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Once well-constructed personnel specifications are available, filtering processes may be more efficient in separating applicants into acceptable and unacceptable categories by

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selecting applications that closely resemble those personnel specifications through distinctions in information and evidence that is provided by selection tools (Cook, 1991;

Fritzsche & Brannick, 2002; Gatewood & Feild, 1987). Both information and evidence provides a foundation for screeners to evaluate individuals when making hiring decisions.

The next sections highlights the common selection tools associated with initial filtering decisions such as applications, autobiographical data, applicant histories (e.g. background checks and substance testing), and digital tools.

2.5.1 Applications

To filter applicants, organisations heavily rely on applications, résumés, and CVs to provide the first information about job-seekers (Robertson & Smith, 2001). Whilst personnel specifications provide the framework to measure credentials and other factual information about individuals, ultimately the process involves human judgement (Drucker, 1995). Searle (2003) suggest that the difference between résumés and CVs only refers to their geographic use: the term résumé is preferred in Australia and the United States. However, MIT (2009) and Salvador (2010) note that there are some differences between the two terms and that CVs are more comprehensive and centre on academic achievement and occupational experiences. However for the purposes of this thesis, the terms are considered interchangeable.

Whilst applicants’ résumés provide a basis for evaluation, screeners must assess the validity and reliability of applicant provided information (Cerrito, 2004; Engleman & Kleiner, 1998). Validity and reliability can be jeopardised when unemployment rises and labour markets become more competitive, as applicants may be more likely to embellish experience and skills to gain a completive advantage over other applicants (Bishop, 2006; Davidson, 1984; Spang, 1984; Van Dam et al., 1985). Some authors (Marcoux, 2006) suggest embellishment is necessary for applicants to obtain an interview whilst others (Bishop, 2006;

Green, 1991) oppose embellishment as it can lead towards deception.

Applications and résumés serve as a primary method for organisations to gage individual accomplishments (Arthur, 2001; Karsh with Pike, 2009; Robertson & Smith, 2001).

Information contained in these applications and résumés determine the course of subsequent interviews and filtering tools (Stanton, 1977). Because résumés are unique to job-seekers, organisations generally require job-seekers to submit a standard application to promote consistency in information presentation. This is also considered best practice and thus advised by scholars (Arthur, 2001). The standard application should include the collection of biographical data (Drakeley et al., 1988; Searle, 2003).

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Based on a job analysis and a composite of characteristics and attributes of successful applicants, biodata focuses on an individual’s histories (i.e. experiences, education, and successes) about applicants to predict potential aptitude and success (Bliesener, 1996; Harvey-Cook & Taffler, 2000; Mount et al., 2000; Oswald et al., 2004).

Applicants answer a series of questions usually using a multiple-choice or Likert scale format (Searle, 2003). Although job-seekers can be presented with a diversity of questions, some legal jurisdictions do not allow organisations to collect or consider certain categories of biodata (Barrick & Zimmerman, 2005; Cerrito, 2004). For examples: Birth information is normally not allowed to be considered in the United States. Even with these restrictions, biodata is overall a good predictor for employee success (Drakeley et al., 1988) and predicting avoidable employee turnover (Barrick & Zimmerman, 2005).

Whilst biodata appears to predict success, flaws exist with the theoretical underpinnings of the tool itself (Searle, 2003). According to Carlson et al. (1999), biodata is generalisable when carefully created; however, Searle (2003) contradicts this generalisability and contends that biodata is not transportable between organisations or positions. That is, biodata is specific to particular organisations (Rothstein et al., 1990) and roles. Due to this specificity, biodata also depress diversity, an essential element to organisational creativity and innovation, by promoting sterility and consistency for a continuation of organisational culture (Searle, 2003). Job selection criteria changes with cultural differences (Peppas, 2006;

Saha et al., 2008) and the use of biodata differ amongst countries and organisation size (Shackleton & Newell, 1991). Furthermore, Bobko et al. (2008) suggest that current needs and changes are not fully integrated within biodata designs as technology can alter job duties. Furthermore, the theory behind biodata needs more research to account for differences in subgroups and in relationships that surround validity (Oswald et al., 2004).

In addition to generalisability, a practical theory is needed for effective biodata prediction (Harvey-Cook & Taffler, 2000; Oswald et al., 2004). Some assessment and filtering processes closely resemble performance evaluations (Lazear, 1996) and are not geared towards identifying the best applicants in a cost efficient manner. The ontology of selection processes may alter hiring decisions (McIntyre et al., 1980). For example, regulations and guidelines covering certain types of information (i.e. race, ethnicity, and gender) may cause screeners to adjust decisions away from better qualified applicants.

The use of biodata could be linked to inconsistent research findings surrounding its effectiveness (Hough & Oswald, 2000); applicant perceptions in biodata fairness (Cook,

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1991; Gilliland, 1993); and legal provisions (McIntyre et al., 1980). Additionally, the use of biodata can increase conflicts between organisations and applicants (Robertson & Smith, 1987, in Cook, 1991). Thus, applicant histories provide additional evidence for predicting applicant success (Owens & Schoenfeldt, 1979; Spell & Blum, 2005; Wells & Gill, 2007).

2.5.3 Applicant Histories

In addition to biodata, organisations can investigate applicant histories through references, criminal checks, credit checks, and substance tests (Doyle, 2009; McKinney &

Tissington, 2009; Spell & Blum, 2005) as future behaviours can be predicted from past behaviours (Owens & Schoenfeldt, 1979). Background checks should be an integrated part of assessment (Edersheim, 2007; Sumners, 2008) with key personnel undergoing more extensive background checks to identify unethical behaviours (Wells & Gill, 2007). A traditional background check is the applicant references which provide historic information on behaviours, work ethics, and skills (Searle, 2003; Shackleton & Newell, 1991). However, most references are overwhelmingly positive since an applicant supplies contact details and referees names, furthermore line managers generally avoid providing negative information about an applicant as this can spur litigation in some jurisdictions, like the United States (Engleman & Kleiner, 1998). Thus, criminal checks are necessary to reduce an organisation’s litigation costs from hiring negligently. Whilst most organisations remove applicants with criminal backgrounds, this should be considered in relation to the specific offense and the specific role in order to avoid providing someone with the opportunity to be fully rehabilitated into society. Furthermore Cappelli (1994) suggests that many formal criminals can offer more loyalty and stability to an organisation’s labour force, particularly in non-professional and manufacturing occupations such as landscaping and assemblers;

therefore it can be argued that filtering processes should not remove all offenders.

To gauge credit worthiness, integrity, honesty, and potential organisational loss, a credit report obtained from a credit bureau or other information broker often summarise liabilities, satisfied financial agreements, and financially related information from a composite of variables and sources into a numeric value (Kolesar & Showers, 1985; Schmidt & Hunter, 1998; Thorne, 2007; Wagner, 2004). Based on organisational guidelines and legal regulations (see FTC, 2009; Garfinkel, 1995; Porterfield, 2001), reports may be used to filter applicants from employment consideration (Applegate, 2001; Lofton, 2007) or terminate current employment contracts (Thorne, 2007) provided proper documentation is available for decisions and selection tests (Crane, 1989). In the United States, the Fair Credit Reporting Act of 1970 (FCRA) regulates what types of information exchanges and collections can take place as report suppliers once neglected to verify and correct information concerning files on

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businesses and individuals (FTC, 2009). For example, the Fair Credit Reporting Act extends to biodata questions relating to credit and financial transactions (see FTC, 2009; Engleman &

Kleiner, 1998).

Substance testing (i.e. drugs and alcohol) uses a sample of body fluid or hair to detect the use of certain substances (TrimegaLabs, 2008) associated with undesirable behaviours that may be disruptive to an organisation (Knudsen et al., 2003). When substances are detected, organisations normally remove applicants from consideration (Fernando, 2007; Jardine-Tweedie & Wright, 1998). In the United States, substance testing as a filtering tool must be done after an offer of employment, which is thus contingent to the negative test results. Most organisations only test once. Problems with test validity and variances exist. Contingent upon a sample (NACD, 2007), an individual’s pigment (Jardine-Tweedie & Wright, 1998), test counter-measures (Comer, 1994; USDTL, 2008), and procedural errors (Riley et al., 2000), tests can provide false positives and false negatives (Comer, 1994; NACD, 2007). Organisations may adopt substance tests for several reasons such as to reduce theft (Busnick, 2005; Minchin et al., 2006), increase safety (Gillian, 2002;

Lauver, 2007), increase productivity (Cholakis & Bruce, 2007), comply with laws (AMA, 1998;

Knowles & Riccucci, 2001), and cultural attributes (Brewis et al., 2006; French et al., 2001;

Gee et al., 2005; NACD, 2007). Substance testing is mostly concentrated within United States organisations (Hanson, 1993; JRF, 2004; McKinney & Tissington, 2009) and substance testing may act as a deterrent against individuals that may potentially be engaged in undesirable behaviours (Wells, 2005). Alternative tools and filtering processes provided by digital technology can expedite and enhance assessment (Ployhart et al., 2003).

2.5.4 Digital Tools

As technology becomes cheaper, some organisational processes shift towards computer-based programmes that gather evidence and test applicant’s skills and knowledge (Guastello et al., 1992; Searle, 2006). For example, Internet and electronic or digital applications are rapidly replacing traditional hard copy fill-in forms (Bartram, 2004; DeMey &

Flowers, 1999; Porterfield, 2002); and biodata is becoming more popular with digital programmes (Searle, 2003) such as expert systems (Shore, 1996). Expert systems may be used as decision tools which could be tailored to specific personnel specifications (Shore, 1996). Whilst an expert system can generate consistent decisions, practical problems exist.

These systems may not be able to deal with rapid changes in the law as expert system learning may be limited. Also, programmers may introduce flaws into the expert system or costs may exceed benefits associated with hiring.

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According to Ployhart et al. (2003), digital tests are more accurate than paper-and-pencil testing. Since digital tests are evaluated using computer programmes, job-seeker responses are assessed more consistently than tests judged by individual screeners. In effect, computer programmes can be used to compile information to develop profiles of job-seekers. Conversely, Arthur (2001) disagrees suggesting electronic profiling has the potential to remove qualified job-seekers that are outside of the pre-programmed desired range of KSAO. Aguinis et al. (2001) contend that the performance of some job-seekers actually declines with digital testing due to a lack of familiarity with the testing environment or assessment system. Furthermore, assessments in virtual reality are subjective and observers can focus on non-relevant observations and information.

Although filtering processes can be enhanced using applications, biodata, applicant histories, and digital tools, errors in judgement still may impact decisions concerning which applicants are to be removed from the hiring processes. As some variances in perception can be attributed to informal decisions, converting theory into practical applications is essential and is discussed in the next section.