Although recent work has begun to address psychometric correlates of innovation and entrepreneurship (Mascarenhas, Singh, Singh, & Veer, 2007; Mascarenhas & Singh, 2012; Mascarenhas & Veer, 2014; Steel, Rinne, & Fairweather, 2012; Geels, 2004) , the real-life impact of chronic stress on the underlying cognitive and motivational variables is not well understood. Stress affects core circuits in the medial prefrontal cortex (Goldwater, Pavlides, Hunter, Bloss, Hof, McEwen, & Morrison, 2009; Shansky & Morrison, 2009) that subserve consciousness (Marino, Bonanno, & Giorgio, 2015) , theory- of-mind functions (Baetens, Ma, Steen, & Van Overwalle, 2014; Isoda & Noritake, 2013) , humor (Franklin & Adams, 2011) , level of construal (Gilead, Liberman, & Maril, 2014) , intentional stance (Spunt, Meyer, & Lieberman, 2015) and reward valence assessment (Hogeveen, Hauner, Chau, Krueger, & Grafman, 2016) .
As a control for agency, we use a reciprocal scale, the Apathy Evaluation Scale (AES), which has been previously described (Clarke, Reekum, Simard, Streiner, Freedman, & Conn, 2007; Yuen, Gunning, & Woods, 2014) . To measure affective theory-of-mind functions, we use the widely employed and previously validated REM test (Olderbak, Wilhelm, Olaru, Geiger, Brenneman, & Roberts, 2015) .
This work aims to measure the impacts of chronic stress on psychometric measures relevant to innovation and entrepreneurship while helping construct a hypothetical framework for future neuropsychological research in this area.
2.1. Participants and Settings
A subset of a previously reported cohort was used in this study. All participants provided informed consent and the study protocol was approved by the Institutional Review Board. Procedures for online data collection from the original 1277 anonymous adult participants have been described in three previously published studies using the eSAIL, a 43-item online inventory for which satisfactory Cronbach alpha and test-retest reliability coefficients have been observed for all component psychometric scales, while discriminant, convergent, and predictive validities have been shown across cohorts (Mascarenhas, Singh, Singh, & Veer, 2007) .
113 individuals from the original cohort who scored high (>0.3 SD above mean) or low (<0.3 SD below mean) scores in the STRESS subscale of the eSAIL in 2005 or 2010 additionally answered the STRESS-6 in 2014 and 2015, and the REM, AES and AGENCY inventories in 2015. Two subsets of women consistently reporting either high (n = 21) or low (n = 24) stress in all three stress inventories over either the 5 or 10-year period were combined for data analysis.
2.3.1. STRESS-6 Questionnaire
The STRESS-6 questionnaire is a six-item subset of a previously validated stress inventory (Cohen, Kamarck, & Mermelstein, 1983) .
2.3.2. eSAIL and Subscales Thereof
2.3.3. REM Test
To measure affective theory-of-mind functions, the widely used and previously described and validated REM test was employed (Olderbak, Wilhelm, Olaru, Geiger, Brenneman, & Roberts, 2015) .
2.4. Data Analysis
Data are presented as means ± SD. Probability values (p values) were computed using Student’s t-test.
Table 1 shows a number of significant differences between the two cohorts. As expected, the Persistent High Stress group scored significantly higher than the Persistent Low Stress group in the STRESS subscale of the eSAIL, as well as in the STRESS-6 inventory.
Table 1. Chronic stress and average Z scores#.
*Subscale of the eSAIL; #Population mean = 0.
In contrast, the IMPROMPTU and MACH subscale scores of the eSAIL showed no significant differences between groups.
4.1. Chronic Stress and Psychometric Data
4.2. Implications and Limitations
This is a preliminary finding in a small cohort. A larger study will be needed to confirm these initial observations. The practical implications of this finding, if confirmed by future studies, are potentially significant for innovation management. The link between psychometric scales and innovation is supported by published data (Mascarenhas, Singh, Singh, & Veer, 2007; Mascarenhas & Singh, 2012; Mascarenhas & Veer, 2014; Steel, Rinne, & Fairweather, 2012; Geels, 2004) . Taken together, these facts may suggest the increasing importance of resilience to stress in the management of knowledge economy within which shorter product cycles create a growing dependence on innovation.
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