Exploring Enterprise Resource Planning (ERP) System Outcomes in Indian Small and Medium Enterprises (SME’s)

Exploring Enterprise Resource Planning (ERP) System Outcomes in Indian Small and Medium Enterprises (SME’s)

1R.M.Bhawarkar, 2DR. L.P. Dhamande,

1 Training & Placement Officer, Acharya Shrimannarayan Polytechnic, Wardha-442001, Maharashtra, India.

2Principal, College of Engineering & Technology, Sant Gadge Baba Amravati University, Dhamangaon, Distt.-Amravati, Maharashtra, India.

Abstract

To improve productivity and overall business performance, Enterprise Resource Planning (ERP) is one of the solutions for the small and medium scale enterprises (SMEs) in order to face the global challenges. The purpose of this paper is to investigate Enterprise Resource Planning (ERP) system outcomes in the context of Indian Small & Medium- sized Enterprises. Most of the former research on ERP outcomes or benefits is based on data from large enterprises. In this paper, we discover and classify ERP system outcomes in Indian Small & Medium size Enterprises (SMEs). An extensive literature review was carried out for identification of various attributes which grouped in various ERP system outcomes or benefits. The instrument consisting of 25 variables was identified after literature review. A 24 item questionnaire was developed from the relevant literature and distributed to 863 SMEs. Data from 219 Indian SMEs were collected for the measurement of effectiveness of critical success factors. Through the study, five factors were identified that attempts to explain

77.349 % of variances. The factors are found to be reliable and valid.

Keywords Enterprise Resource Planning (ERP), Critical Success Factors (CSFs), Small and Medium Size Enterprises (SMEs), ERP system outcomes

1. Introduction

   Enterprise Resource Planning (ERP) system implementations are substantial and long-term investments, expected to yield significant positive outcomes or benefits for organizations undertaking this earnest attempt. Organizations hence need to assess whether they have achieved the intended contribution from their investment, and the ERP literature includes several studies investigating ERP system outcomes in organizations [32]. While it

   could be argued that return on investment is even more critical for small and medium-sized enterprises (SMEs), for whom ERP system implementations constitute a comparatively larger investment than for large enterprises [18], there has yet been limited focus on ERP outcomes in the SME context.

   The majority of existing measurement frameworks has been developed based on data from the large enterprises. Only few studies have tried to attempt within the SME context. Large enterprises have been reported to receive more benefits as compared to small and medium- sized enterprises [31]. Compared to large enterprises SMEs have been reported to be constrained by limited resources and limited IT competence [17]. The organizations which have successfully implemented the ERP systems are reaping the benefits of having integrating working environment, standardized processes and operational benefits to the organization. The objective of this paper is to contribute to the scarce literature on evaluation of ERP system outcomes in SMEs and to develop an instrument for measuring ERP system outcomes or benefits. In this paper, first, we review the literature mainly to identify ERP outcomes in general organizations. Next, we describe the data collection, then we present and discuss the factors that emerged and finally, we present the study contribution and conclusion. The study reveals that about 77.349 % of the variances in ERP system outcomes were explained by the ERP benefits identified in the study.

2. Literature Review:

   Over the years, various approaches to ex-post evaluation of ERP system outcomes have been developed. A significant contribution in this area is the multidimensional model for Enterprise systems success (ESS) measurement developed by Gable [10]. Former research has recognized the effect of organizational size on ERP outcomes. A study conducted by Esteves [9] identified organizational size as a moderator of ERP impact on productivity in

   SMEs. A limited number of studies have focused on ERP system outcomes in SMEs. Esteves [9] conducted a survey to investigate ERP benefits realization in SMEs. Kale [17] investigated performance evaluation of ERP implementation in Indian SMEs. The study employed a survey of 130 SMEs. The ERP performance was studied through a list of 19 ERP benefits. Extensive literature review was carried out for identification of various attributes of ERP outcomes which were grouped into the performance outcomes. Table 1 presents a list of variables selected by author from the literature review.

3. Research Methodology:

   The purpose of this research is to identify ERP outcomes within the SME context. This research was a cross sectional field study that involved the use of survey methodology to obtain data from small & medium scale industries across a variety of

   1. Work Simplification	2. Data Integration
   3. Administration Expenses Reduces	4. Better Inventory Outflow
   5. Increased Work Efficiency	6. Data Transparency
   7. Information Accuracy	8. Business Process Improvements & Increased Capacity
   9. Overall Productivity	10. Substitutability
   11. Data Analysis	12. Information Availability
   13. Data Import / Export	14. Information Timeliness
   15. Production Planning Improvements	16. Enhances Quality of Decision Making
   17. Data Security	18. Up-to-date Data Base Contents
   19. System Extensions / Changes	20. Improves organization wide Communication & Departmental Cooperation
   21. Staff Requirements Reduction	22. System Quality
   23. Information Back Tracking	24. User Interface Flexibility
   25. Improves Workers Participation in the Organization.

   Table 1: List of variables selected from review.

   production environments. A model was developed to include key variables and their relationships in the implementation of ERP system. A questionnaire was developed to collect data from Indian SMEs, for testing these relationships. The survey was implemented using a mixed mode method wherein postal mail procedures were mixed with email delivery.

4. Scale Development for ERP System Outcomes:

   Design of multi-item scales employed to measure the constructs are very vital to empirical research [11]. Establishing the validity of the scales is dependent first upon establishing that they are reliable measures [13]. One of the major goals of this research study is to create reliable and valid multi item scales for measuring the 25 constructs.

5. Survey Methodology:

   Invitations to participate in the survey requested responses from implementers of ERP packages who have basically worked for small & medium scale enterprises based in India and have been associated with the implementation process for their respective organization. Questionnaire survey method was selected and used five point multi-items, liker-typescales for each item where 1 meant not important,

   2 meant somewhat important, 3 meant neutral,

   4 meant important and 5 meant most important. The questionnaire is focused on the ERP system outcomes or benefits that clarified from literature review. It identifies the respondents perception of the importance of ERP system outcomes.

6. Findings and Analysis:

   An analysis is conducted to defect weaknesses in design and instrumentation and to provide proxy data for selection of a probability data. By carrying out the extensive literature review total 25 variables were framed in the research instrument (questionnaire). The main objective of this study is to identify the current ERP scenario in small and medium scale enterprise. Accordingly, to draw meaningful conclusion, sample frame & sample size were decided based on review. Sample frame consist of the all type of small and medium scale enterprises. The questionnaire was sent to 863 organizations &

   219 usable surveys were received making the response rate to be around 25.37%. The respondents came from manufacturing, financial services, healthcare, Insurance, process oriented, unit oriented,

   public service, telecommunication, utility & a variety of other industries.

   1. Reliability of Instrument:

      Reliability is one of the most critical elements in assessing the quality of the construct measures [9], and it is necessary condition for scale validity. A statistically reliable scale provides consistent and stable measures of a construct. There are four methods to measure the reliability of empirical model out of these four, internal consistency method is easy and works effectively in the field studies.

      The internal consistency of a set of measurement variables is to the degree to which items in the set are homogeneous. Internal consistency can be estimated using reliability coefficient such as Cronbachs alpha. With the objective of establishing the reliability of the data collected and that of the study. Cronbachs alpha of the data pertaining to the factors was calculated. Nunnally (1971) suggests that a Cronbachs alpha value larger than 0.7 suggests good internal consistency. The overall Cronbachs alpha for independent variable was found to be 0.964 indicates that the developed model was found to be reliable. Table 2 shows the reliability statistics of output variables, whereas Table 3 shows the reliability for five ERP system outcomes.

      Table 2: Reliability Statistics (output Variables) Reliability Statistics

      Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	N of Items
      .964	.964	25

      Table 3: Internal Consistency – Reliability for five ERP system outcomes. (Output Factors)

      Sr.No	Factor Name	Cronbach Alpha	No. of Items
      1	System Quality	0.959	8
      2	Organizational Impact	0.909	6
      3	Information Quality	0.925	5
      4	Individual Impact	0.905	4
      5	Workgroup Impact	0.915	2

   2. Descriptive Statistics for Variables: The primary data analysis involved the use of descriptive statistical tools such as mean and standard deviation. These measures were utilized to know the data quality. The mean and standard deviation associated with each scale used to measure the ERP system outcomes facilitating ERP system deployment

      are shown in table 4. All the 25 variables showing minimum mean valve of 3.44 & a maximum mean valve of 4.09, which means that five of the mean values are more than 4 and others are nearer to 4. It shows the perception of Indian small & medium ERP firms towards these 25 factors that means these variables were the performance measures of the successful ERP implementation.

      Table 4: Descriptive Statistics of Responses of Performance Measures

      Descriptive Statistics
      	N	Mean	Std. Deviatio n
      Increased Work Efficiency	219	4.09	.985
      Enhances Quality of Decision Making	219	4.08	.967
      Data Integration	219	4.04	1.022
      User Interface Flexibility	219	4.03	1.108
      System Extensions / Changes	219	4.02	1.045
      System Stability	219	3.99	1.084
      Data Security	219	3.99	1.075
      Substitutability	219	3.99	1.056
      Data Analysis	219	3.97	1.004
      Data Transparency	219	3.97	.943
      Data Import / Export	219	3.96	1.068
      Work Simplification	219	3.94	1.012
      Production Planning Improvements	219	3.90	.951
      Administration Expenses Reduces	219	3.88	.926
      Business Process Improvements & Increased Capacity	219	3.85	.932
      Better Inventory Outflow	219	3.84	.837
      Up-to-date Data Base Contents	219	3.81	1.014
      Information Back Tracking	219	3.80	.984
      Information Accuracy	219	3.79	.882
      Information Timeliness	219	3.78	.958
      Staff Requirements Reduction	219	3.77	1.030
      Information Availability	219	3.75	.917
      Overall Productivity	219	3.75	.984
      Improves Organization Wide Communication & Departmental Cooperation	219	3.50	.955

      Improves Workers Participation In The Organization	219	3.44	.962
      Valid N (listwise)	219

   3. Factor Analysis:

      An exploratory factor analysis was conducted on the different measures to purify the model. Factor analysis is most frequently used to identify a small number of factors, which may be used to represent relationship among sets of interrelated variables. Factor analysis is frequently used to develop questionnaires. In this study, factor extraction principal components method was used with original 25 dependent variables.

      The first step is to decide which factors you wish to retain in the analysis. The common sense criterion for retaining factrs is that each retains factors must have some sort of face validity or theoretical validity. The SPSS V 18 default is to keep any factor with an Eigen value larger than 1.0. If a factor less than 1.0, it explains less variance than an original variables and usually for only a few of the factors will the Eigen value be larger than 1.0 there are other criteria for selection such as Scree plot or conceptual reasons that may be used. The Scree plot sometimes used to select how many factors to rotate to a final solution. The traditional construct for interpretation is that the Scree should be ignored and that only factors on the steep portion of the graph should be selected and rotated. We have selected 5 output factors (dependent) based on the observation of the Scree plot (Fig 2). Also, the Eigen value of these variables are lower than 0.4.

      After factor extraction and the rotation, loading of the variables in respective factor was noted down and the naming was done. Table 3 shows the reliability of (internal consistency) co- efficient of input factor which ranged from 0.905 to 0.959. Table 5 shows the rotated component matrix.

      Fig. 2 Scree Plot.

      Table 5: Rotated Component Matrix for ERP system outcomes (Dependent Factors).

      Rotated Component Matrixa

      	Component
      	1	2	3	4	5
      24-User Interface Flexibility	.838
      17-Data Security	.822
      19-System Extensions / Changes	.820
      2-Data Integration	.802
      11-Data Analysis	.788
      22-System Stability	.785
      6-Data Transparency	.780
      13-Data Import / Export	.768
      4-Better Inventory Outflow		.758
      3-Adminstration Expenses Reduces		.724
      7-Information Accuracy 18-Up-to-date Data Base Contents 12-Information Availability 23-Information Back Tracking 14-Information Timeliness 5-Increased Work Efficiency 16-Enhances Quality Of Decision Making 10-Substitutability 1-Work Simplification 25-Improves Workers Participation In The Organization 20-Improves Organization Wide Communication & Departmental Cooperation	.405	.711 .693 .648 .602	.806 .756 .749 .672 .668	.811 .791 .788 .738	.885 .838

      1. Business Process Improvements & Increased Capacity 15-Production Planning Improvements

      2. Overall Productivity 21-Staff Requirements reduction

      Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

      a. Rotation converged in 6 iterations.

   4. Interpretation of Output from the Factor Analysis of ERP System Outcomes or Factor Naming:

      After factor analysis five factors were extracted, this explained total 77.329% of variances which were named as shown in table 6 as per the variables content within that component.

      Table 6: Interpretation of Output from the Factor Analysis of Performance Measures.

      Categorization of Performance Measures in terms of Component	TVE	List of CSFs for Indian SMEs	RCMV
      Component 1:	53.762	24-User Interface
      Named as		Flexibility	.838
      System		17-Data Security	.822
      Quality		19-System Extensions /
      		Changes	.820
      		2-Data Integration	.802
      		11-Data Analysis	.788
      		22-System Stability	.785
      		6-Data Transparency	.780
      		13-Data Import / Export	.768
      Component 2:	8.780	4-Better Inventory
      Named as		Outflow	.758
      Organizationa		3-Adminstration
      l Impact		Expenses Reduces	.724
      		8-Business Process
      		Improvements &
      		Increased Capacity	.711
      		15-Production Planning
      		Improvements	.693
      		9-Overall Productivity	.648
      		21-Staff Requirements
      		reduction	.602

      Component 3:	5.517	7-Information Accuracy	.806
      Named as		18-Up-to-date Data
      Information		Base Contents	.756
      Quality		12-Information
      		Availability	.749
      		23-Information Back
      		Tracking	.672
      		14-Information
      		Timeliness	.668
      Component 4:	4.901	5-Increased Work
      Named as		Efficiency	.811
      Individual		16-Enhances Quality Of
      Impact		Decision Making	.791
      		10-Substitutability	.788
      		1-Work Simplification	.738
      Component 5:	4.389	25-Improves Workers
      Named as		Participation In The
      Workgroup		Organization	.885
      Impact		20-Improves
      		Organization Wide
      		Communication &
      		Departmental	.838
      		Cooperation

   5. Detailed Item Analysis:

   This method is used to evaluate the assignment of variables to scales as per Nunnally's method (1971). As per this method variable should have high co- relation with the scale in whichthe variable is placed than other scales. As seen in table 7, all the variables have high co-relations with the scales to which they had been assigned relative to all others. Therefore it was concluded that all the variables in this instrument had been correctly assigned to respective scale.

   Table 7: Detail factor analysis.

   Correlations

   	OU_SCORE1 _ System Quality	OU_SCORE2 Organizational Impact	OU_SCORE3_ Information Quality	OU_SCORE4 _ Individual Impact	OU_SCORE5 _Workgroup Impact
   24_User Interface Flexibility	.922**	.596**	.614**	.504**	.468**
   17_Data Security	.893**	.570**	.593**	.499**	.441**
   19_System Extensions / Changes	.881**	.561**	.569**	.510**	.470**
   2_Data Integration	.861**	.550**	.565**	.473**	.424**
   11_Data Analysis	.823**	.475**	.538**	.474**	.443**
   22_System Stability	.876**	.590**	.628**	.540**	.501**
   6_Data Transparency	.810**	.468**	.553**	.508**	.360**
   13_Data Import / Export	.831**	.563**	.580**	.499**	.430**

   4_Better Inventory Outflow	.434**	.776**	.516**	.451**	.347**
   3_Adminstration Expenses Reduces	.539**	.847**	.626**	.557**	.439**
   8_Business Process Improvements & Increased Capacity	.563**	.834**	.594**	.580**	.411**
   15_Production Planning Improvements	.578**	.824**	.625**	.575**	.446**
   9_Overall Productivity	.595**	.785**	.623**	.525**	.457**
   21_Staff Requirements reduction	.655**	.808**	.683**	.564**	.466**
   7_Information Accuracy	.506**	.552**	.852**	.537**	.382**
   18_Up-to-date Data Base Contents	.572**	.613**	.887**	.565**	.497**
   12_Information Availability	.567**	.608**	.869**	.590**	.470**
   23_Information Back Tracking	.553**	.624**	.843**	.584**	.537**
   14_Information Timeliness	.530**	.589**	.831**	.608**	.546**
   5_Increased Work Efficiency	.495**	.534**	.560**	.897**	.439**
   16_Enhances Quality Of Decision Making	.557**	.560**	.583**	.898**	.508**
   10_Substitutability	.431**	.482**	.562**	.844**	.397**
   1_Work Simplification	.459**	.467**	.527**	.800**	.445**
   25_Improves Workers Participation In The Organization	.416**	.422**	.448**	.443**	.942**
   20_Improves Organization Wide Communication & Departmental Cooperation	.509**	.462**	.548**	.495**	.937**

   **. Correlation is significant at the 0.01 level (2-tailed).

7. Validity:

The validity of a measure refers to the extent to which it measures what is intended to be measured. There are two different types of validity generally considered.

1. Content Validity: Content validity was subjectively judged by the researchers [30] contents of this instrument was selected based on the extensive literature reviews and discussed with experts and with recent literature regarding the performance measures of ERP system in SME'S. Thus we said that this study have content validity.

2. Construct Validity: The construct validity of each measure was evaluated by factor analyzing the measurement items of each of the factors. A measure has construct validity if it the theoretical construct that it has design to measure. The factor matrices (Table 8) showed that all the output factors were unifactorial with Eigen values greater than the accepted criteria of

   1. The result of this study indicated good construct validity for the developed scales.

Table 8: Summary of Separate factor matrices for each constructs (output Factors)

1. Conclusion :

   The study has identified 25 major ERP system outcomes in the SME context and thus contributes to the research on ERP system implementation projects in small & medium-sized enterprises. The main basic contributions of this paper are the definition of new constructs associated with the ERP system outcomes and the development of new multi-item measurement scales for measuring these constructs. The model which

   proposed was evaluated empirically and was found to be of acceptable reliability and validity. By factor analysis, five ERP system outcomes were identified after grouping and they are System Quality, Organizational Impact, Information Quality, Individual Impact and Workgroup Impact which covers total 25 variables contributing 77.329% of total variances.

2. References:

1. Bajwa, D.S., and Garcia, J.E., An Integrative Framework for the Assimilation of Enterprise Resource Planning Systems: Phases, Antecedents, and Outcomes Journal of Computer Information Systems, Vol. 44, (3), pp 81-90, 2004.

2. Bingi, P., Sharma, M.K, and Godla, J.K Critical Issues Affecting an ERP Implementation. Information Systems Management, Vol. 16, Issue 3, pp. 7 14, 1999.

3. Brent Snider, Giovani J.C. da silveira and Jaydeep Balakrishnan, ERP Implementation at SMEs: analysis of five Canadian cases International Journal of Operations and production Management, Vol. 29, Issue 1, pp 4 -29, 2009.

4. Brehm, L., Heinzl, A. and Markus, M.L. Tailoring ERP Systems: A Spectrum of Choices and their Implications Proceedings of the 34th Hawaii International Conference on Information Systems. Outrigger Wailea Resort, Maui. January 3-6, 2001.

5. Capaldo G., Iandoli L., Rippa P., Mercanti S., troccoli G.,

   An AHP Approach To Evaluate Factors Affecting ERP Implementation Success, Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, 2008.

6. Daniel E. OLeary, Enterprise Resource Planning (ERP) Systems: An Empirical Analysis of Benefits Journal of Emerging Technologies In Accounting, Vol.1, pp 63 72, 2004.

7. Davenport, T.H Putting the Enterprise into the Enterprise System. Harvard Business Review, Vol. 76, Issue 4, pp.121

   131, 1998.

8. DeLone, W.H and McLean, E.R Information Systems Success: The quest for the dependent variable Information Systems Research, Vol.3, Issue 1, pp.60-95, 1992.

9. Esteves, J., A Benefits Realization Road-map framework for ERP Usage In Small & Medium Sized Enterprises, Journal of Enterprise Information Management, pp 22-25, 2009.

10. Gable, G.G., Sedera, D., Chan T., Enterprise Systems Success: A Measurement Model, International conference on Information systems Seattle, USA, pp 576-591, 2003.

11. Gable, G.G.,, Sedera, D., Taizan, C., Re Conceptualizing Information Systems Success: The Is Impact Measurement Model, Journal of The Association for Information Systems, Vol. 9, pp 1-32, 2008.

12. Gore A. & Kess P., Managing The Impact of ERP Implementation on Organization Structure: A Case Study, The Journal for Practicing Managers, Vol. 31, No 1, pp 1-13, 2007.

13. Ifinedo P., Enterprise Systems success Measurement Model: A preliminary Study Journal of Information Technology Management, Vol. 17, Issue 1, pp 14 33, 2006.

14. Ifinedo P., Investigating the Relationship Among ERP Systems Success Dimensions: A Structural Equation Model Issues In Information Systems, Vol. 8, No 2, pp 399 – 405, 2007.

15. Ike C. Ehie, Mogens madsen, Identifying critical issues in enterprise resource planning (ERP) implementation Computers In Industry, Vol. 56, pp 545 557, 2005, Available online at www.sciencedirect.com .

16. Jose Esteves-Sousa, John Pastor-Collado, Towards the Unification of Critical Success Factors for ERP Implementations, 10th annual Business Information Technology Conference, Manchester, 2000.

17. Kale P.T., Banawait S.S, Laroiya S.C., Performance evaluation of ERP Implementation in Indian SMEs Journal of Manufacturing Technology Management, Vol. 21, pp 758- 780, 2010.

18. Levy M., Powell, P. Information System Strategy for small and medium sized enterprises: an organizational perspective, Journal of Strategic Information Systems, Vol 9, pp 63-84, 2000.

19. Light, B. 'The maintenance implications of the customization of ERP software'. Journal of Software Maintenance and Evolution: Research and Practice; Vol.13: pp 415-429, 2001.

20. Mabert,V.A., Soni, A and Venkataramanan, M.A,

    Enterprise resource planning: managing the implementation process; European journal of operational research, Vol. 146, No 2, pp 302-314, 2003.

21. Mahadeo Jaiswal, ERP enabled Best Business Practices for Competitiveness: Case of Auto Components Manufacturing Industry in India6th Annual ISOnEWorld Conference, Las Vegas, 2007.

22. Markus, M.L., Axline, S., Petrie, D., and Tanis, C

    Learning from the Adapters Experiences with ERP: problems encountered and success achieved. Journal of Information Technology, Vol. 15, Issue 4, pp. 245-265, 2000.

23. McAdam, R, Large scale innovations-reengineering methodology in SMEs: Positivistic and phenomenological approaches, International Small Business Journal, Vol.20 No 1, pp 33-50, 2002.

24. Motwani J. Mirchandani D., Madan M. & Gunasekaran

    A. Successful Implementation of ERP Projects: Evidence From Two Case Studies International Journal of Production Economics, Vol.75, pp 83-96, 2002.

25. Nah, F.F.H., Lau, J.L.S., and Kuang, J Critical Factors for Successful Implementation of Enterprise Systems.Business Process Management Journal, Vol. 7 Issue 3, pp. 285 296, 2001.

26. Nah, F.F.H and Delgado, S Critical Success Factors for Enterprise Resource Planning Implementation and Upgrade. Journal of Computer Information Systems, Vol. 47, special issue, pp.99 113, 2006.

27. Nah, F.F., Islam, Z., and Tan, M., Empirical Assessment of factors Influencing Success of Enterprise Resource Planning Implementations, Journal of Database Management, Vol. 18, No.4, pp 26-50, 2007.

28. Parijat Upadhyay, Pranab K Dan, ERP in Indian SMEs: A Post Implementation Study of the Underlying Critical Success Factors International Journal Of Management Innovation System, Vol. 1, No 2 pp 1-10, 2009.

29. Parr, A and Shanks, G A Model of ERP Project Implementation. Journal of Information Technology, Vol. 15, Issue 4, pp.289 303, 2000.

30. Sarph, J., V., Benson, P., G., Shroeder R., G., An instrument for measuring the critical factors of quality management, Decision Sciences, 20, pp 810-829, 1989.

31. Sedara, D., Gable g G , Chan T. Measuring Enterprise System Success: The Importance of a Multiple Stakeholder Perspective European Conference on Information System, 2004.

32. Sherry Finney, Martin Corbett, ERP Implementation: a compilation and analysis of critical success factors, Business Process Management Journal, Vol. 13, No.3, pp 329-347, 2007.

33. Somers, T.M and Nelson, K A taxonomy of players and activities across the ERP project life cycle. Information & Management, Vol.41, Issue 3, pp.257-278, 2004.

34. Staehr, L., Assessing Business Benefits From ERP Systems: An Improved ERP Benefits Framework, International Conference on Information Systems, 2007.

35. Sumner M., Risk Factors in Enterprise-wide/ERP Projects, Journal of Information Technology, pp. 317-327, 2000.

36. Tsai, W. , fan, Y., Leu., J., Chou, L., and Yang, C., The Relationship Between Implementation Variables and Performance Improvement of ERP Systems, International Journal of Technology Management, Vol. 38, No.4, pp 350- 373, 2007.

37. Umble, E.J., Haft, R.R., and Umble, M.M Enterprise resource planning: Implementation procedures and critical success factors. European journal of operational research, Vol. 146, Issue 2, pp. 241 – 257, 2003.