varied from 20 years to 65 years, with an average of 35.90 years (SD = 10.19). Respondents had various HR related jobs and educational backgrounds, ranging from high school to post-graduate degrees.

5.2. Measures Openness to experience, neuroticism, personal innovativeness in IT, computer self-efficacy, TAM perceptions, intentions to use, and voluntariness were measured with the same scales as described in Study 1. All coefficient alphas were adequate (see Table 3). All items were rated on a 5-point Likert scale with 1 = strongly disagree and 5 = strongly agree, except for actual usage, which was rated on a 5-point scale with 1 = never and 5 = daily.

5.2.1. Perceptions of face validity, predictive validity, and fairness Perceptions of face validity were measured with a 3-item adapted scale from Smither et al. (1993) with an alpha of .65. An example item is: ‘‘I can see a clear connection between new selection technologies and what I think is required by the job’’. Perceptions of predictive validity were also measured with a 3-item adapted scale from Smither et al. (1993). The alpha for this scale was .69. An example item is: ‘‘I am confident that new selection technologies can predict how well an applicant will perform on the job’’. Perceptions of fairness were measured with a 5-item adapted scale from Tonidandel and Quiñones (2000) with an alpha of .70. An example item is: ‘‘I think that new selection technologies are fair’’.

5.2.2. Subjective norm Subjective norm was measured with four items, adapted from the two-item scale developed by Taylor and Todd (1995) and the two item-item scale developed by König et al. (2010). An example of an item is: ‘‘Many companies that work in the same field use new selection technologies’’. The alpha of this four-item scale was .84.

5.2.3. Image Image was measured with the three-item adapted scale developed by Moore and Benbasat (1991) and Venkatesh and Davis (2000). An example of an item is: ‘‘Having new selection technologies is a status symbol’’. The alpha of this scale was .91.

5.2.4. Job relevance Job relevance was measured with the two-item adapted scale developed by Davis, Bagozzi, and Warshaw (1992). An example of an item is: ‘‘In my job, usage of new selection technologies is important’’. The alpha of this scale was .87.

(–) (.87) .26** (.70) .28** .14 (.92) .12 .06 .25* .30** .22* (.82) .02 .03 .15 .14 .03 .08 .23* .12 .05 (.61) .01 .13 .10 .08 .03 .16 .12 .26** .06 .07 (–) .14 .07 .13 .08 .06 .07 .22* .11 .03 .04 .02 .19 .06 06 .15 .02

(.84) .49** .43** .28** .33** .02 .13 .16 .14 .24* .26** .27** .19 .19 .23* .30**

(.91) .44** .26* .22* .07 .05 .15 .07 .19 .25* .23* .20 .02 .05 .07

(.87) .36** .41** .08 .07 .18 .29** .27** .32** .34** .03 .09 .30** .30**

(.74) .32** .04 .02 .31** .30** .25* .21* .36** .29** .29** .18 .24*

(.75) .11 .16 .23* .19 .23* .39** .47** .07 .20* .21* .27**

(.85) .22* .33** .51** .24* .16 .32** .32** .31*

(.75) .18 .25** .14 .13 .25* .23* .08

(.92) .26** .18 .20* .21* .42** .12

(.65) .22* .33** .27** .36**

(.69) .50** .12 .10

19 18 17 16 15 14 13 12 11 10 9 8 7 6 5

(–) .07 .08 .01 .03 .03 .02 .07 .00 .20 .08 .06 .02 .07 .04 .07 .08 .05 .20*

(–) .11 .09 .00 .08 .01 .00 .08 .10 .01 .02 .05 .10 .16 .16 .02 .11 .06

4 3 2 1

(–) .25* .02 .09 .02 .02 .02 .18 .01 .19 .09 .18 .34** .03 .18 .02 .12 .09 .04 .01 10.19 0.50 1.00 0.77 0.67 0.74 0.74 0.56 0.54 0.40 0.49 0.74 0.43 0.56 0.50 0.59 0.57 0.48 0.71 0.74

SD M

35.90 0.58 2.76 3.37 3.16 2.83 3.65 3.18 3.61 3.68 2.21 3.47 4.23 3.54 3.79 3.49 3.01 3.29 3.57 2.51 1. Age 2. Gender 3. Educational level 4. Voluntariness 5. Subjective norm 6. Image 7. Job relevance 8. Output quality 9. Results demonstrability 10. Openness to experience 11. Neuroticism 12. Personal innovativeness in IT 13. Computer self-efficacy 14. Perceived usefulness 15. Perceived ease of use 16. Face validity 17. Predictive validity 18. Fairness 19. Intentions 20. Actual usage

Table 3 Means, standard deviations, scale reliabilities, and correlations between all study variables (Study 2).

Note: Scale reliabilities (internal consistencies) are presented on the diagonal, between parentheses. Gender is coded as follows: 0 = male and 1 = female. Educational level is coded as follows: 1 = intermediate vocational education or lower, 2 = higher vocational education, 3 = bachelor degree, 4 = master degree or higher. All study variables are measured on a scale from 1 to 5. N = 109. * p < .05. ** p < .01.

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5.2.5. Output quality The judgment of how new selection technologies perform was measured with the two-item adapted scale developed by Davis et al. (1992). An example of an item is: ‘‘The quality of the output I get from new selection technologies is high’’. The alpha of this scale was .74. 5.2.6. Results demonstrability The tangibility of the results of new selection technologies was measured with the four-item adapted scale developed by Moore and Benbasat (1991). An example of an item is: ‘‘I have no difficulty telling others about the results of using new selection technologies’’. The alpha of this scale was .75. 5.2.7. Actual usage Actual usage was measured with the following statement ‘‘In my work, I have used the following tools to select personnel: computer-based testing, internet-based testing, telephone-based interview, video-conference job interview, and multimedia simulation tests’’. This measure is based on and adapted from another scale used to measure actual usage of internet (McElroy et al., 2007). 6. Results Study 2 6.1. Preliminary analysis Means, standard deviations, scale reliabilities (internal consistencies), and correlations between all variables are presented in Table 3. All variables were adjudged to be normally distributed. Furthermore, residual plots showed that residuals were randomly scattered and that all relationships between the independent and dependent variables were linear. We first looked at significant correlations between demographic characteristics and the study variables. Age was significantly related to computer self-efficacy (r = .34, p < .01). Gender was negatively and significantly related to actual usage (r = .20, p < .05). Male respondents (M = 2.66, SD = 0.77) used new selection technologies more often than female respondents (M = 2.35, SD = 0.70; t = 2.04, p = .04). Because of these significant correlations we controlled for age and gender (coded as 0 = male and 1 = female) in the regression analyses. 6.2. Replication of Hypotheses 1–5 Table 3 shows that openness to experience was not significantly related to the perceived usefulness (r = .08, p = .41), nor to perceived ease of use (r = .03, p = .80). Thus, our first hypothesis, stating that openness is positively related to the perceived usefulness and the perceived ease of use, was again not supported. Hypothesis 2, stating that neuroticism is negatively related to the perceived usefulness and the perceived ease of use was not supported, as neuroticism was not significantly correlated with the perceived usefulness (r = .15, p = .13) and perceived ease of use (r = .14, p = .16). Hypothesis 3 was confirmed as personal innovativeness was significantly and positively correlated with both the perceived usefulness (r = .33, p < .01) and the perceived ease of use (r = .51, p < .01). In addition, personal innovativeness was significantly and positively related to the intentions to use new selection technology (r = .32, p < .01), and the actual usage of new selection technology (r = .31, p = .02). Hypothesis 4, was partly supported because computer selfefficacy was significantly correlated with the perceived ease of

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J.K. Oostrom et al. / Computers in Human Behavior 29 (2013) 2404–2415 Table 4 Summary of hierarchical regression analyses of predictors on perceived usefulness and perceived ease of use (Study 2). Perceived usefulness

DR

.01

.01

R

b Step 1 Age Gender Voluntariness

.10 .06 .11

Step 2 Subjective norm Image Job relevance Output quality Results demonstrability

.03 .03 .08 .08 .10

Step 3 Openness to experience Neuroticism Personal innovativeness in IT Computer self-efficacy

.06 .28** .24* .08

Perceived ease of use 2

2

b

R2

DR2

.04

.04

.25

.21**

.41

.16**

.07 .07 .00 .12

.11 .05 .10 .17 .07 .23*

.25

.13* .06 .15 .41** .08

F(12, 95) = 2.27*

F(12, 95) = 4.80**

Note: Standardized regression weights are for final step. DR2 may appear inconsistent due to rounding. Gender is coded as follows: 0 = male and 1 = female. N = 109. * p < .05. ** p < .01.

use (r = .25, p < .01), but not with the perceived usefulness (r = .18, p = .06). Hypothesis 5 was confirmed as perceptions of ease of use and usefulness were both significantly and positively related to intentions to use new technology (r = .42, p < .01 and r = .30, p < .01, respectively). Controlled for age, gender, and voluntariness, perceived usefulness (b = .34, p < .01) and ease of use (b = .18, p = .06) explained 17% of the variance (DF = 9.76, p < .01, R2 = .21, F = 4.66, p < .01) in the intentions to use new selection technologies. 6.3. Testing of Hypotheses 6–8 Controlled for age, gender, and voluntariness intentions to use new selection technologies (b = .26, p = .01) explained 7% of the variance (DF = 6.71, p = .01, R2 = .11, F = 2.67, p = .04) in the actual usage of new selection technologies. These findings support Hypothesis 6, stating that intentions to use new selection technologies would be positively related to the actual usage of new selection technologies. Perceived usefulness and ease of use were significantly and positively related to intentions of technology use (r = .42, p < .01 and r = .30, p < .01, respectively). After controlling for age, gender, and voluntariness, perceived usefulness (b = .34, p < .01) and perceived ease of use (b = .18, p = .07) explained 17% of the variance (DF = 9.76, p < .01, R2 = .21, F = 4.66, p < .01) in the intentions to use new selection technologies. Controlled for age, gender, and voluntariness, face validity (b = .23, p = .03), perceived predictive validity (b = .02, p = .84), and perceived fairness (b = .20, p = .10) explained 11% of the variance (DF = 3.98, p = .01, R2 = .15, F = 2.56, p = .03) in the intentions of technology use. Perceived usefulness and perceived ease of use displayed incremental validity over and above face validity, perceived predictive validity, and perceived fairness in the prediction of the intentions to use new technologies (DR2 = .11, DF = 6.63, p = .02, R2 = .26, F = 3.82, p < .01). However, face validity, perceived predictive validity, and perceived fairness did not show incremental validity over and above perceived usefulness and perceived ease of use in the prediction of the intentions to use new technologies (DR2 = .06, DF = 2.14, p = .10, R2 = .26, F = 3.82, p < .01). These findings support Hypothesis 7, which stated that perceived usefulness and perceived ease use would explain more variance in the intentions to use new

selection technologies than perceptions of face validity, predictive validity, and fairness. Two hierarchical regression analyses were conducted, with perceived usefulness or perceived ease of use as the dependent variables. Step 1 included the control variables age, gender, and voluntariness. Step 2 included the TAM predictors subjective norm, image, job relevance, output quality, and results demonstrability. Step 3 included the openness, neuroticism, personal innovativeness, and computer self-efficacy. The results of the regression analyses are presented in Table 4. Together, openness, neuroticism, personal innovativeness, and computer self-efficacy explained 13% of the variance in the perceived usefulness and 16% of the variance in the perceived ease of use over and above age, gender, voluntariness, and the established TAM predictors. Therefore, Hypothesis 8 was supported, which stated that openness to experience, neuroticism, personal innovativeness in IT, and computer self-efficacy would have incremental validity in the prediction of perceived usefulness and ease of use over and above established predictors. Specifically, neuroticism and personal innovativeness were relevant predictors of the perceived usefulness and the perceived ease of use, as significant beta weights were found for neuroticism in the prediction of perceived usefulness (b = .28, p < .01) and for personal innovativeness in IT (b = .24, p = .03 and b = .41, p < .01, respectively) in both regression analyses. 7. Conclusion and general discussion The present set of studies provides insight into recruiters’ intentions and their actual use of new selection technologies and underlines the usefulness of TAM in this type of research (Davis, 1989). Studies 1 and 2 both showed that recruiters’ intentions to use new selection technologies in their jobs are related to the extent to which they believe that the new technology is useful and can be used with relatively low effort. In this sense, the perceptions of recruiters play a similar role in technological innovation as in many other professional areas (Davis, 1989, 1993; Venkatesh & Davis, 1996, 2000). However, a specific asset of the present studies is that we incorporated recruiter characteristics into the TAM. In doing so we showed that several of such characteristics predicted perceptions of usefulness and ease of use beyond the well-established predictors of the TAM. Prior studies on individual characteristics and

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the TAM mainly used student samples (Devaraj et al., 2008; McElroy et al., 2007; Nov & Ye, 2008), limiting their ecological validity. In addition, no previous studies looked at the incremental validity of individual characteristics beyond the established TAM predictors such as subjective norm and job relevance. Another finding that was particularly relevant regarding recruiters was that the general technology perceptions included in the TAM (i.e., usefulness and ease of use) were better predictors of intentions to use new technologies than the selection-related reactions to new technology. In other words, recruiters intention to use new technology in their jobs was, for example, more strongly related to whether a particular technology seemed to be relatively easy to use than whether it was perceived to be a valid or fair selection instrument. We elaborate on the different findings below. Studies 1 and 2 showed that, in contrast to our expectations, openness to experience did not show any significant positive relationships with perceptions of usefulness and ease of use. These results were not in line with previous studies (Guadagno et al., 2008; McElroy et al., 2007). A possible explanation could be the relatively small variance in openness to experience in our two samples. The relatively high scores and small standard deviation on openness could indicate that a selection effect has occurred. As study participation was voluntary, it is possible that mainly recruiters volunteered that were interested in new selection technologies as well as in participating in a study on this topic. Further research is needed to understand in which way openness may contribute to explaining the adoption of new selection technologies. We did find support for a role of neuroticism in adopting new selection technologies. Recruiters with higher levels of neuroticism had more negative cognitions about and attitudes toward such new technology. These findings are in accordance with the majority of previous studies in this area (e.g., Anthony et al., 2000; Devaraj et al., 2008), thus further establishing the role of neuroticism in this context. Knowing the role of neuroticism is useful when companies wish to implement new technology in their selection practice. When doing so, they may want to provide additional attention to their employers who score relatively high on neuroticism. Such employees may need some additional guidance or encouragements to actually start using the new technologies. The results of the current study support the notion that personal innovativeness in IT is positively related to both perceived usefulness and ease of use of new selection technologies. In fact, the regression analyses in Studies 1 and 2 showed that of the individual differences measures we used, personal innovativeness was the most important predictor of the perceptions of new technology. Personal innovativeness in IT also turned out to be strongly and directly related to intentions as well as to actual usage of new technology in Study 2. Overall, personal innovativeness in IT, thus, plays a key role in the acceptance of new technology by recruiters. Organizations could benefit from this knowledge, for example by measuring this trait in the selection and training process for recruiters. The results of Studies 1 and 2 confirm that computer self-efficacy is related to the ease of use perceptions of new technology (Venkatesh & Davis, 1996). Individuals with a high computer self-efficacy are likely to perceive new technology as less complex because they believe more in their technical abilities, and therefore have more positive perceptions regarding its usage (Nov & Ye, 2008). In contrast to our expectations, in both studies, computer self-efficacy was not related to the perceived usefulness of new selection technologies. As computer self-efficacy refers to one’s judgment of capability to use a computer (Compeau & Higgins, 1995), it might seem logical that computer self-efficacy is more strongly related to perceived ease of use than to perceived usefulness. Besides, Hasan (2006) found that only system-specific selfefficacy affects perceived usefulness perceptions.

It can be concluded that recruiters who develop positive perceptions of usefulness and ease of use with regard to a specific selection technology will in turn express the intentions to use that technology and will therefore be more likely to actually use that technology. In total, perceptions of usefulness and ease of use explained about 20% of the variance in the intentions to use the technology. Interestingly, when it comes to intentions to use new technology, Study 2 showed that the typical perceptions included in the TAM, namely about usefulness and ease of use, had incremental validity over commonly used perceptions in the applicant reaction literature (i.e., face validity, predictive validity, fairness). Yet, these perceptions did not have incremental validity over perceived usefulness and ease of use in predicting intentions to adopt new selection technology. This shows that different variables play a role in the use and acceptance of new technology by recruiters than by applicants. Findings from the applicant reactions literature thus cannot be generalized to recruiter reactions. Intentions to use the technology explained only 7% of the variance in actual usage, while previous studies typically find an explained variance of about 40% (e.g., Venkatesh et al., 2003; Venkatesh et al., 2012). This finding could be explained by the low overall use of new selection technologies in our sample. Apparently, despite the advantages, recruiters don’t use new selection technologies that often yet. It is also possible that organizational level variables have a large impact on actual usage of new selection technology. For example, Anderson (2003) suggested that organizational level variables such as culture and climate for technological innovations, budget funds and resources, and leadership style play an important role in recruiters’ technology adoption. The present study has several limitations that should be mentioned. Given that measures of all constructs were collected at the same point in time, causality could not be directly assessed. Future research should use longitudinal designs. The findings and implications are based on two studies that examined a specific technology involving a specific user group. It might be interesting to compare various groups of HR professionals (e.g., recruiters, HR managers, HR consultants). In this study, the different groups were too small to make a valid comparison. In addition, various types of organizations (e.g., governmental, multinational, consultancy) could be compared to determine if organizational type influences the adoption of new selection technologies.

8. Implications and suggestions for future research Overall, the results obtained in this study emphasize the role of individual characteristics in the adoption of new selection technology. The findings of this study have several theoretical and practical implications. The present study constitutes the first step in untangling the predictors of the adoption of new technology in the personnel selection practice. Thus far, research on the adoption of new selection technology has focused entirely on the candidate (Hausknecht et al., 2004; Wiechmann & Ryan, 2003) and almost ignored the recruiter, while recruiters are the ones actually utilizing these new selection technologies. Additional studies are necessary in order to discern individual characteristics overlooked by the current study. For instance, locus of control (Rotter, 1982) and risk taking (Levenson, 1990) may impact the use of new selection technology. Based on the present findings managers would be well-advised to be aware of the technology readiness of their recruiters and adjust the training schedule and management strategies accordingly. For example, employees who score low on computer self-efficacy could benefit from computer skills training. Another option would be to implement a reward system that could encourage the recruiters to explore more and get used to new selection technologies (Devaraj et al., 2008). We believe that the

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individual-level variables could have positive effects on the use and adoption of IT in an organization. It is the task of the manager to use these differences between people to the organization’s advantage. Acknowledgements We wish to thank Irina Bocan, Stephanie Bisambher, and Leslie MacLennan for their valuable help with the data collection. Appendix A

Overview of questions Openness to experience Am not interested in abstract ideas (–) Tend to vote for liberal political candidates Do not like art (–) Believe in the importance of art Have a vivid imagination Avoid philosophical discussions (–) Tend to vote for conservative political candidates (–) Carry the conversation to a higher level Do not enjoy going to art museums (–) Enjoy hearing new ideas Neuroticism Feel comfortable with myself (–) Rarely get irritated (–) Panic easily Dislike myself Am not easily bothered by things (–) Often feel blue Have frequent mood swings Seldom feel blue (–) Am very pleased with myself (–) Am often down in the dumps Personal innovativeness in IT If I heard about a new selection technology, I would look for ways to experiment with it Among my peers I am usually the first to try out new selection technologies In general, I am hesitant to try out new selection technologies (–) I like to experiment with new selection technologies Computer self-efficacy I find using the computer easy It would be hard for me to learn to use a computer (–) I learn new computer programs easily I hope I never have a job which requires me to use a computer (–) I get confused with all the different keys and computer commands (–) I feel uneasy when people talk about computers (–) I feel comfortable working with computers I get anxious each time I need to learn something new about computers (–) Perceived usefulness Using new selection technologies in my job would enable me to accomplish tasks more quickly Using new selection technologies would improve my job performance Using new selection technologies in my job would increase

my productivity Using new selection technologies would enhance my effectiveness on the job Using new selection technologies would make it easier to do my job I would find new selection technologies useful in my job Perceived ease of use Learning to operate new selection technologies would be easy for me I would find it easy to get the new selection technologies to do what I want them to do My interaction with new selection technologies would be clear and understandable I would find new selection technologies to be flexible to interact with It would be easy for me to become skillful at using new selection technologies I would find new selection technologies easy to use Intentions to use I intend to increase my use of new selection technologies in the future For future work, I would use new selection technologies Voluntariness In my work, I am allowed to choose the tools for the selection of new personnel Subjective norm People who influence my behavior think that I should use the system People who are important to me think that I should use the system Many companies that work in the same field use new selection technologies New selection technologies are generally often used to select people Image People in my organization who use new selection technologies have more prestige than those who do not People in my organization who use new selection technologies have a high profile Having new selection technologies is a status symbol in my organization Job relevance In my job, usage of new selection technologies is important In my job, usage of new selection technologies is relevant Output quality The quality of the output I get from new selection technologies is high I have no problem with the quality of new selection technologies’ output Results demonstrability I have no difficulty telling others about the results of using new selection technologies I believe I could communicate to others the consequences of using new selection technologies The results of using new selection technologies are apparent to me I would have difficulty explaining why using new selection technologies may or may not be beneficial (–) Face validity (continued on next page)

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I can see a clear connection between new selection technologies and what I think is required by the job The actual content of new selection technologies is related to the job tasks I do not understand what new selection technologies have to do with the job Predictive validity I am confident that new selection technologies can predict how well an applicant will perform on the job The employer can tell a lot about the applicant’s ability to do the job based on the results of new selection technologies Failing to perform well on new selection technologies indicates that the applicant cannot perform well on the job Fairness Using new selection technologies to make selection decisions is unfair (–) New selection technologies are unfair tests of a person’s true ability (–) New selection technologies obtain accurate information about each person’s abilities I think that new selection technologies are fair I have a strong doubt that new selection technologies really measure a person’s ability (–) Actual usage In my work, I have used computer-based testing to select personnel In my work, I have used Internet-based testing to select personnel In my work, I have used telephone-based interview to select personnel In my work, I have used video-conference job interview to select personnel In my work, I have used multimedia simulation tests to select personnel

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