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Abstract

The current meta-analysis provides a comprehensive and updated review on integrity-testing findings across industries and countries in the past 50 years (k = 150, N = 67,016). Integrity tests were coded into the types of overt tests, covert tests, biodata, organizational measures, value/moral reasoning/situational judgment tests, integrity-related cognitive ability tests, and novel measures. The criterion measures of workplace deviance included CWBs, unethical pro-organizational behaviors, and other workplace deviant behaviors. For the information source, both computer and manual searches were performed to locate relevant published and unpublished papers. A variety of sources were examined to avoid publication bias, and publication bias analyses were conducted to uphold the methodological rigor. Results indicated that all the integrity tests analyzed were significant in predicting workplace deviance, with an overall mean validity estimate corrected for indirect range restriction and measurement error as .43 (95% CI [.32; .52]; p < .001). Among the tests, the value-oriented tests and cognitive ability tests indicated relatively large validity estimates of .60 (95% CI [.41; .75]; p < .001) and .65 (95% CI [.53; .74]; p < .001), respectively. The relationship between integrity tests and workplace deviance was found to be significantly moderated by the type of integrity test, industry, country, and criterion source. The effect size of integrity tests was largest in predicting deviance in the military and law enforcement sector, and relatively large in the work samples of Canada, Germany, Israel, Romania, and the United States. However, the moderating effects of the nature of deviance, validation sample, validation strategy, publication status, medium of test, and gender, were nonsignificant. Compared with previous reviews, our study was unique in its cross-cultural direction, which included primary studies of integrity testing in countries with different languages (e.g., publications in Chinese) and associated cultural variations. New insights and comparisons with previous meta-analytic findings were discussed.

Keywords

integrity testing psychological assessment preemployment selection cross-cultural counterproductive work behaviors unethical pro-organizational behaviors workplace ethics meta-analysis

Introduction

Are integrity problems and deviance in the workplace due to “bad apples” or “bad barrels”? According to the interactionist model, there is no simple answer to this question, nor should the question be a simple “either-or” (e.g., Ashkanasy et al., 2006; Markowitz & Levine, 2020). The “bad apple” argument attributes workplace deviant behaviors to dispositional and individual factors (Carless et al., 2007; DeShong et al., 2017; Paulhus & Williams, 2002), while the “bad barrel” argument explains the issues by situational and organizational characteristics (Crelinsten, 2003; Gioia, 1992; Hochstetler et al., 2002; Zimbardo, 2004). Different integrity tests based on varying assumptions and models have thus been developed according to different schools of thought (e.g., “the bad apples,” “the bad barrels” or their interplay such as “a bad apple spoils the barrel,” etc.) for predicting integrity problems and deviant behaviors of employees in an organization (Baloch et al., 2017; Barrett, 2001; Forsyth et al., 2012). Despite the different and varying tests, very limited comparative research investigating the differential validities of integrity measures applied across industries and countries exists. There is also a paucity of research that includes Asian findings or studies that consider possible cultural differences when the tests are put into practice. Given such, this meta-analysis will systematically and empirically review the validity and utility of different integrity tests that predicted deviant work behaviors across industries and countries. Insights will be drawn from the past 50 years of research to provide implications and contributions to empirical and practical developments in the domains of psychological assessment and workplace ethics.

Integrity testing and workplace deviance

Given the multiple types of integrity tests that were constructed according to different assumptions and theoretical beliefs stemming from the never-ending “bad apples–bad barrels” debate (May & Van Niekerk, 2019; Monga, 2016), the development of integrity testing for accurate measurements of integrity in the workplace has thus become complex and multidimensional (e.g., Palanski & Yammarino, 2009; Wanek, 1996) In fact, the multitude and variety of integrity tests available in the field reflects the importance and recognition of integrity testing for preemployment selection and talent development (Brown et al., 2019). Today, integrity testing is a widespread and substantial tool used in a broad spectrum of jobs and industries (e.g., Blumen et al., 2017; Byle & Holtgraves, 2008) for reducing the risk of hiring deviant employees that prone to counterproductive work behaviors (CWBs) (Byle & Holtgraves, 2008; Fine, 2012; Kuhn, 2020), unethical pro-organizational behaviors (UPBs) (Castille et al., 2018; Fehr et al., 2019), and certain integrity problems (Marcus et al., 2007; Ones et al., 1993; Schmidt et al., 2016). Nevertheless, the research effort spent on investigating the predictive validity of integrity tests specific to negative work performance has not kept pace with the prevalence in practice when compared with the mass publications on predicting overall job performance. Given such a knowledge gap, the focal criterion on workplace deviance instead of general job performance in this study aims to align with the recent shift in both research and practice (e.g., Kabiri et al., 2021; Mackey et al., 2021). Our study will thus focus on the differential predictive validity of multiple integrity tests on various workplace deviant behaviors.

Different typologies of deviant workplace behaviors and the focus on organizational workplace deviance as the criterion measure in this study

Workplace deviance is generally defined as “voluntary behaviour that violates significant organizational norms and in so doing threatens the well-being of an organization, its members, or both” (Robinson & Bennett, 1995, p. 556). Sometimes the term “workplace deviance” is used interchangeably with “counterproductive work behaviors.” Yet despite the big overlap between the two terms, they are not identical (Bennett & Robinson, 2000; Hollinger, 1986; Sackett et al., 2006). In particular, there is a subset of workplace deviance that is described as “unethical pro-organizational behaviors” and is not subsumed under CWBs, but represents intentional deviant behaviors that are believed to do good for the organization (Umphress et al., 2010). Employees engaging in UPBs might commit to a pro-organizational clause up front, but would still end up with adverse consequences that harm the organization, such as the loss of reputation and lawsuits (e.g., noble cause corruption; withholding negative information about a product or service of the company with an intention to safeguard the organizational reputation) (Bolino & Grant, 2016).

Workplace deviance would thus be better and more broadly defined with different typologies to classify deviant and counterproductive behaviors. One of these typologies is the four-class typology of work deviance, which includes production deviance, property deviance, political deviance, and personal aggression (Bennett & Robinson, 2000). There is also a five-dimension typology of workplace deviance, which includes CWBs on abuse against others, production deviance, sabotage, theft, and withdrawal (Spector et al., 2006). Dalal (2005) proposed an 11-dimension typology to further classify deviant work behaviors (Dalal, 2005). Additional dimensions proposed by other researchers include task-related or non-task-related dimensions and legal or illegal dimensions (Bowling & Gruys, 2010).

According to another two-dimensional model proposed by Bennett & Robinson (2000), workplace deviance can also be categorized into two types, including “organizational workplace deviance” (OWD) and “interpersonal workplace deviance” (IWD). OWD refers to misbehaviors that are destructive to the organization and violate organizational norms, such as absenteeism, presenteeism, time banditry, theft, knowledge sabotage, cyberloafing/cyberslacking, and damaging company property (Bennett & Robinson, 2000; Malachowski, 2005; Mercado et al., 2018). IWD consists of deviant behaviors that hurt other employees and customers in the organization, such as bullying, harassment, violence, abusive acts, and workplace incivility (Arab et al., 2013; Bennett & Robinson, 2000; Milam et al., 2009).

In the present study, a broad definition of “workplace deviance” is adopted. OWD would be focused on as the criterion measure, with a consideration on the differential predictive validities of integrity tests on varying types of OWD. Nevertheless, the present study attempts to echo the two landmark studies previously mentioned and paint a clearer picture of workplace deviance with better specificity. Given such, IWD is excluded. Employee turnover, one of the criteria measures in Van Iddeginke's classic meta-analysis (2012a), is also excluded because it may not be a good indicator of organizational deviance due to various reasons. On the other hand, both CWBs and UPBs are included because of their nature of illegitimate and voluntary behaviors that violate organizational norms and impede organizational functioning, regardless of their anti-organization or pro-organization intention (Barling et al., 2009; Bowling, Burns, Stewart, & Gruys, 2011). In addition, new forms of OWD, including cyberslacking, which involves distraction and putting off work to “surf the internet” (O’Neill, Hambley, & Chatellier, 2014), are included in this meta-analysis to provide an updated picture of contemporary workplace deviance. Some other types of workplace misbehavior that are usually out of the scope of CWBs and UPBs, such as the violation of disciplines, are also included in the category of WDBs. Therefore, the criterion of OWD constitutes CWBs, UPBs, and WDBs in this study. The potential moderating effect of these three different natures of deviance will also be explored.

Different types of integrity tests with varying assumptions and focuses

Among the different types of integrity tests, the overt integrity tests (clear-purpose tests) and covert integrity tests (personality-based tests) are two essential types of tests that have been applied across settings since the 1980s (Frost & Rafilson, 1989; Sackett, 1994; Sackett et al., 1989, 1998; Sackett & Harris, 1984). Overt (clear-purpose) integrity tests include questions that inquire about workplace deviant attitudes and behaviors directly and explicitly with assumptions about both “apple” and “barrel” theories (Cunningham et al., 1994; Nicol & Paunonen, 2002; Sackett & Harris, 1984; Wanek, 1999). Covert (personality-based) integrity tests are personality-oriented trait tests that assess psychological characteristics and traits related to a broader range of CWBs, with an inclination to the “apple” side that attributes behaviors to dispositional factors (Alliger & Dwight, 2000; Byle & Holtgraves, 2008; Neuman & Baydoun, 1998; Sackett et al., 1989).

However, given the multifaceted nature of integrity and deviance, there exist other types of integrity measures with different focuses in practice that have received increasing attention in recent years. Some major types of these integrity measures included biodata (Aita et al., 2018; McDaniel, 1989), vetting (Delarosa, 2015; Jeske et al., 2019; Minor et al., 2018), integrity-related cognitive ability tests (Dilchert et al., 2007; McHenry et al., 1990; Oppler et al., 2013), value-oriented tests (Grabowski et al., 2019; Matherne III & Litchfield, 2012), moral reasoning tests (Fisher et al., 2021; Galić, 2016), situational judgement tests (de Leng et al., 2018; Husbands et al., 2015; Olaru et al., 2019), organizational measures for person–job fit or person–organization fit (Dalal et al., 2020; Diefendorff & Mehta, 2007; Fine & Edward, 2017; Kong, 2016; Zhang et al., 2019), and other novel measures (Cohen et al., 2016; Halliday, 2019; Ion et al., 2017).

These relatively nonconventional types of integrity measures have brought new perspectives to examine different dimensions of the construct of integrity. For instance, integrity-related cognitive ability tests (e.g., Tureary, 1999; Wonderlic, 2002) were used to investigate the linkage between cognitive ability and workplace deviance. It was found that cognitive ability could be a “forgotten trait (intelligence)” that relates to workplace deviance as job applicants with a higher cognitive ability were found to engage in CWB less frequently (Dilchert et al., 2007). It was explained that integrity-related cognitive ability may be linked to deviant work behaviors in two ways (Dilchert et al., 2007). First, it was hypothesized that employees with higher intelligence would have better foresight in inhibiting their CWBs for long-term benefits, and vice versa for employees with lower levels of cognitive abilities, who might be less thoughtful and more impulsive (Gottfredson, 1998). Another chain of reasoning hypothesized that lower cognitive abilities would be associated with lower education attainment, which makes an individual prone to unfavorable jobs, status, and monetary compensation within an organization. In many cases, this would then be followed by frustration and perceived unfairness in these employed individuals, which may then lead to retaliatory behaviors against the organization (Lau, Au, & Ho, 2003; Skarlicki & Folger, 1997). This presented an additional utility of cognitive ability testing in the workplace as it has long been used for predicting overall job performance mostly with the well-known g (general cognitive) ability (Schmidt & Hunter, 1998; Schmidt et al., 2016).

Likewise, specific to the needs of integrity assessment in some professions, value-oriented tests that comprised of moral reasoning and integrity-based situational judgement tests have been gaining in importance. These tests aimed to assess the inner values and beliefs of job applicants and incumbents to uphold professional work ethics (de Leng et al., 2018; Husbands et al., 2015). They not only captured a new dimension core to the integrity construct, but also have the advantage of easy administration in groups with empirically supported predictive validity (Becker, 2005; de Meijer et al., 2010). While value-oriented measures tend to investigate the “inners,” integrity-oriented organizational measures look at both personal and situational factors in affecting workplace deviance. Some organizational measures aimed at evaluating the degree of person–job fit (Fine & Edward, 2017; Iliescu et al., 2015; Johnson & Johnson, 1996, 2000), whereas others examining person–organization fit (Cable & Judge, 1996; Colquitt, 2001; Dalal et al., 2020; Di Stefano & Scrima, 2016; Furnham & Gunter, 2015; Rahim et al., 2001; Spector & Jex, 1998). These integrity-oriented organizational measures adopted the interactionist assumption in associating integrity testing and workplace deviance.

Apart from the above, there were integrity measures developed in response to contemporary occupational needs with unique perspectives. For example, there has been a trend on “cyber-vetting” in which some employers might evaluate applicants’ social media information (e.g., Instagram, Facebook) as integrity checking to inform personnel decisions (Cooper et al., 2020; Delarosa, 2015; Ghoshray, 2012; Jeske et al., 2019). In addition, there were some relatively novel integrity measures developed in the recent decade, which included the assessment of grit (Duckworth et al., 2007; Duckworth & Quinn, 2009; Ion et al., 2017), guilt-proneness (Cohen et al., 2014, 2016), core self-evaluations (Debusscher et al., 2016; Judge et al., 2003; Kluemper et al., 2019), emotional intelligence (Greenidge & Coyne, 2014; Greenidge et al., 2014; Ma & Liu, 2019; Wong & Law, 2002), attachment style (Brennan et al., 1998; Richards & Schat, 2011), insomnia (Yuan et al., 2018), music preference (Halliday, 2019), and time perspective (Carelli et al., 2015; Wojtkowska et al., 2019; Zimbardo & Boyd, 2015),

A detailed list of integrity tests included in the current meta-analytic review is shown in Table S1. Biodata measures are not listed because they are usually based on the generic measures of background checking and resumes of applicants and incumbents.

Literature review on the use of integrity tests for predicting workplace deviance

According to the two classic empirical studies on the validities of different selection methods conducted by Schmidt and fellows (1998; 2016), integrity testing provided the highest incremental validity on predicting overall job performance when combined with a general mental ability test (composite validity of .78). The advantage of using integrity testing was further supported by Ones, Viswesvaran, Schmidt, and fellows (1993) when they consistently and systematically reviewed the empirical evidence of using integrity tests to predict both productive and counterproductive job performance (Ones et al., 1993, 1996; Schmidt & Hunter, 1998). According to Ones et al.’s landmark meta-analysis of integrity tests (1993), they reported an overall validity (corrected for predictor range restriction and criterion unreliability) of .34 and .47 for integrity tests to predict job performance and CWBs, respectively. Specifically, the predictive validity of integrity tests on CWBs was reported to be substantial, with a true validity estimate of .55 for overt integrity tests and .32 for covert integrity tests. These findings provided the foundational support for the effectiveness of integrity tests in predicting overall job performance and CWBs (e.g., theft, disciplinary problems, and absenteeism). As expected, this study was well-cited by integrity test providers for evidence-based practice.

Van Iddekinge's team conducted another meta-analysis (2012a) and found considerably lower criterion-related validity of integrity tests on predicting job performance and CWBs in comparison to the data reported by Ones et al. (1993). In Van Iddekinge et al.'s meta-analysis (2012a), the overall reported validity was .15 for job performance and .32 for CWBs after corrected for measurement error. Their results reported a corrected validity estimate of .18 for job performance and .36 for CWBs. In addition, the corrected (for measurement error) validity estimate was .38 for overt tests and .27 for covert tests (Van Iddekinge et al., 2012a). Given such, Van Iddekinge et al. (2012a) expressed concern about the possible inflated validity estimates of integrity testing and offered cautionary conclusions and suggestions for further examination on the predictive validity of integrity assessment.

Inferring from these different findings on the varying predictive validities of integrity testing, Sackett and Schmitt (2012) concluded the need for more specific research questions and evidence to shed better light on how integrity testing would function. In particular, there has been a call for moderator analyses on this aspect to investigate potential moderators of this integrity testing–performance/deviance linkage.

Potential moderators

Following Sackett and Schmitt's (2012) views and the issues raised in the “Ones–Van Iddekinge dialogue,” the current meta-analysis will examine a comparable set of moderators and add four additional ones (industry, country, medium of test, and gender) that merit investigation. This study will also adopt the research practices that have been less debated on from each previous study to provide better insight into the issues identified in the field.

In Ones et al.’s research (1993), the moderators of predictor type (type of integrity tests, overt vs. covert), admission criteria (self-admission vs. external measure), breadth of criteria (narrow vs. broad counterproductivity), validation strategy (concurrent vs. predictive), and validation sample (applicants vs. employees) were investigated. They found that overt integrity tests appeared to be better at predicting counterproductivity than covert integrity tests. The conclusion, however, was deemed premature because other potential moderators were not examined. For example, it was reported that criteria breadth might be a moderator with a higher predictive validity on narrow criteria, while admission criteria might not. It was also possible that validation strategy could be a moderator, in which concurrent validity would be higher than predictive validity in the integrity tests included in the research. Moreover, the validation sample was found to be a moderator, with larger validity estimates found in employee samples than applicant samples.

Most of these moderators were also investigated in Van Iddekinge and his team's research (2012a), and they also examined publication status (published vs. unpublished studies) as a moderator. Consistent with Ones’s findings (1993), Van Iddekinge's study (2012a) found that overt tests had higher corrected validities in predicting counterproductivity than covert tests. In addition, validation strategies of concurrent designs reported larger validities than predictive designs. Likewise, larger corrected validities were indicated in incumbent samples than applicant samples (Van Iddekinge et al., 2012a). However, different from Ones's meta-analysis, Van Iddekinge's team reported a larger corrected validity for broad criteria measures than narrow ones. They also found admission criteria to be a significant moderator, in which self-reported counterproductivity had larger validity estimates than external measures. Regarding the moderator analysis of publication status, they found that the corrected validities from published studies were somewhat larger than those from unpublished studies.

In the current meta-analysis, moderators examined in the previous two meta-analyses and potential new moderators will be investigated. The first research focus will be on comparing our findings on the effects of those previously studied moderators with the results in Ones et al. (1993) and Van Iddekinge et al.'s (2012a) reviews. Considering the inclusion of relatively new studies from across different countries and industries, as well as the increase in integrity assessments conducted via a computer-mediated medium since the previous meta-analyses, additional moderator analyses will be carried out on the country, industry, and medium of test (computer-mediated test vs. non-computer test vs. mixed).

Specifically, as most integrity tests were developed in North American and European countries, it is speculated that integrity tests will have larger predictive validity in these sampling countries due to the more indigenous operationalization of the integrity constructs and measures in their “country of origin.” In addition, “industry” could be a potential moderator because industries with clearer guidelines and criteria for assessing integrity problems and deviant behaviors might enable easier measurement and prediction by integrity tests. An example could be the clear disciplinary guidelines in the military and law enforcement industry. Moreover, with newer findings on the possible gender differences in CWBs (Gonzalez-Mulé et al., 2013), the present study will look into gender as a possible demographic moderator. Likewise, the potential moderating effect of the “medium of test” will also be explored due to the trend of using computer-mediated tests.

Aiming to be one of the few updated meta-analytic studies on integrity testing that integrates findings across countries and industries, it is our hope that the addition of these new moderators would bring new insight to the field and increase the generalizability of findings across settings. A summary of the proposed moderators is shown in Table S2.

Objectives of the present study

With the objective to contribute to the body of knowledge pertaining to integrity testing in the workplace, we aim to review and provide a quantitative summary to the overall and differential predictive validities of various integrity assessments. By including previously analyzed moderators as well as newer and potential moderators in the analyses, the study would provide a clearer picture on the predictive validity of integrity tests in different working populations and contexts. In addition, this research endeavors to adhere to the emphasis on methodological rigor by correcting for statistical artifacts of measurement error and range restriction as discussed in the “Ones–Van Iddekinge dialogue.” Answers to the long-debated questions about the validity and generalizability of integrity testing that have been asked since 1980s may be found.

Practically, the present study would guide the evidence-based and ethical use of integrity tests in organizational practices for upholding professional work ethics when an absolute “pass or fail” does not exist for integrity assessment. By incorporating the results on integrity research in both the East and West in the past 50 years and a broader coverage on the types of integrity measures (e.g., novel measures) and criteria of workplace deviance (e.g., UPBs) across industries, a clearer picture on the use of integrity tests would be made more possible. To the best of our knowledge, this is the first meta-analysis on integrity testing that includes publications in Chinese with a consideration of the assumptions and implications of cultural differences as represented by different countries in the present study.

Method

Literature search

We examined a variety of sources to avoid publication bias. We performed computer and manual searches to locate relevant published and unpublished papers including journal articles, book chapters, technical reports documented by test publishers, theses and dissertations, working papers, and conference papers. The time frame for literature search was set from 1917 to November 2021 (if feasible in some databases for tracing back to 1917 at the time of World War I when psychological testing was first officially introduced in army recruitment; Terman et al., 1917). Among the searched papers, the earliest identified relevant paper was published in 1975. As such, the current study covers studies in the past 50 years that were available.

Computer search (based on combinations of keywords such as “integrity test / covert test/ overt test / biodata” and “deviance/CWB/ UPB/misconduct”) involved seven databases: APA PsycInfo, APA PsycArticles, MEDLINE via OVID, Social Science Citation Index, China Journal Net, CINAHL, and ProQuest Dissertations and Theses Database. It yielded 7,723 articles. Manual search involved an issue-by-issue search of papers published in 10 academic journals in the past 10 years. We also inspected the reference lists of selected papers and book chapters (e.g., Ones et al., 1993; Van Iddekinge et al., 2012a). Moreover, we contacted prominent scholars and active consultants in the concerned field as well as test publishers for unpublished papers and technical reports.¹ A manual search added 148 studies. Altogether, we located 7,871 studies. After applying some exclusion criteria (Figure S1 in the supplementary materials), we included 119 studies, which provided 150 independent samples with a pooled sample size of 67,016 (marked with an asterisk in the References section). The present meta-analysis included 97 new studies different from the two classic reviews.² Among these new additions, some of them covered novel measures and some were written in Chinese that represented more updated research in the past 10 years. This literature selection process, and details of the inclusion and exclusion criteria, are illustrated in Figure S1.

Coding scheme

Two authors coded the information on sample characteristics (sample size, gender, validation sample, sampling industry, and country), type of integrity test, validation strategy, criterion measure, medium of test, publication status, and effect size of each independent sample. Table S2 indicates the coding scheme and Table S3 shows the codes of the included studies. The initial rate of agreement on the coding between the coders was 96%, in which inconsistencies were resolved by discussion between the coders.

Meta-analytic procedures

Statistical analyses were performed by the psychmeta and metafor packages of the statistical program R (Viechtbauer, 2019), and the statistical software Comprehensive Meta-Analysis (CMA) Version 3. The observed validity coefficient(s) within each primary study was identified and regarded as the effect size statistics, in which most primary studies reported zero-order correlation(s) between the overall integrity test score and an overall criterion score. For studies that reported other varieties of effect sizes, such as t-value, z-score, β-weights, and chi-square (χ²), they were converted to Pearson's correlation coefficients (Cohen, 1992; Lipsey & Wilson, 2001). For samples reporting effect sizes between multiple scales of a particular integrity test and the criterion variable (e.g., correlations between each of the five scales of a Big Five personality test and the criterion of CWB), a single estimate was computed when standard deviations of these variables and the intercorrelations among these variables were available (Cheng & Chan, 2008; Hunter & Schmidt, 1990). Otherwise, a simple average was computed when such information was unavailable (Cheng & Chan, 2008).

Hunter and Schmidt's (2004) psychometric meta-analytic procedure was employed to correct the statistical artifacts of measurement error and indirect range restriction in order to obtain the “true” correlation between integrity tests and workplace deviance. Missing reliability coefficients of the variables were estimated and substituted by the sample size-weighted mean reliability of the multi-item measure of that variable across the samples (Cheng & Chan, 2008; Sverke et al., 2002). The mean reliability of multi-item measures of integrity test was .78, while the mean reliability for deviance measures was .83. After the correction for measurement error and indirect range restriction, 26 out of the 150 corrected validities were found to exceed the value of 1.0 resulting from the artifact adjustment. As there seemed no consensual way to adjust such effect sizes (Neuber et al., 2021; Wang et al., 2019), the original uncorrected, attenuated correlation of these 26 effect sizes were used for analyses as reference to some updated practices (Neuber et al., 2021). A 95% confidence interval was used for the estimated correlation coefficients.

In this meta-analytic review, the random-effects model was adopted and decided a priori as heterogeneity was expected owing to the variety of integrity tests applied across different industries and countries (Hunter & Schmidt, 2004; Hunter et al., 1990). The results of the random effects model are conservative in that their 95% Confidence Intervals (CIs) are often broad, thus reducing the likelihood of type-II errors (Cheung et al., 2012).

The presence of heterogeneity for identification of moderators was tested by the Cochran's Q homogeneity test (Cochran, 1954) and I² statistic (Higgins & Thompson, 2002) by the CMA statistical software. Moderator analyses were also performed by the same software. Abiding to the practice of reliable subgroup analysis, only the estimates of a subgroup that involved at least three independent samples was included (Cheng & Chan, 2008; Choi et al., 2015).

Publication bias analysis

Given the possible tendency to submit or publish studies based on the strength or direction of the results (Dickersin, 2005), the potential publication bias was examined by the funnel plot (Kepes et al., 2012), Egger's regression intercept (Egger et al., 1997), Begg's rank correlation test (Begg & Mazumdar, 1994), and the trim-and-fill method (Duval & Tweedie, 2000) conducted by the metafor package of statistical program R.

Results

Description of studies

Table 1 provides a summary of the characteristics of the 150 included samples, with the observed and corrected validities.

Table 1.

Overall and subgroup analysis of the moderating effect on the relationship between integrity tests and workplace deviance

Moderator	Subgroup	Q	k	N	ρ	95% CI	p-value
Overall effect^a	/	441397876.88**	150	67016	.43**	[.32, .52]	<.001
Integrity test type		24.40**	150	67016			<.001
	Integrity-related cognitive ability tests		5	7821	.65**	[.53, .74]	<.001
	Value, moral reasoning, situational judgement tests		8	2171	.60**	[.41, .75]	<.001
	Organizational measures		30	11193	.48**	[.28, .63]	<.001
	Covert tests		54	21380	.39**	[.31, .48]	<.001
	Overt tests		33	7944	.38**	[.31, .45]	<.001
	Novel measure		14	3324	.27*	[.039, .47]	.022
	Biodata		3	11941	.50	[−.25, .88]	.182
	Mixed measure		3	1242	.65**	[.43, .80]	<.001
Industry		15.49*	150	67016			<.05
	Military and law enforcement		24	29404	.50**	[.29, .66]	<.001
	Customer services, retail, and sales		24	6521	.47**	[.34, .58]	<.001
	Manufacturing		7	2094	.45**	[.21, .63]	<.001
	Business, banking, and finance		13	4272	.43*	[.16, .64]	.003
	General		73	22633	.40**	[.27, .52]	<.001
	Medical, pharmaceutical, and healthcare		6	997	.24**	[.17, .31]	<.001
	Education		3	1095	.36	[−.15, .72]	.17
Criterion source		6.87*	150	67016			<.05
	Employee record		24	25909	.48**	[.25, .67]	<.001
	Self-report		108	32727	.45**	[.34, .54]	<.001
	Other-report		18	8380	.22*	[.065, .37]	.006
Nature of deviance		1.25	150	67016			.534
	WDB		40	30689	.47**	[.30, .61]	<.001
	CWB		99	33810	.42**	[.30, .53]	<.001
	UPB		11	2517	.30*	[.01, .54]	.041
Country		17.35*	150	67016			<.05
	Israel		11	3860	.47**	[.38, .55]	<.001
	Germany		6	2028	.45*	[.18, .65]	.002
	Canada		8	2226	.44**	[.33, .54]	<.001
	United States of America		68	41384	.43**	[.31, .54]	<.001
	Romania		9	2280	.37*	[.08, .60]	.013
	China		10	1994	.28**	[.20, .35]	<.001
	Turkey		4	1298	.53	[−.20, .88]	.142
	United Kingdom		3	900	.13	[−.15, .40]	.36
	Others^b		28	10831	.49**	[.28, .65]	<.001
Validation sample		.00	150	67016			.983
	Applicants		37	18885	0.43**	[.36, .49]	<.001
	Incumbents		113	48131	0.43**	[.30, .54]	<.001
Validation Strategy		.25	150	67016			.619
	Concurrent		109	32327	.41**	[.35, .47]	<.001
	Predictive		41	34689	.47**	[.23, .65]	<.001
Publication status		1.07	150	67016			.302
	Published		141	64792	.43**	[.33, .53]	<.001
	Unpublished		9	2224	.32*	[.11, .50]	.003
Medium of test		4.32	150	67016			.115
	Computer-mediated tests		66	26069	.44**	[.28, .57]	<.001
	Non-computer tests		81	40136	.42**	[.30, .54]	<.001
	Mixed		3	811	.28**	[.17, .38]	<.001
Gender		.51	150	67016			.916
	Majorly male		37	17298	.45*	[.12, .69]	.010
	Mixed		39	11479	.44**	[.29, .56]	<.001
	Majorly female		33	11002	.41**	[.29, .52]	<.001
	Unspecified		41	27237	.41**	[.22, .57]	<.001

Note: Q = Cochran's Q homogeneity test (Cochran, 1954); ρ = validity estimate corrected for indirect range restriction and measurement error; 95% CI = lower and upper bounds of the 95% confidence interval for ρ; k = number of validity coefficients; N = sample size; WDB = workplace deviance behaviors; CWB = counterproductive work behaviors; UPB = unethical pro-organizational behaviors.

Twenty-six studies were found to have the correlation coefficients corrected for indirect range restriction and measurement error exceeded the value of 1.0. As such, the original uncorrected, attenuated correlation of these 26 effect sizes were used for analyses. The uncorrected estimated correlation coefficient of the overall effect (r_raw) is .29, p < .001. the estimated correlation coefficient corrected for measurement error only of the overall effect (r_c) is .33, p < .001. The proportion of variability due to heterogeneity between studies rather than sampling error (I²) is 100%.* p < .05; ** p < .001.

“Others” covered sampling countries of Australia, Barbados, Belgium, Colombia, Croatia, India, Italy, Korea, Mexico, Pakistan, Peru, Poland, Spain, South Africa, Thailand, The Netherlands, Ukraine, and the United Arab Emirates with two or less than two studies included in this review.

Overall validity of integrity tests for predicting workplace deviance

Figure S2 shows the forest plot of the pooled effect size and the effect sizes from the 150 samples, whereas Table 1 presents the meta-analytic validity estimates for integrity tests and the criteria of workplace deviance. Across the 150 independent samples, the overall observed mean validity estimate was .29, the mean validity estimate corrected for measurement error was .33, and the mean validity estimate corrected for indirect range restriction and measurement error was .43 (ρ = .43; CI [.32; .52]; p < .001). This indicated a statistically significant relationship between integrity tests and workplace deviance.

Moreover, heterogeneity was considerable in view of the significant Q statistics (Q = 441397876.88, p < .001) and high I² value of 100%, which suggested the relationship between integrity tests and workplace deviances was subjected to certain moderating effects. The statistics are reported in Table 1.

Moderator analyses

Table 1 presented the results of moderator analyses. Among the moderators examined by the subgroup analyses, it was indicated that the “type of integrity test,” “industry,” “country,” and “criterion source / admission criterion” were significant moderators. However, the “nature of deviance,” “validation sample,” “validation strategy,” “publication status,” “medium of test,” and “gender,” were statistically nonsignificant in moderating the relationship between integrity tests and workplace deviance. All subgroup analyses were based on the corrected validity estimates whenever applicable.

The significant moderating effect of the type of integrity test

The “type of integrity test” significantly moderated the relationship between integrity tests and workplace deviance (Q = 24.40, p < .001), in which the subgroup of “mixed measures” (k = 3) was excluded because the focus of the present study was on estimating the validity of using a single integrity test. Among the different types of integrity test included, except biodata, all of the tests showed significant validity for predicting workplace deviance but with varying effect sizes. When comparing the validity estimates of the examined test types, covert tests (k = 54; ρ = .39; CI [.31; .48]; p < .001) and overt tests (k = 33; ρ = .38; CI [.31; .45]; p < .001) showed comparable validity in associating with workplace deviance. Subgroup analysis also revealed the validity estimate was largest in the integrity-related cognitive ability tests (k = 5; ρ = .65; CI [.53; .74]; p < .001), followed by the second largest validity estimate of the value, moral reasoning, and situational judgment tests (k = 8; ρ = .60; CI [.41; .75]; p < .001) After that, organizational measures showed a significant effect size of .48 (k = 30; ρ = .48; CI [.28; .63]; p < .001). Remarkably, the validity estimates of value-oriented tests, cognitive tests, and organizational measures were all larger than the covert and overt tests. For novel measures, results indicated that this new test type had a significant effect size, but the corrected validity (k = 14; ρ = .27; CI [.039; .47]; p < .05) was smaller than other types of integrity tests. Further, the effect size of “biodata” was nonsignificant (k = 3; ρ = .50; CI [−.25; .88]; p = .18).

The significant moderating effect of industry

Subgroup analysis showed that “industry” was a significant moderator (Q = 15.49, p < .05) of the association between integrity tests and workplace deviance. For this “industry” moderator, the validity estimate was highest in the “military and law enforcement” industry (k = 24; ρ = .50; CI [.29; .66]; p < .001), which was followed by the “customer services, retail, and sales” industries (k = 24; ρ = .47; CI [.34; .58]; p < .001). The corrected validity indicated in the industry of “manufacturing” sector (k = 7; ρ = .45; CI [.21; .63]; p < .001), “business, banking, and finance” (k = 13; ρ = .43; CI [.16; .64]; p < .05), and “general” (k = 73; ρ = .40; CI [.27; .52]; p < .001), were also significant. Notably, the validity estimate of the “medical, pharmaceutical, and healthcare” industry was significant (k = 6; ρ = .24; CI [.17; .31]; p < .001), but the effect size was relatively small when compared to those in other industries. In addition, the effect size was found to be nonsignificant in the “education” sector (k = 3; ρ = .36; CI [−.15; .72]; p = .17).

The significant moderating effect of country

“Country” was found to be a significant moderator (Q = 17.35, p < .05). Among the countries included, the corrected validity estimates with significant effect sizes ranged from the smallest magnitude of .28 in China (k = 10; ρ = .28; CI [.20; .35]; p < .001) to the largest magnitude of .47 in Israel (k = 11; ρ = .47; CI [.38; .55]; p < .001). In-between were the significant validity estimates in ascending order of .37 in Romania (k = 9; ρ = .37; CI [.08; .60]; p < .05), .43 in the USA (k = 68; ρ = .43; CI [.31; .54]; p < .001), .44 in Canada (k = 8; ρ = .44; CI [.33; .54]; p < .001), and then .45 in Germany (k = 6; ρ = .45; CI [.18; .65]; p < .001). The corrected validity estimates of the “Others”³ subgroup was .47 (k = 31; ρ = .47; CI [.27; .63]; p < .001). However, the effect sizes found in the subgroups of Turkey (k = 4; ρ = .53; CI [−.20; .88]; p = .14) and United Kingdom (k = 3; ρ = .13; CI [−.15; .40]; p = .36) were nonsignificant.

The significant moderating effect of criterion source

The “criterion source” (admission criterion) that included the subgroups of “self-report” (k = 108), “other-report” (k = 18), and “employee record” (k = 24), was a significant moderator (Q = 6.87, p < .05). Specifically, the corrected validity estimate was largest in the “employee record” subgroup (ρ = .48; CI [.25; .67]; p < .001), which indicated the most profound effect of integrity tests on predicting the criterion of workplace deviance as measured by objective employee record. The “self-report” criterion source (ρ = .45; CI [.34; .54]; p < .001) and “other-report” criterion source (ρ = .22; CI [.065; .37]; p < .01) also indicated significant validity in moderating the integrity tests and workplace deviance relationship. However, the source of “other-report” showed the smallest validity estimate of .22 that ranged from .065 to .37 with a significant level at p < .01.

The nonsignificant moderating effect of the “nature of deviance”

The moderating effect of the “nature of deviance” (Q = 1.25, p = .534) was found to be nonsignificant. Subgroup analyses revealed the significant effect sizes of CWBs (k = 99; ρ = .42; CI [.30; .53]; p < .001) and WDBs (k = 40; ρ = .47; CI [.30; .61]; p < .001) as indicated by a validity estimate of .42 and .47, respectively. Remarkably, the validity estimate of UPBs was significant but smaller in magnitude with an effect size of .30 (k = 11; ρ = .30; CI [.01; .54]; p < .05).

The nonsignificant moderating effect of “validation sample,” “validation strategy,” and “publication status”

Results showed that the “validation sample” (Q = .00, p = .983), “validation strategy” (Q = .25, p = .619), and “publication status” (Q = 1.07, p = .302) were nonsignificant moderators of the relationship between integrity tests and workplace deviance. For the “validation sample,” the subgroups of “applicants” and “incumbents” indicated comparable and significant effect sizes. The mean validity estimate was .43 for both “applicants” (k = 37; ρ = .43; CI [.36; .49]; p < .001) and “incumbents” (k = 113; ρ = .43; CI [.30; .54]; p < .001). Similarly, for the “validation strategy,” the validity estimate was .41 for “concurrent study” (k = 109; ρ = .41; CI [.35; .47]; p < .001) and .47 for “predictive study” (k = 41; ρ = .47; CI [.23; .65]; p < .001).

For the subgroup analysis of “publication status,” the validity estimate was .43 for “published study” (k = 141; ρ = .43; CI [.33; .53]; p < .001) and .32 for “unpublished study” (k = 9; ρ = .32; CI [.11; .50]; p < .05). Although both groups showed significant effect sizes, it should be interpreted with caution when the number of included samples in the group of “unpublished study” was much smaller than those included in the “published study” group.

The nonsignificant moderating effect of “medium of test” and “gender”

Results showed that the “medium of test” (Q = 4.32, p = .115) and “gender” (Q = .12, p = .989) were nonsignificant in moderating the relationship between integrity tests and workplace deviance. Regarding the “medium of test,” the validity estimate was .44 for “computer-mediated test” (k = 66; ρ = .44; CI [.28; .57]; p < .001) and .42 for “non-computer test” (k = 81; ρ = .42; CI [.30; .54]; p < .001). The effect size of the “mixed media” subgroup was .28, which was also significant but smaller in magnitude (k = 3; ρ = .28; CI [.17; .38]; p < .001).

For “gender” subgroups, the validity estimates were .41 for “majorly female” group (k = 33; ρ = .41; CI [.29; .52]; p < .001), .45 for “majorly male” group (k = 37; ρ = .45; CI [.12; .69]; p < .05), .44 for “mixed genders” group (k = 39; ρ = .44; CI [.29; .56]; p < .001), and .41 for “unspecified” group (k = 41; ρ = .41; CI [.22; .57]; p < .001).

Publication bias analysis

Publication bias analyses revealed an asymmetric funnel plot and a significant p-value (p < .001) of the Egger's regression intercept that indicated potential publication bias. In addition, Begg's test reported a significant p-value (p < .001) that suggested potential publication bias. However, the trim-and-fill method suggested the imputation of 17 studies, with an adjusted effect size of .42 (p < .001) of integrity tests on workplace deviance. This value was comparable to the unimputed value. Overall, possible publication bias was noted but not a serious concern in the present meta-analysis.

Discussion

Inspired by a handful of important research in integrity testing, the present meta-analysis is the first of its kind to encompass a wide range of integrity measures for the workplace that have been used in the past 50 years. This study also covered a broader range of contemporary workplace deviant behaviors across industries. Compared with previous reviews, our study was unique in its cross-cultural direction, which included primary studies of integrity testing in countries with different languages (e.g., publications in Chinese) and associated cultural variations. We aimed to conduct a comprehensive and updated review on the relationship between integrity tests and workplace deviance, with a spirit to provide theoretical, empirical, and practical insights for research and application in the domains of psychological assessment and workplace ethics.

The overall relationship between integrity tests and workplace deviance only scratched the surface of the wide field of integrity testing

Our findings revealed a significant relationship between integrity tests and deviant work behaviors, indicating an overall corrected mean validity estimate of .43. Implying the predictive value of integrity tests on workplace deviance, the current results were in line with the corrected validities found in the landmark analyses done by Ones et al. in 1993 and Van Iddekinge et al. in 2012a, which was between .47 and .36, respectively. It is in this regard that the present findings provided an important puzzle piece in response to the “Ones–Van Iddekinge dialogue” on the controversial validity of integrity testing. Such empirical findings provided a theoretical implication of the value of integrity testing in looking at the dark side of workplace behaviors.

The overall analysis, however, only scratched the surface of the wide field of integrity testing. Deeper investigations by subgroup analyses indicated that the relationship between integrity tests and workplace deviance was moderated by the type of integrity tests, industry, country, and criterion source. However, such relationship was not moderated by the nature of deviance, validation sample, validation strategy, publication status, medium of test, and gender. These results implied the possibility of generalizing integrity testing in the prediction of various genres of workplace deviance across working populations of different genders, and media of testing. The results also revealed that the effect sizes of integrity tests were unlikely to be compromised by the validation strategy nor publication status. These are important practical implications for practitioners to consider when applying integrity testing in the workplace.

Cognitive, values, and organizational measures could outperform conventional covert and overt integrity tests in predicting workplace deviance

Examination of the significant moderating effect of different types of integrity tests revealed that integrity-related cognitive ability tests yielded the largest validity estimate in predicting workplace deviance. The findings supported the theories proposing that intelligence might affect someone's foresight in inhibiting deviant acts (Gottfredson, 1998), education attainment, or job choices as well (Lau et al., 2003; Skarlicki & Folger, 1997). It also demonstrated that cognitive ability, which is sometimes regarded as a “forgotten trait” when discussing misconduct and counterproductivity (Dilchert et al., 2007), may in fact be a factor that increases the proneness or prohibition of an employee's negative work behaviors (Gottfredson, 1998).

Second to cognitive measures, the type of value, moral reasoning, and situational judgement tests was also revealed to be a significant and strong predictor of deviant work behaviors. These value-oriented measures assessed the inner values and moral beliefs of their applicants or incumbents, including moral disengagement, ethical decision-making, and moral identity (Aquino & Reed II, 2002; Chen et al., 2016; Collins, 2009). Not being commonly used to assess the “bright side” of good job performance, these value-oriented measures could nevertheless be important predictors of the “dark side” of integrity-related workplace deviance. Indeed, the essence of integrity is “the quality of moral consistency, honesty, and truthfulness with oneself and others” (American Psychological Association, 2020). The emphasis on “consistency” of inner values defining “integrity” could be a reason to explain for the largest validities of value measures because deviant work behaviors would be more likely when there is incongruence between an employee's inner beliefs and the perceived organizational values.

Organizational-oriented measures ranked third as a predictor of workplace deviance. While values and cognitive ability appear to be more dispositional in nature and are considered as the “apples,” organizational measures are more based on an interactionist model that considers both personal (“apples”) and situational (“barrels”) factors to assess the person–organization fit and person–job fit (Cable & Judge, 1996; Iliescu et al., 2015). This provided insights into the importance of looking at both dispositional and situational factors when applying integrity tests on predicting workplace deviance.

Comparing overt tests and covert tests in predicting workplace deviance, both types of tests were significant predictors and showed comparable validities in the present study. These findings were different from the previously reported pattern in Ones et al. (1993) and Van Iddekinge et al.’s (2012a) reviews, when overt tests were found to outperform covert tests. The difference might be due to the investigation of a wider array of integrity tests and workplace deviant behaviors in this study, versus the focus on two types of integrity tests and CWBs in the previous reviews. In addition, a closer look into the primary studies of this meta-analysis suggested that the inclusion of newer covert tests and faking-resistant measures (e.g., Thibault & Kelloway, 2020; Trent et al., 2020) with well-tested psychometric properties may be another reason for the improved validity of covert tests. Nevertheless, Ones et al. and Van Iddekinge et al.'s studies generally agreed that both overt and covert integrity tests had significant correlations with CWBs, while our findings provided further empirical support to the validities of both types of tests on predicting workplace deviance.

Among the different integrity tests, novel measures reported a significant but smallest mean validity in association with deviant work behaviors. These novel measures also indicated the largest variation of validity estimates ranging from .039 to .47, with a larger mean validity for measures such as music preference (Halliday, 2019), insomnia scale (Yuan et al., 2018), core self-evaluation (Kluemper et al., 2019), guilt-proneness (Cohen et al., 2016), and emotional intelligence (Greenidge et al., 2014), but a smaller mean validity for attachment style (Richards & Schat, 2011), time perspective (Wojtkowska et al., 2019), and grit (Ion et al., 2017). Given the large variations observed and the very distinct nature of these novel measures, the present findings are relatively exploratory and preliminary, which open for further examination. In general, the present findings revealed significant validities of most types of integrity tests, suggesting their substantial value in predicting workplace deviance.

Predictive validity of integrity testing varied across industries and most significant in the law enforcement profession

The relationship between integrity tests and workplace deviance was found to be significantly moderated by industry in our study. This indicated the effect of integrity tests in predicting workplace deviance would be better in certain industries. In this review, the integrity tests yielded the largest predictive validity on deviance in the military and law enforcement profession, which was followed by the second largest predictive validity of integrity tests in the field of customer services, retail, and sales. The validity estimates were also significant, with moderately large magnitude in the manufacturing industry and the business/banking/finance sector. In contrast, the predictive validity was relatively small but significant in the medical, pharmaceutical, and healthcare sector, while the validity in the education sector was nonsignificant. For the “general” subgroup that consisted of work samples across industries, the validity was significant and moderate in magnitude. Inferring from the findings, it implies that industries with clearer criteria on misconducts (e.g., inappropriate weapon use and discipline problems in law enforcement), more observable misbehaviors (e.g., substandard efficiency and theft of products in manufacturing), and better scrutiny of deviance (e.g., customer complaint system in the service industry, fraud in the finance sector) would be more predictable by integrity measures. On the other hand, certain professions (e.g., medical and educational) with vague and obscure criteria may make measuring possible mistakes and wrongdoings difficult. These may consequently weaken the integrity test and deviance relationship as the observation and reporting of such deviant work behaviors could be barricaded.

Predictive validity of integrity tests varied with countries that called for indigenous measures

Investigation of the relationship between integrity tests and workplace deviance across different countries, which was not previously examined in other studies, revealed that “country” was a significant moderator. Among the countries, significant and larger predictive validities were found in Canada, Germany, Israel, Romania, and the United States. The smallest but significant validity estimate was found in China, while the effect sizes were nonsignificant in Turkey and the United Kingdom. The varying validities across countries, specifically the relatively small validity in China and nonsignificant validities in several other countries, implies that integrity testing would have differential predictive validity across countries and that further examination of cultural differences is needed. Indeed, as most of the existing integrity measures were rooted and developed in the Western cultures, it calls for a closer look at the operationalization of integrity for developing more indigenous integrity tests with higher validity in these non-origin countries and cultures.

The significant moderating effect of criterion source that addressed the need for more objective or multiple measures on employees' misbehaviors

The criterion source (admission criteria) was revealed to be a significant moderator of the integrity–deviance relationship, in which the employee record of misbehaviors yielded the largest validity, followed by the significant validity of “self-report.” The subgroup of “other-report” consisting of ratings by supervisors or peers indicated a smaller yet significant validity. Again, our findings were different from previous findings when self-report measures were found to outperform external measures and employee records (Ones et al., 1993; Van Iddekinge et al., 2012a). A possible explanation on the different results could be the improvement of employee records with the advanced human resources analytics in recent decades (Safrinal et al., 2021; Shamim et al., 2020). This improvement may have facilitated record-keeping, making employee records a more reliable criterion source than self-report and other-report measures.

Implying the generalizability of integrity testing from the nonsignificant moderating effect of different natures of deviance, validation samples, validation strategy, and publication status

The nature of deviance as a criterion was found to be nonsignificant in its moderating effect. Results indicated that CWBs, UPBs, and WDBs all had a significant validity estimate in associating with integrity tests. Nevertheless, the mean predictive validity of integrity tests on UPBs was relatively small in magnitude. Such difference might be because UPBs are a newly defined type of workplace deviance in recent decades (Umphress et al., 2010). Most of the existing integrity measures have thus not been designed or validated to capture the UPB construct. More focal research to investigate this specific workplace deviance of UPB and relevant measures would be required to strengthen such a predictor–criterion linkage.

Similarly, the moderating effect of validation samples, validation strategy, and publication status examined in previous reviews was not shown to be a significant moderator in our study. For validation samples, the current results showed comparable effect sizes of both incumbents and applicants. Likewise, both concurrent and predictive validation strategy indicated significant and comparable predictive validities in this review. This provides some support for the ecological validity and utility of integrity testing in real work settings. It also helps to respond to the query of possible overestimation of the predictive validity of integrity testing in practice for preemployment “prediction,” when previous reviews reported a larger validity for validation strategy of concurrent design that imposed such a query.

In addition, the potential moderating effect of “publication status” examined in Van Iddekinge et al.’s (2012a) study was not replicated in this review. Nevertheless, our findings reported a consistent pattern indicating that the significant validity estimates from published studies were somewhat larger than those from unpublished studies. However, given the notable imbalanced number of nine unpublished primary studies and 141 published studies in this review, the present findings are premature in addressing the possible moderating effect of publication status and concern of publication bias.

Potential applications of integrity testing to the contemporary diversified and digitalized workplace

The potential moderators of “gender” and “medium of test” of the relationship between integrity tests and workplace deviance had not yet been investigated prior to the present study. Examination of these moderating effects revealed them to be nonsignificant. For all the “gender” subgroups of “majorly female” “majorly male,” “unspecified,” and “mixed,” the mean validity estimates were comparable and significant. These preliminary findings are of important practical value when being applied and generalized to increasingly welcoming workplaces inclusive of conventional and unconventional LGBTQ (lesbian, gay, bisexual, transgender, queer/questioning) workforces.

The preliminary findings for the “medium of test” were also encouraging. Specifically, comparable and significant effect sizes of integrity tests in predicting deviance that were delivered by both computer- and non-computer media were shown. The predictive validity of integrity tests was also significant in the “mixed-media” subgroup. With the increasing prevalence of computer-mediated and online integrity tests, the present findings strengthened the confidence of transforming current practices to those in line with the digital world, if not the metaverse.

Limitations and future research directions

Some methodological and practical limitations of the present study should be addressed and may provide possible directions for future research. Methodologically, the artifact adjustment of 26 coded primary studies with a corrected effect size value that exceeded 1.0 might have limited the estimation of a more precise “true” validity on the predictor–criterion relationship. Unfortunately, there is not yet any consensual way of handling such effect-size outliers. For example, some researchers have used the original uncorrected, attenuated correlation for analyses as we adopted in this study (Neuber et al., 2021), while others have used the method of Winsorization to cap the effect size value at .80 (Aguinis et al., 2013; Wang et al., 2019). No matter what approach is used, there is still no guarantee of the absence of bias as invalid effect sizes are possible for all kinds of corrections. Researchers should thus pay attention to these kinds of statistical artifacts in meta-analytic studies.

In addition, despite the effort to collect unpublished studies from various sources, the relatively small number of unpublished studies included in this dataset could still result in publication bias. Indeed, the publication bias analyses carried out in this review did indicate a possible publication bias, although it could also be attributed to heterogeneity (Sterne et al., 2011) and/or “true” differences between large and small samples (Kepes et al., 2012). Particularly, the funnel plot asymmetry was atypical (Sterne et al., 2011) and the adjusted overall effect size after imputation of missing studies did not show any significant difference in magnitude from our unimputed results. Nevertheless, it is suggested that future meta-analyses of integrity testing should include as many unpublished studies as possible to reduce the possibility of publication bias.

Moreover, attention should be given to the imbalanced number of studies across moderator levels that might entail unfair comparisons (Cooper & Richardson, 1986) and second-order sampling errors (Hunter & Schmidt, 2004). Likewise, the lack of investigation of the intercorrelations among moderators, which might distort the overall picture as the unique effect of moderators could confound the results, should be noted (Ones et al., 1993; Wang et al., 2019).

Practically, the limitation of collecting sufficient studies on popular integrity tests that assess workplace deviance, such as biodata and structured interviews, may have impeded the application and generalizability of the current findings in real work settings. In particular, the increased use of “cyber vetting” (Jacobson & Gruzd, 2020; Raymond, 2019; Schroeder et al., 2020) as an integrity measure in preemployment selection was not included in this analysis. Besides, following the practices of previous meta-analyses in their exclusion of polygraphy or psychophysiological measures due to dubious validities (Ones et al., 1993; Van Iddekinge et al., 2012a), the newer theoretical perspective of “ethical impulse” (Kish-Gephart et al., 2010; Sonenshein, 2007) and the controversial misguided practices of sole reliance on polygraph for hiring (White, 2020) could not be testified in this meta-analysis.

In addition, although the present study included studies across 26 counties with a good will to uncover some cultural differences assumed in different countries, the cross-cultural dimensions and implications should be carefully and thoroughly examined in future studies to explain the cultural influences on integrity testing in depth. Therefore, it would be valuable if future reviews could cumulate and investigate further on these missing puzzles to provide more practical and up-to-date insights.

Conclusion

The present meta-analysis aims to serve as an updated and balanced overview of the use of integrity tests on predicting workplace deviance across industries and countries.

In conclusion, our findings lend support to the criterion-related validity of integrity tests. Theoretically, it provided support to the interactionist model in looking at the dark side of workplace behaviors. Empirically, the present study provided refined quantitative input to the important “Ones–Van Iddeginke dialogue” about the “true” validity of integrity testing by upholding methodological rigor. Practically, results from the moderator analyses responded to the call for specific focus on investigating the utility of integrity tests in the domains of psychological assessment and workplace ethics. It is our hope that the present attempt would encourage more research efforts, as well as scrutiny in practice, on integrity testing in both the East and West.

Supplemental Material

sj-docx-1-pac-10.1177_18344909231171729 - Supplemental material for Predictive validity of integrity tests for workplace deviance across industries and countries in the past 50 years: A meta-analytic review

Supplemental material, sj-docx-1-pac-10.1177_18344909231171729 for Predictive validity of integrity tests for workplace deviance across industries and countries in the past 50 years: A meta-analytic review by Rebecca Wing-Man Lau, Darius Kwan-Shing Chan, Fan Sun and Grand Hak-Land Cheng in Journal of Pacific Rim Psychology

Footnotes

Acknowledgments

The authors would like to acknowledge Wai Chan and Fiona Ho for their valuable advice on this research. The authors would also like to thank Vince Ho, Ingrid Mak, and other fellows of the Psychological Services Group for their support.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Contribution statement

Lau conceived the research idea, performed the literature search, and wrote the manuscript. Lau and Sun jointly analyzed the data and discussed the results. Chan supervised Lau and Sun by providing guidance and feedback throughout the research and manuscript preparation process. Cheng reviewed the methodology, data analyses, and results. All authors discussed and contributed to the final manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Rebecca Wing-Man Lau

Grand Hak-Land Cheng

Supplemental material

Supplemental material for this article is available online.

Notes

References

* Adam

B. J.

(2013). Comparative analysis of select scales on the M-PULSE Inventory and MMPI-2: Predicting police officer performance from pre-employment psychological evaluations (Publication No. 3541628) [Doctoral dissertation, Alliant International University]. ProQuest Dissertations Publishing.

Aguinis

Gottfredson

R. K.

Joo

(2013). Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods, 16(2), 270–301. https://doi.org/10.1177/1094428112470848

* Ahmad

Jamaluddin

(2009). Employees’ attitude toward cyberloafing in Malaysia [Paper presentation]. Creating global economies through innovation and knowledge management: Theory and practice – Proceedings of the 12th International Business Information Management Association Conference, Kuala Lumpur, Malaysia.

* Aita

S. L.

Hill

B. D.

Musso

M. W.

Gouvier

W. D.

(2018). Can we identify bad cops based on history? Base rates of historical markers in law enforcement pre-employment evaluations. Journal of Police and Criminal Psychology, 33(3), 201–208. https://doi.org/10.1007/s11896-018-9258-0

* Al-Ali

O. E.

(2011). Police selection via psychological testing: A United Arab Emirates study (Publication No. 10701261) [Doctoral dissertation, Sheffield Hallam University]. ProQuest Dissertations Publishing.

Alliger

G. M.

Dwight

S. A.

(2000). A meta-analytic investigation of the susceptibility of integrity tests to faking and coaching. Educational and Psychological Measurement, 60(1), 59–72. https://doi.org/10.1177/00131640021970367

American Psychological Association. (2020). APA Dictionary of Psychology. https://dictionary.apa.org/integrity

Arab

Sheykhshabani

E. H.

Beshlideh

(2013). Antecedents of workplace incivility: Investigating some personal and organizational variables. Journal of Psychology, 17(3), 294–309. https://doi.org/10.5465/amr.1989.4278999

* Ash

(1975). Predicting dishonesty with the Reid Report. Polygraph, 5, 139–153.

10.

Ashforth

B. E.

Mael

(1989). Social identity theory and the organization. Academy of Management Review, 14(1), 20–39. https://doi.org/10.5465/amr.1989.4278999

11.

Ashkanasy

N. M.

Windsor

C. A.

Treviño

L. K.

(2006). Bad apples in bad barrels revisited: Cognitive moral development, just world beliefs, rewards, and ethical decision-making. Business Ethics Quarterly, 16(4), 449–473. https://doi.org/10.5840/beq200616447

12.

Ashton

M. C.

Lee

(2007). Empirical, theoretical, and practical advantages of the HEXACO model of personality structure. Personality and Social Psychology Review, 11(2), 150–166. https://doi.org/10.1177/1088868306294907

13.

Ashton

M. C.

Lee

De Vries

R. E.

(2014). The HEXACO honesty–humility, agreeableness, and emotionality factors: A review of research and theory. Personality and Social Psychology Review, 18(2), 139–152. https://doi.org/10.1177/1088868314523838

14.

Aquino

Reed

II (2002). The self-importance of moral identity. Journal of Personality and Social Psychology, 83(6), 1423–1440. https://doi.org/10.1037/0022-3514.83.6.1423

15.

Babalola

M. T.

Mawritz

M. B.

Greenbaum

R. L.

Ren

Garba

O. A.

(2021). Whatever it takes: How and when supervisor bottom-line mentality motivates employee contributions in the workplace. Journal of Management, 47(5), 1134–1154. https://doi.org/10.1177/0149206320902521

16.

Bakker

A. B.

Demerouti

Taris

T. W.

Schaufeli

W. B.

Schreurs

P. J.

(2003). A multigroup analysis of the job demands–resources model in four home care organizations. International Journal of Stress Management, 10(1), 16–38. https://doi.org/10.1037/1072-5245.10.1.16

17.

Baloch

M. A.

Meng

Cepeda-Carrion

Bari

M. W.

(2017). Dark triad, perceptions of organizational politics and counterproductive work behaviors: The moderating effect of political skills. Frontiers in Psychology, 8, 1972. https://doi.org/10.3389/fpsyg.2017.01972

18.

Barling

Dupré

K. E.

Kelloway

E. K.

(2009). Predicting workplace aggression and violence. Annual Review of Psychology, 60, 671–692. https://doi.org/10.1146/annurev.psych.60.110707.163629

19.

Barrett

(2001). Pre-employment integrity testing: Current methods, problems, and solutions [Paper presentation]. British computer society: Information security group, Milton Hill, Oxford, UK.

20.

Bebeau

M. J.

Rodriguez

Maeda

(2002, March). Using DIT moral schema profiles to evaluate educational intervention effects. Paper presented at the Annual Meeting of the Association for Practical and Professional Ethics, Cincinnati, OH, February.

21.

Becker

T. E.

(2005). Development and validation of a situational judgment test of employee integrity. International Journal of Selection and Assessment, 13(3), 225–232. https://doi.org/10.1111/j.1468-2389.2005.00319.x

22.

Begg

C. B.

Mazumdar

(1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 1088–1101. https://doi.org/10.2307/2533446

23.

Bennett

R. J.

Robinson

S. L.

(2000). Development of a measure of workplace deviance. Journal of Applied Psychology, 85(3), 349–360. https://doi.org/10.1037/0021-9010.85.3.349

24.

* Beutler

L. E.

Storm

Kirkish

Scogin

Gaines

J. A.

(1985). Parameters in the prediction of police officer performance. Professional Psychology: Research and Practice, 16(2), 324–335. https://doi.org/10.1037/0735-7028.16.2.324

25.

* Blickle

Kückelhaus

B. P.

Kranefeld

Schütte

Genau

H. A.

Gansen-Ammann

D.-N.

Wihler

(2020). Political skill camouflages Machiavellianism: Career role performance and organizational misbehavior at short and long tenure. Journal of Vocational Behavior, 118, 103–401. https://doi.org/10.1016/j.jvb.2020.103401

26.

* Blumen

Bayona

Givoli

Pecker

Fine

(2017). Validation study of a multi-method integrity test in a Peruvian sample. Revista de Psicología, 35(1), 347–370. https://doi.org/10.18800/psico.201701.012

27.

Bolino

M. C.

Grant

A. M.

(2016). The bright side of being prosocial at work, and the dark side, too: A review and agenda for research on other-oriented motives, behavior, and impact in organizations. Academy of Management Annals, 10(1), 599–670. https://doi.org/10.5465/19416520.2016.1153260

28.

* Bolton

L. R.

Becker

L. K.

Barber

L. K.

(2010). Big Five trait predictors of differential counterproductive work behavior dimensions. Personality and Individual Differences, 49(5), 537–541. https://doi.org/10.1016/j.paid.2010.03.047

29.

Bowling

N. A.

Burns

G. N.

Stewart

S. M.

Gruys

M. L.

(2011). Conscientiousness and agreeableness as moderators of the relationship between neuroticism and counterproductive work behaviors: A constructive replication. International Journal of Selection and Assessment, 19(3), 320–330. https://doi.org/10.1111/j.1468-2389.2011.00561.x

30.

* Bowling

N. A.

Gruys

M. L.

(2010). Overlooked issues in the conceptualization and measurement of counterproductive work behavior. Human Resource Management Review, 20(1), 54–61. https://doi.org/10.1016/j.hrmr.2009.03.008

31.

* Boye

M. W.

Wasserman

A. R.

(1996). Predicting counterproductivity among drug store applicants. Journal of Business and Psychology, 10(3), 337–349. https://doi.org/10.1007/BF02249607

32.

Brennan

K. A.

Clark

C. L.

Shaver

P. R.

(1998). Self-report measurement of adult attachment: An integrative overview. In Simpson

J. A.

Rholes

W. S.

(Eds.), Attachment theory and close relationships (pp. 46–76). The Guilford Press.

33.

* Brock

M. E.

Martin

L. E.

Buckley

M. R.

(2013). Time theft in organizations: The development of the time banditry questionnaire. International Journal of Selection and Assessment, 21(3), 309–321. https://doi.org/10.1111/ijsa.12040.

34.

Brown

D. P.

Buede

Vermillion

S. D.

(2019). Improving insider threat detection through multi-modelling/data fusion. Procedia Computer Science, 153, 100–107. https://doi.org/10.1016/j.procs.2019.05.060

35.

* Burns

G. N.

Morris

M. B.

Wright

C. P.

(2014). Conceptual and statistical interactions: An illustration with the AB5C and CWBs. Journal of Business and Psychology, 29(1), 47–60. https://doi.org/10.1007/s10869-013-9287-8

36.

* Burtaverde

Iliescu

(2019). Emic vs etic frame of reference personality assessment in the prediction of work-related outcomes. Career Development International, 24(7), 686–701. https://doi.org/10.1108/CDI-10-2018-0273

37.

Butcher

J. N.

Dahlstrom

W. G.

Graham

J. R.

Tellegen

Kaemmer

(1989). MMPI-2: Manual for administration and scoring. University of Minnesota Press.

38.

Byle

K. A.

Holtgraves

T. M.

(2008). Integrity testing, personality, and design: Interpreting the personnel reaction blank. Journal of Business and Psychology, 22(4), 287–295. https://doi.org/10.1007/s10869-008-9059-z

39.

Cable

D. M.

Judge

T. A.

(1996). Person–organization fit, job choice decisions, and organizational entry. Organizational Behavior and Human Decision Processes, 67(3), 294–311. https://doi.org/10.1006/obhd.1996.0081

40.

Carelli

M. G.

Wiberg

Åström

(2015). Broadening the TP profile: Future negative time perspective. In Time perspective theory; review, research and application (pp. 87–97). Springer.

41.

Carless

S. A.

Fewings-Hall

Hall

Hay

Hemsworth

P. H.

Coleman

G. J.

(2007). Selecting unskilled and semi-skilled blue-collar workers: The criterion-related validity of the PDI-employment inventory. International Journal of Selection and Assessment, 15(3), 335–340. https://doi.org/10.1111/j.1468-2389.2007.00392.x

42.

* Castille

C. M.

Buckner

J. E.

Thoroughgood

C. N.

(2018). Prosocial citizens without a moral compass? Examining the relationship between Machiavellianism and unethical pro-organizational behavior. Journal of Business Ethics, 149(4), 919–930. https://doi.org/10.1007/s10551-016-3079-9

43.

* Catano

V. M.

O’Keefe

D. F.

Francis

R. E.

Owens

S. M.

(2018). Construct-based approach to developing a short, personality-based measure of integrity. International Journal of Selection and Assessment, 26(1), 75–92. https://doi.org/10.1111/ijsa.12197

44.

* Chang

Smithikrai

(2010). Counterproductive behaviour at work: An investigation into reduction strategies. The International Journal of Human Resource Management, 21(8), 1272–1288. https://doi.org/10.1080/09585192.2010.483852

45.

* Chen

Chen

C. C.

Sheldon

O. J.

(2016). Relaxing moral reasoning to win: How organizational identification relates to unethical pro-organizational behavior. Journal of Applied Psychology, 101(8), 1082–1096. https://doi.org/10.1037/apl0000111

46.

Cheng

G. H. L.

Chan

D. K. S.

(2008). Who suffers more from job insecurity? A meta-analytic review. Applied Psychology, 57(2), 272–303. https://doi.org/10.1111/j.1464-0597.2007.00312.x

47.

Cheung

M. W. L.

R. C.

Lim

Mak

(2012). Conducting a meta-analysis: Basics and good practices. International Journal of Rheumatic Diseases, 15(2), 129–135. https://doi.org/10.1111/j.1756-185X.2012.01712.x

48.

Chirumbolo

(2015). The impact of job insecurity on counterproductive work behaviors: The moderating role of honesty–humility personality trait. The Journal of Psychology, 149(6), 554–569. https://doi.org/10.1080/00223980.2014.916250

49.

Choi

I.-S.

Colbert

A. E.

(2015). Understanding organizational commitment: A meta-analytic examination of the roles of the five-factor model of personality and culture. Journal of Applied Psychology, 100(5), 1542–1567. https://doi.org/10.1037/apl0000014

50.

Cochran

W. G.

(1954). The combination of estimates from different experiments. Biometrics, 10(1), 101–129. https://doi.org/10.2307/3001666

51.

Cohen

(1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

52.

* Cohen

T. R.

Kim

Jordan

K. P.

Panter

A. T.

(2016). Guilt-proneness is a marker of integrity and employment suitability. Personality and Individual Differences, 92, 109–112. https://doi.org/10.1016/j.paid.2015.12.026.

53.

Cohen

T. R.

Kim

Panter

(2014). The five-item guilt proneness scale (GP-5). Differences, 92, 109–112. https://doi.org/10.17605/OSF.IO/G389S

54.

Colby

Kohlberg

Speicher

Hewer

Candee

Gibbs

Power

(1987). The measurement of moral judgement: Volume 2, Standard issue scoring manual, Vol. 2. Cambridge University Press.

55.

Collins

(2009). Determining a job candidate's ethics profile: Integrity, personality, and moral reasoning level tests. Doing Well and Good: The Human Face of the New Capitalism, 167–185.

56.

Colquitt

J. A.

(2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386

57.

* Cooper

D. A.

Slaughter

J. E.

Gilliland

S. W.

(2020). Reducing injuries, malingering, and workers’ compensation costs by implementing overt integrity testing. Journal of Business and Psychology, 1–18. https://doi.org/10.1007/s10869-020-09681-9.

58.

Cooper

W. H.

Richardson

A. J.

(1986). Unfair comparisons. Journal of Applied Psychology, 71(2), 179–184. https://doi.org/10.1037/0021-9010.71.2.179

59.

Costa

McCrae

(1989). NEO five-factor inventory (NEO-FFI). Psychological Assessment Resources.

60.

Costa

P. T.

McCrae

R. R.

(1992). Neo personality inventory-revised (NEO PI-R). Psychological Assessment Resources.

61.

Crelinsten

R. D.

(2003). The world of torture: A constructed reality. Theoretical Criminology, 7(3), 293–318. https://doi.org/10.1177/13624806030073003

62.

Cunningham

M. R.

Wong

D. T.

Barbee

A. P.

(1994). Self-presentation dynamics on overt integrity tests: Experimental studies of the Reid Report. Journal of Applied Psychology, 79(5), 643–658. https://doi.org/10.1037/0021-9010.79.5.643

63.

Dahlstrom

W. G.

Welsh

G. S.

Dahlstrom

L. E.

(1972). An MMPI Handbook, Vol. 1. University of Minnesota Press.

64.

Dalal

R. S.

(2005). A meta-analysis of the relationship between organizational citizenship behavior and counterproductive work behavior. Journal of Applied Psychology, 90(6), 1241. https://doi.org/10.1037/0021-9010.90.6.1241

65.

Dalal

R. S.

Alaybek

Sheng

Holland

S. J.

Tomassetti

A. J.

(2020). Extending situational strength theory to account for situation-outcome mismatch. Journal of Business and Psychology, 35(3), 273–296. https://doi.org/10.1007/s10869-019-09632-z

66.

Davis

Rostow

(2008). Matrix-predictive uniform law enforcement selection evaluation inventory. Multi-Health Systems.

67.

* Debusscher

Hofmans

De Fruyt

(2016). The effect of state core self-evaluations on task performance, organizational citizenship behaviour, and counterproductive work behaviour. European Journal of Work and Organizational Psychology, 25(2), 301–315. https://doi.org/10.1080/1359432X.2015.1063486.

68.

Delarosa

. (2015). From due diligence to discrimination: Employer use of social Media vetting in the hiring process and potential liabilities. Loyola of Los Angeles Entertainment Law Review, 35(3), 249. https://digitalcommons.lmu.edu/elr/vol35/iss3/1

69.

De Leng

W. E.

Stegers-Jager

K. M.

Born

M. P.

Themmen

A. P. N.

(2018). Integrity situational judgement test for medical school selection: Judging “what to do” versus “what not to do”. Medical Education, 52(4), 427–437. https://doi.org/10.1111/medu.13498

70.

De Meijer

L. A. L.

Born

M. P.

van Zielst

van der Molen

H. T.

(2010). Construct-driven development of a video-based situational judgment test for integrity. European Psychologist, 15(3), 229–236. https://doi.org/10.1027/1016-9040/a000027

71.

* Demir

(2011). Effects of organizational justice, trust and commitment on employees’ deviant behavior. Anatolia, 22(2), 204–221. https://doi.org/10.1080/13032917.2011.597934

72.

DeShong

H. L.

Helle

A. C.

Lengel

G. J.

Meyer

Mullins-Sweatt

S. N.

(2017). Facets of the dark triad: Utilizing the five-factor model to describe Machiavellianism. Personality and Individual Differences, 105, 218–223. https://doi.org/10.1016/j.paid.2016.09.053

73.

Dickersin

(2005). Publication bias: Recognizing the problem, understanding its origins and scope, and preventing harm. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, 9–33.

74.

* Diefendorff

J. M.

Mehta

(2007). The relations of motivational traits with workplace deviance. Journal of Applied Psychology, 92(4), 967–977. https://doi.org/10.1037/0021-9010.92.4.967

75.

* Dilchert

Ones

D. S.

Davis

R. D.

Rostow

C. D.

(2007). Cognitive ability predicts objectively measured counterproductive work behaviors. Journal of Applied Psychology, 92(3), 616–627. https://doi.org/10.1037/0021-9010.92.3.616

76.

Di Stefano

Scrima

(2016). The psychometric properties of the Italian version of the organizational culture assessment instrument (OCAI). BPA-Applied Psychology Bulletin (Bollettino di Psicologia Applicata), 64(276).

77.

* Di Stefano

Scrima

Parry

(2019). The effect of organizational culture on deviant behaviors in the workplace. The International Journal of Human Resource Management, 30(17), 2482–2503. https://doi.org/10.1080/09585192.2017.1326393

78.

Duckworth

A. L.

Peterson

Matthews

M. D.

Kelly

D. R.

(2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087

79.

Duckworth

A. L.

Quinn

P. D.

(2009). Development and validation of the short grit scale (GRIT–S). Journal of Personality Assessment, 91(2), 166–174. https://doi.org/10.1080/00223890802634290

80.

Duval

Tweedie

(2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x

81.

Egger

Smith

G. D.

Schneider

Minder

(1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629

82.

* Farasat

Azam

(2020). Supervisor bottom-line mentality and subordinates’ unethical pro-organizational behavior. Personnel Review. https://doi.org/10.1108/PR-03-2020-0129

83.

Fehr

Welsh

Yam

K. C.

Baer

Wei

Vaulont

(2019). The role of moral decoupling in the causes and consequences of unethical pro-organizational behavior. Organizational Behavior and Human Decision Processes, 153, 27–40. https://doi.org/10.1016/j.obhdp.2019.05.007

84.

* Fernández-del-Río

Ramos-Villagrasa

P. J.

Barrada

J. R.

(2020). Bad guys perform better? The incremental predictive validity of the Dark Tetrad over Big Five and Honesty-Humility. Personality and Individual Differences, 154, 109700. https://doi.org/10.1016/j.paid.2019.109700

85.

Fine

(2008). IntegriTEST: technical manual 3.0. Midot Ltd, Bnei Brak.

86.

Fine

(2012). Estimating the economic impact of personnel selection tools on counterproductive work behaviors. Economics and Business Letters, 1(4), 1–9. https://doi.org/10.17811/ebl.1.4.2012.1-9

87.

* Fine

(2013). A look at cross-cultural integrity testing in three banks. Personnel Review, 42(3), 266–280. https://doi.org/10.1108/00483481311320408

88.

Fine

(2010a). Cross-cultural integrity testing as a marker of regional corruption rates. International Journal of Selection and Assessment, 18(3), 251–259. https://doi.org/10.1111/j.1468-2389.2010.00508.x

89.

Fine

(2010b). Pre-employment integrity testing across multiple industries. Psychological Reports, 107(2), 607–610. https://doi.org/10.2466/08.PR0.107.5.607-610

90.

* Fine

Edward

(2017). Breaking the rules, not the law: The potential risks of counterproductive work behaviors among overqualified employees. International Journal of Selection and Assessment, 25(4), 401–405. https://doi.org/10.1111/ijsa.12194

91.

* Fine

Goldenberg

Noam

(2016a). Beware of those left behind: Counterproductive work behaviors among nonpromoted employees and the moderating effect of integrity. Journal of Applied Psychology, 101(12), 1721–1729. https://doi.org/10.1037/apl0000156

92.

* Fine

Goldenberg

Noam

(2016b). Integrity testing and the prediction of counterproductive behaviours in the military. Journal of Occupational and Organizational Psychology, 89(1), 198–218. https://doi.org/10.1111/joop.12117

93.

* Fine

Horowitz

Weigler

Basis

(2010). Is good character good enough? The effects of situational variables on the relationship between integrity and counterproductive work behaviors. Human Resource Management Review, 20(1), 73–84. https://doi.org/10.1016/j.hrmr.2009.03.010

94.

* Fisher

D. M.

Good

Toich

M. J.

Schutt

(2021). Measuring moral disengagement with a situational judgment test: Advancing the assessment of an important workplace construct. International Journal of Selection and Assessment, 29(1), 114–133. https://doi.org/10.1111/ijsa.12318

95.

* Flaherty

Moss

S. A.

(2007). The impact of personality and team context on the relationship between workplace injustice and counterproductive work behavior. Journal of Applied Social Psychology, 37(11), 2549–2575. https://doi.org/10.1111/j.1559-1816.2007.00270.x

96.

Forsyth

D. R.

Banks

G. C.

McDaniel

M. A.

(2012). A meta-analysis of the Dark Triad and work behavior: A social exchange perspective. Journal of Applied Psychology, 97(3), 557. https://doi.org/10.1037/a0025679

97.

* Fortmann

Leslie

Cunningham

(2002). Cross-cultural comparisons of the Reid Integrity scale in Latin America and South Africa. International Journal of Selection and Assessment, 10(1-2), 98–108. https://doi.org/10.1111/1468-2389.00196

98.

* Frost

A. G.

Rafilson

F. M.

(1989). Overt integrity tests versus personality-based measures of delinquency: An empirical comparison. Journal of Business and Psychology, 3(3), 269–277. https://doi.org/10.1007/BF01023045

99.

* Furnham

Gunter

(2015). Corporate Assessment (Routledge Revivals): Auditing a Company’s Personality. Routledge.

100.

* Galić

(2016). Conditional reasoning test for aggression: Further evidence about incremental validity. International Journal of Selection and Assessment, 24(1), 24–33. https://doi.org/10.1111/ijsa.12126

101.

Ghoshray

(2012). Emerging reality of social media: Erosion of individual privacy through cyber-vetting and law’s inability to catch up. The John Marshall Review of Intellectual Property. Law, 12, IV.

102.

Ghoshray

(2013). The emerging reality of social Media: Erosion of individual privacy through cyber-vetting and law’s inability to catch up, 12 J. Marshall review of intellectual property law, 551 (2013). UIC Review of Intellectual Property Law, 12(3), 4.

103.

Gioia

D. A.

(1992). Pinto fires and personal ethics: A script analysis of missed opportunities. Journal of Business Ethics, 11(5), 379–389. https://doi.org/10.1007/BF00870550

104.

* Goffin

R. D.

Jang

Skinner

(2011). Forced-choice and conventional personality assessment: Each may have unique value in pre-employment testing. Personality and Individual Differences, 51(7), 840–844. https://doi.org/10.1016/j.paid.2011.07.012

105.

* Goffin

R. D.

Spring

T. M.

(2016). Is the perceived ability to deceive confounded by general mental ability? Personality and Individual Differences, 101, 356–359. https://doi.org/10.1016/j.paid.2016.06.031

106.

Gonzalez-Mulé

DeGeest

D. S.

Kiersch

C. E.

Mount

M. K.

(2013). Gender differences in personality predictors of counterproductive behavior. Journal of Managerial Psychology, 28(4), 333–353. https://doi.org/10.1108/JMP-12-2012-0397

107.

Gottfredson

L. S.

(1998). Jensen, Jensenism, and the sociology of intelligence. Intelligence, 26(3), 291–299. https://doi.org/10.1016/S0160-2896(99)80013-0

108.

Gough

H. G.

(1965). Reliability as a qualitative factor in employees’ work. Bollettino di Psicologia Applicata, 69–70.

109.

Gough

H. G.

Arvey

R. D.

Bradley

(1972). Manual for the personnel reaction blank. Consulting Psychologists Press.

110.

Gough

H. G.

Bradley

(1996). The California psychological inventory manual. Consulting Psychologists Press.

111.

Grabowski

Chudzicka-Czupała

Chrupała-Pniak

Mello

A. L.

Paruzel-Czachura

(2019). Work ethic and organizational commitment as conditions of unethical pro-organizational behavior: Do engaged workers break the ethical rules? International Journal of Selection and Assessment, 27(2), 193–202. https://doi.org/10.1111/ijsa.12241

112.

* Greenidge

Coyne

(2014). Job stressors and voluntary work behaviours: Mediating effect of emotion and moderating roles of personality and emotional intelligence. Human Resource Management Journal, 24(4), 479–495. https://doi.org/10.1111/1748-8583.12044

113.

Greenidge

Devonish

Alleyne

(2014). The relationship between ability-based emotional intelligence and contextual performance and counterproductive work behaviors: A test of the mediating effects of job satisfaction. Human Performance, 27(3), 225–242. https://doi.org/10.1080/08959285.2014.913591

114.

* Guo

Zhao

Cheng

Luo

(2020). The relationship between abusive supervision and unethical pro-organizational behavior: Linear or curvilinear? Leadership & Organization Development Journal, 41(3), 369–381. https://doi.org/10.1108/LODJ-05-2019-0214

115.

Guo

Xie

Chen

Wang

(2019). The relationship between opportunities for professional development and counterproductive work behaviors: The mediating role of affective well-being and moderating role of task-contingent conscientiousness. International Journal of Mental Health Promotion, 21, 111–122. https://doi.org/10.32604/IJMHP.2019.011040

116.

* Gürlek

(2021). Shedding light on the relationships between Machiavellianism, career ambition, and unethical behavior intention. Ethics & Behavior, 31(1), 38–59. https://doi.org/10.1080/10508422.2020.1764846

117.

* Hakstian

A. R.

Farrell

Tweed

R. G.

(2002). The assessment of counterproductive tendencies by means of the California Psychological Inventory. International Journal of Selection and Assessment, 10(1–2), 58–86. https://doi.org/10.1111/1468-2389.00194

118.

* Halliday

A. J.

(2019). Bridging music and organizational psychology: Everyday music uses and preferences and the prediction of organizational behaviour. Personality and Individual Differences, 139, 263–276. https://doi.org/10.1016/j.paid.2018.10.042

119.

* Harold

C. M.

I. S.

Holtz

B. C.

Han

Giacalone

R. A.

(2016). Fit and frustration as drivers of targeted counterproductive work behaviors: A multifoci perspective. Journal of Applied Psychology, 101(11), 1513–1535. https://doi.org/10.1037/apl0000150

120.

Harris

W. G.

Jones

J. W.

Klion

Arnold

D. W.

Camara

Cunningham

M. R.

(2012). Test publishers’ perspective on “An updated meta-analysis”: Comment on Van Iddekinge, Roth, Raymark, and Odle-Dusseau (2012). https://doi.org/10.1037/a0024767.

121.

Helle

A. C.

DeShong

H. L.

Lengel

G. J.

Meyer

N. A.

Butler

Mullins-Sweatt

S. N.

(2018). Utilizing Five Factor Model facets to conceptualize counterproductive, unethical, and organizational citizenship workplace behaviors. Personality and Individual Differences, 135, 113–120. https://doi.org/10.1016/j.paid.2018.06.056

122.

Higgins

J. P.

Thompson

S. G.

(2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186

123.

* Hitlan

R. T.

Noel

(2009). The influence of workplace exclusion and personality on counterproductive work behaviours: An interactionist perspective. European Journal of Work and Organizational Psychology, 18(4), 477–502. https://doi.org/10.1080/13594320903025028

124.

Hochstetler

Copes

DeLisi

(2002). Differential association in group and solo offending. Journal of Criminal Justice, 30(6), 559–566. https://doi.org/10.1016/S0047-2352(02)00177-0

125.

Hogan

(1989). How to measure employee reliability. Journal of Applied Psychology, 74(2), 273–279. https://doi.org/10.1037/0021-9010.74.2.273

126.

* Hogan

Jacobson

Hogan

Thompson

(1987). Development and validation of a service operations dispatcher selection inventory. Hogan Assessment Systems.

127.

Holland

J. L.

Fritzsche

B. A.

Powell

A. B.

(1994). “The” Self-Directed Search (SDS): 1994 edition. Psychological Assessment Resources.

128.

Holland

Jeske

(2018). Case 1: To cyber-vet or not to cyber-vet: An ethics question for HRM. In Strategic human resource management and employment relations (pp. 157–162). Springer.

129.

Hollinger

R. C.

(1986). Acts against the workplace: Social bonding and employee deviance. Deviant Behavior, 7(1), 53–75. https://doi.org/10.1080/01639625.1986.9967695

130.

Hollwitz

J. C.

Pawlowski

D. R.

(1997). The development of a structured ethical integrity interview for pre-employment screening. The Journal of Business Communication (1973), 34(2), 203–219. https://doi.org/10.1177/002194369703400206

131.

Hunter

J. E.

Schmidt

F. L.

(1990). Dichotomization of continuous variables: The implications for meta-analysis. Journal of Applied Psychology, 75(3), 334–349. https://doi.org/10.1037/0021-9010.75.3.334

132.

Hunter

J. E.

Schmidt

F. L.

(2004). Methods of meta-analysis: Correcting error and bias in research findings. Sage.

133.

Hunter

J. E.

Schmidt

F. L.

Judiesch

M. K.

(1990). Individual differences in output variability as a function of job complexity. Journal of Applied Psychology, 75(1), 28–42. https://doi.org/10.1037/0021-9010.75.1.28

134.

Husbands

Rodgerson

M. J.

Dowell

Patterson

(2015). Evaluating the validity of an integrity-based situational judgement test for medical school admissions. BMC Medical Education, 15(1), 1–9. https://doi.org/10.1186/s12909-015-0424-0

135.

* Iliescu

Ilie

Ispas

(2011). Examining the criterion-related validity of the Employee Screening Questionnaire: A three-sample investigation. International Journal of Selection and Assessment, 19(2), 222–228. https://doi.org/10.1111/j.1468-2389.2011.00550.x

136.

* Iliescu

Ispas

Sulea

Ilie

(2015). Vocational fit and counterproductive work behaviors: A self-regulation perspective. Journal of Applied Psychology, 100(1), 21–39. https://doi.org/10.1037/a0036652

137.

Inwald

(1982). A psychological screening test for police, Corrections. Criminal Justice Journal, 1(8).

138.

Inwald

Knatz

Shusman

(1983). Inwald Personality Inventory Manual. Hilson Research, Inc.

139.

* Ion

Mindu

Gorbănescu

(2017). Grit in the workplace: Hype or ripe? Personality and Individual Differences, 111, 163–168. https://doi.org/10.1016/j.paid.2017.02.012

140.

Jackson

D. N.

(1999). Personality Research Form Manual (3rd ed.). Sigma Assessment Systems.

141.

Jackson

D. N

. (2009b). Employee Screening Questionnaire 2: Technical Manual. Sigma Assessment Systems.

142.

Jackson

D. N.

(2009a). Technical Manual for the Employee Screening Questionnaire 2. Sigma Assessment Systems.

143.

Jacobson

Gruzd

(2020). Cybervetting job applicants on social media: The new normal? Ethics and Information Technology, 22(2), 175–195. https://doi.org/10.1007/s10676-020-09526-2

144.

James

L. R.

McIntyre

M. D.

(2000). Conditional reasoning test of aggression: Test manual. Innovative Assessment Technology.

145.

Jenkins

C. D.

Jono

R. T.

Stanton

B. A.

(1996). Predicting completeness of symptom relief after major heart surgery. Behavioral Medicine, 22(2), 45–57. https://doi.org/10.1080/08964289.1996.9933764

146.

Jensen

J. M.

Patel

P. C.

(2011). Predicting counterproductive work behavior from the interaction of personality traits. Personality and Individual Differences, 51(4), 466–471. https://doi.org/10.1016/j.paid.2011.04.016

147.

* Jeske

Holland

(2019). Employer and employee vetting: Reputation management challenges in the information age. In Holland

(Ed.), Contemporary HRM issues in the 21st century (pp. 149–157). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78973-457-720191018

148.

Jeske

Lippke

Shultz

K. S.

(2019). Predicting self-disclosure in recruitment in the context of social media screening. Employee Responsibilities and Rights Journal, 31(2), 99–112. https://doi.org/10.1007/s10672-019-09329-8

149.

John

O. P.

Donahue

E. M.

Kentle

R. L.

(1991). Big five inventory. Journal of Personality and Social Psychology. https://doi.org/10.1037/t07550-000

150.

Johnson

G. J.

Johnson

W. R.

(1996). Perceived overqualification and psychological well-being. The Journal of Social Psychology, 136(4), 435–445. https://doi.org/10.1080/00224545.1996.9714025

151.

Johnson

G. J.

Johnson

W. R.

(2000). Perceived overqualification and dimensions of job satisfaction: A longitudinal analysis. The Journal of Psychology, 134(5), 537–555. https://doi.org/10.1080/00223980009598235

152.

* Jones

J. W.

(1980). Attitudinal correlates of employees’ deviance: Theft, alcohol use, and nonprescribed drug use. Psychological Reports, 47(1), 71–77. https://doi.org/10.2466/pr0.1980.47.1.71

153.

* Jones

J. W.

(1981). Dishonesty, burnout, and unauthorized work break extensions. Personality and Social Psychology Bulletin, 7(3), 406–409. https://doi.org/10.1177/014616728173007

154.

* Jones

J. W.

Brasher

E. E.

Huff

J. W.

(2002). Innovations in integrity-based personnel selection: Building a technology-friendly assessment. International Journal of Selection and Assessment, 10(1–2), 87–97. https://doi.org/10.1111/1468-2389.00195

155.

Jones

J. W.

Terris

(1983). Predicting employees’ theft in home improvement centers. Psychological Reports, 52(1), 187–201. https://doi.org/10.2466/pr0.1983.52.1.187

156.

Judge

T. A.

Erez

Bono

J. E.

Thoresen

C. J.

(2003). The core self-evaluations scale: Development of a measure. Personnel Psychology, 56(2), 303–331. https://doi.org/10.1111/j.1744-6570.2003.tb00152.x

157.

Judge

T. A.

Klinger

Simon

L. S.

Yang

I. W. F.

(2008). The contributions of personality to organizational behavior and psychology: Findings, criticisms, and future research directions. Social and Personality Psychology Compass, 2(5), 1982–2000. https://doi.org/10.1111/j.1751-9004.2008.00136.x

158.

Kabiri

Choi

Kruis

Shadmanfaat

S. M.

Lee

(2021). Social concern as a means of understanding the risk of workplace deviance. Deviant Behavior, 1–20. https://doi.org/10.1080/01639625.2021.1942328

159.

Kacmar

K. M.

Ferris

G. R.

(1991). Perceptions of organizational politics scale (POPS): Development and construct validation. Educational and Psychological Measurement, 51(1), 193–205. https://doi.org/10.1177/0013164491511019

160.

Kass

R. A.

Mitchell

K. J.

Grafton

F. C.

Wing

(1983). Factorial validity of the armed services vocational aptitude battery (ASVAB), Forms 8, 9 and 10: 1981 Army applicant sample. Educational and Psychological Measurement, 43(4), 1077–1087. https://doi.org/10.1177/001316448304300417

161.

Kepes

Banks

G. C.

McDaniel

Whetzel

D. L.

(2012). Publication bias in the organizational sciences. Organizational Research Methods, 15(4), 624–662. https://doi.org/10.1177/1094428112452760

162.

* Khattak

M. N.

Khan

M. B.

Fatima

Shah

S. Z. A.

(2019). The underlying mechanism between perceived organizational injustice and deviant workplace behaviors: Moderating role of personality traits. Asia Pacific Management Review, 24(3), 201–211. https://doi.org/10.1016/j.apmrv.2018.05.001

163.

* Kim

del Carmen Triana

Chung

(2016). When do employees cyberloaf? An interactionist perspective examining personality, justice, and empowerment. Human Resource Management, 55(6), 1041–1058. https://doi.org/10.1002/hrm.21699

164.

Kish-Gephart

J. J.

Harrison

D. A.

Treviño

L. K.

(2010). Bad apples, bad cases, and bad barrels: Meta-analytic evidence about sources of unethical decisions at work. Journal of Applied Psychology, 95(1), 1–31. https://doi.org/10.1037/a0017103

165.

* Kluemper

D. H.

Mossholder

K. W.

Ispas

Bing

M. N.

Iliescu

Ilie

(2019). When core self-evaluations influence employees’ deviant reactions to abusive supervision: The moderating role of cognitive ability. Journal of Business Ethics, 159(2), 435–453. https://doi.org/10.1007/s10551-018-3800-y

166.

* Knatz

H. F.

Inwald

R. E.

Brockwell

A. L.

Tran

L. N.

(1992). IPI And MMPI predictions of counterproductive job behaviors by racial group. Journal of Business and Psychology, 7(2), 189–201. https://doi.org/10.1007/BF01013928

167.

Kohlberg

(1981). The philosophy of moral development moral stages and the idea of justice. https://philpapers.org/archive/KOHTPO-6.pdf.

168.

* Kong

D. T.

(2016). The pathway to unethical pro-organizational behavior: Organizational identification as a joint function of work passion and trait mindfulness. Personality and Individual Differences, 93, 86–91. https://doi.org/10.1016/j.paid.2015.08.035

169.

Kuhn

K. M.

(2020). The why and when of background checks: Situational factors moderate effects of criminal and financial stigma. International Journal of Selection and Assessment, 28(3), 283–296. https://doi.org/10.1111/ijsa.12283

170.

Lau

V. C.

W. T.

J. M.

(2003). A qualitative and quantitative review of antecedents of counterproductive behavior in organizations. Journal of Business and Psychology, 18(1), 73–99. https://doi.org/10.1023/A:1025035004930

171.

* Le

I.-S.

Robbins

S. B.

Ilies

Holland

Westrick

(2011). Too much of a good thing: Curvilinear relationships between personality traits and job performance. Journal of Applied Psychology, 96(1), 113–133. https://doi.org/10.1037/a0021016

172.

* Le

Donnellan

M. B.

Spilman

S. K.

Garcia

O. P.

Conger

(2014). Workers behaving badly: Associations between adolescent reports of the Big Five and counterproductive work behaviors in adulthood. Personality and Individual Differences, 61, 7–12. https://doi.org/10.1016/j.paid.2013.12.016

173.

* Lee

Schwarz

Newman

Legood

(2019). Investigating when and why psychological entitlement predicts unethical pro-organizational behavior. Journal of Business Ethics, 154(1), 109–126. https://doi.org/10.1007/s10551-017-3456-z

174.

Lee

Ashton

M. C.

(2004). Psychometric properties of the HEXACO personality inventory. Multivariate Behavioral Research, 39(2), 329–358. https://doi.org/10.1207/s15327906mbr3902_8

175.

Levine

S. Z.

Jackson

C. J.

(2002). Aggregated personality, climate and demographic factors as predictors of departmental shrinkage. Journal of Business and Psychology, 17(2), 287–297. https://doi.org/10.1023/A:1019693717563

176.

Lilienfeld

S. O.

Alliger

Mitchell

(1995). Why integrity testing remains controversial. American Psychologist, 50(6), 457–458. https://doi.org/10.1037/0003-066X.50.6.457

177.

Lim

V. K.

(2002). The IT way of loafing on the job: Cyberloafing, neutralizing and organizational justice. Journal of Organizational Behavior, 23(5), 675–694. https://doi.org/10.1002/job.161

178.

Lipsey

M. W.

Wilson

D. B.

(2001). Practical meta-analysis. Sage Publications, Inc.

179.

London House Inc (1977). Personnel Security Inventory, PSI-5. London House Management Consultants.

180.

London House Inc (1980). The Personnel Selection Inventory. London House Press.

181.

London House Inc (1986). The Employment Productivity Index (EPI). London House Press.

182.

London House Inc (1997). Applicant Potential Inventory. London House Management Consultants.

183.

Loviscky

G. E.

Treviño

L. K.

Jacobs

R. R.

(2007). Assessing managers’ ethical decision-making: An objective measure of managerial moral judgment. Journal of Business Ethics, 73(3), 263–285. https://doi.org/10.1007/s10551-006-9206-2

184.

* Luo

Hao

R. P.

Liu

H. Y.

(2010). An experimental study of faking of candidates in occupational selection situations. Academic Journal of Jinyang, 2, 74–77.

185.

Lynam

D. R.

Widiger

T. A.

(2001). Using the five-factor model to represent the DSM-IV personality disorders: An expert consensus approach. Journal of Abnormal Psychology, 110(3), 401–412. https://doi.org/10.1037/0021-843X.110.3.401

186.

* Ma

Liu

(2019). The moderating effect of emotional intelligence on the relationship between supervisor conflict and employees’ counterproductive work behaviors. International Journal of Conflict Management, 30(2), 227–245. https://doi.org/10.1108/IJCMA-11-2017-0140

187.

Mackey

J. D.

McAllister

C. P.

Ellen

B. P.

Carson

J. E.

(2021). A meta-analysis of interpersonal and organizational workplace deviance research. Journal of Management, 014920631986261. https://doi.org/10.1177/0149206319862612

188.

Mael

Ashforth

B. E.

(1992). Alumni and their alma mater: A partial test of the reformulated model of organizational identification. Journal of Organizational Behavior, 13(2), 103–123. https://doi.org/10.1002/job.4030130202

189.

* Mahlendorf

M. D.

Matějka

Weber

(2018). Determinants of financial managers’ willingness to engage in unethical pro-organizational behavior. Journal of Management Accounting Research, 30(2), 81–104. https://doi.org/10.2308/jmar-51957

190.

Malachowski

(2005). Wasted time at work costing companies billions. San Francisco Chronicle, 11(3).

191.

* Marcus

(2006). Relationships between faking, validity, and decision criteria in personnel selection. Psychological Test and Assessment Modeling, 48(3), 226.

192.

* Marcus

Lee

Ashton

M. C.

(2007). Personality dimensions explaining relationships between integrity tests and counterproductive behavior: Big five, or one in addition? Personnel Psychology, 60(1), 1–34. https://doi.org/10.1111/j.1744-6570.2007.00063.x

193.

* Marcus

Schuler

(2004). Antecedents of counterproductive behavior at work: A general perspective. Journal of Applied Psychology, 89(4), 647–660. https://doi.org/10.1037/0021-9010.89.4.647

194.

* Marcus

Schuler

Quell

Hümpfner

(2002). Measuring counterproductivity: Development and initial validation of a German self-report questionnaire. International Journal of Selection and Assessment, 10(1–2), 18–35. https://doi.org/10.1111/1468-2389.00191

195.

Markowitz

D. M.

Levine

T. R.

(2020). It’s the situation and your disposition: A test of two honesty hypotheses. Social Psychological and Personality Science, 12(2), 213–224. https://doi.org/10.1177/1948550619898976

196.

* Matherne

C. F.

III Litchfield

S. R

. (2012). Investigating the relationship between affective commitment and unethical pro-organizational behaviors: The role of moral identity. Journal of Leadership, Accountability and Ethics, 9(5), 35–46.

197.

May

M. S.

Van Niekerk

(2019). Co-constructing integrity: A conceptual framework. SA Journal of Industrial Psychology, 45(1), 1–12. https://doi.org/10.4102/sajip.v45i0.1498

198.

* McDaniel

M. A.

(1989). Biographical constructs for predicting employee suitability. Journal of Applied Psychology, 74(6), 964–970. https://doi.org/10.1037/0021-9010.74.6.964

199.

McDaniel

M. A.

Jones

J. W.

(1988). Predicting employee theft: A quantitative review of the validity of a standardized measure of dishonesty. Journal of Business and Psychology, 2(4), 327–345. https://doi.org/10.1007/BF01013765

200.

* McHenry

J. J.

Hough

L. M.

Toquam

J. L.

Hanson

M. A.

Ashworth

(1990). Project A validity results: The relationship between predictor and criterion domains. Personnel Psychology, 43(2), 335–354. https://doi.org/10.1111/j.1744-6570.1990.tb01562.x

201.

Mercado, B. K., Dilchert, S., Giordano, C., & Ones, D. S. (2018). Counterproductive work behaviors. In D. S. Ones, N. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 109–211). Sage Reference.

202.

* Michel

J. S.

Bowling

N. A.

(2013). Does dispositional aggression feed the narcissistic response? The role of narcissism and aggression in the prediction of job attitudes and counterproductive work behaviors. Journal of Business and Psychology, 28(1), 93–105. https://doi.org/10.1007/s10869-012-9265-6

203.

Milam

A. C.

Spitzmueller

Penney

L. M.

(2009). Investigating individual differences among targets of workplace incivility. Journal of Occupational Health Psychology, 14(1), 58. https://doi.org/10.1037/a0012683

204.

Miller

M. J.

Woehr

D. J.

Hudspeth

(2002). The meaning and measurement of work ethic: Construction and initial validation of a multidimensional inventory. Journal of Vocational Behavior, 60(3), 451–489. https://doi.org/10.1006/jvbe.2001.1838

205.

* Minor

Persico

Weiss

D. M.

(2018). Criminal background and job performance. IZA Journal of Labor Policy, 7(1), 1–49. https://doi.org/10.1186/s40173-018-0101-0

206.

Mishra

Ghosh

Sharma

(2021). Unethical pro-organizational behavior: A systematic review and future research agenda. Journal of Business Ethics, 1–25. https://doi.org/10.1007/s10551-021-04764-w

207.

Monga

(2016). Integrity and its antecedent: A unified conceptual framework of integrity. The Journal of Developing Areas, 50(5), 415–421. https://www.jstor.org/stable/26415605 https://doi.org/10.1353/jda.2016.0040

208.

Moore

Detert

J. R.

Klebe Treviño

Baker

V. L.

Mayer

D. M.

(2012). Why employees do bad things: Moral disengagement and unethical organizational behavior. Personnel Psychology, 65(1), 1–48. https://doi.org/10.1111/j.1744-6570.2011.01237.x

209.

* Mouw

T. A.

(2020). Assertiveness and Aggressiveness as Predictors of Aviation Safety Attitudes in Military Pilots (Publication No. 27739763) [Doctoral dissertation, Grand Canyon University]. ProQuest Dissertations Publishing.

210.

* Mufson

D. W.

Mufson

M. A.

(1998). Predicting police officer performance using the Inwald Personality Inventory: An illustration from Appalachia. Professional Psychology: Research and Practice, 29(1), 59–62. https://doi.org/10.1037/0735-7028.29.1.59

211.

Muzio

Faulconbridge

Gabbioneta

Greenwood

(2016). Bad apples, bad barrels and bad cellars: A “boundaries” perspective on professional misconduct. In Palmer

Smith-Crowe

Greenwood

(Eds.), Organizational wrongdoing (pp. 141–175). Cambridge University Press.

212.

Neuber

Englitz

Schulte

Forthmann

Holling

(2021). How work engagement relates to performance and absenteeism: A meta-analysis. European Journal of Work and Organizational Psychology, 1–24. https://doi.org/10.1080/1359432X.2021.1953989

213.

Neuman

G. A.

Baydoun

(1998). An empirical examination of overt and covert integrity tests. Journal of Business and Psychology, 13(1), 65–79. https://doi.org/10.1023/A:1022971016454

214.

Nicol

A. A. M.

Paunonen

S. V.

(2002). Overt honesty measures predicting admissions: An index of validity or reliability. Psychological Reports, 90(1), 105–115. https://doi.org/10.2466/pr0.2002.90.1.105

215.

Niehoff

B. P.

Moorman

R. H.

(1993). Justice as a mediator of the relationship between methods of monitoring and organizational citizenship behavior. Academy of Management Journal, 36(3), 527–556. https://doi.org/10.5465/256591

216.

* O’Connell

Delgado

Lawrence

Kung

Tristan

(2017). Predicting workers’ compensation claims and disciplinary actions using SecureFit®: Further support for integrative models of workplace safety. Journal of Safety Research, 61, 77–81. https://doi.org/10.1016/j.jsr.2017.02.010

217.

* O’Connor

P. J.

Stone

Walker

B. R.

Jackson

C. J.

(2017). Deviant behavior in constrained environments: Sensation-seeking predicts workplace deviance in shallow learners. Personality and Individual Differences, 108, 20–25. https://doi.org/10.1016/j.paid.2016.11.062

218.

* Oh

I. S.

Lee

Ashton

M. C.

De Vries

R. E.

(2011). Are dishonest extraverts more harmful than dishonest introverts? The interaction effects of honesty-humility and extraversion in predicting workplace deviance. Applied Psychology, 60(3), 496–516. https://doi.org/10.1111/j.1464-0597.2011.00445.x

219.

* Olaru

Burrus

MacCann

Zaromb

F. M.

Wilhelm

Roberts

R. D.

(2019). Situational Judgment Tests as a method for measuring personality: Development and validity evidence for a test of dependability. PloS One, 14(2), e0211884. https://doi.org/10.1371/journal.pone.0211884

220.

* O’Neill

T. A.

Hambley

L. A.

Chatellier

G. S.

(2014). Cyberslacking, engagement, and personality in distributed work environments. Computers in Human Behavior, 40, 152–160. https://doi.org/10.1016/j.chb.2014.08.005

221.

Ones

D. S.

Schmidt

F. L.

Viswesvaran

Lykken

D. T.

(1996). Controversies over integrity testing: Two viewpoints. Journal of Business and Psychology, 10(4), 487–501. https://doi.org/10.1007/BF02251783

222.

Ones

D. S.

Viswesvaran

Schmidt

F. L.

(1993). Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance. Journal of Applied Psychology, 78(4), 679–703. https://doi.org/10.1037/0021-9010.78.4.679

223.

* Oppler

S. H.

McCloy

R. A.

Campbell

J. P.

(2013). The prediction of supervisory and leadership performance. In Exploring the limits in personnel selection and classification (pp. 415–435). Psychology Press.

224.

* Özbağ

G. K.

(2019). The Role of Personality in Counterproductive Work Behaviour. In Joint Conference: 14th ISMC and 8th ICLTIBM-2018, Kocaeli, Turkey.

225.

Palanski

M. E.

Yammarino

F. J.

(2009). Integrity and leadership: A multi-level conceptual framework. The Leadership Quarterly, 20(3), 405–420. https://doi.org/10.1016/j.leaqua.2009.03.008

226.

Paulhus

D. L.

Williams

K. M.

(2002). The dark triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36(6), 556–563. https://doi.org/10.1016/S0092-6566(02)00505-6

227.

Pekdemir

I. M.

Turan

(2015). The relationships among love of money, Machiavellianism and unethical behavior. Canadian Social Science, 11(6), 48–59. https://doi.org/10.3968/7112

228.

* Penney

L. M.

Hunter

E. M.

Perry

S. J.

(2011). Personality and counterproductive work behaviour: Using conservation of resources theory to narrow the profile of deviant employees. Journal of Occupational and Organizational Psychology, 84(1), 58–77. https://doi.org/10.1111/j.2044-8325.2010.02007.x

229.

* Peterson

D. K.

(2002). The relationship between unethical behavior and the dimensions of the ethical climate questionnaire. Journal of Business Ethics, 41(4), 313–326. https://doi.org/10.1023/A:1021243117958

230.

* Peterson

M. H.

Griffith

R. L.

Isaacson

J. A.

O’Connell

M. S.

Mangos

P. M.

(2011). Applicant faking, social desirability, and the prediction of counterproductive work behaviors. Human Performance, 24(3), 270–290. https://doi.org/10.1080/08959285.2011.580808

231.

* Rafilson

F. M.

(1988). Development of a standardized measure to predict employee productivity. Journal of Business and Psychology, 3(2), 199–213. https://doi.org/10.1007/BF01014489

232.

Rahim

M. A.

Magner

N. R.

Antonioni

Rahman

(2001). Do justice relationships with organization-directed reactions differ across US and Bangladesh employees? International Journal of Conflict Management. https://doi.org/10.1108/eb022862

233.

Rammstedt

John

O. P.

(2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41(1), 203–212. https://doi.org/10.1016/j.jrp.2006.02.001

234.

* Ramos Villagrasa

P. J.

Fernández del Río

Koopmans

Barrada

J. R.

(2019). Assessing job performance using brief self-report scales: The case of the individual work performance questionnaire. Journal of Work and Organizational Psychology, 35(3), 195–205. https://doi.org/10.5093/jwop2019a21

235.

Raymond

L. C.

(2019). Bias During Cyber-Vetting Using Social Media and Online Searches (Publication No. 13898014) [Doctoral dissertations, Robert Morris University]. ProQuest Dissertations Publishing.

236.

Reid

J. E.

(1967). The Reid Report. John E. Reid & Associates.

237.

Reid

J. E.

(1971). The Reid Report. John E. Reid & Associates.

238.

Reid

J. E.

(1992). The Abbreviated Reid Report (28th ed.). John E. Reid & Associates.

239.

Rentfrow

P. J.

Gosling

S. D.

(2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236. https://doi.org/10.1037/0022-3514.84.6.1236

240.

Rest

J. R.

(1980). Moral judgment research and the cognitive-developmental approach to moral education. The Personnel and Guidance Journal, 58(9), 602–605. https://doi.org/10.1002/j.2164-4918.1980.tb00462.x

241.

* ReyhanoĞLu

Özden

(2020). The effect of workplace conditions on employee job performance. Ege Akademik Bakış Dergisi, 20(1), 57–74. https://doi.org/10.21121/eab.510839

242.

Richards

D. A.

Schat

A. C. H.

(2011). Attachment at (not to) work: Applying attachment theory to explain individual behavior in organizations. Journal of Applied Psychology, 96(1), 169–182. https://doi.org/10.1037/a0020372

243.

* Ripley

A. M.

(2019). Examining the Utility of the HEXACO-PI-R for Predicting Positive and Negative Indicators of Police Officer Performance (Publication No. 13880616) [Doctoral dissertation, Walden University]. ProQuest Dissertations Publishing.

244.

Robinson

S. L.

Bennett

R. J.

(1995). A typology of deviant workplace behaviors: A multidimensional scaling study. Academy of Management Journal, 38(2), 555–572. https://doi.org/10.5465/256693

245.

Sackett

P. R.

(1994). Integrity testing for personnel selection. Current Directions in Psychological Science, 3(3), 73–77. https://doi.org/10.1111/1467-8721.ep10770422

246.

Sackett

P. R.

Berry

C. M.

Wiemann

S. A.

Laczo

R. M.

(2006). Citizenship and counterproductive behavior: Clarifying relations between the two domains. Human Performance, 19(4), 441–464. https://doi.org/10.1207/s15327043hup1904_7

247.

Sackett

P. R.

Burris

L. R.

Callahan

(1989). Integrity testing for personnel selection: An update. Personnel Psychology, 42(3), 491–529. https://doi.org/10.1111/j.1744-6570.1989.tb00666.x

248.

Sackett

P. R.

Gruys

M. L.

Ellingson

J. E.

(1998). Ability–personality interactions when predicting job performance. Journal of Applied Psychology, 83(4), 545–556. https://doi.org/10.1037/0021-9010.83.4.545

249.

Sackett

P. R.

Harris

M. M.

(1984). Honesty testing for personnel selection: A review and critique. Personnel Psychology, 37(2), 221–245. https://doi.org/10.1111/j.1744-6570.1984.tb01447.x

250.

Sackett

P. R.

Schmitt

(2012). On reconciling conflicting meta-analytic findings regarding integrity test validity. The Journal of Applied Psychology, 97(3), 550–556. https://doi.org/10.1037/a0028167

251.

Safrinal

Sukendi

Supriyanto

Afrizal

(2021). Use of a webpage in designing employee data collection at the Aceh language center office. International Journal Software Engineering and Computer Science (IJSECS), 1(1), 1–6. https://doi.org/10.35870/ijsecs.v1i1.321

252.

* Sarwar

Irshad

Zhong

J. Y.

Sarwar

Pasha

(2020). Effects of social undermining in families on deviant workplace behaviours in Pakistani nurses. Journal of Nursing Management, 28(4), 938–947. https://doi.org/10.1111/jonm.13027

253.

* Schilbach

Baethge

Rigotti

(2020). Why employee psychopathy leads to counterproductive workplace behaviours: An analysis of the underlying mechanisms. European Journal of Work and Organizational Psychology, 29(5), 693–706. https://doi.org/10.1080/1359432X.2020.1739650

254.

Schmidt

F. L.

Hunter

J. E.

(1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274. https://doi.org/10.1037/0033-2909.124.2.262

255.

Schmidt

F. L.

I.-S.

Shaffer

J. A.

(2016). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 100 years. Retrieved July, 3, 2018. https://home.ubalt.edu/tmitch/645/session%204/Schmidt%20&%20Oh%20MKUP%20validity%20and%20util%20100%20yrs%20of%20research%20Wk%20PPR%202016.pdf.

256.

Schneider

T. J.

Goffin

R. D.

(2012). Perceived ability to deceive and incremental prediction in pre-employment personality testing. Personality and Individual Differences, 52(7), 806–811. https://doi.org/10.1016/j.paid.2012.01.015

257.

Schroeder

A. N.

Odd

K. R.

Whitaker

J. H.

(2020). Agree to disagree: Examining the psychometrics of cybervetting. Journal of Managerial Psychology, 35(5), 435–450. https://doi.org/10.1108/JMP-09-2018-0420

258.

Shamim

Zeng

Choksy

U. S.

Shariq

S. M.

(2020). Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level. International Business Review, 29(6), 101604. https://doi.org/10.1016/j.ibusrev.2019.101604

259.

* Shen

(2011). The application of a person-oriented criterion-related configural approach to the relationship between personality traits and work behaviors (Publication No. 3464618) [Doctoral dissertations, University of Minnesota]. ProQuest Dissertations Publishing.

260.

Shipley

W. C.

(1940). A self-administering scale for measuring intellectual impairment and deterioration. The Journal of Psychology, 9(2), 371–377. https://doi.org/10.1080/00223980.1940.9917704

261.

* Shusman

E. J.

Inwald

R. E.

(1991). Predictive validity of the Inwald Personality Inventory. Criminal Justice and Behavior, 18(4), 419–426. https://doi.org/10.1177/0093854891018004004

262.

* Shusman

E. J.

Inwald

R. E.

Landa

(1984). Correction officer job performance as predicted by the IPI and MMPI: A validation and cross-validation study. Criminal Justice and Behavior, 11(3), 309–329. https://doi.org/10.1177/0093854884011003004

263.

Skarlicki

D. P.

Folger

(1997). Retaliation in the workplace: The roles of distributive, procedural, and interactional justice. Journal of Applied Psychology, 82(3), 434–443. https://doi.org/10.1037/0021-9010.82.3.434

264.

Smithikrai

(2008). Moderating effect of situational strength on the relationship between personality traits and counterproductive work behaviour. Asian Journal of Social Psychology, 11(4), 253–263. https://doi.org/10.1111/j.1467-839X.2008.00265.x

265.

Somer

Korkmaz

Tatar

(2002). Beflfaktör Kiflilik Envanteri’nin Gelifltirilmesi-I: Ölçek ve Alt Ölçeklerin Oluflturulmas›. Türk Psikoloji Dergisi, 17(49), 21–33.

266.

Sonenshein

(2007). The role of construction, intuition, and justification in responding to ethical issues at work: The sensemaking-intuition model. Academy of Management Review, 32(4), 1022–1040. https://doi.org/10.5465/amr.2007.26585677

267.

Spector

P. E.

Fox

Penney

L. M.

Bruursema

Goh

Kessler

(2006). The dimensionality of counterproductivity: Are all counterproductive behaviors created equal. Journal of Vocational Behavior, 68(3), 446–460. https://doi.org/10.1016/j.jvb.2005.10.005

268.

Spector

P. E.

Jex

S. M.

(1998). Development of four self-report measures of job stressors and strain: Interpersonal conflict at work scale, organizational constraints scale, quantitative workload inventory, and physical symptoms inventory. Journal of Occupational Health Psychology, 3(4), 356–367. https://doi.org/10.1037/1076-8998.3.4.356

269.

Sterne

J. A.

Sutton

A. J.

Ioannidis

J. P.

Terrin

Jones

D. R.

Lau

Schmid

C. H.

(2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ, 343(July 22), d4002–d4002. https://doi.org/10.1136/bmj.d4002

270.

Sverke

Hellgren

Näswall

(2002). No security: A meta-analysis and review of job insecurity and its consequences. Journal of Occupational Health Psychology, 7(3), 242–264. https://doi.org/10.1037/1076-8998.7.3.242

271.

Tepper

B. J.

(2000). Consequences of abusive supervision. Academy of Management Journal, 43(2), 178–190. https://doi.org/10.5465/1556375

272.

Terman

L. M.

Otis

A. S.

Dickson

Hubbard

O. S.

Norton

J. K.

Howard

Cassingham

C. C.

(1917). A trial of mental and pedagogical tests in a civil service examination for policemen and firemen. Journal of Applied Psychology, 1(1), 17–29. https://doi.org/10.1037/h0073841

273.

Terpstra

D. A.

Mohamed

A. A.

Kethley

R. B.

(1999). An analysis of federal court cases involving nine selection devices. International Journal of Selection and Assessment, 7(1), 26–34. https://doi.org/10.1111/1468-2389.00101

274.

* Terris

Jones

(1982). Psychological factors related to employees’ theft in the convenience store industry. Psychological Reports, 51(Suppl 3), 1219–1238. https://doi.org/10.2466/pr0.1982.51.3f.1219

275.

* Terris

Jones

J. W.

(1980). Attitudinal and personality correlates of theft among supermarket employees. Journal of Security Administration, 3(2), 65–78.

276.

Thibault

Kelloway

E. K.

(2020). The dark tetrad at work. Human Performance, 33(5), 406–424. https://doi.org/10.1080/08959285.2020.1802728

277.

* Trent

J. D.

Barron

L. G.

Rose

M. R.

Carretta

T. R.

(2020). Tailored Adaptive Personality Assessment System (TAPAS) as an indicator for counterproductive work behavior: Comparing validity in applicant, honest, and directed faking conditions. Military Psychology, 32(1), 51–59. https://doi.org/10.1080/08995605.2019.1652481

278.

Tureary

A. S.

(1999). Manual of Cognitive Ability Test. Dar Elketab Aljamey Press.

279.

* Umphress

E. E.

Bingham

J. B.

Mitchell

M. S.

(2010). Unethical behavior in the name of the company: The moderating effect of organizational identification and positive reciprocity beliefs on unethical pro-organizational behavior. Journal of Applied Psychology, 95(4), 769–780. https://doi.org/10.1037/a0019214

280.

* Van Hein

J. L.

Kramer

J. J.

Hein

(2007). The validity of the Reid Report for selection of corrections staff. Public Personnel Management, 36(3), 269–280. https://doi.org/10.1177/009102600703600306

281.

Van Iddekinge

C. H.

Roth

P. L.

Raymark

P. H.

Odle-Dusseau

H. N.

(2012a). The criterion-related validity of integrity tests: An updated meta-analysis. Journal of Applied Psychology, 97(3), 499–530. https://doi.org/10.1037/a0021196

282.

Van Iddekinge

C. H.

Roth

P. L.

Raymark

P. H.

Odle-Dusseau

H. N.

(2012b). The critical role of the research question, inclusion criteria, and transparency in meta-analyses of integrity test research: A reply to Harris et al. (2012) and Ones, Viswesvaran, and Schmidt (2012). The Journal of Applied Psychology, 97(3), 543–542. https://doi.org/10.1037/a0026551

283.

* Van Scotter

J. R.

Roglio

K. D. D.

(2020). CEO Bright and dark personality: Effects on ethical misconduct. Journal of Business Ethics, 164(3), 451–475. https://doi.org/10.1007/s10551-018-4061-5

284.

Victor

Cullen

J. B.

(1988). The organizational bases of ethical work climates. Administrative Science Quarterly, 101–125. https://doi.org/10.2307/2392857

285.

Viechtbauer

(2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

286.

Viechtbauer

. (2019). metafor: Meta-analysis package for R (Version 2.1-0). Computer Software Inc.

287.

Wanek

J. E.

(1996). The construct of integrity: Item level factor analysis of the dimensions underlying honesty testing and Big-Five measures of personality (Publication No. 9541372) [Doctoral dissertations, University of Minnesota]. ProQuest Dissertations Publishing.

288.

Wanek

J. E.

(1999). Integrity and honesty testing: What do we know? How do we use it? International Journal of Selection and Assessment, 7(4), 183–195. https://doi.org/10.1111/1468-2389.00118

289.

Wang

Cheng

G. H. L.

Chen

Leung

(2019). Team creativity/innovation in culturally diverse teams: A meta-analysis. Journal of Organizational Behavior, 40(6), 693–708. https://doi.org/10.1002/job.2362

290.

Webster

G. D.

DeWall

C. N.

Pond

R. S.

Jr Deckman

Jonason

P. K.

B. M.

Bator

R. J.

(2014). The brief aggression questionnaire: Psychometric and behavioral evidence for an efficient measure of trait aggression. Aggressive Behavior, 40(2), 120–139. https://doi.org/10.1002/ab.21507

291.

Weiss

P. A.

Vivian

J. E.

Weiss

W. U.

Davis

R. D.

Rostow

C. D.

(2013). The MMPI-2 L Scale, reporting uncommon virtue, and predicting police performance. Psychological Services, 10(1), 123–130. https://doi.org/10.1037/a0029062

292.

* Werner

S. H.

Jones

J. W.

Steffy

B. D.

(1989). The relationship between intelligence, honesty, and theft admissions. Educational and Psychological Measurement, 49(4), 921–927. https://doi.org/10.1177/001316448904900415

293.

White

D. L.

(2020). Police candidate selection: Assessing the effectiveness of pre-employment polygraph screening. Policing: A Journal of Policy and Practice, 14(4), 1138–1150. https://doi.org/10.1093/police/pay099

294.

* Wojtkowska

Stolarski

Matthews

(2019). Time for work: Analyzing the role of time perspectives in work attitudes and behaviors. Current Psychology, 1–12. https://doi.org/10.1007/s12144-019-00536-y

295.

Wonderlic & Associates (2002). Wonderlic Personnel Test manual Libertyville. Wonderlic.

296.

Wong

C. S.

Law

K. S.

(2002). The effects of leader and follower emotional intelligence on performance and attitude: An exploratory study. The Leadership Quarterly, 13(3), 243–274. https://doi.org/10.1016/S1048-9843(02)00099-1

297.

* Woolley

R. M.

Hakstian

A. R.

(1993). A comparative study of integrity tests: The criterion-related validity of personality-based and overt measures of integrity. International Journal of Selection and Assessment, 1(1), 27–40. https://doi.org/10.1111/j.1468-2389.1993.tb00081.x

298.

* Yuan

Barnes

C. M.

(2018). Bad behavior keeps you up at night: Counterproductive work behaviors and insomnia. Journal of Applied Psychology, 103(4), 383–398. https://doi.org/10.1037/apl0000268

299.

* Zhang

Akhtar

M. N.

Zhang

Sun

(2019). Are overqualified employees bad apples? A dual-pathway model of cyberloafing. Internet Research: Electronic Networking Applications and Policy, 30(1), 289–313. https://doi.org/10.1108/INTR-10-2018-0469

300.

Zimbardo

P. G.

(2004). Does psychology make a significant difference in our lives? American Psychologist, 59(5), 339–351. https://doi.org/10.1037/0003-066X.59.5.339

301.

Zimbardo

P. G.

Boyd

J. N.

(2015). Putting time in perspective: A valid, reliable individual-differences metric. In Time perspective theory; review, research and application (pp. 17–55). Springer. https://doi.org/10.1007/978-3-319-07368-2_2

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