The Genos employee motivation assessment Gilles E. Gignac and Benjamin R. Palmer
Gilles E. Gignac and Benjamin R. Palmer are based at Genos, Waterloo, Australia.
Abstract Purpose – This paper aims to describe a new measure of employee motivational fit, namely the Genos employee motivation assessment (GEMA), its predictive validity and use in learning and organizational development activities. Design/methodology/approach – Within three different organizations, employees completed GEMA via an online web survey system. Correlation analyses were then performed with a series of job performance and employee engagement data. Findings – Motivational fit (i.e. the degree of alignment between what an individual is motivated by and experiences in their work), within four areas measured by GEMA (namely, role fit, management fit, team fit, and organization fit), were found to be associated with average predictive validity correlation coefficients equal to 0.46, .073, 0.67, and 0.52, respectively. Research limitations/implications – Statistical analyses at the individual level would be beneficial in future research. Additionally, whether motivational fit can be improved via learning and/or organizational development interventions, and whether such improvement leads to corresponding improvements in performance and engagement remains to be determined. Practical implications – Intervention initiatives designed to improve motivational fit need to be designed and tested. The findings of this study suggest that successful interventions may result in improvements in job performance and employee engagement. Originality/value – This paper will be of interest to professionals in recruitment, learning and organizational development interested in the improvement of job performance and employee engagement. This is the first study to examine the validity of GEMA scores and to propose the potential use of motivational fit as an intervention medium to improve these areas. Keywords Motivation (psychology), Employees, Job satisfaction, Organizational culture Paper type Research paper
ost leaders know that at the heart of every productive and successful business lies a thriving organizational culture. A culture where hard working people collaborate passionately to produce great results. This notion has been supported by extensive meta-analytic research in the area of employee engagement. For example, Harter et al. (2009) found that organizations at the 75th percentile on employee engagement experienced 16 percent greater profitability than those organizations at the 25th percentile. While there are many tools available (such as engagement surveys and reports), to help managers and human resource professionals build highly engaged and productive workplace cultures, these cultures are seemingly very difficult to develop. As a result they are also seen as a critical lever of sustainable competitive advantage.
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q Copyright Genos Pty Ltd 2010
DOI 10.1108/00197851111108908
In this paper, we describe a new assessment designed to help managers and human resource professionals better understand the motivational characteristics of their employees and how this compares with their perceptions of the environment within which they work. We posit that such insight and information may prove useful in the conceptualization of strategies to facilitate employee engagement and job performance more broadly, at the group and individual level. We describe the model of motivation the assessment has been
VOL. 43 NO. 2 2011, pp. 79-87, Emerald Group Publishing Limited, ISSN 0019-7858
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‘‘ PE fit is defined as the ‘compatibility between an individual and a work environment that occurs when their characteristics are well matched’. ’’
designed to assess, report some preliminary research findings on its predictive validity, and, finally, describe GEMA’s potential utility as a learning and organizational development tool.
Background Within academic realms, this new assessment, namely the Genos employee motivation assessment (GEMA), may be identified within the broader person-environment (PE) fit literature. PE fit is defined as the ‘‘compatibility between an individual and a work environment that occurs when their characteristics are well matched’’ (Kristof et al., 2005, p. 281). Kristof (1996) sub-categorized PE fit into four areas: person-job (PJ), person-supervisor (PS), person-group (PG), and person-organization (PO). Based on a meta-analysis, Kristof et al. (2005) reported predictive validity associated with all four areas of PE fit; for example, variables such as job satisfaction, organizational commitment, and intention to quit. It will be noted, however, that Kristof et al. (2005) failed to identify any PE fit area as a predictor of job performance. Rather than a measure of PE fit, we refer to GEMA as a measure of motivational fit. Theoretically, the differences between PE fit and motivational fit may be relatively minor and principally semantic in nature. However, the items within GEMA are framed explicitly within the context of motivational drivers, rather than values or preferences, which is typically the case within the PE assessment literature (Edwards, 2008). We define employee motivational fit as the alignment between what motivates an employee to work and the degree to which those motivational characteristics are experienced at work. On the same basis that PE fit has been found to be a predictor of variables such as a job satisfaction, organizational commitment, and intention to quit (Kristof et al., 2005), it is theoretically proposed that the degree of employee motivational fit will predict an array of important variables such as organizational commitment, absenteeism, and job satisfaction, as well as more objective or outcome-focused measures of job performance. We base this hypothesis on the simple notion that the more an individual does what they are motivated to do, and the more an individual works with the type of people they find motivating, the more likely they are to perform at their best and find satisfaction in their work.
GEMA model of motivational fit A number of PE fit measures have been published to-date. Fields (2002) lists and describes seven measures of fit, most of which have been used or adapted in several empirical investigations (see Kristof et al., 2005). However, based on our survey of the literature, none of the existing measures could be said to explicitly and comprehensively measure all four areas of PE fit, as delineated by Kristof (1996). Instead, the existing measures tend to focus principally upon PO fit related characteristics, and to some degree PJ fit; however, only relatively little are PS and PG fit represented. Thus, given the abundant research that has established the importance of management and teams in the workplace (e.g. Hoegl and Gemuenden, 2001; den Hartog and Koopman, 2001), we set out to design an alignment measure that provided breadth of measurement across all four areas. Additionally, instead of using terms such as values or preferences, we chose to develop an alignment measure within the context of motivation. The importance of motivation in the workplace has a long history (Goodman, 1971), and, furthermore, may be said to be experiencing a re-emergence in popularity within the business management literature (e.g. Nohria et al., 2008). Additionally, from an applied perspective, addressing and discussing individual differences
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in alignment with respect to motivation may be considered less confronting, in comparison to doing so from the perspective of personal values. The model of motivational fit that underlies GEMA is derived from the four areas of PE fit first identified by Kristof (1996). However, rather than use the words job, supervisor, group, and organization, we opted for alternatives that may be viewed more receptively by practitioners. Specifically, the four areas of motivational alignment within the GEMA model are: role, management, team, and organization.
GEMA motivational characteristics Within each of the four GEMA areas are motivational characteristics that may be defined as relatively narrow facets of work relevant motivation for which one would expect non-negligible individual differences. Table I lists the GEMA motivational characteristics associated with each respective area. It can be seen that the role area has 15 motivational characteristics, while the remaining three areas have ten each. There are of course additional motivational characteristics that, arguably, should be included within the current GEMA model. However, some consideration was placed on achieving a balance between breadth of representation and manageability of administration and report interpretation. In addition to the balance of breadth and manageability, an important criterion used in selecting motivational characteristics was non-negligible individual differences. By this we mean not all employees would be substantially motivated by the presence of the attribute in the workplace. Consider, for example, an organizational attribute such as ‘‘career opportunities’’, or a management attribute such as ‘‘feedback’’. It would be expected that nearly all employees would be motivated to work in an organization with career opportunities or for management that provided feedback. Such variables can be considered ‘‘universal motivation drives’’. Of course, measuring the degree to which employees experience career opportunities within their organization, or the degree to which they receive feedback from management, may comprise attributes of an important assessment and indeed are often included as drivers of engagement in engagement surveys. However, we viewed that such motivators should not be included in GEMA, where the expectation was that respondents could learn about their workplace motivational profile as relatively distinct from others. In summary, the results reported in Kristof et al. (2005) suggest strong support for the development of a comprehensive applied measure of fit. Consequently, in this study, we report some predictive validity results associated with GEMA, a comprehensive measure of motivational alignment.
Table I The four areas and 45 motivational drivers measured by GEMA Role
Management
Team
Organization
Systems and processes Protect Finance Intellectual stimulation Design Technology Importance Decision making Influence Interpersonal-interaction Customer-interaction Contribution Variety Pace Employment flexibility
Directive Implementer Empowering Specialist Competitive Performance-oriented People-oriented Networker Decisiveness Rotating management
Organizing/planning Norms Inclusive Purpose Quality/output Achievement oriented Camaraderie Support Rotating teams Virtual teams
Bureaucracy Structure Thought leader Experimental Remuneration Competitiveness Social responsibility People culture Multi-faceted Improvising
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Method Sample The analyses were based on three samples. The first sample consisted of 82 employees (76 percent female) working in a call center within an Australian industrial company. The employees were categorized into nine units with an average of 9.11 employees within each unit. The second sample consisted of 139 employees (77 percent female) working in a call center within an Australian insurance company. The employees were categorized into seven units with an average of 19.9 employees within each unit. The third sample consisted of 460 employees (31 percent female) working in a UK utility company. The employees were categorized into ten units with an average of 46 employees within each unit. Measures The Genos employee motivational assessment (GEMA) was used to measure the degree of motivational alignment between employees and their role, management, team, and organization motivational characteristics. GEMA consists of 45 item dyads whereby respondents rate the degree to which a motivational characteristic motivates them to work and the degree to which that motivational characteristic is present in their work environment. Each pair of items are rated on a six-point scale (0 to 5) based on the verbal anchors provided in Table II. Four example items within GEMA are listed in Table II. A substantial amount of discussion has surrounded the issue of determining how fit scores should be calculated (Edwards, 1995). For the applied researcher and practitioner, a delicate balance between simplicity and sophistication must be achieved. Ultimately, precisely what type of score should represent fit versus non-fit is to some degree arbitrary. However, some demarcation criterion must be chosen to facilitate score interpretations and statistical analyses. Consequently, we specified that an average absolute deviation score between desire and experience items within a particular area equal to 1.0 or less was considered ‘‘aligned’’. An average absolute deviation score greater than 1.0 was considered unaligned. Using such a procedure allowed for the calculation of a fit score for each employee, which in turn allowed for the calculation of the percentage of employees within a unit or organization that were aligned with their role, management, team, and/or organization[1]. Within the industrial company call center, there were six objectively determined job performance indicators, all of which were provided to us by the organization’s management. The job performance indicators included: appointment conversion rate (i.e. percentage of telephone calls made to the call center by the general public that resulted in the booking of Table II Example GEMA items
1a 1b 2a 2b 3a
3b 4a 4b
I am motivated by making a contribution to society in my work My role involves making a contribution to society through my work I am motivated by management that makes decisions quickly I am supervised by management that makes decisions quickly I am motivated by a team where there is the opportunity to regularly work with different team members I work in a team where there is the opportunity to regularly work with different team members I am motivated by an organization that has clear levels of management and authority I work in an organization that has clear levels of management and authority
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Not at all
Very little
Little
Somewhat
Much
Very much
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an appointment to see a sales consultant or the booking of a job), calls answered (i.e. number of calls answered), availability (average percentage of time while at work prepared to answer calls), hold time (average amount of time callers are placed on hold), percentage talking (percentage of time while engaged with a phone call that the employee spends talking), and staff utilization (percentage of time employees spend engaged in work activities). All metrics correspond to data collected over a three-month period during 2009. Engagement in the insurance company call center was measured with Hewitt’s engagement survey. In this investigation, total engagement scores for each unit were provided to us by management and were utilized in the analyses. Absenteeism, also provided by management, represented the number of hours spent away from work. Finally, engagement in the utility company was measured with PriceWaterCooper’s engagement survey. Total engagement scores for each unit were provided to us by management. Procedure Employees within all three organizations were contacted by an HR representative via e-mail for the purposes of inviting them to participate in a study that would help the participating organizations to understand their work relevant motivational preferences. The employees were assured that their responses would remain anonymous and that the data would be analyzed only at the group level by an organization external to their place of work. Typically, the organizations allowed the employees two weeks to complete the survey, which was administered online by Genos Pty. Response rates were 75, 100, and 28 percent for the industrial company call center, the insurance company call center, and the utility company, respectively. Data analysis In all three samples, a series of Pearson correlations were performed between GEMA fit levels and outcome variables. As the three samples were considered either complete or quasi representations of the populations of interest (i.e. a substantial percentage of employees within the population were included in the sample), it was not considered necessary to estimate statistical significance levels associated with the correlation coefficients.
Results As can be seen in Table III, the overall mean percentage of employees who were aligned with their role corresponded to 49.8 percent. The area associated with the lowest percentage of aligned employees corresponded to the organization area (43.0) percent. The management and team areas were associated with approximately equal levels of alignment, at 59.2 percent and 56.3 percent, respectively. Thus, it may be suggested that there was a relatively significant percentage of employees who were not aligned with their role, management, team, or organization. As can be seen in Table IV, one or more GEMA fit area scores were found to correlate substantially with all of the dependent variables, where a large correlation is defined as equal to or greater than 0.50 (Cohen, 1992). Table III Means and standard deviations associated with percentage of employees who were categorized as well-fitting across groups within the three samples of data Role Mean SD Call center – industrial Call center – insurance Utility company Overall mean
31.3 42.4 75.8 49.8
11.5 9.1 13.6
Management Mean SD 49.0 59.4 69.3 59.2
13.6 10.3 20.5
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Team Mean SD 47.7 61.1 60.1 56.3
25.0 13.4 16.7
Organization Mean SD 31.2 49.0 48.7 43.0
8.9 12.7 16.1
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Table IV Pearson correlations between group-level motivational-fit and various HR metrics Role
Management
Team
Organization
Call center – industrial Appointment conversion rate Calls answered Availability Hold time Percentage talking Staff utilization
0.71 0.40 0.04 20.72 0.47 0.49
0.94 0.91 0.35 20.79 0.77 0.82
0.81 0.70 0.11 20.90 0.84 0.80
0.64 0.57 0.11 20.63 0.70 0.70
Call center – insurance Engagement Absenteeism
0.47 20.34
0.50 20.68
0.48 20.70
0.10 20.48
0.46 0.46
0.73 0.73
0.70 0.67
0.75 0.52
Utility company Engagement jAvgj
Notes: Correlations were calculated as if the population of interest was available; consequently, levels of statistical significance were not reported; jAvgj corresponds to the average of the absolute correlations, i.e. the negative correlations (hold time and absenteeism) were reflected for the purposes of calculated the area average correlations
With respect to the industrial company call center sample, very substantial correlations were found associated to all of the objective job performance metrics. We note, in particular, the substantial effects between management fit and ‘‘appointment conversion rate’’ (r ¼ 0:94). This effect means that higher levels of motivational alignment between employees and management is associated with greater numbers of sales opportunities from calls. Based on the correlations in Table IV, it may also be said that greater levels of motivational alignment are positively associated with a greater number of calls answered, somewhat greater levels of availability, more time spent talking to clients, and greater staff utilization rates. With respect to insurance company call center sample, substantial effects were observed between motivational alignment and engagement. Specifically, role, management, and team motivational alignment were substantially and positively correlated with self-reported levels of engagement within units (see Table IV). Furthermore, substantial negative correlations were obtained between management and team motivational alignment and absenteeism (r ¼ 20:68 and r ¼ 20:70, respectively). Thus, higher levels of motivational alignment within units was associated with less absenteeism within units. Finally, with respect to the utility company, greater levels of motivational alignment were associated with greater levels of employee engagement. The management and team areas were, again, two of the larger predictors, which was an observed pattern across all three samples. In fact, based on the averages of the absolute correlations across all metrics and samples, the management area was associated with the most substantial predictive validity (r ¼ 0:73) followed by the team area (r ¼ 0:67).
Discussion The results of this preliminary investigation on the predictive validity of GEMA are encouraging. All four of the GEMA area fit scores were found to predict HR outcome variables and employee engagement to a substantial degree. Perhaps most noteworthy were the effects associated with the management and team areas, which tended to be associated with greatest degree of predictive validity. It is interesting to note that, based on Kristof et al.’s (2005) comprehensive meta-analysis of the PE fit research, both person-supervisor and person-group fit were associated with far fewer empirical investigations, in comparison to person-job and person-organization fit. Thus, based on the preliminary results associated with this investigation, it would appear that researchers
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should focus more substantially upon management and team motivational alignment, as far as predictive validity is concerned. To our knowledge, GEMA is the only fit assessment that measures all four commonly regarded areas of fit within a work environment. In contrast to the majority of other investigations in the motivational-fit literature, which tend to use self-report job satisfaction and self-reported intent to leave as dependent variables (see Kristof et al., 2005), this investigation used a number of objectively determined HR metrics, such as call conversion rate, average hold time, and absenteeism. The substantial, negative correlations between motivational-fit and absenteeism may be suggested to have important practical implications, as they cannot be explained by method effects (i.e. unlike self-reported intent to leave). Wheeler et al. (2005) provided a theoretical model for the effects of fit on job related behaviors. They theorized that, in absence of alternative job opportunities, low fitting employees may stay in their current job, but engage in counter-productive behaviors such as inactivity and absenteeism. Our investigation supports Wheeler et al.’s (2005) model in that PE fit was found to be substantially and negatively associated with absenteeism. Thus, while low fitting employees may not necessarily leave their job, they may nonetheless engage in behaviors that lead to lower levels of productivity at substantial expense to an organization. In addition to measuring all four areas of motivational-alignment, another possible explanation for the substantial predictive effects reported in this investigation may be due to the fact that GEMA measures each area fit in a relatively comprehensive manner. That is, some motivational-fit investigations have used as few as three items to measure P-E fit (Cable and Judge, 1996; Lauver and Kristof-Brown, 2001). Furthermore, the items used in previous investigations frequently tend to be non-characteristic specific, for example, ‘‘My values match or fit the values of the organization’’ (Wheeler et al., 2007). In contrast, GEMA measures a total of 45 specific motivational drivers to help respondents consider, in more detail, whether their motivational drives match their experiences and circumstances at work. Much discussion in the PE fit literature has revolved around the issue of scoring and statistical analysis (Edwards, 1995). In this investigation, we used arguably one of the most straightforward approaches: absolute difference scores and simple correlations between the percentage of individuals who were considered aligned (based on a relatively arbitrary criterion of less than 1.0) and the outcome variables. The limitations associated with the use of differences scores have been well articulated, particularly in the context of a lack of reliability (Peter et al., 1993). However, in this investigation, the validity coefficients were so substantial and consistent that there would appear to be strong support for the contention that the methodology is robust. Ultimately, as GEMA is first and foremost an applied measure, its predictive validity should be the number criterion for its psychometric evaluation, as opposed to other considerations such as internal consistency reliability, factorial validity, and discriminant validity, for example. Limitations A limitation associated with this investigation is that the data were analyzed at the group/unit level. Thus, unit or group fit levels were correlated with the various HR metrics. Ideally, individual levels of motivational-fit would have been correlated with individual scores on the HR metrics. Such an approach would likely have yielded more robust results, as the number of correlated data points would have been larger (i.e. from an average of nine units to as high as 460 individuals in the case of the utility company). Furthermore, the motivational-fit data at the individual level were associated with a greater range of scores, which, all other things equal, would have yielded, on average, more substantial predictive validity effects (i.e. range restriction is known to reduce the magnitude of correlations; Stauffer, 2001). However, we chose to ensure the anonymity of employee responses (a concern expressed to us by several participants), which precluded the possibility of matching up the individual level fit data with the corresponding individual HR metric data. Thus, conducting predictive validity in this area at the individual level should be acknowledged as a challenge. Nonetheless, although the method of analysis was not ideal, combined across all three samples, the pattern of results were remarkably consistent, which suggest the possibility of substantial utility of a measure such as GEMA in the workplace.
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Future research Of course, replication of the effects reported in this investigation across a number of samples and industries would be valuable. Additionally, it would be useful to implement various strategies to facilitate motivational alignment within employees to assess whether there were any concomitant increases in employee engagement and job performance more generally. Such a study would support a causal connection between the two constructs. Applications In addition to facilitating research, GEMA was designed to facilitate useful and practical applications in the workplace. In this section, we briefly describe uses of GEMA from an applied perspective within the context of ‘‘finding fit’’ and ‘‘shaping fit’’. In finding fit, GEMA could be used in recruitment to help identify candidates who are motivated by what they are likely to experience on the job in terms of the role, manager, team and ultimately the organization itself. For example, if a small company was comprised of a number of individuals who scored very high on the camaraderie (team) driver, it may be beneficial to hire candidates applying for work within that company that similarly report being substantially motivated by camaraderie. Additionally, GEMA could also be used in career guidance and counseling. That is, by putting individuals through the assessment (what they find motivating only), the results may help form the basis of useful coaching/counseling conversations about different occupations, managers, teams and indeed organizations they might find motivating to work for. In shaping fit, GEMA can be used at the group level to identify those motivational characteristics for which there is a substantial misalignment between desire and experience, and then implement initiatives to help improve the gap. For example, one organization that used GEMA with their graduate population found that their graduates were highly motivated by the role driver intellectual stimulation, but were not experiencing it in their work. As a result, the organization asked the graduates to form their own ‘‘think-tank’’ within the organization, to survey customers, and to brainstorm what could be done to improve customer service. Similarly, in shaping fit, GEMA can be used at the group level to identify things that a team of employees experience a lot of that are de-motivating. For example, at an airline we were working with, flight attendants reported experiencing a lot of customer interaction (a GEMA role driver), however, their GEMA results suggested they were not motivated by it. Focus group findings revealed that the flight attendants felt they were taught many aspects of their job (such as serving food and drinks and providing safety procedures), with the exception of delivering customer service under difficult circumstances (e.g. dealing with an angry customer who’s in-flight entertainment system is not working). As a result, the organization provided some personal resiliency training to their flight attendants to help them better cope with this aspect of their work. In summary, the results of this investigation suggest that motivational alignment, as conceptualized and measured by GEMA, may be a useful tool for application in industry. As its potential applications are many and varied, we look forward to both ourselves and others uncovering further its validity and utility in the workplace.
Note 1. We appreciate that it would be more statistically powerful to analyze the data at the individual level. That is, correlate individual motivational alignment scores with individual levels of performance. However, the employees completed the assessment anonymously, which precluded the possibility of combining performance scores with GEMA fit scores.
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den Hartog, D.N. and Koopman, P.L. (2001), ‘‘Leadership in organizations’’, in Anderson, N., Ones, D.S., Kepir-Sinangil, H. and Viswesvaran, C. (Eds), International Handbook of Industrial, Work and Organizational Psychology, Vol. 2, Sage, London. Edwards, J.R. (1995), ‘‘Alternatives to difference scores as dependent variables in the study of congruence in organizational research’’, Organizational Behaviour and Human Decision Processes, Vol. 64 No. 3, pp. 307-24. Edwards, J.R. (2008), ‘‘Person-environment fit in organisations: an assessment of theoretical progress’’, The Academy of Management Annals, Vol. 2, pp. 167-230. Fields, D.L. (2002), Taking the Measure of Work: A Guide to Validated Scales for Organizational Research and Diagnosis, Sage, Thousand Oaks, CA. Goodman, T.H. (1971), ‘‘Employee motivation’’, Library Trends, Vol. 20, July, pp. 39-47. Harter, J.K., Schmidt, F.L., Killham, E.A. and Agrawal, S. (2009), Q12 Meta-analysis: The Relationship between Engagement at Work and Organizational Outcomes, Gallop, Omaha, NE. Hoegl, M. and Gemuenden, H.G. (2001), ‘‘Teamwork quality and the success of innovative projects: a theoretical concept and empirical evidence’’, Organization Science, Vol. 12, July-August, pp. 435-49. Kristof, A. (1996), ‘‘Person-organisation fit: an integrative review of it’s conceptualisations, measurement, and implications’’, Personnel Psychology, Vol. 49, pp. 1-49. Kristof, A., Zimmerman, R.D. and Johnson, E.C. (2005), ‘‘Consequences of individuals’ fit at work: a meta-analysis of person-job, person-organization, person-group and person-supervisor fit’’, Personnel Psychology, Vol. 58, pp. 281-342. Lauver, K.J. and Kristof-Brown, A. (2001), ‘‘Distinguishing between employees’ perceptions of person-job and person-organization fit’’, Journal of Vocational Behavior, Vol. 59 No. 3, pp. 454-70. Nohria, N., Groysberg, B. and Lee, L.-E. (2008), ‘‘Employee motivation: a powerful new model’’, Harvard Business Review, Vol. 86, July-August, pp. 78-84. Peter, J.P., Churchill, G.A. and Brown, T.J. (1993), ‘‘Caution in the use of difference scores in consumer research’’, Journal of Consumer Research, Vol. 19, pp. 655-62. Stauffer, J.M. (2001), ‘‘On the proper sequence for correcting correlation coefficients for range restriction and unreliability’’, Psychometrika, Vol. 66 No. 1, pp. 63-8. Wheeler, A.R., Gallagher, V.C., Brouer, R.L. and Sablynski, C.J. (2007), ‘‘When person-organization (mis)fit and dis(satisfaction) lead to turnover: the moderating role of perceived job mobility’’, Journal of Managerial Psychology, Vol. 22 No. 2, pp. 203-19. Wheeler, A.R., Buckley, M.R., Halbesleben, J.R., Brouer, R.L. and Ferris, G.R. (2005), ‘‘‘The elusive criterion of fit’ revisited: toward an integrative theory of multidimensional fit’’, in Martocchio, J. (Ed.), Research in Personnel and Human Resource Management, Vol. 24, Elsevier/JAI Press, Greenwich, CT, pp. 155-304.
Further reading Edwards, J.R. (1996), ‘‘Person-organization fit: an integrative review of conceptualizations, measurement, and implications’’, Personnel Psychology, Vol. 49, pp. 1-9. McCulloch, M.C. and Turban, D.B. (2007), ‘‘Using person-organization fit to select employees for high turnover jobs’’, International Journal of Selection and Assessment, Vol. 15, pp. 63-71. Yu, K.Y.T. (2009), ‘‘Affective influences in person-environment fit theory: exploring the role of affect as both cause and outcome of P-E fit’’, Journal of Applied Psychology, Vol. 94, pp. 1210-26.
Corresponding author Gilles E. Gignac can be contacted at:
[email protected]
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