Industrial Question: Discuss the role of the Ability to Identify Criteria (ATIC) in predicting interview performance and subsequent job performance. Provide theoretical explanations for these relationships. Propose potential moderators that may moderate the relationship between ATIC and interview performance and discuss possible mechanisms.
One foundational building block of an effective and profitable organization is having the right employees in the right positions. Because of this, a lot of emphasis goes into the process of employee selection. While there are many methods used (e.g. assessment centers, personality questionnaires, cognitive ability measures, etc.), the number one staple used by the majority of organizations for selection purposes is the interview (Lievens, Highhouse, & de Corte, 2005; Salgado, Viswesvaran, & Ones, 2001). Erker, Cosentino, and Tamanini (2010) estimate that nearly 100% of organizations use some form of an interview at some point in their selection process. With use of the interview being so wide spread, many researchers have investigated what role that individual differences might play. Constructs such as personality (Cook, Vance, & Spector, 2000), self-monitoring (Anderson, Silvester, Cunningham-Snell, & Haddleton, 1999), emotional and general intelligence (Fox & Spector, 2000), speech styles (Parton, Siltanen, Hosman, & Langenderfer, 2002), the ability to identify criteria (Melchers, Klehe, Richter, Kleinmann, König, & Lievens, 2009) and more have been studied in relation to interview outcomes and its predictive validity of future performance.
The ability to identify criteria (ATIC), an individual difference variable, is defined as an individual’s ability to accurately perceive performance criteria in evaluative situations (Kleinmann et al., 2011). In simpler terms, ATIC is the ability of an individual to accurately determine what a selection procedure (e.g. interview question) is actually measuring. For example, an individual with high ATIC would be able to determine that an interview question about “a disagreement with a coworker” is actually evaluating your ability to cooperate with others, and then would adjust their response appropriately. On the other hand, an individual with low ATIC may inaccurately identify the criteria as measuring ones assertiveness, and thus their responses would be incorrectly tailored to match their inaccurate hypothesis about the criterion.
Much research has been done on the construct of ATIC since its development in the early 1990s. For example, ATIC has been found to positively relate to performance in numerous selection settings such as assessment centers, interviews, and integrity tests (Kleinman, 1993; Konig, Melchers, Kleinman, Richter, & Khele, 2007; Konig, Melchers, Kleinman, Richter, & Khele, 2006; Konig & Khele, 2004). In other words, those who are able to identify the criteria of the selection procedure perform better on said procedure than those who are unable to correctly identify the evaluation criteria. Additionally, ATIC has been shown to have cross-situational convergence, with ATIC in one selection exercise transferring to other similar and dissimilar exercises (Speer, Christiansen, Melchers, Konig, & Kleinmann, 2014).
Despite the amount of research that has already been conducted, there is still much to learn about how ATIC works and why it is predictive of performance. This paper will begin with a brief overview of employment interviews. Next the theoretical basis of ATIC and its connection with interviews and future job performance will be discussed. Further, possible moderators of the ATIC-interview performance relationship, such as attachment style, situational characteristics, self-monitoring, motivational factors, and situational characteristics will be discussed.
The interview is a selection procedure intended to help predict future job performance and behaviors based on the responses of the applicant, and is one of the most common personnel selection methods used in the workplace (McDaniel, Whetzel, Schmidt, & Maurer, 1994). Though interviews have been traditionally conducted face-to-face, technological advances have made it possible for other forms of interview settings, such as over the telephone (Oliphant, Hansen, & Oliphant, 2008) and computer-mediated video conferencing (Chapman & Rowe, 2002), to become more frequently used. With any type of interview setting, there are three main types of interview formats: structured, semi-structured, and unstructured. The more structured the interview, the more systematic the information collection, the questions being asked, and the response scoring. For example, structure can come in the form of asking the same questions to all interviewees, limiting prompting (or follow-up) questions, using behaviorally anchored rating scales, taking specified notes, and controlling for ancillary information (Levashina, Hartwell, Morgeson, & Campion, 2014).
Of interviewing formats in general, the structured interview stands out as the most reliable and valid (Levashina, et al., 2014). Structured interviews leave less room for interviewer differences and bias to occur. A structured interview differs from an unstructured in its ability to accurately focus on different constructs or criteria of interest to the employer, while also limiting interviewer bias that may come about when using an unstructured or semi-structured interviewing format. Structured interviews can vary in relation to the type of question that is being asked, with situational and past behavior type questions being the two most common.
Levashina et al. (2014) describes situational interview questions, based on goal setting theory, as those that ask applicants to respond to a hypothetical work-related situation under the premise that their answers show their intentions which, in turn, predict future performance. Past behavior questions, on the other hand, ask applicants to describe what they did in a previous work-related situation under the premise that past behavior will predict future performance. Meta-analytic studies have found that both situational and past behavior interview questions have criterion-related validity, but past behavior questions show slightly higher validity (Day & Carroll, 2003; Gibb & Taylor, 2003; Klehe & Latham, 2006). In addition to question type, researchers have also explored interview transparency as a possible way to increase construct-related validity.
Interview transparency is defined as “the extent to which the interview dimensions being assessed are made known to the applicants before or during the interview” (p. 250; Levashina, Hartwell, Morgeson, & Campion, 2014). In other words, an interview can be made transparent if the applicant is informed of what value/trait/characteristic is being assessed. Interview transparency can be thought of as existing on a continuum, where there are varying degrees of transparency ranging from fully transparent to nontransparent. For example, an interview can be nontransparent when the applicant is not cued in on what dimensions are being assessed, partially transparent when they are given general information on the dimensions being assessed, or more transparent when they are given detailed information about the dimensions.
Even though there is some evidence to support the idea that a more transparent interview leads to higher construct validity (Khele, Konig, Richter, Kleinmann, & Melchers, 2008), the predictive validity of performance is not better than non-transparent interviews, and low transparency may reduce faking behavior (McFarland & Ryan, 2000). Additionally, measuring an applicant’s ability to identify criteria, which has been found to relate to future performance, is more valuable when the selection procedure is non-transparent, as transparency eliminated the need to identify the criteria (Jansen et al., 2013; Khele et al., 2008).
The Ability to Identify Criteria
The ability to identify criteria has been a source of recent interest in personnel selection research. Kleinmann et al. (2011) define ATIC as “a person’s ability to correctly perceive performance criteria when participating in an evaluative situation” (p. 129). Their claim is that applicants actively strive for good evaluations in a personnel selection setting, and this striving should drive the applicants to actively try to discern what the employer is looking for in order to adjust their behavior appropriately. Kleinmann et al. (2011) operationalize ATIC as the amount of correspondence between an applicant’s perceptions of what is being evaluated and the actual performance criteria in a selection procedure, which has been consensually determined by subject matter experts (SMEs). ATIC has also been explained in relation to an individual’s ability to read situational cues during the selection process. In particular, the cognitive-affective personality system theory (CAPS; Mischel & Shoda, 1995) has been used to conceptualize selection as a psychological situation in which participants differ in terms of their perception and interpretation of different situational cues (Griffin, 2014). CAPS theory argues that an individual’s perception of the available environmental cues guides their understanding and response behaviors. In short, ATIC pertains to the correct identification of selection criteria using available cues.
If an individual is unable to identify the criteria of the selection procedure, it may limit their ability to do well because they may incorrectly assume that an ineffective behavior is in fact effective. For example, if an applicant incorrectly assumes the interview question is assessing assertiveness when it is actually assessing cooperation, they will respond in a way that reflects how they are able to be assertive, which would lead to a lower score than if they were to respond in a way that reflects their ability to work well with others.
Research on ATIC has explored its use in various selection procedures including interviewing (Melchers et al., 2009), assessment centers (Kleinmann, 1993; König, Melchers, Kleinmann, Richter, & Klehe, 2007), personality tests (Khele et al., 2012), and integrity tests (König, Melchers, Kleinmann, Ritcher, & Khele, 2006). Results show that ATIC scores positively relate to selection test score, which means that high ATIC aids in performance in the selection procedures (Jansen, König, Kleinmann, & Melchers, 2012). ATIC scores have also been found to demonstrate incremental validity over other selection test scores, like cognitive ability, in predicting future job performance (Kleinmann et al., 2011).
To fully understand what ATIC is, it is important to understand how it is measured and scored. In general, after participants have completed the selection procedure, whether it be an interview or assessment center, they are asked what they believe the particular exercise or question was intended to assess. More specifically, there are a few commonly used methods for questioning participants about their impressions of what is being measured. One method involves an open ended question that has participant develop their own hypotheses and these hypotheses are then scored by trained assistants to determine their closeness to the actual target criteria. Another common method is similar in that it asks participants to develop their own hypotheses, but they are then given a list of dimensions they may have been used. Participants are asked to evaluate their original responses in comparison to the provided dimensions. More recently, Christiansen et al. (2012) developed a new method of scoring ATIC in which participants are asked to determine, from a list, which dimensions are relevant and which are not for each assessment center exercise.
Proper scoring is hinged upon proper analysis of the underlying constructs, which is typically done by SMEs. These SMEs must determine the target dimensions, and from there, other individuals can be trained on how to assess participants’ hypotheses against these target dimensions. In a low-stakes situation, individuals participating may score their own hypotheses, while in a high-stakes situation it is more common to have unbiased scorers. These scores determine whether each hypothesis corresponds to the target dimension (as determined by the SMEs) and to what degree. The postulation with the highest fit for the target dimension is used to compute an overall ATIC score, which is the average of all of the ratings across every interview question (or AC exercise, etc.) with a high score corresponding to higher ATIC (Melchers et al., 2009).
ATIC and Cognitive Ability
When taken at face value, ATIC may appear to be a form of general mental ability. Indeed, Schmidt and Hunter (1998) argued that performance in many selection tests is largely a function of cognitive ability, which would lead one further believe that ATIC, which corresponds with assessment performance, is just a measure of cognitive ability. To further this position, a meta-analysis found that general mental ability was correlated at .65 with overall assessment scores (Collins et al., 2003).
In support of this notion, Melchers and colleagues (2009) found that ATIC was positively correlated with cognitive ability. However, others have found nonsignificant or small effects in relation to cognitive ability and ATIC (Griffin, 2012; Melchers et al., 2012). Additionally, research has also indicated that while ATIC may be influenced by cognitive ability, it is able to predict performance even after controlling for cognitive ability (Konig et al., 2007). Therefore, while ATIC may be related to cognitive ability in some form, it is still distinct from it for it provides additional unique information in regards to participant performance.
ATIC and Interviews
ATIC levels are only useful when the selection procedure is non-transparent. This is because a transparent procedure puts all of the applicants on a level playing field, so to speak, because it is no longer necessary to discern important criteria as it is provided for them. In a non-transparent selection procedure, such as the structured interview, an applicant must go through a two-step process (Kleinman et al., 2011). Applicants must first recognize what is being measured, and then perform in accordance with their hypothesized criteria (Kleinman et al., 2011). Therefore, in an interview an applicant must first accurately decipher what the interview question is asking, and then craft an appropriate response that expresses the criteria they identified.
Research on ATIC has shown it is predictive of both selection assessment and future job performance. Kleinmann et al. (2011) postulate that ATIC predicts performance because those with higher ATIC scores are more able to show dimension-relevant behavior, which will lead to higher performance scores. Jansen et al. (2013) found that individual differences in situational assessment (i.e. ATIC) predicted performance in behavior-based selection procedures. Melchers et al. (2009) found that individuals with higher ATIC received better interview evaluations, and an interviewee’s ATIC accounted for a substantial amount of variance even after controlling for cognitive ability. Similarly, Oostrom and colleagues found that ATIC was related to performance in both the interview- and work-related situations. They posit that this relationship was due to the interviewee’s ability to read situational demands in varying social setting such as interviews and work contexts.
ATIC and Future Performance
In addition to identifying performance criteria in a selection interview setting, ATIC is hypothesized to influence performance on the job (Melchers et al., 2009). ATIC may be used to recognize job demands as they arise, which will lead to improved job performance. This will occur because individuals with high ATIC are able to accurately perceive situational cues in social context, whether that be a selection situation or on the job (Oostrom, Melchers, Ingold, & Kleinmann, 2016). In theory, individuals are motivated to identify the demands of the situation, and this is accomplished by evaluating the situational cues available. In an interview context, this occurs because individuals are motivated to actively identify and respond to the selection criteria in order to attain positive feedback (Kleinmann et al., 2011). Similarly, individuals are motivated to identify the demand criteria of a work situation in order to perform their tasks effectively, which may lead to positive performance evaluations.
Multiple studies have supported the hypothesized relationship between ATIC and job performance. For example, Khele et al. (2012) found that ATIC was related to job performance in 28 work simulations (4 high fidelity and 24 low fidelity). In a field-based study, it was found that an interviewee’s ATIC scores predicted job performance as rated by their supervisors (Ingold, Kleinmann, Konig, Melchers, & Iddekinge 2015). This positive relationship to future job performance makes measuring ATIC beneficial for finding important predictive information independent of test performance.
Moderators of the ATIC-Performance Relationship
Although ATIC has been shown to be predictive across different selection contexts (interviews, assessment centers, personality tests, integrity tests) (see Khele et al., 2012; Konig, Melchers, Kleinmann, Ritcher & Khele, 2006; Melchers et al., 2009), it is still unclear how ATIC relates to other constructs, and what mechanisms contribute to its predictive ability in these selection situations. More specifically, while the relationship between ATIC and interview performance has been well established, potential moderators should be explored in order to further our understanding of the construct of ATIC and why it is predictive of performance.
The application of attachment theory to the workplace is a fast-emerging topic of interest in the field of industrial and organizational psychology. Some work has already been done to explore the possibility of its connection to various theories in the work place. This includes studies on attachment and transformational leadership (Popper & Mayseless, 2003), group processes (Rom & Mikulincer, 2003), mentoring (Wang, Greenberger, & Noe, 2009), Workaholism (Tziner & Tanami, 2013) leaders as attachment figures (Davidovitz, Mikulincer, Shaver, & Popper, 2007), individual work behavior (Richards & Schat, 2011), and leader member exchange (Richards & Hackett, 2012) to name a few. Each of these studies draws parallels between adult attachment style functioning and how it might affect different relationship within the workplace. Although attachment research has already begun to expand to the field of IO psychology, there have been no studies (to my knowledge) that examine the effect attachment styles might have on the interview process and outcomes.
An anxious attachment style results from inconsistent responses from a parent or attachment figure, in regards to their infant’s signals of distress. When interacting with the infant, the parental figure may be overly intrusive sometimes, and absent or unavailable at other times (Hazan & Shaver, 1990). The infant will learn that their attachment figure is inconsistent and unpredictable. This leads to overdependence on others stemming from a negative self-view, in which they believe they are the reason for the mixed responses (Mikulincer & Shaver, 2005). As an adult, this negative view of self will translate into self-blame when failure occurs. An anxiously attached adult will tend to have an intense fear of rejection, jealousy or fear of abandonment, a preoccupation with relationships (Brennan et al., 1998), and a tendency to ruminate on distress and negative emotions (Mikulincer & Shaver, 2007).
In an interview context, anxious attachment may moderate the relationship between ATIC and interview performance, such that higher anxious attachment scores will strengthen the relationship. I speculate that this may be due to anxious individuals’ fear of rejection by others (Brennan et al., 1998). This fear may cause the individual to attempt to impress the interviewer by putting their “best foot forward” in order to avoid being rejected. Hazan and Shaver (1990) found that anxiously attached individuals’ main motivation might be to gain respect and admiration from others in the workplace. They will work hard to please others, such as supervisors or interviewers, because they are sensitive to others’ negative evaluations of them (Fraley, Niedenthal, Marks, Brumbaugh, & Vicary, 2006). This behavior may be present in the interview process as the anxiously attached applicant strives to achieve a positive evaluation from the interviewer, and a subsequent job offer. Because of their strong tendency to desire the acceptance of others, if an anxiously attached interviewee is able to figure out the selection criteria of the interview question, they will try harder to act on those identified criteria in order to please the interviewer. This will help them to better convert their ATIC into effective interview performance.
Future research should examine whether anxiously attached individuals have higher vigilance to cues relevant to appraising and monitoring the emotion and responsiveness of others, as this may be a potential mechanism to explain potential the moderating effect of anxious attachment. Fraley et al. (2006) found that anxiously attached individuals are more attuned to the emotional expressions of others, and are better able to perceive changes in emotional expression than others. This may help them better apply their ATIC to obtain higher interview performance scores.
CAPS theory posits that beheviors are a result of activated cognitive scripts which reflect the individual’s perception of the situation and cues available (Mischel & Shoda, 1995). Using this as a framework, future studies should test whether different interview-specific situational aspects (i.e. cues) effect the relationship between ATIC and interview performance.
Adjustments made to the number of situational cues available may moderate the relationship between ATIC and interview performance. This is because the more cues that are available may allow individuals with lower levels of ATIC to also identify the criteria, which will reduce the predictive power of ATIC. However, if these additional situational details also included irrelevant information, the relationship may be strengthened as only those with high ATIC would be able to identify the criteria by using the appropriate situational cues while ignoring the irrelevant ones. Future research should examine the impact of different situational cues by examining controllable variables such as the amount of time spent building rapport before an interview, the details provided in the question, the amount of probing questions asked, the environment of the interviewing room, and the type of interview (i.e. over the phone, in person, or computer mediated).
Another potential research avenue to explore the effect of situational cues available is by comparing the average ATIC of interviewees who are subjected to face-to-face interview to those of interviewees who engage in a computer mediated interview. As these different formats may vary in their amount of richness. Media richness theory (Daft & Lengel, 1986) asserts that communication media vary in their amount of inherent richness, with face-to-face being the richest medium of communication because of the verbal (the words used), nonverbal (facial expressions, etc.), and paraverbal (vocal tone, etc.) cues available. A computer mediated interview, on the other hand would be reduced in richness because there are no longer nonverbal or paraverbal cues available to the interviewee; therefore, they may have a more difficult time identifying criteria.
Another potential moderating variable of the ATIC-interview performance relationship is motivational factor. In a high-stakes situation, such as an employment interview, interviewees may be motivated to do well for a variety of reasons, such as the desirability of the position, the fear of rejection, or societal pressures (i.e. need for power, money, or prestige). This motivation will be the driving force behind them wanting to identify the criteria in order to obtain their desired outcome (such as getting the position). These different motivational factors may affect the ATIC-interview performance relationship in a variety of ways at differing levels. For example, an individual who is in need of a job because they are on the brink of bankruptcy, may be more motivated to identify the criteria of the selection procedure than an individual who is driven by the desirability of the position. In this example, the individual who is driven by the need for money (i.e. bankruptcy) may view obtaining this position as a solution to their current largest problem (i.e. financial woes). On the other hand, the individual who is driven by the desirability of the position will also want to get hired, but they (with all else being equal) do not need the position as badly as other equally desirable positions may exist, and thus they may not work as hard to identify the criteria and respond appropriately.
This can be explained within the framework of Vroom’s (1964) expectancy theory, which states that the extent to which an individual is motivated to engage in a course of action is dictated by the multiplicative product of the situational judgments of outcome desirability, task-specific self-efficacy, and the perceived correspondence between task performance and a desired outcome (valence, instrumentality, and expectancy judgements). Thus one could use Vroom’s theory to predict an applicant’s ability to convert their ATIC into an appropriate response.
While there has been research conducted on ATIC in both low-stakes (i.e. lab study typically done with college students), artificial high-stakes (i.e. lab studies with a deception component relating to a job opportunity), and real world high-stakes (i.e. selection process) situations, there has been no direct comparison between these different settings. This situational stakes comparison deserves further interest as it may act as a moderating variable because of the motivating (or lack there of) component involved.
For instance, participants in a study on ATIC for class credit may not be motivated to attempt to accurately identify criteria because doing so would not gain them any benefits. Additionally, these same participants may lack the focus needed to utilize their ATICs because they are able to let their minds wander to other matters as there is no negative consequence for not doing well on the simulated selection procedure, and therefore they do not need to focus their full attention on it. Therefore, while they may have a high ATIC, they may lack the motivation required to transform their identified criteria into appropriate responses. On the other hand, if the study is conducted in a real world selection context, there is the added motivational component of the opportunity to get hired for the position being selected for. This may motivate the participants to focus their full effort on identifying the criteria and crafting appropriate responses based on the criteria they identified. Thus, the stakes involved in the situation may moderate the relationship between ATIC and interview performance through the mechanism of effort put forth based on the individual’s motivation to do well.
Self-monitoring includes being aware of social cues and being willing to respond appropriately to them with a particular interest in meeting the expectations of others (Snyder, 1974). When assessing self-monitoring, individuals are sorted into a category of “high self-monitoring” or “low self-monitoring” based on their responses to a self-monitoring inventory (Snyder and Monson, 1975). According to Snyder, individuals who are considered high self-monitors are able to fully contemplate social situations as they related to their self-presentation, and respond appropriately.
The concept of self-monitoring seems similar to ATIC when taken at face value, but there are two major differences between the two. One is that self-monitoring requires a strong motivational component because it is concerned with status and meeting expectations, while ATIC is an ability which implies there is no motivational requirement. The second is there is not a significant correlation between the two, which shows that they are conceptually distinct constructs (Klehe et al., 2011). However, Khele and colleagues measured general self-monitoring and did not measure self-monitoring specific to the selection context.
Individuals who do not typically engage in general self-monitoring may be motivated to do so during a selection context, such as an interview, because of their desire to get selected for the position. If an individual engages in interview specific self-monitoring, when they do not engage in self-monitoring on a general basis, they will need to devote more cognitive resources to their self-presentation, which may not leave them with enough cognitive resources to respond in alignment with their identified criteria. This is because humans have a limited amount of cognitive resources to allocate to tasks, so when the cognitive load increases (by adding more tasks or increasing the difficulty) task performance declines in one or all of the tasks being performed (Norman & Bobrow, 1975). So, it follows that individual who devote their cognitive resources to self-monitoring will be more focused on regulating their self-presentation than on adequately answering the specific interview questions being asked. If, in this same situation, that individual decides to designate cognitive resources toward the application of their ATIC, then their visual performance (face-to-face appearance) will suffer, and this could also affect their overall interview performance rating by biasing the interviewer’s opinions of them. Therefore, future research should assess whether the difference between interview specific and general self-monitoring moderates the ATIC-interview performance relationship.
In line with the impact of limited cognitive resources discussed with regards to self-monitoring, interview specific rumination may be another individual difference variable that may moderated the ATIC-interview performance relationship because of an individual’s limited resources. Rumination is defined as a “cognitive process characterized by thinking about concerns and problems in unproductive, repetitive ways, and experiencing difficulties terminating these chains of thought” (Jong-Meyer, Beck, & Riede, 2009). It is a fixation on problems caused by distress, which interferes with more productive actions that could be occurring. Rumination has been found to correlate with a range of maladaptive cognitive styles such as pessimism, self-criticism, and neediness (Ciesla & Roberts, 2002). In other words, ruminative thoughts have a tendency to be negative in nature. These pervasive thoughts tend to consist of being focused on past events and wondering why they happened, or what could have been done differently.
Response styles theory (Nolen-Hoeksema, 1991) describes rumination as prolonging distress through making it more likely that people will use negative thoughts and memories to understand their current circumstances, and they will have more pessimistic and fatalistic thinking. Individuals who are prone to ruminate will seek to make sense of their current situation through a negative lens of past situations that, in their opinion, did not go well. The tendency to ruminate has also been found to remain relatively stable over time (Bagby, Rector, Bacchiochi, & McBride, 2004), and to lead to less confidence in solutions during problem solving tasks (Lyubomirsky, Tucker, Caldwell, & Berg, 1999).
In an interview context, I believe this will manifest in the form of the applicant spending time thinking of how they could have answered the previous question(s) better instead of focusing on adequately answering the current question being asked, so they will not be using their high levels of ATIC (if available) to their advantage, which will drive their interview rating down. They will ruminate on their responses, by thinking of alternative ways they could have answered the previous question(s) instead of concentrating on sufficiently answering the current question being asked. Because of this, their performance will suffer.
This rumination will use the individual’s limited cognitive resources, which will leave little for them to use to attempt to craft answers that model the selection criteria they have identified. This is because humans are unable to carry out more than one cognitively demanding task at the same time without declines in performance in at least one of the tasks (Kahneman, 1973). Therefore, engaging in rumination during an interview will lower the individual’s capability to translate the selection criteria they have identified into suitable interview responses.
For example, in an interview context, an individual with low ATIC’s interview performance will be lower regardless of their level of rumination. However, for individuals with high ATIC, their performance is also determined by their level of rumination, where more higher levels of rumination will weaken the ATIC-performance link because it interferes with the ability to translate their high ATIC into appropriate interview responses.
While not a potential moderating factor, abstraction ability may help explain the ATIC-interview performance relationship by being an antecedent of ATIC. Abstraction can be defined as “the process of extracting the core meaning and central aspects of whatever one thinks about by peeling away peripheral, less essential aspects of the object of thought”(p.123; Weisner, 2015). Construal level theory (CLT) argues that the process of abstraction allows individual to direct their thoughts at actions, object, persons, or situations that are outside of their direct experience (Liberman & Trope, 1998). The resulting mental representations of thought will affect and individual’s predictions, evaluations, and actions (Liberman & Trope, 2008). According to CLT, a lower-level of construal is concrete representations that include incidental features of events, while a higher-level construal is abstract representations that emphasize relatively invariant features (Halamish, Borovoi, & Liberman, 2017). The way in in which an individual construes an object affects their judgment and decision making in relation to that object (Ho, Ke, & Liu, 2015).
In an interview context, a low level of construal may interpret the interview question in a more concrete, literal way, while a higher level of construal may lead an individual to view the question more abstractly. This abstraction may lead an individual to be better able to identify the underlying criteria. For example, if a situational interview question describes a situation where they have made a mistake, an individual who construes this question at a lower level might respond with their immediate actions to remedy the mistake, while an individual who construes the question at a higher level might view the issue in more abstract terms of personal accountability (the criteria being assessed), and then respond accordingly. Future research should examine how extant research on CLT and decision making may apply to an individual’s use of ATIC during an interview.
Effective employee selection is paramount to organizational success as employees play a vital role in many desirable organizational outcomes. As such, identifying variables that aid in the predictive validity of selection measures is of principal importance. One such factor is the ability to identify criteria, which is an applicant’s ability to accurately identify what the selection procedure is actually measuring. ATIC has been shown to positively to related to performance in many selection settings such as interviews, assessment centers, personality tests, and integrity tests.
Much research has been done on the construct of the ability to identify criteria since its development in the early 1990s. However, there is still much that is not yet understood. Future research should examine potential moderators of the ATIC-interview performance relationship, such as situational characteristics, selection stakes, motivational factors, and individual differences such as attachment style.
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