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The role of perceived usefulness in the relationship between task — technology fit and individual job performance in ERP implementation — evidence from Vietnam’s enterprises






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Abstract

In this context, the aim of the study was to examine the role of perceived usefulness in the relationship between individual job performance and task – technology fit in ERP environment. The study was done by 225 individuals. The results of the PLS_SEM analysis reveal ed that task — technology fit was significantly and positively related to perceived usefulness and individual job performance in ERP environment. Furthermore, perceived usefulness was significantly to individual job performance in ERP context. The results of this study added to the empirical evidence in the application of background theories including TTF, TAM and TTF models combined, DeLone and McLean IS Success Model, and ECM. Besides, they also added to the theoretical background of ERP's success. Furthermore, they support for companies who are planning to use ERP systems and the ERP vendors and implementers become more knowledgeable about ERP's success and forecast success when using ERP systems. Based on these results, the enterprise can plan the application to improve the efficiency of ERP systems. At the same time, the ERP vendors and developers can better advise and support their customers when delivering and deploying ERP systems

Introduction

Companies all over the world have adopted enterprise resource planning (ERP) systems to integrate their business processes and stay competitive 1 . Because ERP is information system (IS), user perceptions about an ERP system play an important role in both usage and success of ERP 2 . Some organizations have applied the ERP system but users are non-adoption of the system 3 . In this case, job performance and job satisfaction are lower and turnover rates are higher 4 . In addition, if the job performance of individual is low, it will adversely affect the performance of organization 5 . As a critical indicator of ERP implementation success, it is important to examine possible factors that affect employee job performance 4 .

Bradford and Florin 6 developed and tested a model of ERP implementation success which is measured by perceived organizational performance and user satisfaction. While the study of Bradford and Florin 6 examined perceived organizational performance, the others explored the factors that can impact individual performance when using ERP systems such as Kositanurit et al. (2006), Park et al. (2007), Sykes et al . (2014), Sykes (2015) 8 , 4 , 7 , 5 . Kositanurit et al. provides evidence that system quality, utilization, and ease of use are the three important factors bearing on individual performance in ERP environment 5 . The study of Park et al. found that the users’ ability to understand ERP knowledge influenced its performance and organizational support moderated the relationship between their absorptive capacity and performance 7 . Sykes et al. show workflow advice and software advice are associated with job performance 4 . Besides, that study found that the interactions of workflow and software get-advice, workflow and software give-advice, and software get- and give-advice impacted job performance. Similarly, Sykes disclosures both traditional support structures and peer advice ties were found to influence the various outcomes including system satisfaction, job stress, job satisfaction and job performance 8 . On the individual level, the factors that are proven to have an impact on the job performance in ERP environment include system quality, utilization, ease of use, users’ ability to understand ERP knowledge, workflow advice, software advice, traditional support structures and peer advice ties. Similar to the above studies, this work seeks to examine post — implementation employee job performance. However, we look for the impact of other factors including perceived usefulness and task — technology fit on job performance of users in ERP context. Based on Task- Technology Fit theory (TTF) 11 , 10 , 9 , TAM and TTF models combined 12 , DeLone and McLean IS Success Model 13 , 2 and ECM (Expectation–Confirmation Model) 14 , this study examined the role of perceived usefulness in the relationship between individual job performance and task — technology fit in ERP environment.

This article is structured as follows. First, we describe the theoretical background on individual job performance, perceived usefulness and task — technology fit. Next, we present the research methodology used. Then, we present the findings. Finally, we present some conclusions and further work.

Literature review

Individual Job Performance

According to TTF theory, performance benefits improve a range of performance outcomes 15 . The specific outcomes that have been examined in prior TTF research include system use/intention to use, job performance, satisfaction with technology, opinions regarding a technology, appropriation changes made, decision efficiency, decision quality, decision strategy employed, joint profit attained, number of ideas generated, quality of solution, task completion time, task accuracy, ability to perform tasks, perceived ease of use, perceived usefulness, perceived playfulness, perceived risk… 15 .

High performance implies that there is a mix between improved efficiency, improved effective and/ or improved quality 10 . It was often confused with productivity 16 . However, it is actually measured by more global variables such as the quality of outputs, job knowledge, leadership, or judgment 16 . In the traditional office, job performance is largely established by scanning for employees’ presence and through direct and indirect observations 16 .

There exists a wide range of employee outcomes in ERP implementation, such as systems satisfaction, job stress, job satisfaction, and individual job performance 8 . Job performance is a way to measure employee outcomes. Job performance is a good way of performing an employee's work 8 .

This study defined individual job performance in ERP environment was that the employee feels that with the help of ERP systems he/she can improve a range of performance outcomes.

Task — Technology Fit

Task — technology fit concept derives from the Task-Technology Fit theory (TTF) 11 , 10 , 9 . According to TTF, the task — technology fit represents the degree of matching or alignment between the capabilities of an information system and the demands of the tasks that must be performed 15 . Base on TTF, this study defined t ask — technology fit is the degree of relevance between the ability of ERP system and the tasks which an employee must perform.

Based on three theories including TTF, TAM and TTF models combined, and the DeLone and McLean IS Success Model (2013) 13 , this study develops the hypothesis H1. Firstly, according TTF, task — technology fit impact on performance benefit 10 while individual job performance is used as an indicator of performance benefit 15 . Secondly, TAM and TTF models combined show that task — technology fit have significant effect on actual tool use 12 . At the same time, the user behavior has an impact on the job performance 17 . Finally, Peter et al . 18 suggested that task compatibility impact on the IS success. Task compatibility is the fit or consistency between the task and the IS that supports that task 13 . In this study, task — technology fit was defined similarly to task compatibility. Petter et al . 18 measured the IS success based on the update DeLone and McLean 2 IS success model, including information quality, system quality, service quality, intention to use/ use, user satisfaction, and net benefits. In this study, individual job performance was considered an indicator of net benefits. Based on the above arguments, this study developed hypothesis H1.

This hypothesis is also supported by several studies, such as Norzaidi et al . (2009), Bhattacherjee (2001), Teo and Bing (2008), Kositanurit et al . (2006), D'Ambra and Wilson (2004a), D'Ambra and Wilson (2004b), Wongpinunwatana et al . (2000), Goodhue et al. (1997), Goodhue and Thompson (1995), Goodhue (1995), Henseler (2015) 25 , 24 , 10 , 9 , 23 , 22 , 21 , 5 , 20 , 14 , 19 . In particular, Staples and Seddon 18 show that task — technology fit had an impact on individual job performance in both kinds of IT use, voluntary and mandatory. In this study, if an enterprise is using an ERP system, employees are required to use it. So Staples and Seddon 18 strongly support the hypothesis H1.

H1: The task – technology f it has a positive effect on the job performance of employee in ERP environment.

Perceived Usefulness

In TAM, perceived usefulness is “the degree to which a person believes that using particular system would enhance his or her job performance” 26 (p.320). Perceived usefulness is a dimension of performance expectancy in UTAUT 27 . In this study, perceived usefulness of ERP systems was understood as the level at which the user believes ERP systems delivers good results for their work.

According TTF, task – technology fit impact on performance benefit 10 and perceived usefulness can be used as an indicator of performance benefit 28 , 15 . Besides, Dishaw et al . 12 suggested TAM and TTF models combined that demonstrated task — technology fit to have a significant impact on perceived usefulness. Based on TTF, TAM and TTF models combined and some studies such as Norzaidi et al. (2009), Tjahjono (2009), Chang (2008), Wu et al. (2007), Klopping and McKinney (2004), Dishaw and S trong (1999), Goodhue (1995) 10 , 9 , 33 , 32 , 31 , 30 , 29 , 19 hypothesis H2 is stated as follows:

H2: The task — technology fit has a positive effect o n the perceived usefulness in ERP environment.

Based on three theories, namely TAM and TTF models combined, ECM (Expectation–Confirmation Model) 14 and DeLone and McLean IS Success Model (2013) 2 , this study developed the hypothesis H3. The first, according TAM and TTF models combined, perceived usefulness has effect on attitude toward use, then attitude toward use impact on intention to use, after that intention to use effect on actual tool use 12 . Next, ECM shows that perceived usefulness impact on IS continuance intention 14 . Last, user expectations was proven to have an impact on the IS success 2 . User expectations is the degree to which the user’s perceptions about the IS are consistent with the actual IS 10. In this study, perceived usefulness was close to the user expectations used in Petter et al . (2013) 13 . In general, TAM and TTF models combined, ECM and DeLone and McLean IS Success Model (2013) 2 have shown that the perceived usefulness of the information system had a positive impact on user behavior. At the same time, the user behavior had an impact on the job performance 17 .

Furthermore, some studies include Rajan and Baral (2015), Furneaux (2012), Sternad and Bobek (2013), Soto-Acosta et al . (2013), Elkhani et al. (2014), Zhang et al . (2013), Keong et al. (2012), Norzaidi et al . (2009), Youngberg et al. (2009), Calisir et al . (2009), Lee et al . (2010), Chang (2008), Wu et al. (2007), Ramayah and May- Chiun Lo (2007), Seymour et al. (2007) and Amoako- Gyampah and Salam (2004) have also shown that the perceived usefulness of IS/ ERP had a positive impact on user behavior 44 , 43 , 42 , 31 , 30 , 41 , 40 , 39 , 19 , 38 , 37 , 36 , 35 , 34 , 15 , 17 . In addition, Goodhue demonstrated that the perceived usefulness of IS has a positive impact on the individual job performance in IT context 9 . Based on the above arguments, this study hypothesized the following:

H3: The perceived usefulness of the ERP has a positive effect on the job performance of employee in ERP environment.

Figure 1 represents the proposed research model that was used for this research.

Figure 1 . Proposed research model.

Methodology

Measures

All research constructs included in this study had multi-item scales derived from the relevant literature. Each item in the survey employed a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), and a not applicable (NA) option was available for the respondents to choose. We now elaborate our measures for the constructs. This study accepted scale of the individual job performance in ERP environment (PER) from Goodhue and Thompson 10 . PER is a first – order construct and reflective measures with 2 items. We measured task – technology fit (TTF) using a11- item scale adapted from Kositanurit et al . 5 that captures the eight dimensions including currency (CURR), right data (RDAT), right level of detail (RDET), meaning (MEAN), ease of use (EOU), training (TRAI), authorization (AUT) and system reliability (REL). TTF is a high – order construct and reflective – reflective measures. The perceived usefulness (PU) was measured with 6 items adapted from Calisir and Calisir 45 (from the TAM model). PU is a first – order construct and reflective measures.

Data collection

This study was conducted using quantitative means as it aimed to validate the proposed relationships between factors affecting individual job performance in ERP context. The research instrument was used a questionnaire distributed to the end-users (employees). The data were collected from June 2017 to August 2017. The questionnaires were sent by email or postal mail to an initial sample of 5 00 employees who are using ERP system. We collected 265 responses (response rate was 53 %). Baruch 46 points out that for surveys addressed to individual, the average response rate is 52.7%. Thus, the response rate of our study seems to be above average. Of the 265 employees, 225 employees from 49 companies gave usable responses at all points of measurement.

Table 1 presents the sample characteristics age, gender, education and average computer experience. The sample consisted of 161 (71.6 %) female and 64 (28.4 %) male. The Table 1 shows that 75.5% of the sampled individuals were fewer than 35. In addition, 72% of the sampled individuals had bachelor degree. Average experience using an ERP system was 2.56 years.

Table 1 Sample characteristics (n = 225)
Category Frequency Percentages (%) Category Frequency Percentages (%)
Age Education
< 35 170 75.5 Master 7 3.1
35 – 45 47 20.9 Bachelor 162 72
> 45 8 3.6 Colleges 43 19.1
Gender Others 13 5.8
Male 64 28.4 Average computer experience (years) 2.56
56 Female 161 71.6

The Issue of Common Method Bias

Because there was only one respondent for each individual, common method bias (CMB) was a potential problem. In this study, we took a number of steps suggested by Podsakoff et al. 47 to reduce the possibility of common method bias. Firstly, we used multiple items for each construct and ensured the neutral wording of the items. Secondly, we assured respondents of the anonymity of their responses and emphasized that there were no right or wrong answers; each of these actions enabled them to answer questions as honestly as possible. Thirdly, we separated the measurement of predictors and criterion variables in the questionnaire to diminish the respondent’s ability and motivation to use his/her prior responses to answer subsequent questions. Finally, we also used the Harman’s single-factor test and the marker variable approach to control for common method variance (CMV). Results are discussed in data analysis and results section.

Data analysis and results

Measurement model

We estimated the internal consistency reliability, convergent validity, and discriminant validity of each measurement scale to assess the measurement model. We used two criterions for internal consistency were composite reliability (CR) and Cronbach’s alpha. All the reflective constructs in our model show in Table 2 have a Cronbach’s alpha over the cut off of 0.70, as suggested by Hair et al. 48 . Similarly, a composite reliability (CR) of all the constructs is also higher than 0.7, as suggested by Fornell and Larcker 49 , implying high internal consistency.

Convergent validity is verified through the t-statistic for each factor loading. In PLS_SEM, we can use an indicator's outer loading. An outer loading should be above 0.7 and the t-statistic for each outer loading significant 48 . Results of measurement models show that the items including AUT1, AUT2, REL1, REL2 and TRAI have outer loading was above 0.7 but the t-statistic for each outer loading was not significant. As such, the AUT1, AUT2, REL1, REL2 and TRAI were excluded from the TTF scales. Table 2 shows that results of final measurement models. A ll factor loadings are greater than the typical cut off value of 0.7 48 and significant at the p <0.001 level. In this study, we also used the average variance extracted (AVE) to assess convergent validity. An AVE value of 0.50 or higher indicates that, on average, the construct explains more than half of the variance of its indicators.

Table 2 Results summary of measurement models
Latent variable Indicators Convergent validity Internal consistency reliability Discriminant validity
Loadings Indicator reliability AVE Composite reliability Cronbach’s Alpha
> 0.7 >0.5 > 0.5 0.6 – 0.95 0.6 – 0.95
PER PER1: ERP systems system has a positive impact on my productivity in my job 0.966*** 0.933 0.937 0.932 0.967 Yes
PER2 : ERP systems is an important aid to me in the performance of my job 0.970*** 0.941
PU PU1 : Using ERP systems in my job increased my productivity 0.884*** 0.781 0.768 0.939 0.952 Yes
PU2: I found ERP systems useful in my job 0.855*** 0.731
PU3: Using ERP systems improved my job performance 0.854*** 0.729
PU4: Using ERP systems enhanced my effectiveness on the job 0.913*** 0.834
PU5: Using ERP systems in my job enabled to accomplish tasks more quickly 0.900*** 0.810
PU6: Using ERP systems made it easier to do my job 0.849*** 0.721
CURR CURR: The data provide by ERP systems is up-to-date enough for my purposes 1.000 1.000 1.000 1.000 1.000 Yes
RDAT RDAT: ERP systems available to me is missing critical data that are very useful to me in my job 1.000 1.000 1.000 1.000 1.000 Yes
RDET RDET : ERP systems maintains data at an appropriate level of detail for my group’s tasks 1.000 1.000 1.000 1.000 1.000 Yes
MEAN MEAN: The exact definition of data fields relating to my tasks is easy to find out 1.000 1.000 1.000 1.000 1.000 Yes
EOU EOU1: It is easy to learn how to use ERP systems 0.953*** 0.908 0.912 0.904 0.954 Yes
EOU2: ERP systems I use is convenient and easy to use 0.957*** 0.916

To establish discriminant validity, we used the HTMT criterion, Fornell – Larcker criterion and cross loadings. The results of discriminant validity are show in Table 3 . Cross-factor loadings are reported in Appendix A .

Table 3 Results of discriminant validity
Mean SD CURR EOU MEAN PER PU RDAT RDET
CURR 4.50 1.530 1.000
EOU 4.428 1.383 0.390 0.955
0.410
MEAN 4.62 1.346 0.387 0.535 1.000
0.387 0.562
PER 5.029 1.327 0.351 0.716 0.634 0.968
0.362 0.779 0.657
PU 5.231 1.157 0.314 0.576 0.532 0.767 0.876
0.324 0.621 0.547 0.817
RDAT 3.96 1.565 0.143 0.301 0.147 0.110 0.026 1.000
0.143 0.317 0.147 0.114 0.049
RDET 4.69 1.338 0.618 0.375 0.519 0.492 0.418 0.125 1.000
0.618 0.394 0.519 0.508 0.433 0.125

The square root of the AVE of each construct should be higher than its highest correlation with any other construct 49 . Table 3 shows that the square root of AVE exceeds the correlation between other constructs. In addition, all HTMT of constructs are significantly smaller than 1 25 . These results imply satisfactory discriminant validity.

Structural model

The structural model was examined to test the hypotheses. The R 2 , which is generated for each regression equation, indicates the explanatory power or variance explained of the latent endogenous variable. Figure 2 shows the structural model result.

Figure 2 . Result of proposed research model.

The PLS path analysis results show that task – technology fit was significantly related to individual job performance (β = 0.434, p < 0.001) and perceived usefulness (β = 0.607, p < 0.001) supporting hypotheses H1 and H2. Perceived usefulness was significantly related to individual job performance (β = 0.504, p < 0.001) supporting hypotheses H3.

The external variables including task – technology fit and perceived usefulness could explain 70.7 percent variance in individual job performance (R 2 = 0.707). Task – technology fit explained 36.8 percent of variance of perceived usefulness (R 2 = 0.368).

Next, we assessed the predictive relevance of the path model by Q 2 values. All Q 2 values are considerably above zero (Q 2 of individual job performance is 0.631 and Q 2 of perceived usefulness is 0.261), thus providing support for the model's predictive relevance regarding the endogenous latent variables.

The final assessments address the f 2 and q 2 effect sizes. Table 4 summarizes the results of the f 2 and q 2 effect sizes with respect to all the relationships in the model. Target constructs appear in the first row, whereas the predecessor constructs are in the first column. Table 4 shows TTF has a large effect size of 0.405 (0.290) on PER and of 0.583 (0.404) on PU. Similarly, PU has a large effect size of 0.546 (0.345) on PER.

Table 4 f 2 and q 2 effect sizes
f2 effect sizes q2 effect sizes
PER PU PER PU
TTF 0.405 0.583 0.290 0.345
PU 0.546 0.404

Additionally, the variance inflation factor (VIF) was assessed to check multicollinearity. The collinearity diagnostics given in Table 5 shows that VIF for the independent variables higher than 0.20 (lower than 5) which further suggests that multicollinearity does not exist among the independent variables.

Table 5 Collinearity statistic
Construct VIF
Task – technology fit (TTF) 1.583
Perceived usefulness (PU) 1.583

Mediation analysis

In this study, we examined a mediator variable, intervenes between two other related constructs. Specifically, we examined the role of perceived usefulness in the relationship from task – technology fit to individual job performance in ERP context. Table 6 shows result of mediator variable. We find that both direct effect and indirect effect are significant. Our finding provided empirical support for the mediating role of perceived usefulness in the relationship from task – technology fit to individual job performance in ERP context.

Because path coefficient of the relationship from task – technology fit to individual job performance was 0.434 and significant, path coefficient of the relationship from task – technology fit to perceived usefulness was 0.607 and significant, and path coefficient of the relationship from perceived usefulness to individual job performance was 0.504 and significant, perceived usefulness represents complementary mediation of the relationship from task – technology fit to individual job performance in ERP context.

Table 6 Significance analysis of the direct and indirect effects
Direct effect 95% confidence interval of the direct effect t value Significance (p < 0.05)? Indirect effect 95% confidence interval of the indirect effect t value Significance (p < 0.05)?
TTF -> PER 0.434 [0.314; 0.541] 7.326 Yes 0.306 [0.230; 0.394] 7.143 Yes

The Issue of Common Method Bias

We used the Harman’s single-factor test and the marker variable approach to control for CMV in PLS analysis. Result of Harman’s single-factor test by EFA shows that one factor only account for 40.537% of the total variance. In this case, CMV is not a serious problem 50 .

The marker variable approach was conducted by using marker variable. The first stage, we involved survey questionnaire that had a question was “Do you really like black coffee?” – this question was a marker variable 24 . The next stage, we used PLS to test path coefficient of the relationship from marker variable to other variable in proposal model including perceived usefulness, task – technology fit and individual job performance. Analysis results showed that all path coefficients of the relationships from marker variable to perceived usefulness, task – technology fit and individual job performance were less than 0.3 (-0.102, 0.094 and -0.060). This finding suggests that CMV was not a serious problem in this study.

Besides, we also based on VIF to test CMB. Table 5 show that all VIFs resulting from a full collinearity test were lower than 3.3, the model can be considered free of CMB.

Discussion

The results of this research supported most of the proposed relationships in the structural model. Most were consistent with the previous study results. Task – technology fit was significantly and positively related to perceived usefulness and individual job performance in ERP environment (H1 and H2 are supported). Perceived usefulness was significantly and positively related to individual job performance in ERP context (H3 is supported).

In ERP context, Kositanurit et al . 5 found that task – technology fit was the important factor bearing on individual performance. The result of this study is similar to the result of Kositanurit et al. 5 . The new findings of this study are that we provided empirical evidence on the impact of task – technology fit to perceived usefulness and of perceived usefulness to individual job performance in ERP environment.

Conclusions

This study added to the empirical evidence in the application of background theories including TTF, TAM and TTF models combined, DeLone and McLean IS Success Model, and ECM. Besides, the results of this study also added to the theoretical background of ERP’s success, namely, the individual job performance of employee in ERP context. Specifically, factors including perceived usefulness and tasks and technology fit have a significant impact on the individual job performance employee in ERP context. Furthermore, the results of this study help companies who are planning to use ERP systems and the ERP vendors and implementers become more knowledgeable about ERP’s success and forecast success when using ERP systems. In this study, the ERP success was measured by job performance of employee. The factors that have been tested are the impact on the individual job performance of employee in ERP context including perceived usefulness and tasks and technology fit. Based on these results, the enterprise can plan the application to improve the efficiency of ERP systems. At the same time, the ERP vendors and developers can better advise and support their customers when delivering and deploying ERP systems.

This study has a few limitations. ERP implementations are complex and take time to complete 52 , 51 . However, this study was restricted to the shakedown phase of the implementation, which is widely acknowledged to be the most critical in terms of continuation or abandonment of ERP 53 . It could be that these findings might change over time, with some support structures gaining or losing influence on the outcomes of interest. Work that gives greater consideration to time would enrich our understanding of this phenomenon. Thus, an area for possible future work would be to examine ERP implementations and support structures over a significantly longer period of time—that is, across all phases of an implementation. Besides, this study collected data from ERP user in many kinds of enterprises; therefore, there is a restriction related to applicability of this study for each specific enterprise group.

This study chose an approach for the employee to assess his or her job performance in ERP context that is not evaluated by the supervisor of the employee. Future research should collect data through supervisors to measure the job performance of the employee in ERP context.

ABBREVIATIONS

AVE: Average variance extracted

CMB: Common method bias

CMV: Common method variance

CR: Composite reliability

ECM: Expectation–Confirmation Model

EFA: Exploratory factor analysis

ERP: Enterprise Resource Planning

IS: Information system

HTMT: Heterotrait-monotrait ratio of correlations

PLS_SEM: Partial Least Squares Based Structural Equation Modeling

TAM: Technology Acceptance model

TTF: Task-Technology Fit theory

UTAUT: Unified theory of acceptance and use of technology

VIF: Variance inflation factor

COMPETING INTERESTS

The authors declare that they have no conflicts of interest.

AUTHORS’ CONTRIBUTIONS

Vo Van Nhi and Pham Tra Lam have contributed in conducting experiments, getting hold of data and writing the manuscript. Pham Tra Lam has contributed explanation of data and revising the manuscript.

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Article Details

Issue: Vol 3 No 4 (2019)
Page No.: 451-461
Published: Feb 9, 2020
Section: Research article
DOI: https://doi.org/10.32508/stdjelm.v3i4.587

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Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Nhi, V. V., & Lam, P. T. (2020). The role of perceived usefulness in the relationship between task — technology fit and individual job performance in ERP implementation — evidence from Vietnam’s enterprises. Science & Technology Development Journal - Economics - Law and Management, 3(4), 451-461. https://doi.org/https://doi.org/10.32508/stdjelm.v3i4.587

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