Error reports in the light of error management climate, task complexity and personnel composition

Haid, Marco; Graschitz, Sabine; Heimerl, Peter Article Error reports in the light of error management climate, task complexity and personnel composition International Journal of Business and Economic Sciences Applied Research (IJBESAR) Provided in Cooperation with: International Hellenic University (IHU), Kavala Suggested Citation: Haid, Marco; Graschitz, Sabine; Heimerl, Peter (2019) : Error reports in the light of error management climate, task complexity and personnel composition, International Journal of Business and Economic Sciences Applied Research (IJBESAR), ISSN 2408-0101, International Hellenic University (IHU), Kavala, Vol. 12, Iss. 3, pp. 14-23, https://doi.org/10.25103/ijbesar.123.02 This Version is available at: https://hdl.handle.net/10419/242206 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ †Corresponding Author: Marco Haid Email: marco.ha[email protected]t DOI: 10.25103/ijbesar.123.02 International Journal of Business and Economic Sciences Applied Research IJBESAR ijbesar.ihu.gr Error Reports in the Light of Error Management Climate, Task Complexity and Personnel Composition Marco Haid1†, Sabine Graschitz2, Peter Heimerl3 1,3UMIT - Private University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, 6060 Hall in Tirol, Austria 2University of Innsbruck, Universitätsstraße 15, 6020 Innsbruck, Austria ARTICLE INFO ABSTRACT Article History Received 02 December 2019; Accepted 30 January 2020 Purpose: This study examines the impact of error management climate, task complexity and personnel composition on the willingness to report errors in audit firms. A high willingness to report errors enables the proper conduction of an audit and is associated with higher organizational performance. Design/methodology/approach: We use a mixed-subject case-based experiment to gain data for the examination. A total of 53 certified Austrian and German auditors participated in the experiment. For the data analysis, we use descriptive statistics and nonparametric test procedures to investigate significant differences. Findings: The results indicate that a proper error management climate is the most important factor for the willingness to report errors, which increases under an “error-tolerant” error management climate. The impact of the error management climate is independent of the task complexity and the personnel composition in which the error occurred. Research limitations/implications: The use of other methods and approaches to manipulate and operationalize the variables may lead to different results. Originality/value: This study contributes to the growing body of literature on error management climate. The analysis of the influence of task complexity and personnel composition on the willingness to report errors offers new insights into the impact and significance of error management climate in audit firms. JEL Classifications M40, M42 Keywords: Error Management Climate, Personnel Composition, Error Report, Audit Quality, Task Complexity ©International Hellenic University 1. Introduction The purpose of auditing is to ensure that the financial information has been prepared in accordance with the relevant legal framework. Therefore auditors aim to identify any material misstatements in financial statements. During the audit process, errors can occur on the part of the auditors. These errors may lead to misstatements not being detected. As the accuracy of the audit process is of severe relevance for the audit quality, it is incredibly relevant to know which factors affect the audit team members’ willingness to report (hereinafter referred to as WTR) such errors. Specifically, in audit firms, the WTR should be high, as otherwise, high liability claims could lead to seriously high costs. So it is essential to create a motivating environment for the employees (e.g. Herzberg, 1968 or Steven, 2013) to increase their WTR. As reporting errors may cause severe costs (e.g. damaged reputation or increasing work effort), individuals consider a trade-off between costs and benefits of an error report (Zhao and Olivera, 2006). Error management is especially designed to handle errors quickly and efficiently in order to reduce potential losses and damages (Li, 2016). Error management climate (hereinafter referred to as EMC) is a critical factor for the individual´s WTR. The EMC is the attitude of an organization concerning errors made by their members (e.g. Gold DOI: 10.25103/ijbesar.123.02 15 et al., 2014; Seckler et al., 2017). It mainly includes organizational error related methods, practices, and procedures (Li, 2016). Task complexity is a crucial factor that influences audit quality and assessments made throughout the audit process (Bonner, 2008). Task complexity decisively determines the necessary resources and the necessary audit intensity (Bonner, 2008; Pummerer et al., 2013). Previous research examined the impact of the error type (mechanical vs. conceptual errors) on the WTR. Thus far, however, there is no study on the influence of task complexity on the WTR. Joint-audits are discussed intensively with regard to audit quality and the audit process (e.g. Cameran et al., 2017; Baldauf and Steckel, 2012). As joint-audits and teamwork are becoming more and more common, errors may occur in different personnel compositions. So far, different studies examined the effect of the error originator (own vs. peer error, often called “whistleblowing”) on the WTR. Until now, however, no study has dealt with errors committed in different personnel compositions with shared responsibility. Due to these insights, we aim to analyze the impact of (1) EMC, (2) task complexity, and (3) the personnel composition on the auditor´s WTR. We pay particular attention to the overall effect of EMC. Hence, the following research questions occur: RQ 1: How does the error management climate (EMC) affect the willingness to report (WTR) in audit firms? RQ 2: How does task complexity affect the willingness to report (WTR) in audit firms under different error management climates (EMC)? RQ 3: How does the personnel composition affect the willingness to report (WTR) in audit firms with diverse EMC? The study should lead to a better understanding of the impact of the EMC, task complexity, and different personnel compositions on the auditor´s WTR. Therefore, our contribution aims to identify existing differences in the WTR between self-made errors and joint errors in small and large teams towards task complexity and the EMC. The results should help to improve organizational structures in audit firms. Seckler et al. (2017) show that various levels and measures are needed to provide a suitable error management climate. This is also a link, our study refers to, as we analyze the effects of different factors. Further our paper brings new insights to the behavioral literature on error management climate by elaborating on the complementing and supplementing effects of task complexity and personnel composition. The remainder of the paper is structured as follows: After the introduction, where the problem statement, the research motivation as well as the research questions are presented, a literature review with the development of the hypotheses on the covered topics is following. Afterwards, the research method and design, as well as the results, are described and discussed. The last section contains the conclusion and discussion of the findings. 2. Literature Review and Development of Hypotheses 2.1 Error Management Climate Literature on error management from different disciplines consents that errors and their management are crucial to understand the quality of work (Seckler et al., 2017). An error is defined as the unintentional deviation behavior from the original plans, objectives, rules or standards (Keith and Frese, 2011; Frese and Keith, 2015). Generally, preventing and handling errors is an important task to be fulfilled by the auditor and therefore is important for the auditor’s daily work (Seckler et al., 2017). In addition to handling errors, error management also contributes to the further development (learning from errors) and prevention of possible future errors (Li, 2016; Homsma, 2009; Frese et al. 1991). The EMC, as part of the organizational climate, is determined by the behavior and the treatment of an organization with errors of its members. In addition to the visible and outward value concept system, EMC also includes an internalized atmosphere (Li, 2016). Van Dyck et al. (2005) define EMC as the sum of norms and common practices regarding the organizational treatment of errors. They classify EMC into error management culture and error avoidance culture. Gold et al. (2014) have a similar approach and divide EMC into “open oriented” and “blame oriented”. An open oriented or error-tolerant (hereinafter referred to as “high”) EMC tolerates errors and accepts them as normal, humanly and an opportunity for further development. By contrast in a blame oriented or error-averse (hereinafter referred to as “low”) EMC, errors are attributed to incompetence and lead to blame and incomprehension (e.g. Gold et al., 2019; Gold et al., 2014; van Dyck et al., 2005). Seckler et al. (2017) developed a multi-level model of error management and identify error management activities on the organizational, the team and the individual level. According to their model, a firm’s error management is dependent on organizational structures, the prevention procedures and resilient practices on the team level as well as the individual’s error orientation. They suggest considering the role of formal error prevention and the role of information error resilience as well as their interaction. Hence, various levels and measures are needed to provide a suitable error management climate. This is also a link, our study refers to, as we analyze the effects of different factors. Non-audit related studies show a positive effect of EMC on the organizational performance (Wang, 2000), on the employee innovative behavior (Zhu and Pei, 2014), and on the knowledge socialization and internalization (Ma, 2015). Kaptein (2011) links a positive organizational climate to a high WTR. Gold et al. (2014) and Gronewold et al. (2013) show that the WTR increases when the EMC is error-tolerant (“high”). Hence, we develop the following hypothesis: H1: If the EMC is error-tolerant (high), the WTR increases. DOI: 10.25103/ijbesar.123.02 16 2.2 Task Complexity In addition to person-specific and environmental-specific factors, task complexity in particular influences the quality, difficulty, and effectiveness of the audit process (Bonner, 2008; Stuart et al., 2012; Mala and Chand, 2015). Increasing task complexity harms the judgment performance of the auditor and requires a higher use of resources in order to achieve a corresponding audit quality (Haid, 2018; Bonner, 2008). Due to the significant impact of task complexity on the audit, the question arises whether it also influences the WTR. So far, studies have concentrated only on the impact of the error type on the WTR. In this regard, Gold et al. (2014) used the categorization of Ramsay (1994) and divided errors in “mechanical” and “conceptual” errors. Mechanical errors relate to deviations from accuracy or completeness, and conceptual errors relate to complex, subjective, significant matters and the accuracy of the audit work as a whole (Gold et al., 2014; Ramsay, 1994). Gold et al. (2014) assume that in an open EMC, the superior is unlikely to invoke sanctions or attribute the error to incompetence. By contrast, a conceptual error might lead the superior assigning easier tasks to the auditor in order to gain more experience. The auditor may perceive this consequence as “punishment-like” and may, therefore, be worried about reporting a conceptual error (Gold et al. 2014). The results of the study by Gold et al. (2014) show that a high EMC results in an increase in the WTR of mechanical errors, but not to an increase of conceptual errors. Based on the argumentation of Gold et al. (2014) regarding the influence of the error type on the WTR, we assume that errors occurred under a very complex situation can be assigned to inherent auditor incompetence, whereas errors under a situation with low complexity can be more assigned to sheer carelessness or inattentiveness. While the report of an error that has occurred in a simple task can lead to calls for mindfulness, the report of an error in high complex tasks can lead to degradation or mandatory additional training to withstand complexity in the future. Hence, reporting an error made in a high complex task may cause more costs than in a low complex task which leads us to the following hypothesis: H2a: Increasing task complexity leads to a lower WTR. Following the results of previous studies (e.g. Gold et al., 2019; Gold et al. 2014; Gronewold et al., 2013; van Dyck et al., 2005), we further assume that a high EMC increases the WTR of errors that occur in both highly complex and low complex tasks. Hence, we develop the following hypothesis: H2b: If the EMC is error-tolerant (high), the WTR increases in highly complex and low complex tasks. 2.3 Personnel Composition Previous studies find that the error origin affects the WTR. The study by Gold et al. (2014) examines the effect of the EMC on the WTR of errors caused by different error originators. It is examined whether the WTR of own errors differs from the WTR of errors made by other people (peer error). The results show that a high EMC increases the WTR of peer errors. Rathert and May (2007) find that individuals feel more comfortable when reporting self-made errors than reporting peer´s errors. Miller and Thomas (2005) show that the WTR is the lowest if they were committed by fellow team members. Teams consisting of members with equal positions tend to share the responsibility. Cameran et al. (2017) synthesize a broad body of literature and point out the relevance of auditing teams, their efficiency, dynamics and management for audit quality. That means, research on personnel composition is able to identify factors of how to improve audit quality. Based on the different results of former studies on the influence of the error originator on the WTR, we assume that different personnel compositions have no impact in this respect. However, following the results of Gold et al. (2014), we assume that a high EMC will have a positive effect on the WTR in different personnel compositions, which leads us to the following hypotheses: H3a: The personnel composition does not influence the WTR. H3b: If the EMC is error-tolerant (high), the WTR increases for (a) self-made errors (b) joint errors of a small team and (c) joint errors of a large team. 3. Research Design 3.1 Research Method The empirical examination of the impact of the EMC, task complexity and the personnel composition on the WTR requires the measurement of all variables involved. We use a laboratory experiment to test the hypotheses. The choice of the research method enables us to control each variable and to measure the impact on the WTR in a standardized setting. This method seems to be most suitable as we can observe individual´s behavior in various situations, and it allows the identification of cause-effect-relationships. Furthermore, the direct measurement of the variables enhances construct validity compared to archival studies. Concerning the use, challenges and constraints of experimental accounting and auditing research see, e.g. Stefani (2003), Libby et al. (2002) or Smith (2011). 3.2 Study Design We conduct the laboratory experiment in a 2x2x3 mixed-subject design to examine the impact of the EMC, the task complexity and the personnel composition on the WTR. Thus, the independent variables are the EMC, the task complexity and the personnel composition. The WTR is the dependent variable. We manipulate the EMC (low vs. high) between-subject; the task complexity, and the personnel complexity were manipulated within-subject. The participants are randomly assigned to one of the EMC groups (low vs. high). The WTR of all participants is then DOI: 10.25103/ijbesar.123.02 17 measured both in the low complexity manipulation and in the high complexity manipulation, each under three different personnel compositions (self-made, joint/small team and joint/large team error). Hence, we perform six measurements of the WTR per participant. Figure 1 illustrates the study design. Figure 1: Overview of the study design Hence, the participants are divided into two EMC groups (low vs. high); and the WTR of all participants is measured six times. 3.3 Data Collection and Manipulation Check The experiment is divided into three parts. Part 1 contains the description of a fictitious audit firm. The participants work for this firm in the experiment. Part 2 consists of several scenarios in which errors have occurred. The WTR of the participants is measured in each of these scenarios. In the last part, we perform manipulation checks and collect demographic data. The research design was pre-tested several times with students as participants before it was finalized. Data collection was carried out between 2016 and 2018. We use the data collection period and questions regarding the audit procedure as control variables. Manipulation checks are used to check whether the manipulation of the independent variables is recognized by the participants. The experiment was conducted using an online survey. 3.4 Participants We have collected a total of 59 data sets. Six had to be excluded due to missing data. In order to ensure that the participants received and understood the information and descriptions correctly, manipulation checks were carried out on the EMC, the task complexity, and the personnel composition. All participants have passed the manipulation checks. In total, 53 valid data sets are used for the analysis. The participants of the study are Austrian and German auditors with an active professional certification. Sixteen persons are female (30.19%), and 37 persons are male (69.81%). On average, the participants are 41.6 years old, and over 90 % (48 individuals) graduated from university. Thirty-six participants work for the Big Four. Twenty-nine participants were assigned to the high EMC and twenty-four to the low EMC. 3.5 Variables 3.5.1 EMC For the operationalization of the EMC, we use the categorization and insights of Gold et al. (2014), Gronewold et al. (2013) and van Dyck (2005). We manipulate EMC (between-subject) using descriptions of two fictitious audit firms with different EMC. Thus, the participants receive either a description of a firm with low EMC in which errors are not tolerated or a description of a firm with high EMC, which tolerates errors and treats errors as an opportunity for development. We take the description of the two audit firms from the study by Gronewold et al. (2013). Table 1 contains the wording as it was used in our experiment. DOI: 10.25103/ijbesar.123.02 18 Table 1: Manipulation of the EMC (Gronewold et al. 2013) Low (error-averse) EMC Open (error-tolerant) EMC The overall climate of your firm is noted for a “getting it right the first time” mentality that reflects the office managing partner’s own beliefs and actions. Errors in carrying out audit procedures are seen as signs of incompetence. You have seen top seniors coming from review meetings with managers and partners - after discussions about errors in files - almost in tears. Performance evaluations document such problems complicating possibilities for future promotion within the firm. The overall climate of your firm is noted for an “open for improvement” mentality that reflects the office managing partner’s own beliefs and actions. Errors in carrying out audit procedures are seen as a natural part of learning. You have seen top seniors coming from review meetings with managers and partners - after discussions about errors in files - with a renewed determination to work harder next time. Performance evaluations do not document such Problems if the person does not repeat them or is making progress in addressing them, allowing possibilities for future promotion within the firm. The table above includes a description of the two different EMCs. Whereas the lower or error averse EMC is characterized by a “getting it right the first time” mentality, the open or error-tolerant EMC sees errors as a potential for improvement and essential part of learning. 3.5.2 Task Complexity We manipulate the task complexity within-subject using descriptions of two fictional tasks with different complexity levels. A classification for complexity divides it into (1) a subjective experience, (2) an interchange from taskand personspecific aspects and (3) into an objective sphere wherein principle measurable criteria are crucial for the degree of complexity (Gill and Hicks, 2006; Campbell, 1988). The classification following the (1) subjective psychological experience treats complexity as a measure of the potential that the task is perceived as complex by the individual (Gill and Hicks, 2006; O’Donnel et al., 2005) or defines task complexity in terms of the task´s potential to induce a state of arousal or enrichment in the individual (Gill and Hicks, 2006; Nordquist et al., 2004). Besides this subjective view, the classification based on (3) objective characteristics aim at different task inherent (objective measurable) properties like the number of alternatives and attributes (Gill and Hicks, 2006; Fisher et al., 2003) or the complexity of the underlying systems or environment (Gill and Hicks, 2006; Funke, 1991). Another objective criterion is the degree of uncertainty, whereas the complexity is classified by the unpredictability of the outset of the task (Kishore et al., 2004). For manipulation of the task complexity, we make use of the objective task characteristics (1) task size, (2) amount of information to be processed and (3) number of solution paths (Gill and Hicks, 2006; Fisher et al., 2003; Campbell, 1988). We vary these attributes in the fictitious tasks. Table 2 contains the manipulation of task complexity. Table 2: Manipulation of the task complexity Low Complexity High complexity For the audit of the medium-sized company "Maier GmbH" with one headquarters and two production halls, you have to audit the conformity of the sums in the balance sheet to check the conformity whether values have been transferred correctly. For the audit of the big company "Big Business AG" the completeness and the correct valuation of the provisions and liabilities have to be checked. The company has 14 subsidiaries, 9 of them are abroad. Besides its headquarters, there are seven branch offices for the management of the company as well as 11 factory halls and seven stores For our experiment, we used the above-mentioned descriptions. Thereby we manipulate (highlighted: underlined) the company size, the legal form of the entity, the number of factories and branches, the number of subsidiaries and internationality. Additionally, the tasks to be carried out is described shortly (highlighted: bold). 3.5.3 Personnel Composition The third independent variable is the personnel composition. We examine the impact of self-made errors, joint errors in a small team and joint errors in a large team. The variable is manipulated within-subject inspired by Gronewold et al. (2013) through information about the personnel composition. The wording used is included in Table 3. Table 3: Manipulation of the personnel composition (inspired by Gold et al., 2014) Self-made Joint (small team) Joint (large team) You do a part of the task alone. A bit of time after the completion, you notice an error. You do a part of the task with a colleague. A bit of time after the completion, you notice an error. Both of you are equally responsible You do a part of the task within a group of 6 people. A bit of time after the completion, you notice an error. Everybody in the team is DOI: 10.25103/ijbesar.123.02 19 for it. equally responsible for it. Regarding the personnel composition, the participants were either working (1) alone, in (2) small teams or in (3) large teams. Errors that occurred where thereby either caused by (1) themselves (self-made error), (2) jointly by a team of two or (3) jointly by six people. This manipulation represents different personnel compositions that usually occur in auditing and may affect individual’s WTR. 3.5.4 WTR WTR is the dependent variable of interest. In line with former studies (e.g. Gold et al., 2014; Gronewold et al., 2013), we used the question, “Do you want to report the error to your supervisor?” to measure WTR. The variable WTR is measured using an 11-point Likert scale, which reached from “want to report the error” (1) to “do not want to report the error”(11). 4. Results The characteristics of the collected data require nonparametric test procedures to investigate significant differences. Hence, we use the Mann-Whitney U test, the Wilcoxon rank-sum test, the non-parametric Friedman test (Janssen and Laatz, 2010). These tests compare data from different groups with each other and therefore show if the distribution is the same among the groups. The alpha value is 0.05 for all statistical tests. Regarding the dependent variable WTR, a lower number means a higher WTR. 4.1 Descriptive Statistics and Correlations Table 4 shows the results of the descriptive statistics of the dependent variable WTR. Table 4: Descriptive Statistics N Min Max Mean Std. Deviation WTR 318 1.00 11.00 1.8239 1.47763 The average WTR across all 318 scenarios (6 scenarios x 53 participants) is relatively high at 1.824. Table 5 summarizes the correlations between the dependent variable WTR and the independent variables EMC, task complexity, and personnel composition. Table 5: Correlations (Spearman´s Rho) EMC (1-OpenEMC; 2-lowEMC) Task Complexity (1-lowTC; 2-highTC) Personnel Composition (1-selfmade; 2smallTeam; 3-largeTeam) WTR Correlation Coefficient 0.321** -0.029 0.036 Sig. (2-tailed) 0.000 0.604 0.519 N 318 318 318 **. Correlation is significant at the 0.01 level (2-tailed) There is a significant positive correlation between the WTR and the EMC. 4.2 EMC Despite the generally high WTR across all 318 scenarios (Mean = 1.824), the WTR in the high EMC scenario (mean rank = 135.52) is significantly higher than the WTR in the low EMC scenario (mean rank = 188.48), U = 8355.50, p = 0.00, d = 0.32. Table 6 summarizes the results. Table 6: Mann-Whitney U test (WTR all scenarios) Ranks EMC N Mean Rank Sum of Ranks WTR open EMC 174 135.52 23580.50 low EMC 144 188.48 27140.50 Total 318 Test Statisticsa WTR Mann-Whitney U 8355.500 Wilcoxon W 23580.500 Z -5.722 Asymp. Sig. (2-tailed) 0.000 a. Grouping Variable: EMC DOI: 10.25103/ijbesar.123.02 20 In an open EMC, the overall WTR of the participants is significantly higher than in a low EMC. Hence, we support hypothesis H1. In the following sections, we examine whether the impact of EMC on the WTR will persist even with different levels of task complexity and different personnel composition. 4.3 Task Complexity We use the responses to the WTR of “self-made” errors 1 to analyze the impact of task complexity. For the examination of significant differences between low and high complexity, we analyze both the group with the high EMC and the group with the low EMC. Table 7 contains the results: Table 7: Wilcoxon signed ranks test (high complexity vs. low complexity) Ranks N Mean Rank Sum of Ranks highEMChighTCd – highEMClowTC Negative Ranks 2 1.50 3.00 Positive Ranks 1 3.00 3.00 Ties 26 Total 29 lowEMChighTC – lowEMClowTC Negative Ranks 4 2.50 10.00 Positive Ranks 0 0.00 0.00 Ties 20 Total 24 Test Statisticsa highEMChighTC – highEMClowTC lowEMChighTC - lowEMClowTC Z 0.000b -1.841c Asymp. Sig. (2-tailed) 1.000 0.066 a. Wilcoxon Signed Ranks Test b. The sum of negative ranks equals the sum of positive ranks. c. Based on positive ranks. d. TC = task complexity No significant difference between low and high complexity could be found for the group with the high EMC (Z = 0.000, p = 1.00) and for the group with low EMC (Z = - 1.841, p = 0.066). According to the results, task complexity has no significant impact on the WTR. Hence, we reject hypothesis H2a. We further investigate whether the EMC has an influence on the WTR at different levels of task complexity. Data analysis shows that both, at high task complexity and low task complexity, a high EMC leads to a significantly higher WTR than a low EMC. Table 8 summarizes the results: Table 8: Mann-Whitney U test (EMC and task complexity) Ranks EMC N Mean Rank Sum of Ranks low task complexity high EMC 29 22.69 658.00 low EMC 24 32.21 773.00 Total 53 high task complexity high EMC 29 23.16 671.50 low EMC 24 31.65 759.50 Total 53 Test Statisticsa low task complexity high task complexity Mann-Whitney U 223.000 236.500 Wilcoxon W 658.000 671.500 Z -2.544 -2.283 Asymp. Sig. (2-tailed) 0.011 0.022 a. Grouping Variable: 1-openEMC; 2-lowEMC For low task complexity the results indicate a significantly higher WTR (mean rank = 22.69) for a high EMC than for a low EMC (mean rank = 32.21), U = 223.00, p = 0.011, d = 0.35. Also with high task complexity, the WTR of a high EMC (mean rank = 23.16) is significantly higher than the WTR of a low EMC (mean rank = 31.65), U = 1 The data analysis was also carried out with the responses to the WTR of errors made in small and large teams for control purposes. The analysis came to the same conclusion as the analysis of the WTR of self-made errors. Therefore, the influence of task complexity on WTR remains constant across different personnel compositions. DOI: 10.25103/ijbesar.123.02 21 236.5, p = 0.022, d = 0.31. Thus, the effect of EMC remains with different task complexity. According to the results, we support hypothesis H2b. 4.4 Personnel Composition To examine the impact of the personnel composition on the WTR we use the responses to the low task complexity scenarios 2 . We investigate the relationship in both the high EMC and the low EMC scenario. The data analysis for the high EMC shows a slightly higher WTR for self-made errors (mean rank = 1.91) than for joint small-team errors (mean rank = 2.02) and for joint large-team errors (mean rank = 2.07). However, according to the non-parametric Friedman test of differences among repeated measures, these differences are not significant, Chi-square = 4.667, p = 0.097. Table 9 summarizes the results: Table 9: Non-parametric Friedman test (Personnel Composition (high EMC)) Mean Rank Self-made error 1.91 Small-team error 2.02 Large-team error 2.07 Test Statistics N 29 Chi-Square 4.667 Df 2 Asymp. Sig. 0.097 Table 10 contains the results of the low EMC scenario. Also, in the low EMC scenario, the WTR of self-made errors is the highest (mean rank = 1.98). As in the high EMC scenario, the differences are not significant after the non-parametric Friedman test, Chi-square = 0.118, p = 0.943. Table 10: Non-parametric Friedman test (Personnel Composition (low EMC)) Mean Rank Self-made error 1.98 Small-team error 2.02 Large-ream error 2.00 Test Statistics N 24 Chi-Square 0.118 df 2 Asymp. Sig. 0.943 As predicted, data analysis shows no significant impact of the personnel composition on the WTR. Thus, we support hypothesis H3a. We further analyze the impact of the EMC on the WTR in different personnel compositions. The results are shown in Table 11: Table 11: Mann-Whitney U test: EMC and different personnel compositions Ranks EMC N Mean Rank Sum of Ranks Self-made error high EMC 29 22.69 658.00 low EMC 24 32.21 773.00 Total 53 Small-team error high EMC 29 22.53 653.50 low EMC 24 32.40 777.50 Total 53 Large-team error high EMC 29 22.90 664.00 low EMC 24 31.96 767.00 Total 53 Test Statisticsa Self-made error Small-team error Large-team error Mann-Whitney U 223.000 218.500 229.000 Wilcoxon W 658.000 653.500 664.000 2 The data analysis was also carried out with the answers of the high complexity scenario for control purposes. The analysis came to the same conclusion as the analysis of the low complexity scenario. Therefore, the influence of the personnel composition on WTR remains constant across different levels of task complexity.