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Potential applicants’ expectation-confirmation and intentions
Computers in Human Behavior 44 (2015) 183–190
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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh
Potential applicants’ expectation-confirmation and intentions Daniel M. Eveleth ⇑, Lori J. Baker-Eveleth 1, Robert W. Stone 1 College of Business & Economics, 875 Perimeter Drive, MS 3161, University of Idaho, Moscow, ID 83844-3161, United States
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Article history:
Keywords: Expectation-confirmation Website usability Job applicant recruitment
a b s t r a c t Companies dedicate a substantial amount of financial resources and efforts toward recruiting potential employees. A company’s website plays an important role in this process, but little is known about how job-seekers’ experiences on the website affects their attitudes and intentions toward the company. This research examines the extent to which job-seekers’ expectations about the company are confirmed or disconfirmed by their experiences with the website and the degree that these expectations affect their intentions. The presented theoretical model is based on the expectation confirmation model adapted to include website usability and its determinants. Data to empirical test the model were collected by administering a survey to university students and WebTurk contract workers. The sample consisted of 199 usable responses. The empirical analysis used structural equations modeling. The fit of the measured theoretical model to the data was also good and all the paths in the measurement and structural models were statistically significant. The structural model shows that expectation-confirmation influences respondents’ intentions, indirectly, through satisfaction with the website and perceived usefulness of the site. Furthermore, engagement, website content and interactivity influence website usability while website usability influences expectation confirmation and perceived usefulness. Implications and conclusions based on these findings were also provided. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction An organization’s human capital can be a source for a sustainable competitive advantage. This is because the skills and knowledge that new employees bring and current employees acquire are often rare, durable and imperfectly imitable (Barney, 1991). For this reason the process of recruiting and selecting talent from the labor market is a key concern for any organization. Organizations dedicate a substantial amount of financial resources and efforts toward attracting and screening potential employees. One survey found that U.S. organizations spent approximately $124 billion in 2011 on recruitment and selection efforts for a rate of approximately $3500 per new employee (Bersin, 2013). A significant amount of the recruitment portion of these efforts is more recently been directed at motivating potential applicants to visit organizations’ websites for the purposes of learning more about the companies and submitting applications (Thompson, Braddy, & Wuensch, 2008). Much like a marketing department that uses such things as advertising, personal selling and sales promotions to encourage potential customers to visit a ⇑ Corresponding author. Tel.: +1 208 885 6788. E-mail addresses: [email protected] (D.M. Eveleth), [email protected] (L.J. Baker-Eveleth), [email protected] (R.W. Stone). 1 Tel.: +1 208 885 6788. http://dx.doi.org/10.1016/j.chb.2014.11.025 0747-5632/Ó 2014 Elsevier Ltd. All rights reserved.
company website for the purposes of purchasing products, human resource departments commonly drive job seekers to the career portion of the company’s website using similar promotion-oriented tactics. With respect to influencing job-seekers, a significant challenge for organizations is that once a potential applicant responds to the organization’s early recruitment efforts and arrives at the company’s website, the individual’s decision to submit an application or return to the site later is largely a function of the experience he or she has with the site. Understanding the website-related factors that affect a potential-applicant’s intentions is, therefore, essential. One key factor in determining a prospective applicant’s intentions may be the extent to which the job-seeker’s experience with the website confirms or disconfirms the expectations the individual had prior to the experience. While a company’s early recruitment efforts (e.g., personal selling at a career fair, public relations through press releases, advertisements about specific positions) help a prospective applicant form an initial expectation about the company or a specific position, the experience the individual has with the website helps them determine the extent to which their initial expectations are confirmed. The purpose of this research is to extend current thinking about the role of website characteristics and the user’s experience in recruiting. While a significant amount of research has looked at
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the factors that affect website usability and perceived usefulness (Gregory, Meade, & Thompson, 2013; Maurer & Cook, 2011; Thompson et al., 2008), there remains a limited understanding about how website usability and perceived usefulness affect potential applicants’ intentions. We suggest that potential applicants’ intentions can be explained, in part, by the extent to which the website confirms or disconfirms potential applicants’ expectations about the company. If expectation-confirmation is a critical factor in the intentions of potential applicants then organizations should consider the interplay between messaging about the company or position that is received from the website and the messaging about the company or position that is received from other pre-websitevisit sources (e.g., recruiters). The paper is organized into four additional sections. The first section summarizes the theory on potential applicant’s intentions and the factors that are thought to affect them, including website usability and perceived usefulness, expectations-confirmation and potential applicants’ satisfaction with the site. In this section the theoretical model is presented. The second section summarizes the methods used. In the third and fourth sections we describe the empirical results and discuss these results.
website can serve as a signal (Breaugh, 2008) upon which potential applicants can draw inferences about the company we can conclude that a job-seekers’ intentions can be affected by their perceptions of the website’s usefulness and their level of satisfaction with the site. Furthermore, because there is consistent evidence to support the link between perceived usefulness of an object and satisfaction with the object (e.g., Stone & Baker-Eveleth, 2013) we also conclude that a job-seekers’ perceptions about the usefulness of a company website will have a positive effect on their satisfaction with the site. Hypothesis 1 (H1). Website satisfaction has a significant and positive influence on the users’ behavioral intentions to the company. Hypothesis 2 (H2). Website perceived usefulness has a significant and positive influence on the users’ behavioral intentions to the company. Hypothesis 3 (H3). Website perceived usefulness has a significant and positive influence on website satisfaction.
2. Theory 2.2. Expectation confirmation 2.1. Intentions to apply While there are several definitions of recruiting in the literature, a common theme across the definitions is that recruiting, at a minimum, involves the activities an organization performs to affect the number and types of individuals who apply (Chapman, Uggerslev, Carroll, Piasentin, & Jones, 2005). Barber (1998) and others (e.g., Roberson, Collins, & Oreg, 2005) have argued that an organization’s pre-application recruitment efforts are critical, because talented job-seekers who do not apply will obviously not be affected by the more interpersonal recruitment-oriented efforts that occur simultaneously with selection or applicant-screening activities. Therefore, understanding the factors that affect a prospective applicant’s decision to apply is critical for designing and implementing effective recruitment efforts. In situations where a behavior (i.e., submitting an application or returning to the career site later) is volitional, evidence from previous research concludes that individuals form specific intentions about whether to perform the behavior and that these intentions are a good predictor of subsequent behavioral outcomes (Ajzen & Fishbein, 1973). Because behavioral intentions (Ajzen & Fishbein, 1973) are conceptualized as the consequence of cognitive judgments about one’s expectancies and the valences with respect to performing the potential behavior the intentions construct provides a useful mechanism for understanding how potential applicants’ experiences with a company’s website affect whether they will submit an application or not. Signaling theory (Spence, 1973) offers one explanation of how recruitment efforts, such as the design of the company website, can affect a job-seeker’s intentions to apply. The theory holds that because information receivers (e.g., job-seekers) lack complete information about an object or person (e.g., a position or company) they draw inferences based on signals or cues they receive from a signaler (e.g., the recruiting company). When applied to the recruitment process researchers have found that job-seekers’ attraction to an organization and intentions to apply are affected by inferences they draw from such things as recruiter behaviors (Rynes & Miller, 1983), organization image (Gatewood, Gowan, & Lautenschlager, 1993), corporate social performance efforts (Jones, Willness, & Madey, 2014) and the type of information in an advertisement (Roberson et al., 2005). Because a company’s
The expectation-confirmation model (ECM) is a theoretical framework that has been used extensively to explain and understand such things as consumer satisfaction, trust, purchase and switching decisions (Fan & Suh, 2014; Kim, Ferrin, & Rao, 2009) and the acceptance, use, or adoption of technology (Bhattacherjee, 2001; Bhattacherjee & Premkumar, 2004; Halilovic & Cicic, 2013; Stone & Baker-Eveleth, 2013). When applied to consumer purchase decisions, for example, the ECM framework suggests that consumers base their original purchase decision, in part, on an initial expectation they have about the good or service. Subsequent information they gain about the product by consuming or using it helps them determine if their initial expectations about the product were accurate. Whether the expectations were confirmed or disconfirmed will affect the consumer’s intentions to repurchase (Anderson & Sullivan, 1993) or complain (Wu, 2013). In organizational behavior literature the extent to which jobrelated expectations are met is related to organizational commitment, absenteeism, turnover, job performance, intention to quit and organizational citizenship behaviors, among other critical outcomes (Brown, Venkatesh, Kuruzovich, & Massey, 2008; Turnley & Feldman, 2000; Wanous, Poland, Premack, & Davis, 1992). Additionally, across a wide set of domains, met expectations (Porter & Steers, 1973) has consistently been associated with satisfaction (Brown et al., 2008; Greenhaus, Seidel, & Marinis, 1983; Kopalle & Lehmann, 2001; Stone & Baker-Eveleth, 2013; Szajna & Scamell, 1993). A logical extension of these findings to a job-seeker’s experience with a company’s website is to conclude that the extent to which a job-seeker’s expectations about the company or position are met will directly or indirectly affect many of his or her subsequent attitudes, intentions and behaviors. When a jobseeker responds to a company’s early recruitment efforts (e.g., word-of-mouth from an existing employee, personal selling by a recruiter at a career fair, reading a job announcement on a job board) by visiting the company’s website he or she arrives at the site with initial expectations about the company or position. After experiencing the website, the extent to which job-seekers’ expectations are confirmed or disconfirmed will likely influence their perceptions of the website’s usefulness and their level of satisfaction with the site. These relationships are summarized by Hypotheses 4 and 5.
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Hypothesis 4 (H4). The user’s expectation-confirmation after using the website has a significant and positive influence on their satisfaction with the website. Hypothesis 5 (H5). The user’s expectation-confirmation after using the website has a significant and positive influence on the website’s perceived usefulness.
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Hypothesis 7 (H7). Website usability has a significant and positive influence on website satisfaction. Hypothesis 8 (H8). Website usability has a significant and positive influence on expectation confirmation after website use. Hypothesis 9 (H9). The engagement of website users has a significant and positive influence on website usability.
2.3. Website usability When we imagine a job-seeker first landing at a company’s website it is easy to assume that their initial impressions of the site, and subsequent attitudes and intentions about the site and the company, will in part be a function of how easy it is for them to learn how to navigate the website, the level of simplicity of the site, the speed with which they can find information that they want or need, and the level of control that they have over the experience. These factors are largely captured in a construct often labeled as ‘‘usability’’ (Davis, 1989). Website usability is associated with a variety of outcomes. Flavián, Guinalíu, and Gurrea (2006), for example, found a relationship between website usability and user satisfaction. Website usability is also related to online trust (Gefen, Karahanna, & Straub, 2003), perceived usefulness (Saadé & Bahli, 2005), self-efficacy and outcome expectancy/usefulness (Stone & Baker-Eveleth, 2013). Uggerslev, Fassina, and Kraichy (2012) suggest that recruitment activities, including such things as the extent to which the company’s website is ‘‘user-friendly’’ (p. 598), are useful cues for jobseekers during early stages of the recruitment and selection process when their levels of knowledge about the company and position are low and when they have a lack of commitment to any single opportunity. Similarly, Maurer and Liu (2007) suggest that ‘‘Source Features’’ (p. 306), such as website content, interactivity and vividness play an important role in determining potential applicants’ attitudes and intentions. Cober, Brown, Levy, Cober, and Keeping (2003) found that website content, esthetics and usability influence potential applicants’ attraction to the organization and Allen, Mahto, and Otondo (2007) found that the amount of job and organization information on a website influences potential applicants’ attitudes toward the recruitment website. More recently, Allen, Biggane, Pitts, Otondo, and Van Scotter (2013) found that website design affects job-seekers’ evaluation of the website. While a user might evaluate their experience with a website on the extent to which the site information or design meets their instrumental or extrinsic-motivation needs, some have suggested that users’ attitudes and beliefs about technology use, in general, may also be explained by intrinsic-motivation that derives from ‘‘their holistic experiences with the technology’’ (Agarwal & Karahanna, 2000, p. 666). Intrinsic-motivation-type constructs, such as engagement (Webster & Ho, 1997), enjoyment (Davis, Bagozzi, & Warshaw, 1992; Trevino & Webster, 1992) and absorption (Agarwal & Karahanna, 2000) have been found to influence users’ attitudes toward and use of technology. By logical extension, the extent to which a job-seeker is engaged with a career website should influence their attitudes toward the site’s usefulness. Given the substantial evidence that website usability affects satisfaction with the website and perceptions of the site’s usefulness and that website design and user engagement with the site affect the users’ attitudes toward the site, the following hypotheses are proposed. Hypothesis 6 (H6). Website usability has a significant and positive influence on website perceived usefulness.
Hypothesis 10 (H10). The content on the website has a significant and positive influence on website usability. Hypothesis 11 (H11). The feedback provided the user by the website has a significant and positive influence on website usability.
2.4. Theoretical model Based on the literature presented above and the corresponding hypotheses, a theoretical model and the hypotheses are displayed in Fig. 1. From left to right on the figure, the model suggests that a user’s evaluation of a site’s interactivity or feedback (Hypothesis 11), content (Hypothesis 10), and their level of engagement with a site (Hypothesis 9), influence their perceptions of website usability. The user’s perceptions of website usability influence the extent to which the site confirms or disconfirms their expectations about the company (Hypothesis 8), their levels of satisfaction with the site (Hypothesis 7), and the website’s perceived usefulness (Hypothesis 6). Furthermore, the user’s expectation-confirmation influences their satisfaction of the site (Hypothesis 4) and the website’s perceived usefulness (Hypothesis 5). Website perceived usefulness influences website satisfaction (Hypothesis 3) and intentions to the company (Hypothesis 2). Finally, website satisfaction influences intentions to the company (Hypothesis 1). 3. The method 3.1. The sample The data were collected by distributing a questionnaire to potential respondents using two distribution methods. The first method used students enrolled in required business courses at a medium-sized university in the western United States. The second was to contract with WebTurk on Amazon.com to have their contract workers complete the questionnaire and associated activities. Given the focus of the research, the target population from which to sample are individuals with either recent or current experience searching for employment or intentions to do so in the near future. Such individuals possess the insights regarding job searches and the role of web-based enhanced employment searches. The student respondents were all near completion of their junior year in a business degree program when they provided their responses. These students would have experiences searching for employment for internships and would soon be directly engaged in permanent employment search over the next 6–12 months. Eighty-two percent of the Mechanical Turk respondents reported job search activities either currently, or in the recent past, or intentions to do so in the near future. Thus, based on the research focus, the experiences and intentions of the respondents provide an appropriate sample for this research. The total sample size was 199 respondents composed of 99 completed questionnaires from students and 100 completed by contract individuals through WebTurk. The data collection took
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Website Perceived Usefulness
Engagement
H1
H2 Content
H10
H4
H9
Website Usability
Website Satisfaction
H5
H11
Behavioral Intentions to Company
H7 H6 H8
H3*
Expectation Confirmation of Company After Website Use
Feedback
Fig. 1. The theoretical model and hypotheses.
place in March of 2014. The sampling method was a systematic random design. The systematic portion of the sampling design was provided by assigning company Web sites to respondents by selecting each sequentially from their respective lists. The random element was introduced by randomly selecting the starting point on each of the company list and the respondent list. This breakdown of the sample is shown in Table 1. The questionnaire administration process had several steps. Respondents were first asked to complete a subset of the questionnaire items. Next, they were given the name of one of four companies and the name of an open position at that company. They were instructed to go to the company website, find the position listing and initiate the job application process. Respondents were then
directed to search to identify another potential position of interest for the future. After completing these activities on the assigned website, respondents completed the remaining items on the questionnaire. While the names of the companies whose websites were used are not revealed for privacy reasons, the percentage of responses for each company included in the sample are shown in Table 1. The breakdown of the representation of these companies in the sample is roughly 40%, 25%, 20%, and 15%. The final demographic on the sample was the declared major of the students in the sample. These percentages of majors ranged from 1.51% to 14.07%. The most interesting value was that the largest response category was non-students and missing responses at 27.64%. From this result it appears that a number of the contract workers at WebTurk are business students who reported their majors on the questionnaire.
Table 1 The sample demographics. Frequency Observation source WebTurk Students
100 99
50.25 49.75
Total
199
100.00
80 50 39 30
40.20 25.13 19.60 15.08
199
100.01b
Company 1 2 3 4 Total
a
Majors of the student respondents in the sample Accounting 25 Business economics 9 Finance 28 Information systems 7 Management and human resources 24 Marketing 22 Operations management 17 PGA golf management 3 Other 9 Non-students and missing responses 55 Total a b
Percentage in sample
199
Company names redacted. Details do not sum to 100% due to rounding.
12.56 4.52 14.07 3.52 12.06 11.06 8.54 1.51 4.52 27.64 100.00
3.2. The measures The measures to operationalize and empirically test the theoretical model were formed by collecting responses to appropriate questionnaire items. The indicants of these measures are shown in Table 2 along with the measures’ corresponding psychometric properties. All the standardized path coefficients and psychometric properties were generated based on a confirmatory factor analysis using procedure Calis in PC SAS version 9.2. In the confirmatory factor analysis all measures were reflective in their indicants and allowed to pairwise correlate. The summary statistics of the quality of the fit between the data and the estimated model indicate an acceptable fit (Hair, Anderson, Tatham, & Black, 1992; Hooper, Coughlan, & Mullan, 2008). The goodness of fit measure was 0.89 and adjusted for degrees of freedom it was 0.84, while the parsimonious goodness of fit was 0.70. The root mean square residual and its standardized counterpart were both 0.041. The chi-square statistic was 280.40 with 181 degrees of freedom, which was statistically significant at a 1% level. The normed chi-square statistic was 1.55. The root mean square error of approximation was estimated to be 0.054 with 90% upper and lower confidence intervals of 0.041–0.066. Bentler’s comparative fit index was 0.97 while the incremental fit indexes (i.e., Bentler & Bonett’s non-normed and normed indexes; Bollen’s normed and non-normed indexes) ranged from 0.89 to 0.97.
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D.M. Eveleth et al. / Computers in Human Behavior 44 (2015) 183–190 Table 2 The indicants, measures and psychometric properties using standardized path coefficients. Indicant Engagement This website . . . . . 1. Stimulated my imagination 2. Is intrinsically interesting
Standardized path coefficient
0.84 0.88 0.77
Feedback 6. This website facilitates two-way communicate between the visitors and the site 7. The website is effective in gathering visitors’ feedback
0.81 0.73
Website Usability When I think about this company’s website . . . . . 8. I find this company’s website easy to use 9. I find it easy to locate the information that I need in this company’s website 10. I believe that it is easy to do what I want to do while on this company’s website
0.89 0.90 0.79
Website Satisfaction 11. Overall, I am satisfied with this place 12. I think I did the right thing when I decided to use this website 13. Overall, I am satisfied with this website experience
0.84 0.81 0.88
Behavioral Intentions to Company In the future how likely would you be to . . . . . 17. Consider joining this organization? 18. Consider joining this organization as an intern or after graduation? 19. Consider joining this organization sometime during your life? 20. Search their website in the future about jobs with this organization? Expectation Confirmation of Company After Website Use If I accepted a job at this company . . . . . 21. I would have opportunities to get ahead 22. The job would be outstanding
As mentioned earlier, the measures of the constructs were formed by gathering responses to appropriate questionnaire items. For all items, the respondents were given a statement and a scale upon which to respond. The response scale used was a five-point scale with anchors of 1-Strongly Disagree; 2-Disagree; 3-Neither Agree or Disagree; 4-Agree; and 5-Strongly Agree. All the items were taken from previously published research and modified to fit this current application. Engagement was measured by two items with standardized path coefficients of 0.80 and 0.95. The composite reliability coefficient calculated from these path coefficients was 0.75 and the percentage of shared variance extracted was 59%. The content measure used three items with standardized path coefficients of 0.84, 0.88, and 0.77. Its calculated composite reliability was 0.87 and percentage of shared variance extracted of 69%. Two indicants were used to measure feedback. These path coefficients were 0.81 and 0.73 with a reliability coefficient of 0.75 and shared variance extracted of 59%. Website usability, website satisfaction, and website perceived usefulness were each measured using three questionnaire items. For website usability these path coefficients were 0.89, 0.90, and 0.79 with composite reliability coefficient of 0.90 and shared variance extracted of 74%. Similarly, website satisfaction had estimated path coefficients of 0.84, 0.81, and 0.88 and resulting reliability and shared variance estimates of 0.88 and 71%, respectively. Website perceived usefulness had estimated standardized path coefficients of 0.81, 0.88, and 0.81 which produced
Percentage of shared variance extracted
0.75
59
0.87
69
0.75
59
0.90
74
0.88
71
0.87
70
0.94
80
0.76
61
0.80 0.95
Content The website includes . . . . . 3. Good job information 4. A variety of information 5. Information about each job that is helpful
Website Perceived Usefulness Regarding the company’s website . . . . . 14. Overall, I find this company’s website useful 15. The content on this company’s website is helpful to me 16. I think this company’s website is valuable to me
Composite reliability
0.81 0.88 0.81
0.92 0.86 0.94 0.86
0.72 0.84
a composite reliability estimate of 0.87 and a shared variance extracted of 70%. The final two measures were behavioral intentions to the company and expectation-confirmation of the company after website use. The first had estimated path coefficients of 0.92, 0.86, 0.94, and 0.86. Its reliability coefficient was 0.94 with a shared variance extracted of 80%. The final measure used two indicants with estimates of 0.72 and 0.84 leading to a composite reliability coefficient of 0.76 and 61% shared variance extracted. Based on the magnitudes of the standardized path coefficients that ranged from 0.72 to 0.95, it can be argued that item reliability was satisfied (Rainer & Harrison, 1993). In terms of composite reliability, all the measures demonstrated acceptable values based on the calculated reliability coefficients of 0.75–0.94 (Rainer & Harrison, 1993). All these reliability measures exceeded the generally accepted cutoff level of 0.70 (Nunnally, 1978). Additionally, all the shared variance extracted percentages were above 50%. The combination of these results indicates that the measures satisfy convergent validity (Igbaria & Greenhaus, 1992; Rainer & Harrison, 1993). Discriminant validity was also examined by comparing, for each pair of measures, its squared correlation to the individual percentages of shared extracted variance. If discriminant validity is satisfied, the items within a measure share greater common variation among themselves than between the two measures. This is demonstrated when for each measure pair, the individual measures’ percentage of extracted shared variances are greater than the squared correlation between the two measures (Fornell & Larcker, 1981).
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Table 3 The correlations and squared correlations among the measures. Measure pairs
Correlation
Squared correlation
Engagement-Feedback Engagement-Website Usability Engagement-Content Engagement-Website Satisfaction Engagement-Website Perceived Usefulness Engagement-Behavioral Intentions to Company Feedback-Website Usability Feedback-Content Feedback-Website Satisfaction Feedback-Website Perceived Usefulness Feedback-Behavioral Intentions to Company Website Usability-Content Website Usability-Website Satisfaction Website Usability-Website Perceived Usefulness Website Usability-Behavioral Intentions to Company Content-Website Usability Content-Website Perceived Usefulness Content-Behavioral Intentions to Company Website Satisfaction-Website Perceived Usefulness Website Satisfaction-Behavioral Intentions to Company Website Perceived Usefulness-Behavioral Intentions to Company Expectation Confirmation of Company After Website Use-Engagement Expectation Confirmation of Company After Website Use-Feedback Expectation Confirmation of Company After Website Use-Website Usability Expectation Confirmation of Company After Website Use-Content Expectation Confirmation of Company After Website Use-Website Satisfaction Expectation Confirmation of Company After Website Use-Website Perceived Usefulness Expectation Confirmation of Company After Website Use-Behavioral Intentions to Company
0.35 0.31 0.28 0.54 0.54 0.35 0.47 0.29 0.50 0.46 0.43 0.40 0.65 0.60 0.36 0.55 0.59 0.34 0.71 0.61 0.53 0.48 0.58 0.42 0.50 0.70 0.66 0.55
0.12 0.10 0.08 0.29 0.29 0.12 0.22 0.08 0.25 0.21 0.18 0.16 0.42 0.36 0.13 0.30 0.35 0.12 0.50 0.37 0.28 0.23 0.34 0.18 0.25 0.49 0.44 0.30
Table 4 The statistics summarizing the fit of the model to the data.
**
Statistic
Value
Goodness of fit index Adjusted goodness of fit Parsimonious GFI Chi-square statistic Degrees of freedom Normed chi-square statistic Root Mean Square Residual (RMSR) Standardized RMSR Root Mean Square Error of Approximation (RMSEA) RMSEA lower and upper 90% confidence interval Bentler’s comparative fit index Bentler & Bonnet’s normed index Bentler & Bonnet’s non-normed index Bollen normed index Bollen non-normed index
0.85 0.81 0.72 387.90** 195 1.99 0.106 0.106 0.072 0.062–0.083 0.93 0.88 0.92 0.85 0.93
Significant at a 1% level.
All the squared correlations were calculated using the confirmatory factor analysis and are reported in Table 3. The percentages of shared variance extracted were also calculated from the confirmatory factor analysis and are shown in Table 2. From these values, it is seen that discriminant validity was satisfied for all the measure. As a result, for all the measures, it can be concluded that construct validity is satisfied (Hair et al., 1992). 4. Results The estimation of the model and the corresponding tests of the hypotheses were performed using a structural equations approach. Specifically, the estimation technique was procedure Calis in PC SAS version 9.2 and maximum likelihood estimation. The goodness of fit index was 0.85 and the adjusted goodness of fit was 0.81. The corresponding parsimonious goodness of fit index was 0.72. The chi-square statistic was 387.90 with 195 degrees of freedom. It
was statistically significant at a 1% level. The resulting normed chi-square statistic was 1.99. The root mean square residual and its standardized version were both 0.106. The root mean square error of approximation was estimated to be 0.072 with 90% upper and lower confidence intervals of 0.062–0.083. Bentler’s comparative fit index was estimated to be 0.93 with incremental fit indexes (i.e., Bentler & Bonnet’s as well as Bollen’s normed and nonnormed indexes) ranged from 0.085 to 0.93. All these values are displayed in Table 4. These summary statistics, while presenting somewhat mixed results, do indicate an acceptable fit between the data and the model (Hair et al., 1992; Hooper, Coughlan, & Mullan, 2008). The details of the measurement and structural model’s estimation are shown in Fig. 2 using standardized path coefficients. In the measurement model, all the paths which were free to vary were statistically significant at a 1% level. All the structural paths among the measures were statistically significant at a 1% level, except one. This path was between website perceived usefulness and behavioral intentions to organization which was statistically significant at a 5% level. All the structural paths had the hypothesized signs. 5. Discussion Much is known about how and why job-seekers make jobchoice decisions. Fit between job-seeker’s values and organization values (Cable & Judge, 1996), company image (Gatewood et al., 1993), recruiter behaviors (Chapman et al., 2005), and compensation (Cable & Judge, 1994) are among the identified factors that influence the decision to accept or reject a job offer. In addition, Barber (1998) suggests that decision criteria change as an individual moves through the recruitment and selection process. Early in the process job-seekers consider a broad set of criteria and their attraction to a company is typically more fragile because they have less information upon which to base a final decision (Turban, Eyriung, & Campion, 1993). The factors that help encourage (or discourage) job-seekers to accept an offer are more focused and are interpersonal-oriented variables (e.g., interviewer behavior) that
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Fig. 2. The estimated model using standardized path coefficients.
come later in the process (Boswell, Roehling, LePine, & Moynihan, 2003). Thus, as companies rely more heavily on web-based recruitment activities in the competition for talent, one of their major challenges is how to encourage qualified job-seekers to submit an application after experiencing the website. If potential applicants land at a company’s website with limited information and potentially tenuous attraction to the company and if job-choice decisions are most influenced by interpersonal interactions that occur during the post-application stage of the process, then it is critical to understand how a website positively or negatively influences applicants’ intentions to submit an application or revisit the site in the future. The results of this study offer some help in this regard. The results indicate that the antecedents of website usability of engagement, content, and feedback have meaningful influences on this usability conceivably through their ability to produce intrinsic and extrinsic motivation for the user. In this context implying that if the managers’ responsible for developing and maintaining their company’s career website make sure that the site engages potential applicants, provides meaningful content and feedback that these will influence potential applicants’ behavioral intentions to the company. These influences are indirect through website usability to website perceived usefulness, expectation-confirmation of the job, and website satisfaction. In similar fashion, those managing the company’s career website should also be concerned with assuring that the website confirms job-seekers’ pre-visit expectations. Because job-seekers land at company websites with expectations that are in some part formed by early recruitment efforts (e.g., speaking with a recruiter at a career fair, reading a job announcement, public relations), consistency between website content and design and pre-website messaging is essential. Met expectations directly influence job-seekers’ satisfac-
tion with the website and indirectly, through this satisfaction, jobseekers’ behavioral intentions regarding the company. 6. Conclusions Considerable research has been done to increase our understanding of the website user experience from the perspective of customers. We know, for example, that website characteristics have an effect on such outcomes as potential customers’ intentions to purchase online or repurchase (Cronin Jr., Brady & Hult, 2000; Harris & Mark, 2010; Ranganathan & Jha, 2007; Taylor & Baker, 1994; van Birgelen, de Jong, & de Ruyter, 2006; Van der Heijden, Verhagen, & Creemers, 2003). The results of this study continue the work that is seeking to gain similar levels of understanding about the user experience when the user is a potential applicant. The research illustrates the importance of engagement, content, and feedback on influencing website usability, and better explains how website usability affects potential applicants’ intentions (i.e., indirectly through perceived usefulness, satisfaction and expectation confirmation). References Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and belies about information technology usage. MIS Quarterly, 24(4), 665–694. Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27(1), 41. Allen, D. G., Biggane, J. E., Pitts, M., Otondo, R., & Van Scotter, J. (2013). Reactions to recruitment web sites: Visual and verbal attention, attraction, and intentions to pursue employment. Journal of Business and Psychology, 28(3), 263–285. Allen, D. G., Mahto, R. V., & Otondo, R. F. (2007). Web-based recruitment: Effects of information, organizational brand, and attitudes toward a Web site on applicant attraction. Journal of Applied Psychology, 92(6), 1696.
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Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143. Barber (1998). Recruiting employees: Individual and organizational perspectives. Thousand Oaks, CA: Sage. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. Bersin, J. (2013). Corporate recruiting explodes: A new breed of service providers. Forbes, Inc..
Igbaria, M., & Greenhaus, J. H. (1992). Determinants of MIS employee’s turnover intentions: A structural equation model. Communications of the ACM, 35, 35–49. Jones, D., Willness, C., & Madey, S. (2014). Why are job seekers attracted by corporate social performance? Experimental and field tests of three signalbased mechanisms. Academy of Management Journal, 57(2), 383–404. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237–257. Kopalle, P. K., & Lehmann, D. R. (2001). Strategic management of expectations: The role of disconfirmation sensitivity and perfectionism. Journal of Marketing Research, 38(3), 386–394. Maurer, S. D., & Cook, D. P. (2011). Using company web sites to e-recruit qualified applicants: A job marketing based review of theory-based research. Computers in Human Behavior, 27(1), 106–117. Maurer, S. D., & Liu, Y. (2007). Developing effective e-recruiting websites: Insights for managers from marketers. Business Horizons, 50(4), 305–314. Nunnally, J. (1978). Psychometric methods (2nd ed.). New York: McGraw-Hill. Porter, L. W., & Steers, R. M. (1973). Organizational, work, and personal factors in employee turnover and absenteeism. Psychological Bulletin, 80(2), 151. Rainer, R. K., Jr., & Harrison, A. W. (1993). Toward development of the end user computing construct in a university setting. Decision Sciences Journal, 24(6), 1187–1202. Ranganathan, C., & Jha, S. (2007). Examining online purchase intentions in B2C ecommerce: Testing an integrated model. Information Resources Management Journal, 20(4), 48–64. Roberson, Q. M., Collins, C. J., & Oreg, S. (2005). The effects of recruitment message specificity on applicant attraction to organizations. Journal of Business and Psychology, 19(3), 319–339. Rynes, S. L., & Miller, H. E. (1983). Recruiter and job influences on candidates for employment. Journal of Applied Psychology, 68(1), 147. Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317–327. Spence, A. M. (1973). Job marketing signaling. Quarterly Journal of Economics, 87, 355–374. Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984–990. Szajna, B., & Scamell, R. W. (1993). The effects of information system user expectations on their performance and perceptions. MIS Quarterly, 17, 493–516. Taylor, S. A., & Baker, T. L. (1994). An assessment of the relationship between service quality and customer satisfaction. Journal of Retailing, 70(2), 163–178. Thompson, L. F., Braddy, P. W., & Wuensch, K. L. (2008). E-recruitment and the benefits of organizational web appeal. Computers in Human Behavior, 24(5), 2384–2398. Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication electronic mail and voice mail evaluation and impacts. Communication Research, 19(5), 539–573. Turban, D. B., Eyriung, A. R., & Campion, J. E. (1993). Job attributes: Preferences compared with reasons given for accepting and rejecting job offers. Journal of Occupational and Organizational Psychology, 66(1), 71–81. Turnley, W. H., & Feldman, D. C. (2000). Re-examining the effects of psychological contract violations: Unmet expectations and job dissatisfaction as mediators. Journal of Organizational Behavior, 21(1), 25–42. Uggerslev, K. L., Fassina, N. E., & Kraichy, D. (2012). Recruiting through the stages: A meta-analytic test of predictors of applicant attraction at different stages of the recruiting process. Personnel Psychology, 65(3), 597–660. van Birgelen, M., de Jong, A., & de Ruyter, K. (2006). Multi-channel service retailing: The effects of channel performance satisfaction on behavioral intentions. Journal of Retailing, 82(4), 367–377. Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48. Wanous, J. P., Poland, T. D., Premack, S. L., & Davis, K. S. (1992). The effects of met expectations on newcomer attitudes and behaviors: A review and metaanalysis. Journal of Applied Psychology, 77(3), 288. Webster, J., & Ho, H. (1997). Audience engagement in multimedia presentations. ACM SIGMIS Database, 28(2), 63–77. Wu, I. L. (2013). The antecedents of customer satisfaction and its link to complaint intentions in online shopping: An integration of justice, technology, and trust. International Journal of Information Management, 33(1), 166–176.
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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh
Potential applicants’ expectation-confirmation and intentions Daniel M. Eveleth ⇑, Lori J. Baker-Eveleth 1, Robert W. Stone 1 College of Business & Economics, 875 Perimeter Drive, MS 3161, University of Idaho, Moscow, ID 83844-3161, United States
a r t i c l e
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Article history:
Keywords: Expectation-confirmation Website usability Job applicant recruitment
a b s t r a c t Companies dedicate a substantial amount of financial resources and efforts toward recruiting potential employees. A company’s website plays an important role in this process, but little is known about how job-seekers’ experiences on the website affects their attitudes and intentions toward the company. This research examines the extent to which job-seekers’ expectations about the company are confirmed or disconfirmed by their experiences with the website and the degree that these expectations affect their intentions. The presented theoretical model is based on the expectation confirmation model adapted to include website usability and its determinants. Data to empirical test the model were collected by administering a survey to university students and WebTurk contract workers. The sample consisted of 199 usable responses. The empirical analysis used structural equations modeling. The fit of the measured theoretical model to the data was also good and all the paths in the measurement and structural models were statistically significant. The structural model shows that expectation-confirmation influences respondents’ intentions, indirectly, through satisfaction with the website and perceived usefulness of the site. Furthermore, engagement, website content and interactivity influence website usability while website usability influences expectation confirmation and perceived usefulness. Implications and conclusions based on these findings were also provided. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction An organization’s human capital can be a source for a sustainable competitive advantage. This is because the skills and knowledge that new employees bring and current employees acquire are often rare, durable and imperfectly imitable (Barney, 1991). For this reason the process of recruiting and selecting talent from the labor market is a key concern for any organization. Organizations dedicate a substantial amount of financial resources and efforts toward attracting and screening potential employees. One survey found that U.S. organizations spent approximately $124 billion in 2011 on recruitment and selection efforts for a rate of approximately $3500 per new employee (Bersin, 2013). A significant amount of the recruitment portion of these efforts is more recently been directed at motivating potential applicants to visit organizations’ websites for the purposes of learning more about the companies and submitting applications (Thompson, Braddy, & Wuensch, 2008). Much like a marketing department that uses such things as advertising, personal selling and sales promotions to encourage potential customers to visit a ⇑ Corresponding author. Tel.: +1 208 885 6788. E-mail addresses: [email protected] (D.M. Eveleth), [email protected] (L.J. Baker-Eveleth), [email protected] (R.W. Stone). 1 Tel.: +1 208 885 6788. http://dx.doi.org/10.1016/j.chb.2014.11.025 0747-5632/Ó 2014 Elsevier Ltd. All rights reserved.
company website for the purposes of purchasing products, human resource departments commonly drive job seekers to the career portion of the company’s website using similar promotion-oriented tactics. With respect to influencing job-seekers, a significant challenge for organizations is that once a potential applicant responds to the organization’s early recruitment efforts and arrives at the company’s website, the individual’s decision to submit an application or return to the site later is largely a function of the experience he or she has with the site. Understanding the website-related factors that affect a potential-applicant’s intentions is, therefore, essential. One key factor in determining a prospective applicant’s intentions may be the extent to which the job-seeker’s experience with the website confirms or disconfirms the expectations the individual had prior to the experience. While a company’s early recruitment efforts (e.g., personal selling at a career fair, public relations through press releases, advertisements about specific positions) help a prospective applicant form an initial expectation about the company or a specific position, the experience the individual has with the website helps them determine the extent to which their initial expectations are confirmed. The purpose of this research is to extend current thinking about the role of website characteristics and the user’s experience in recruiting. While a significant amount of research has looked at
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the factors that affect website usability and perceived usefulness (Gregory, Meade, & Thompson, 2013; Maurer & Cook, 2011; Thompson et al., 2008), there remains a limited understanding about how website usability and perceived usefulness affect potential applicants’ intentions. We suggest that potential applicants’ intentions can be explained, in part, by the extent to which the website confirms or disconfirms potential applicants’ expectations about the company. If expectation-confirmation is a critical factor in the intentions of potential applicants then organizations should consider the interplay between messaging about the company or position that is received from the website and the messaging about the company or position that is received from other pre-websitevisit sources (e.g., recruiters). The paper is organized into four additional sections. The first section summarizes the theory on potential applicant’s intentions and the factors that are thought to affect them, including website usability and perceived usefulness, expectations-confirmation and potential applicants’ satisfaction with the site. In this section the theoretical model is presented. The second section summarizes the methods used. In the third and fourth sections we describe the empirical results and discuss these results.
website can serve as a signal (Breaugh, 2008) upon which potential applicants can draw inferences about the company we can conclude that a job-seekers’ intentions can be affected by their perceptions of the website’s usefulness and their level of satisfaction with the site. Furthermore, because there is consistent evidence to support the link between perceived usefulness of an object and satisfaction with the object (e.g., Stone & Baker-Eveleth, 2013) we also conclude that a job-seekers’ perceptions about the usefulness of a company website will have a positive effect on their satisfaction with the site. Hypothesis 1 (H1). Website satisfaction has a significant and positive influence on the users’ behavioral intentions to the company. Hypothesis 2 (H2). Website perceived usefulness has a significant and positive influence on the users’ behavioral intentions to the company. Hypothesis 3 (H3). Website perceived usefulness has a significant and positive influence on website satisfaction.
2. Theory 2.2. Expectation confirmation 2.1. Intentions to apply While there are several definitions of recruiting in the literature, a common theme across the definitions is that recruiting, at a minimum, involves the activities an organization performs to affect the number and types of individuals who apply (Chapman, Uggerslev, Carroll, Piasentin, & Jones, 2005). Barber (1998) and others (e.g., Roberson, Collins, & Oreg, 2005) have argued that an organization’s pre-application recruitment efforts are critical, because talented job-seekers who do not apply will obviously not be affected by the more interpersonal recruitment-oriented efforts that occur simultaneously with selection or applicant-screening activities. Therefore, understanding the factors that affect a prospective applicant’s decision to apply is critical for designing and implementing effective recruitment efforts. In situations where a behavior (i.e., submitting an application or returning to the career site later) is volitional, evidence from previous research concludes that individuals form specific intentions about whether to perform the behavior and that these intentions are a good predictor of subsequent behavioral outcomes (Ajzen & Fishbein, 1973). Because behavioral intentions (Ajzen & Fishbein, 1973) are conceptualized as the consequence of cognitive judgments about one’s expectancies and the valences with respect to performing the potential behavior the intentions construct provides a useful mechanism for understanding how potential applicants’ experiences with a company’s website affect whether they will submit an application or not. Signaling theory (Spence, 1973) offers one explanation of how recruitment efforts, such as the design of the company website, can affect a job-seeker’s intentions to apply. The theory holds that because information receivers (e.g., job-seekers) lack complete information about an object or person (e.g., a position or company) they draw inferences based on signals or cues they receive from a signaler (e.g., the recruiting company). When applied to the recruitment process researchers have found that job-seekers’ attraction to an organization and intentions to apply are affected by inferences they draw from such things as recruiter behaviors (Rynes & Miller, 1983), organization image (Gatewood, Gowan, & Lautenschlager, 1993), corporate social performance efforts (Jones, Willness, & Madey, 2014) and the type of information in an advertisement (Roberson et al., 2005). Because a company’s
The expectation-confirmation model (ECM) is a theoretical framework that has been used extensively to explain and understand such things as consumer satisfaction, trust, purchase and switching decisions (Fan & Suh, 2014; Kim, Ferrin, & Rao, 2009) and the acceptance, use, or adoption of technology (Bhattacherjee, 2001; Bhattacherjee & Premkumar, 2004; Halilovic & Cicic, 2013; Stone & Baker-Eveleth, 2013). When applied to consumer purchase decisions, for example, the ECM framework suggests that consumers base their original purchase decision, in part, on an initial expectation they have about the good or service. Subsequent information they gain about the product by consuming or using it helps them determine if their initial expectations about the product were accurate. Whether the expectations were confirmed or disconfirmed will affect the consumer’s intentions to repurchase (Anderson & Sullivan, 1993) or complain (Wu, 2013). In organizational behavior literature the extent to which jobrelated expectations are met is related to organizational commitment, absenteeism, turnover, job performance, intention to quit and organizational citizenship behaviors, among other critical outcomes (Brown, Venkatesh, Kuruzovich, & Massey, 2008; Turnley & Feldman, 2000; Wanous, Poland, Premack, & Davis, 1992). Additionally, across a wide set of domains, met expectations (Porter & Steers, 1973) has consistently been associated with satisfaction (Brown et al., 2008; Greenhaus, Seidel, & Marinis, 1983; Kopalle & Lehmann, 2001; Stone & Baker-Eveleth, 2013; Szajna & Scamell, 1993). A logical extension of these findings to a job-seeker’s experience with a company’s website is to conclude that the extent to which a job-seeker’s expectations about the company or position are met will directly or indirectly affect many of his or her subsequent attitudes, intentions and behaviors. When a jobseeker responds to a company’s early recruitment efforts (e.g., word-of-mouth from an existing employee, personal selling by a recruiter at a career fair, reading a job announcement on a job board) by visiting the company’s website he or she arrives at the site with initial expectations about the company or position. After experiencing the website, the extent to which job-seekers’ expectations are confirmed or disconfirmed will likely influence their perceptions of the website’s usefulness and their level of satisfaction with the site. These relationships are summarized by Hypotheses 4 and 5.
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Hypothesis 4 (H4). The user’s expectation-confirmation after using the website has a significant and positive influence on their satisfaction with the website. Hypothesis 5 (H5). The user’s expectation-confirmation after using the website has a significant and positive influence on the website’s perceived usefulness.
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Hypothesis 7 (H7). Website usability has a significant and positive influence on website satisfaction. Hypothesis 8 (H8). Website usability has a significant and positive influence on expectation confirmation after website use. Hypothesis 9 (H9). The engagement of website users has a significant and positive influence on website usability.
2.3. Website usability When we imagine a job-seeker first landing at a company’s website it is easy to assume that their initial impressions of the site, and subsequent attitudes and intentions about the site and the company, will in part be a function of how easy it is for them to learn how to navigate the website, the level of simplicity of the site, the speed with which they can find information that they want or need, and the level of control that they have over the experience. These factors are largely captured in a construct often labeled as ‘‘usability’’ (Davis, 1989). Website usability is associated with a variety of outcomes. Flavián, Guinalíu, and Gurrea (2006), for example, found a relationship between website usability and user satisfaction. Website usability is also related to online trust (Gefen, Karahanna, & Straub, 2003), perceived usefulness (Saadé & Bahli, 2005), self-efficacy and outcome expectancy/usefulness (Stone & Baker-Eveleth, 2013). Uggerslev, Fassina, and Kraichy (2012) suggest that recruitment activities, including such things as the extent to which the company’s website is ‘‘user-friendly’’ (p. 598), are useful cues for jobseekers during early stages of the recruitment and selection process when their levels of knowledge about the company and position are low and when they have a lack of commitment to any single opportunity. Similarly, Maurer and Liu (2007) suggest that ‘‘Source Features’’ (p. 306), such as website content, interactivity and vividness play an important role in determining potential applicants’ attitudes and intentions. Cober, Brown, Levy, Cober, and Keeping (2003) found that website content, esthetics and usability influence potential applicants’ attraction to the organization and Allen, Mahto, and Otondo (2007) found that the amount of job and organization information on a website influences potential applicants’ attitudes toward the recruitment website. More recently, Allen, Biggane, Pitts, Otondo, and Van Scotter (2013) found that website design affects job-seekers’ evaluation of the website. While a user might evaluate their experience with a website on the extent to which the site information or design meets their instrumental or extrinsic-motivation needs, some have suggested that users’ attitudes and beliefs about technology use, in general, may also be explained by intrinsic-motivation that derives from ‘‘their holistic experiences with the technology’’ (Agarwal & Karahanna, 2000, p. 666). Intrinsic-motivation-type constructs, such as engagement (Webster & Ho, 1997), enjoyment (Davis, Bagozzi, & Warshaw, 1992; Trevino & Webster, 1992) and absorption (Agarwal & Karahanna, 2000) have been found to influence users’ attitudes toward and use of technology. By logical extension, the extent to which a job-seeker is engaged with a career website should influence their attitudes toward the site’s usefulness. Given the substantial evidence that website usability affects satisfaction with the website and perceptions of the site’s usefulness and that website design and user engagement with the site affect the users’ attitudes toward the site, the following hypotheses are proposed. Hypothesis 6 (H6). Website usability has a significant and positive influence on website perceived usefulness.
Hypothesis 10 (H10). The content on the website has a significant and positive influence on website usability. Hypothesis 11 (H11). The feedback provided the user by the website has a significant and positive influence on website usability.
2.4. Theoretical model Based on the literature presented above and the corresponding hypotheses, a theoretical model and the hypotheses are displayed in Fig. 1. From left to right on the figure, the model suggests that a user’s evaluation of a site’s interactivity or feedback (Hypothesis 11), content (Hypothesis 10), and their level of engagement with a site (Hypothesis 9), influence their perceptions of website usability. The user’s perceptions of website usability influence the extent to which the site confirms or disconfirms their expectations about the company (Hypothesis 8), their levels of satisfaction with the site (Hypothesis 7), and the website’s perceived usefulness (Hypothesis 6). Furthermore, the user’s expectation-confirmation influences their satisfaction of the site (Hypothesis 4) and the website’s perceived usefulness (Hypothesis 5). Website perceived usefulness influences website satisfaction (Hypothesis 3) and intentions to the company (Hypothesis 2). Finally, website satisfaction influences intentions to the company (Hypothesis 1). 3. The method 3.1. The sample The data were collected by distributing a questionnaire to potential respondents using two distribution methods. The first method used students enrolled in required business courses at a medium-sized university in the western United States. The second was to contract with WebTurk on Amazon.com to have their contract workers complete the questionnaire and associated activities. Given the focus of the research, the target population from which to sample are individuals with either recent or current experience searching for employment or intentions to do so in the near future. Such individuals possess the insights regarding job searches and the role of web-based enhanced employment searches. The student respondents were all near completion of their junior year in a business degree program when they provided their responses. These students would have experiences searching for employment for internships and would soon be directly engaged in permanent employment search over the next 6–12 months. Eighty-two percent of the Mechanical Turk respondents reported job search activities either currently, or in the recent past, or intentions to do so in the near future. Thus, based on the research focus, the experiences and intentions of the respondents provide an appropriate sample for this research. The total sample size was 199 respondents composed of 99 completed questionnaires from students and 100 completed by contract individuals through WebTurk. The data collection took
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Website Perceived Usefulness
Engagement
H1
H2 Content
H10
H4
H9
Website Usability
Website Satisfaction
H5
H11
Behavioral Intentions to Company
H7 H6 H8
H3*
Expectation Confirmation of Company After Website Use
Feedback
Fig. 1. The theoretical model and hypotheses.
place in March of 2014. The sampling method was a systematic random design. The systematic portion of the sampling design was provided by assigning company Web sites to respondents by selecting each sequentially from their respective lists. The random element was introduced by randomly selecting the starting point on each of the company list and the respondent list. This breakdown of the sample is shown in Table 1. The questionnaire administration process had several steps. Respondents were first asked to complete a subset of the questionnaire items. Next, they were given the name of one of four companies and the name of an open position at that company. They were instructed to go to the company website, find the position listing and initiate the job application process. Respondents were then
directed to search to identify another potential position of interest for the future. After completing these activities on the assigned website, respondents completed the remaining items on the questionnaire. While the names of the companies whose websites were used are not revealed for privacy reasons, the percentage of responses for each company included in the sample are shown in Table 1. The breakdown of the representation of these companies in the sample is roughly 40%, 25%, 20%, and 15%. The final demographic on the sample was the declared major of the students in the sample. These percentages of majors ranged from 1.51% to 14.07%. The most interesting value was that the largest response category was non-students and missing responses at 27.64%. From this result it appears that a number of the contract workers at WebTurk are business students who reported their majors on the questionnaire.
Table 1 The sample demographics. Frequency Observation source WebTurk Students
100 99
50.25 49.75
Total
199
100.00
80 50 39 30
40.20 25.13 19.60 15.08
199
100.01b
Company 1 2 3 4 Total
a
Majors of the student respondents in the sample Accounting 25 Business economics 9 Finance 28 Information systems 7 Management and human resources 24 Marketing 22 Operations management 17 PGA golf management 3 Other 9 Non-students and missing responses 55 Total a b
Percentage in sample
199
Company names redacted. Details do not sum to 100% due to rounding.
12.56 4.52 14.07 3.52 12.06 11.06 8.54 1.51 4.52 27.64 100.00
3.2. The measures The measures to operationalize and empirically test the theoretical model were formed by collecting responses to appropriate questionnaire items. The indicants of these measures are shown in Table 2 along with the measures’ corresponding psychometric properties. All the standardized path coefficients and psychometric properties were generated based on a confirmatory factor analysis using procedure Calis in PC SAS version 9.2. In the confirmatory factor analysis all measures were reflective in their indicants and allowed to pairwise correlate. The summary statistics of the quality of the fit between the data and the estimated model indicate an acceptable fit (Hair, Anderson, Tatham, & Black, 1992; Hooper, Coughlan, & Mullan, 2008). The goodness of fit measure was 0.89 and adjusted for degrees of freedom it was 0.84, while the parsimonious goodness of fit was 0.70. The root mean square residual and its standardized counterpart were both 0.041. The chi-square statistic was 280.40 with 181 degrees of freedom, which was statistically significant at a 1% level. The normed chi-square statistic was 1.55. The root mean square error of approximation was estimated to be 0.054 with 90% upper and lower confidence intervals of 0.041–0.066. Bentler’s comparative fit index was 0.97 while the incremental fit indexes (i.e., Bentler & Bonett’s non-normed and normed indexes; Bollen’s normed and non-normed indexes) ranged from 0.89 to 0.97.
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D.M. Eveleth et al. / Computers in Human Behavior 44 (2015) 183–190 Table 2 The indicants, measures and psychometric properties using standardized path coefficients. Indicant Engagement This website . . . . . 1. Stimulated my imagination 2. Is intrinsically interesting
Standardized path coefficient
0.84 0.88 0.77
Feedback 6. This website facilitates two-way communicate between the visitors and the site 7. The website is effective in gathering visitors’ feedback
0.81 0.73
Website Usability When I think about this company’s website . . . . . 8. I find this company’s website easy to use 9. I find it easy to locate the information that I need in this company’s website 10. I believe that it is easy to do what I want to do while on this company’s website
0.89 0.90 0.79
Website Satisfaction 11. Overall, I am satisfied with this place 12. I think I did the right thing when I decided to use this website 13. Overall, I am satisfied with this website experience
0.84 0.81 0.88
Behavioral Intentions to Company In the future how likely would you be to . . . . . 17. Consider joining this organization? 18. Consider joining this organization as an intern or after graduation? 19. Consider joining this organization sometime during your life? 20. Search their website in the future about jobs with this organization? Expectation Confirmation of Company After Website Use If I accepted a job at this company . . . . . 21. I would have opportunities to get ahead 22. The job would be outstanding
As mentioned earlier, the measures of the constructs were formed by gathering responses to appropriate questionnaire items. For all items, the respondents were given a statement and a scale upon which to respond. The response scale used was a five-point scale with anchors of 1-Strongly Disagree; 2-Disagree; 3-Neither Agree or Disagree; 4-Agree; and 5-Strongly Agree. All the items were taken from previously published research and modified to fit this current application. Engagement was measured by two items with standardized path coefficients of 0.80 and 0.95. The composite reliability coefficient calculated from these path coefficients was 0.75 and the percentage of shared variance extracted was 59%. The content measure used three items with standardized path coefficients of 0.84, 0.88, and 0.77. Its calculated composite reliability was 0.87 and percentage of shared variance extracted of 69%. Two indicants were used to measure feedback. These path coefficients were 0.81 and 0.73 with a reliability coefficient of 0.75 and shared variance extracted of 59%. Website usability, website satisfaction, and website perceived usefulness were each measured using three questionnaire items. For website usability these path coefficients were 0.89, 0.90, and 0.79 with composite reliability coefficient of 0.90 and shared variance extracted of 74%. Similarly, website satisfaction had estimated path coefficients of 0.84, 0.81, and 0.88 and resulting reliability and shared variance estimates of 0.88 and 71%, respectively. Website perceived usefulness had estimated standardized path coefficients of 0.81, 0.88, and 0.81 which produced
Percentage of shared variance extracted
0.75
59
0.87
69
0.75
59
0.90
74
0.88
71
0.87
70
0.94
80
0.76
61
0.80 0.95
Content The website includes . . . . . 3. Good job information 4. A variety of information 5. Information about each job that is helpful
Website Perceived Usefulness Regarding the company’s website . . . . . 14. Overall, I find this company’s website useful 15. The content on this company’s website is helpful to me 16. I think this company’s website is valuable to me
Composite reliability
0.81 0.88 0.81
0.92 0.86 0.94 0.86
0.72 0.84
a composite reliability estimate of 0.87 and a shared variance extracted of 70%. The final two measures were behavioral intentions to the company and expectation-confirmation of the company after website use. The first had estimated path coefficients of 0.92, 0.86, 0.94, and 0.86. Its reliability coefficient was 0.94 with a shared variance extracted of 80%. The final measure used two indicants with estimates of 0.72 and 0.84 leading to a composite reliability coefficient of 0.76 and 61% shared variance extracted. Based on the magnitudes of the standardized path coefficients that ranged from 0.72 to 0.95, it can be argued that item reliability was satisfied (Rainer & Harrison, 1993). In terms of composite reliability, all the measures demonstrated acceptable values based on the calculated reliability coefficients of 0.75–0.94 (Rainer & Harrison, 1993). All these reliability measures exceeded the generally accepted cutoff level of 0.70 (Nunnally, 1978). Additionally, all the shared variance extracted percentages were above 50%. The combination of these results indicates that the measures satisfy convergent validity (Igbaria & Greenhaus, 1992; Rainer & Harrison, 1993). Discriminant validity was also examined by comparing, for each pair of measures, its squared correlation to the individual percentages of shared extracted variance. If discriminant validity is satisfied, the items within a measure share greater common variation among themselves than between the two measures. This is demonstrated when for each measure pair, the individual measures’ percentage of extracted shared variances are greater than the squared correlation between the two measures (Fornell & Larcker, 1981).
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Table 3 The correlations and squared correlations among the measures. Measure pairs
Correlation
Squared correlation
Engagement-Feedback Engagement-Website Usability Engagement-Content Engagement-Website Satisfaction Engagement-Website Perceived Usefulness Engagement-Behavioral Intentions to Company Feedback-Website Usability Feedback-Content Feedback-Website Satisfaction Feedback-Website Perceived Usefulness Feedback-Behavioral Intentions to Company Website Usability-Content Website Usability-Website Satisfaction Website Usability-Website Perceived Usefulness Website Usability-Behavioral Intentions to Company Content-Website Usability Content-Website Perceived Usefulness Content-Behavioral Intentions to Company Website Satisfaction-Website Perceived Usefulness Website Satisfaction-Behavioral Intentions to Company Website Perceived Usefulness-Behavioral Intentions to Company Expectation Confirmation of Company After Website Use-Engagement Expectation Confirmation of Company After Website Use-Feedback Expectation Confirmation of Company After Website Use-Website Usability Expectation Confirmation of Company After Website Use-Content Expectation Confirmation of Company After Website Use-Website Satisfaction Expectation Confirmation of Company After Website Use-Website Perceived Usefulness Expectation Confirmation of Company After Website Use-Behavioral Intentions to Company
0.35 0.31 0.28 0.54 0.54 0.35 0.47 0.29 0.50 0.46 0.43 0.40 0.65 0.60 0.36 0.55 0.59 0.34 0.71 0.61 0.53 0.48 0.58 0.42 0.50 0.70 0.66 0.55
0.12 0.10 0.08 0.29 0.29 0.12 0.22 0.08 0.25 0.21 0.18 0.16 0.42 0.36 0.13 0.30 0.35 0.12 0.50 0.37 0.28 0.23 0.34 0.18 0.25 0.49 0.44 0.30
Table 4 The statistics summarizing the fit of the model to the data.
**
Statistic
Value
Goodness of fit index Adjusted goodness of fit Parsimonious GFI Chi-square statistic Degrees of freedom Normed chi-square statistic Root Mean Square Residual (RMSR) Standardized RMSR Root Mean Square Error of Approximation (RMSEA) RMSEA lower and upper 90% confidence interval Bentler’s comparative fit index Bentler & Bonnet’s normed index Bentler & Bonnet’s non-normed index Bollen normed index Bollen non-normed index
0.85 0.81 0.72 387.90** 195 1.99 0.106 0.106 0.072 0.062–0.083 0.93 0.88 0.92 0.85 0.93
Significant at a 1% level.
All the squared correlations were calculated using the confirmatory factor analysis and are reported in Table 3. The percentages of shared variance extracted were also calculated from the confirmatory factor analysis and are shown in Table 2. From these values, it is seen that discriminant validity was satisfied for all the measure. As a result, for all the measures, it can be concluded that construct validity is satisfied (Hair et al., 1992). 4. Results The estimation of the model and the corresponding tests of the hypotheses were performed using a structural equations approach. Specifically, the estimation technique was procedure Calis in PC SAS version 9.2 and maximum likelihood estimation. The goodness of fit index was 0.85 and the adjusted goodness of fit was 0.81. The corresponding parsimonious goodness of fit index was 0.72. The chi-square statistic was 387.90 with 195 degrees of freedom. It
was statistically significant at a 1% level. The resulting normed chi-square statistic was 1.99. The root mean square residual and its standardized version were both 0.106. The root mean square error of approximation was estimated to be 0.072 with 90% upper and lower confidence intervals of 0.062–0.083. Bentler’s comparative fit index was estimated to be 0.93 with incremental fit indexes (i.e., Bentler & Bonnet’s as well as Bollen’s normed and nonnormed indexes) ranged from 0.085 to 0.93. All these values are displayed in Table 4. These summary statistics, while presenting somewhat mixed results, do indicate an acceptable fit between the data and the model (Hair et al., 1992; Hooper, Coughlan, & Mullan, 2008). The details of the measurement and structural model’s estimation are shown in Fig. 2 using standardized path coefficients. In the measurement model, all the paths which were free to vary were statistically significant at a 1% level. All the structural paths among the measures were statistically significant at a 1% level, except one. This path was between website perceived usefulness and behavioral intentions to organization which was statistically significant at a 5% level. All the structural paths had the hypothesized signs. 5. Discussion Much is known about how and why job-seekers make jobchoice decisions. Fit between job-seeker’s values and organization values (Cable & Judge, 1996), company image (Gatewood et al., 1993), recruiter behaviors (Chapman et al., 2005), and compensation (Cable & Judge, 1994) are among the identified factors that influence the decision to accept or reject a job offer. In addition, Barber (1998) suggests that decision criteria change as an individual moves through the recruitment and selection process. Early in the process job-seekers consider a broad set of criteria and their attraction to a company is typically more fragile because they have less information upon which to base a final decision (Turban, Eyriung, & Campion, 1993). The factors that help encourage (or discourage) job-seekers to accept an offer are more focused and are interpersonal-oriented variables (e.g., interviewer behavior) that
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Fig. 2. The estimated model using standardized path coefficients.
come later in the process (Boswell, Roehling, LePine, & Moynihan, 2003). Thus, as companies rely more heavily on web-based recruitment activities in the competition for talent, one of their major challenges is how to encourage qualified job-seekers to submit an application after experiencing the website. If potential applicants land at a company’s website with limited information and potentially tenuous attraction to the company and if job-choice decisions are most influenced by interpersonal interactions that occur during the post-application stage of the process, then it is critical to understand how a website positively or negatively influences applicants’ intentions to submit an application or revisit the site in the future. The results of this study offer some help in this regard. The results indicate that the antecedents of website usability of engagement, content, and feedback have meaningful influences on this usability conceivably through their ability to produce intrinsic and extrinsic motivation for the user. In this context implying that if the managers’ responsible for developing and maintaining their company’s career website make sure that the site engages potential applicants, provides meaningful content and feedback that these will influence potential applicants’ behavioral intentions to the company. These influences are indirect through website usability to website perceived usefulness, expectation-confirmation of the job, and website satisfaction. In similar fashion, those managing the company’s career website should also be concerned with assuring that the website confirms job-seekers’ pre-visit expectations. Because job-seekers land at company websites with expectations that are in some part formed by early recruitment efforts (e.g., speaking with a recruiter at a career fair, reading a job announcement, public relations), consistency between website content and design and pre-website messaging is essential. Met expectations directly influence job-seekers’ satisfac-
tion with the website and indirectly, through this satisfaction, jobseekers’ behavioral intentions regarding the company. 6. Conclusions Considerable research has been done to increase our understanding of the website user experience from the perspective of customers. We know, for example, that website characteristics have an effect on such outcomes as potential customers’ intentions to purchase online or repurchase (Cronin Jr., Brady & Hult, 2000; Harris & Mark, 2010; Ranganathan & Jha, 2007; Taylor & Baker, 1994; van Birgelen, de Jong, & de Ruyter, 2006; Van der Heijden, Verhagen, & Creemers, 2003). The results of this study continue the work that is seeking to gain similar levels of understanding about the user experience when the user is a potential applicant. The research illustrates the importance of engagement, content, and feedback on influencing website usability, and better explains how website usability affects potential applicants’ intentions (i.e., indirectly through perceived usefulness, satisfaction and expectation confirmation). References Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and belies about information technology usage. MIS Quarterly, 24(4), 665–694. Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27(1), 41. Allen, D. G., Biggane, J. E., Pitts, M., Otondo, R., & Van Scotter, J. (2013). Reactions to recruitment web sites: Visual and verbal attention, attraction, and intentions to pursue employment. Journal of Business and Psychology, 28(3), 263–285. Allen, D. G., Mahto, R. V., & Otondo, R. F. (2007). Web-based recruitment: Effects of information, organizational brand, and attitudes toward a Web site on applicant attraction. Journal of Applied Psychology, 92(6), 1696.
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Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143. Barber (1998). Recruiting employees: Individual and organizational perspectives. Thousand Oaks, CA: Sage. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. Bersin, J. (2013). Corporate recruiting explodes: A new breed of service providers. Forbes, Inc..
Igbaria, M., & Greenhaus, J. H. (1992). Determinants of MIS employee’s turnover intentions: A structural equation model. Communications of the ACM, 35, 35–49. Jones, D., Willness, C., & Madey, S. (2014). Why are job seekers attracted by corporate social performance? Experimental and field tests of three signalbased mechanisms. Academy of Management Journal, 57(2), 383–404. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237–257. Kopalle, P. K., & Lehmann, D. R. (2001). Strategic management of expectations: The role of disconfirmation sensitivity and perfectionism. Journal of Marketing Research, 38(3), 386–394. Maurer, S. D., & Cook, D. P. (2011). Using company web sites to e-recruit qualified applicants: A job marketing based review of theory-based research. Computers in Human Behavior, 27(1), 106–117. Maurer, S. D., & Liu, Y. (2007). Developing effective e-recruiting websites: Insights for managers from marketers. Business Horizons, 50(4), 305–314. Nunnally, J. (1978). Psychometric methods (2nd ed.). New York: McGraw-Hill. Porter, L. W., & Steers, R. M. (1973). Organizational, work, and personal factors in employee turnover and absenteeism. Psychological Bulletin, 80(2), 151. Rainer, R. K., Jr., & Harrison, A. W. (1993). Toward development of the end user computing construct in a university setting. Decision Sciences Journal, 24(6), 1187–1202. Ranganathan, C., & Jha, S. (2007). Examining online purchase intentions in B2C ecommerce: Testing an integrated model. Information Resources Management Journal, 20(4), 48–64. Roberson, Q. M., Collins, C. J., & Oreg, S. (2005). The effects of recruitment message specificity on applicant attraction to organizations. Journal of Business and Psychology, 19(3), 319–339. Rynes, S. L., & Miller, H. E. (1983). Recruiter and job influences on candidates for employment. Journal of Applied Psychology, 68(1), 147. Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317–327. Spence, A. M. (1973). Job marketing signaling. Quarterly Journal of Economics, 87, 355–374. Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984–990. Szajna, B., & Scamell, R. W. (1993). The effects of information system user expectations on their performance and perceptions. MIS Quarterly, 17, 493–516. Taylor, S. A., & Baker, T. L. (1994). An assessment of the relationship between service quality and customer satisfaction. Journal of Retailing, 70(2), 163–178. Thompson, L. F., Braddy, P. W., & Wuensch, K. L. (2008). E-recruitment and the benefits of organizational web appeal. Computers in Human Behavior, 24(5), 2384–2398. Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication electronic mail and voice mail evaluation and impacts. Communication Research, 19(5), 539–573. Turban, D. B., Eyriung, A. R., & Campion, J. E. (1993). Job attributes: Preferences compared with reasons given for accepting and rejecting job offers. Journal of Occupational and Organizational Psychology, 66(1), 71–81. Turnley, W. H., & Feldman, D. C. (2000). Re-examining the effects of psychological contract violations: Unmet expectations and job dissatisfaction as mediators. Journal of Organizational Behavior, 21(1), 25–42. Uggerslev, K. L., Fassina, N. E., & Kraichy, D. (2012). Recruiting through the stages: A meta-analytic test of predictors of applicant attraction at different stages of the recruiting process. Personnel Psychology, 65(3), 597–660. van Birgelen, M., de Jong, A., & de Ruyter, K. (2006). Multi-channel service retailing: The effects of channel performance satisfaction on behavioral intentions. Journal of Retailing, 82(4), 367–377. Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48. Wanous, J. P., Poland, T. D., Premack, S. L., & Davis, K. S. (1992). The effects of met expectations on newcomer attitudes and behaviors: A review and metaanalysis. Journal of Applied Psychology, 77(3), 288. Webster, J., & Ho, H. (1997). Audience engagement in multimedia presentations. ACM SIGMIS Database, 28(2), 63–77. Wu, I. L. (2013). The antecedents of customer satisfaction and its link to complaint intentions in online shopping: An integration of justice, technology, and trust. International Journal of Information Management, 33(1), 166–176.