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An exploration of factors that impact individual performance in an ERP environment: an analysis using multiple analytical techniques

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European Journal of Information Systems

Abstract

This study explores the factors that can impact individual performance when using enterprise resource planning (ERP) systems. Starting from the proposition that organizational performance depends on individuals' task accomplishments, we test a structural model of task–technology fit, ERP user satisfaction, and individual performance in ERP environments. This research utilizes a survey method to examine the perceptions of ERP users. We performed factor and reliability analyses to assess the validity of the survey instrument. Six factors were identified as having an impact on individual performance: System Quality, Documentation, Ease of use, Reliability, Authorization, and Utilization. To explore the relationships among these factors, we conducted regression and multivariate adaptive regression splines analysis, and compared the findings from these two analytical techniques. The study provides evidence that System Quality, Utilization, and Ease of Use are the most important factors bearing on individual performance in ERP environments. Our findings also provide IT managers and researchers with knowledge of how these factors can be manipulated to improve individual performance when using ERP systems.

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Notes

  1. The survey approach was chosen because we wanted to extend the existing models that were developed via the survey methodology. Other research methods (e.g. qualitative approaches), while valid, would have been: (1) inefficient due to the range of organizations in our study and (2) would have had limited immediate impact on extending the existing models because they would require another step (the survey method) to collect data in order to validate the proposed model.

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Acknowledgements

This research was supported in part by a grant from the 2004 Summer Research Program of the School of Business of Virginia Commonwealth University, Richmond, VA, U.S.A.

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Authors and Affiliations

  1. Fiscal Policy Office, Information and Communication Technology Center, Ministry of Finance, Thailand

    Boontaree Kositanurit

  2. Research Institute for Tfechnology Management and Organizational Learning, School of Information Technology Management, Ryerson University, Toronto, Canada

    Ojelanki Ngwenyama

  3. Department of Information Systems and The Information Systems Research Institute, Virginia Commonwealth University, Richmond, VA, U.S.A.

    Kweku-Muata Osei-Bryson

Authors
  1. Boontaree Kositanurit
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  2. Ojelanki Ngwenyama
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  3. Kweku-Muata Osei-Bryson
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Corresponding author

Correspondence to Ojelanki Ngwenyama.

Appendices

Appendix A

Table A1

Table 10 Questionnaire Items and Acronyms Used
Full size table

Appendix B: Overview of RS

RS is a method for flexible regression modeling (Friedman, 1991). While traditional regression analysis uses a single slope to represent the relationship between a dependent and an independent variable, RS allows more than one slope, thus it provides a more flexible model than regression analysis. The RS method uses a set of piecewise polynomials to describe the relationships between the independent variables and the dependent variable (Chen, 1997). Each piecewise polynomial, called a basis function (BF), represents a sub-region of the domain of the spline. Basis functions are used by RS to implement the concepts of knots and piecewise linear RS (Briand et al., 2002).

MARS is a computer-assisted approach to RS analysis that allows for automatic selection of the knots (Hastie & Tibshirani, 1990; Friedman, 1991). MARS builds the RS model in a two-phase process, using a forward-stepwise regression selection and backward-stepwise deletion strategy. In the first phase, an overfitted model is built by adding basis functions. In the second phase, basis functions that have the least contribution to the model are deleted where removal causes the smallest increase in residual squared error. The MARS model is optimized based on the Generalized Cross Validation measure (Hastie et al., 2001). A MARS model can be generated that allows no interaction between the input variables, or to permit interactions between two or more variables. In MARS, analysis interactions between variables have a hierarchical, tree-like structure, with parent and child relationships between basis functions. A MARS model that does not involve interaction between the variables (or factors) can be expressed as a linear combination of nonlinear functions (i.e., BF jk ) of the independent variables such that y=b0+Σ j f j (x j ). Equivalently, it can be expressed as a linear combination of basis functions BF jk such that y=β0+Σ j Σk∈Kj β jk BF jk where f j (x j )=Σk∈Kj β jk BF jk , β0 is a constant that is equivalent to the intercept in the regression model, BF jk is a basis function of the independent variable (or factor) that has one of the following forms: max(0, X j −c jk ), or max(0, c jk −X j ), and β jk is a coefficient of the basis function BF jk . MARS must both identify the most relevant knots (e.g. c jk ) as well as the coefficients. The coefficients are the coefficients β jk and are estimated by minimizing the sum of square errors. RS performs a polynomial fit in each region with constraints at the knots using the least squares criterion.

Appendix C: Details of factor analysis

Independent variables:

We conducted several iterations of factor analyses in order to find a meaningful or interpretable grouping of the questionnaire items. Based on the results of the initial iteration, RDATA, TRAIN, FUNC2, FUNC3, FUNC4, FUNC5, SUPP1, and SUPP2 variables were omitted from further consideration because they have cross loadings (i.e., the load was very close on more than one factors and the difference in the loadings was less than 0.1) and low communalities. Although the difference in the loadings of MEAN on the System Quality factor (i.e., factor 1) and the Ease of Use factor (i.e., factor 3) was less than 0.1 (i.e., loading on factor 1 and 3 were 0.607 and 0.519, respectively), it was retained for the following reasons: (1) the loading on the System Quality factor was 0.088 higher than the loading on the Ease of Use factor; (2) the alpha of System Quality with MEAN was as high as 0.9603; and (3) the reliability analysis of the System Quality factor did not suggest that MEAN should be deleted.

In our second iteration, factor analysis and reliability analysis were re-performed on the other 26 items (cf. Table C1). The five factors produced by the factor analysis explained 70.996% of the total variance. Reliability analysis was then performed on each of the factors suggested by the factor analysis. Although the reliability analysis recommended that RDETAIL should be dropped, the item was maintained because the alpha of System Quality is quite high (0.9603) and if the item is deleted, the increase in the alpha is not significant (0.0002).

Table 11 Factor analysis and reliability analysis of TTF and user satisfaction items
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The exploratory factor analysis confirmed the overlap of the instruments, as described by Goodhue (1998). The questionnaire items regarding Ease of Use from the TTF instrument and from the user satisfaction instrument of Etezadi-Amoli and Farhoomand were loaded together into one factor (Ease of Use). In addition, the currency item from the TTF instrument and the timeliness item from the user satisfaction instrument of Etezadi-Amoli and Farhoomand were loaded on the System Quality factor.

Dependent Variables:

Utilization and Individual Performance are dependent variables in this study. However, Utilization is also an independent variable for Individual Performance. Thus, factor analyses were performed separately on Individual Performance items and Utilization items.

Items from Individual Performance (i.e., PERFO1, PERFO2, PERFO3) were loaded together into one factor with 81.79% total variance explained. The reliability analysis recommended the item PERFO3 to be deleted. However, it was maintained since the alpha with the three items was as high as 0.927, and the alpha would be increased by only 0.02, if PERFO3 is deleted. Items from Utilization were loaded together into one factor with 28% total variance explained. Results of factor analysis and reliability analysis are presented in Tables C2 and C3.

Table 12 Factor analysis and reliability analysis of individual performance items
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Table 13 Factor analysis and reliability analysis of utilization items
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Kositanurit, B., Ngwenyama, O. & Osei-Bryson, KM. An exploration of factors that impact individual performance in an ERP environment: an analysis using multiple analytical techniques. Eur J Inf Syst 15, 556–568 (2006). https://doi.org/10.1057/palgrave.ejis.3000654

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