The Effect Of Critical Success Factors Accounting Essay

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Literature review revealed that there is a relationship between Critical Success Factors and the success of Six-Sigma programs implementation. However, there seems to be little empirical evidence about specific Critical Success Factors that should be adopted and their expected influence in developing countries. Hence, it deem appropriate to identify these factors and their influence on applying Six-Sigma programs. It is claimed that deductive approach is most appropriate for this research. Reliability test using Cronbach's Alpha was used to test the goodness and validity of response data. Factor analysis was deployed to test the validity of the measures (Critical Success Factors) and describe the underline structure in data matrix variable resulted in six variables. The final step in the analysis was stepwise regression analysis through which the research succeeded to identify variables affecting variation of Six-Sigma programs implementation with only four variables namely; Foundations, Communication and Support, Alignment, and Resource Management. Furthermore, the research provides empirical evidence that proper/improper selection of Critical Success Factors significantly affect the success of Six-Sigma programs implementation.

Key words: Six-Sigma, Critical Success Factors, Egyptian organizations.

Introduction

Improving quality of products and services is fundamental to business success. Accordingly, organizations have to pursue different continuous improvement programs / techniques (Zu et al., 2010). Six-Sigma is considered one of the latest improvement programs commonly used by different organizations (Chakravorty, 2009; Su and Chou, 2008). Different business sectors such as; Manufacturing, Financial, Healthcare, Engineering, Construction, and Research and development show growing interest in applying Six-Sigma (Kwak and Anbari, 2006). In addition, literature review revealed that not only large organizations but also small- and medium-sized organizations perceived Six-Sigma to be an effective program to enhance their performance (Antony et al., 2005).

Six-Sigma supports organizations to improve productivity; provide basis for improvements; strengthen organizations' competitive advantage (Bratić, 2011); reduce cost (Bratić, 2011; Antony et al., 2005); increase profitability; reduce process variability (Antony et al., 2005); increase employees' improvement efforts and commitment to quality (Linderman et al., 2003); improve operation performance; and consequently enhance customer satisfaction and loyalty (Chakravorty, 2009). Major areas including; process design, variables investigation, analysis and reasoning, focus and process improvement, broad participation in problem solving, knowledge sharing, goal setting, suppliers efficiency and effectiveness, and decision making are affected positively by applying Six-Sigma programs (Mehrjerdi, 2011).

Although many enterprises demonstrated substantial return on investment as a result of Six-Sigma implementation (Klefsjö et al., 2001), several researches criticized Six-Sigma as offering nothing new and simply repackaging traditional quality management practices (Zu et al., 2008). In addition, there are some organizations that adopted Six-Sigma had to scrape their entire Six-Sigma program after spending significant amount of money (Mehrjerdi, 2011). Key argument is that the large returns from Six-Sigma at some organizations were not due to Six-Sigma itself as an improvement program but were attributable to poor quality level before adopting any quality program (Stamatis, 2000 cited in Zu et al., 2008).

Moreover, Kumar et al. (2008) emphasized that required implementation cost for successful Six-Sigma initiatives can be considerably high for many companies, especially those companies with small profit margins and limited resources. Therefore, in spite of the recognized benefits achieved by applying Six-Sigma, many companies have chosen not to apply this program (Raisinghani et al., 2005). Accordingly, Chakravorty (2009) argued that there is an increasing concern about the success or failure of Six-Sigma programs despite the growing popularity and the wide-spread adoption of Six-Sigma. In that sense, several publications (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011) highlighted that proper/improper identification of Critical Success Factors (CSFs) will affect Six-Sigma programs success or failure.

Research Problem

The aforementioned discussion reveals inconsistency between researchers regarding the effectiveness of implementing Six-Sigma programs. Moreover, it reveals that proper identification of CSFs have direct effect on Six-Sigma programs success. Therefore, this research intends to identify the relationship between CSFs and the success of Six-Sigma programs implementation. The practical implication of this research is to provide practitioners with better opportunity to successfully conclude Six-Sigma projects, while the theoretical implication is to provide researchers with a different perspective to investigate success and failure of Six-Sigma projects.

Research Methodology

This research aims to identify significant CSFs that affect Six-Sigma implementation. Therefore, this research will examine the literature to theoretically identify the CSFs and consequently, test and verify empirically the relationship between those CSFs and Six-Sigma implementation. It is claimed that deductive approach is most appropriate for this research as it is a theory testing process in which, research starts with an established theory or hypothesis about a set of variables and seeks to test and verify the relationship of these variables (Sekaran, 2003; Greener, 2008). This will be followed by adopting survey research for gathering and analyzing data required for testing the research hypotheses. Survey research is considered appropriate for this research as survey research is used to collect information from individuals about themselves or about the social units to which they belong (Forza, 2002).

Literature Review

Background and Definitions

Total Quality Management (TQM) can be considered as the father of Six-Sigma as many of the principles constituting the basis of TQM are paramount in Six-Sigma (Brun, 2011). This was confirmed by Schroeder et al. (2008) whom argued that Six-Sigma is grown out of traditional Quality Management (QM) methods and practices, they also affirmed the merit of Six-Sigma is that it provides an organizational structure not previously seen. Moreover, the literature revealed that the original structure of Six-Sigma was derived from statistics aiming to reduce process variation to less than 3.4 defects per million opportunities (Bratić, 2011).

Several Six-Sigma definitions were presented in the literature. These definitions are; project-driven management methodology aims to improve organization's products, services and processes by continually reducing defect rate (Bratić, 2011); preventing mistakes within processes that add value to customers (Su and Chou, 2008); improving yield which, in turn, boost customer satisfaction with the ultimate goal to enhance net income (Raisinghani et al., 2005); powerful business strategy that is essential for achieving and sustaining operational and service excellence (Antony, 2004); organization efficiency and effectiveness enhancement (Sokovic et al., 2005); sustaining competitive advantage by integrating process knowledge with statistics, engineering, and project management (Kwak and Anbari, 2006).

Reviewing the mentioned above definitions revealed that there is consistency between researchers about the core roles and functions that Six-Sigma aims to achieve. These roles and functions can be summarized as; improve processes efficiency and effectiveness through reducing defect rate, improve process capabilities, eliminate waste, better utilization of existing knowledge, enhance organization profitability aiming to sustain organization growth and competitiveness. Hence, Six-Sigma can be defined as a methodology used to enhance customer satisfaction through identifying, measuring, analyzing and improving critical processes that affect organization stakeholders and held control among these processes in order to sustain organization growth and profitability.

Six-Sigma Practices

Six-Sigma aims to achieve organization strategic objectives through using certain specialists whom apply a structured method and performance metrics (Bratić, 2011). Thus, Six-Sigma approach has three unique features; an overall approach (known as DMAIC) that implies improvement tools sequences and links; integration of both human and process elements using a belt based organization (Champion, Master Black Belt, Black Belt and Green Belt); and monitoring bottom-line results and sustaining gains (Su and Chou, 2008). In the same vein, Zu et al. (2008) empirically validated three new Six-Sigma practices. These new practices are (1) Six-Sigma role structure, (2) Six-Sigma structured improvement procedure, and (3) Six-Sigma focus on matrices.

Six-Sigma role structure

In an organization, Six-Sigma is a top-down initiative led by the company CEO who designates hierarchical trained personnel working as improvement specialists; Champion, Master Black Belt (MBB), Black Belt (BB), and Green Belt (GB) thus, constituting the infrastructure of a Six Sigma project (Linderman et al.,2003; Antony, 2004; Su and Chou, 2008). This hierarchy should include a coordination mechanism for quality improvement across multiple organizational levels (Zu et al., 2008) as well as clear routines for control and reporting (Klefsjö et al., 2001). In that sense, leaders (Champions) initiate, support, and review key improvement projects while Black Belts serve as project leaders whom mentor Green Belts in problem-solving efforts (Schroeder et al., 2008).

Six-Sigma structured improvement procedure

Six-Sigma is a highly disciplined process that helps an organization to focus on developing and delivering near-perfect products and services (Su and Chou, 2008). The implementation of Six-Sigma incorporates a wide range of tools and methodologies used to improve organization performance and profitability (Ingle and Roe, 2001).

Six-Sigma projects are led, from concept to completion, by a structured method named DMAIC (Define, Measure, Analyze, Improve, and Control) for process improvement (Sokovic et al., 2005) and DMADV (Define, Measure, Analyze, Design, and Verify) for product/service design improvement projects (Zu et al., 2008). The improvement cycle comes into play to meet the customer needs consistently and perfectly (Su and Chou, 2008). These structured procedures offer a standardized approach that guides the teams to break complex tasks into its elementary components that reduce task complexity, thus increasing their productivity (Linderman et al., 2006). This is achieved through using appropriate tools for designated steps, as well as systematic project management tools, which enhance problem-solving ability (Kwak and Anbari, 2004). For a specific project or problem, the standardized approach is tailored with focus on making the studied process more robust and less subject to errors (Raisinghani et al., 2005).

Six-Sigma focus on matrices

Six-Sigma is a process-focused approach that aims to highlight process improvement opportunities through systematic measurement (Raisinghani et al., 2005). The main benefit of a Six-Sigma program is the elimination of subjectivity in decision-making, by creating a system where everyone in the organization collects, analyzes, and displays data in a consistent way (Su and Chou, 2008). Thus, implementing Six-Sigma emphasizes using a variety of quantitative matrices in continuous improvement, such as process Sigma measurements, critical-to-quality matrices and defect measures as well as traditional quality measures like process capability (Zu et al., 2008).

An empirical research held by Zu et al. (2008) in 226 organizations illustrated three vital Six-Sigma practices namely; Six-Sigma role structure, Six-Sigma structured improvement procedure, and Six-Sigma focus on matrices. Their research showed also that applying these practices will contribute directly to; improve performance; coordinate and control work; ensure that tactics match overall business strategy; guide improvement projects; reduce corporate use of political agendas to drive solutions; reduce performance variability; increase employees' improvement efforts and finally increase the magnitude of improvements. Hence, the research will adopt these practices as variables that represent successful implementation of Six-Sigma programs.

Six-Sigma Critical Success Factors

Antony (2006) alleged that the difference between success (substantial return on investment) and failure (waste of resources, effort, time and money) in a six sigma improvement project could be attributed to the proper/improper identification of critical success factors during the implementation of a Six-Sigma program. With the attempt to successfully implement Six-Sigma, several researches (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011) introduced different Critical Success Factors (CSFs) that are considered as the base that guarantee successful implementation.

Reviewing the literature revealed several CSFs that were mentioned by several researches such as; visible management commitment, support and involvement (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011), encouraging and accepting organizational cultural change (Brun, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Yusr et al., 2011), active communication process (Brun, 2011; Mehrjerdi, 2011; Chung et al., 2008), organizational infrastructure (Brun, 2011; Antony et al., 2005; Yusr et al., 2011), continuous education and Six-Sigma training (Brun, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011), linking Six-Sigma to corporate business strategy and objectives (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Antony, 2006; Yusr et al., 2011), linking Six-Sigma projects to clearly defined customers' requirements (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011), linking Six-Sigma to human resources (Brun, 2011; Antony et al., 2005; Yusr et al., 2011), linking Six-Sigma to suppliers (Brun, 2011; Antony et al., 2005; Yusr et al., 2011), understanding the DMAIC methodology, tools, techniques and key matrices (Brun, 2011; Antony et al., 2005; Antony, 2006; Yusr et al., 2011), proper skills for project selection, project management and project control (Brun, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Yusr et al., 2011), prioritization and selection of projects based on their significant savings for the organization (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Antony, 2006; Chung et al., 2008; Yusr et al., 2011), executive management must be active in providing rewards (Mehrjerdi, 2011), clear performance matrices for collecting facts and data in support of all decisions to be made (Mehrjerdi, 2011; Chakrabarty and Tan, 2007), generating a regularly written communications about Six-Sigma (Mehrjerdi, 2011), asking managers at different level to be supportive and the advocate of Six-Sigma (Mehrjerdi, 2011), provide news of Six-Sigma's success to the company and how it benefits the companies' bottom line and employees (Mehrjerdi, 2011), prepare a list of one-year Six-Sigma projects and review and refresh them regularly (Mehrjerdi, 2011; Chung et al., 2008), selection of team members and teamwork (Kwak and Anbari, 2006; Chung et al., 2008), attaching the success to financial benefits (Antony, 2006; Chakrabarty and Tan, 2007), and organizational understanding of work processes (Chakrabarty and Tan, 2007). Table (1) summarizes different CSFs for Six-Sigma implementation.

Table 1: CSFs for Six-Sigma implementation

#

CSFs

1

Visible management commitment, support and involvement.

2

Encouraging and accepting Organizational cultural change.

3

Active communication process.

4

Organizational infrastructure.

5

Continuous education and Six-Sigma training.

6

Linking Six-Sigma to corporate business strategy and objectives.

7

Link Six-Sigma projects to clearly defined customers' requirements.

8

Linking Six-Sigma to human resources.

9

Linking Six-Sigma to suppliers.

10

Understanding the DMAIC methodology, tools, techniques and key matrices.

11

Project selection, management and control skills.

12

Prioritization and selection of projects based on their significant savings for the organization.

13

Executive management must be active in providing rewards.

14

Using clear performance matrices for collecting facts and data in support of all decisions to be made.

15

Generating a regularly written communications about Six-Sigma.

16

Asking managers at different level to be supportive and the advocate of Six-Sigma.

17

Provide news of Six-Sigma's success to the company and how it benefits the companies' bottom line and employees.

18

Prepare a list of one-year Six-Sigma projects and review and refresh them regularly.

19

Selection of team members and teamwork.

20

Attaching the success to financial benefits.

21

Organizational understanding of work processes.

Research Variables and Hypotheses

The main theme of this research is to empirically identify the relationship between CSFs (independent variables) and the success of Six-Sigma practices implementation (dependent variables) namely; Six-Sigma role structure, Six-Sigma structured improvement procedure, and Six-Sigma focus on matrices. The aim is to identify that there is a variation in importance between the different CSFs in reaching successful Six-Sigma projects. Thus, this research hypothesized the following:

H1. CSFs have a significant influence on the level of Six-Sigma role structure.

H2. CSFs have a significant influence on the level of Six-Sigma structured improvement procedure.

H3. CSFs have a significant influence on the level of Six-Sigma focus on matrices.

Research Instrument

Survey instrument

To investigate the influence of CSFs on the implementation of Six-Sigma practices, a questionnaire is suggested as an effective tool for gathering the data representing the respondents' perceptions about the proposed relationships. Questionnaire allows the collection of information from large groups of individuals about themselves or about the social units which they belong (Forza, 2002).

The questionnaire is intended to measure the research dependent variables namely, (1) Six-Sigma role structure, (2) Six-Sigma structured improvement procedure, and (3) Six-Sigma focus on matrices (adopted from Zu et al., 2010). Five-point Likert scale with 1 = Extremely not implemented, 2 = Not implemented, 3 = Moderate implementation, 4 = Implemented and 5 = Totally implemented were used to measure these dependent variables. In addition, the degree to which each participant value the 21 CSFs (independent variables) are assessed by asking respondents to rank each of the CSFs on five-point Likert scale where 1 = Least Important, 2 = Less Important, 3 = Important, 4 = Very Important and 5 = Crucial.

The questionnaire was translated from English to Arabic to ensure participants ability to understand questionnaire items properly. The translated version of the questionnaire was first pre-tested by an expert in this field to ensure adequate translation. To refine the questionnaire contents, the initial version was first reviewed by two academic members. Then, further pre-test were held by five BBs/GBs holders. Evaluation was done based on how each scale matches the variable that it intended to measure and respondents ability to understand each item clearly. Obtaining their feedback helped the researchers to revise the questionnaire in a comprehensive and understandable perspective.

Sample and data collection

It is necessary to identify the research unit of analysis; the object, event, entity, individual, decisions, programs, implementation process, etc under investigation. The unit of analysis in this research is the individual BB/GB who leads the implementation of Six-Sigma within their organizations. The population of interest for this study is considered to be all BBs and GBs working in Egyptian organizations that implement Six-Sigma practices. It appeared extremely difficult to identify the exact population for the research as there was great hurdle to find a database that collates all certified GB/BB in any official and reliable database in Egypt. Therefore, the research had been forced to use snowball sampling technique. This type of sampling is used commonly when it is difficult to identify members of the desired population and is thus considered the only possibility to investigate the research phenomena (Saunders et al., 2012). Researches using snowball sampling usually make contact with one or two cases in the population, ask these cases to identify further cases, ask these new cases to identify further new cases and finally stop when either no new cases are given or the sample is as large as it is manageable (Saunders et al., 2012). The limitations of this technique are in the difficulty of making initial contact, and the huge bias that arise as respondents are most likely to identify other potential respondents who are similar to themselves resulting in a homogenous sample (Saunders et al., 2012). The first limitation is not valid to this research as the researchers were able to identify five initial contacts (forming a primary list) before starting the empirical work. The diversity in the primary contact list allowed the researchers to reduce the effect of potential respondent's bias (second limitation) that may arise from selecting respondents similar to them.

Accordingly, the BBs and GBs in the primary list were contacted by phone to provide a brief introduction, objectives and type of required data to be gathered. Then, the questionnaire is sent to them by e-mail and a second e-mail is sent after two weeks as a reminder. After receiving initial mails, respondents were asked to identify potential respondents obtaining same characteristics. Then, either an E-mails or direct meetings with potential new respondents were used to fill-in the questionnaire. Finally, 81 questionnaires were returned as no new cases were given.

Analysis and Results

The goodness and validity of response data will be accomplished through conducting reliability test using the Cronbach's Alpha (Sekaran, 2003). Factor analysis will be used to test the validity of the measures (CSFs) and describe the underline structure in data matrix variable. Factor analysis attempts to explain the correlation among a large number of factors in terms of a smaller number of constructs. That is, all the factors within a particular group (construct) are highly correlated among themselves but have relatively smaller correlations with factors in a different construct (Mukhopadhyay, 2009).

Stepwise regression analysis will be used as it is though as a suitable methodology for statistically identifying significant variables. Stepwise analysis aims to either enter or remove variables, one at a time, by taking into account the marginal contribution of each variable to the model controlling for the contribution of the other variables already presented in the model (Tamhane and Dunlop, 2000; Milton and Arnold, 2003).

Reliability analysis

The validity of the collected data for the CSFs scale and the three Six-Sigma implementation practices are identified by calculating Cronbach's alpha (Table 2). Since the calculated Cronbach's alpha values are higher than 0.6, the research can rely on the collected data for testing the research hypotheses (Sekaran, 2003).

Table 2 - Reliability analysis

Scale

No. of indicators

Cronbach's alpha

All CSFs

21

0.900

Six-Sigma role structure

6

0.792

Six-Sigma structured improvement procedure

6

0.735

Six-Sigma focus on matrices

13

0.933

All Six-Sigma practices

25

0.913

Factor analysis

An assessment of the suitability of the data for exploratory factor analysis was first done. First requirement is using an interval scale measurement (Hair, et al., 2010). This was successfully employed in this study through utilizing a 5- point Likert scale survey questionnaire. Strong relationship between the variables is another requirement for conducting factor analysis. The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity are used to examine the strong relationship between the variables (Mukhopadhyay, 2009). The results of KMO test (Table 3) showed 0.808 which exceeds the minimum recommended value 0.8 (Mukhopadhyay, 2009). Bartlett's Test of Sphericity was 867.574 with an associated statistical significance (p-value = 0.000). This implies that data is appropriate for factor analysis.

Table 3: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy

0.808

Bartlett's Test of Sphericity

Approx. Chi-Square

867.574

df

210

Sig.

0.000

It is considered that a factor loading above 0.3 is considered significant; loadings of 0.40 are considered more important; if the loadings are 0.50 or greater, they are considered very significant (Mukhopadhyay, 2009). Table 4 shows that all the 21 items have a loading values range from 0.517 to 0.849. This implies that all the items are statistically significant at a 0.05 significance level.

Table 4: Communalities of CSFs

Item

Initial

Extraction

Item

Initial

Extraction

Item

Initial

Extraction

F1

1

0.802

F8

1

0.730

F15

1

0.774

F2

1

0.791

F9

1

0.594

F16

1

0.738

F3

1

0.688

F10

1

0.642

F17

1

0.705

F4

1

0.772

F11

1

0.665

F18

1

0.533

F5

1

0.680

F12

1

0.690

F19

1

0.521

F6

1

0.799

F13

1

0.771

F20

1

0.769

F7

1

0.740

F14

1

0.517

F21

1

0.849

Moreover, Factor analysis has resulted in six suggested principal components (six new variables) as shown in Table 5. The results show that only two out of the 21 CSFs has to be excluded. Hence, it is vital to reallocate the elements among the new variables (derived from factor analysis) before performing any further analysis to investigate the relationship between the new variables and Six-Sigma implementation practices.

Table 5: Rotated Component Matrix

Items

CSF 1

CSF 2

CSF 3

CSF 4

CSF5

CSF6

F10

Understanding the DMAIC methodology, tools, techniques and key matrices.

0.744

F20

Attaching the success to financial benefits.

0.738

F21

Organizational understanding of work processes.

0.714

F14

Using clear performance matrices for collecting facts and data in support of all decisions to be made.

0.653

F3

Active communication process.

0.630

F12

Prioritization and selection of projects based on their significant savings for the organization.

0.598

F15

Generating a regularly written communications about Six-Sigma.

0.813

F17

Provide news of Six-Sigma's success to the company and how it benefits the companies' bottom line and employees.

0.772

F16

Asking managers at different level to be supportive and the advocate of Six-Sigma.

0.654

F18

Prepare a list of one-year Six-Sigma projects and review and refresh them regularly.

0.588

F6

Linking Six-Sigma to corporate business strategy and objectives.

0.795

F7

Link Six-Sigma projects to clearly defined customers' requirements.

0.742

F2

Encouraging and accepting Organizational cultural change.

0.702

F11

Project selection, management and control skills.

0.510

F4

Organizational infrastructure.

0.846

F8

Linking Six-Sigma to human resources.

0.759

F13

Executive management must be active in providing rewards.

0.683

F5

Continuous education and Six-Sigma training.

0.501

0.512

F1

Visible management commitment, support and involvement.

0.859

The research proposed title for each variable based on its general theme. These titles are; CSF1 (Foundations), CSF2 (Communication and Support), CSF3 (Alignment), CSF4 (Organizational Infrastructure), CSF5 (Resource Management), and CSF6 (Management Commitment and Involvement).

Descriptive analysis

Basic descriptive statistics are conducted to ensure that there is negligible distortion of the questionnaire outputs. Descriptive analysis (Table 6) illustrated that both trimmed mean and median are close to mean. This indicates that extreme scores do not have influence on calculated mean. In addition, the absolute values of the skewness coefficients are relatively low. This means that there is only a weak distortion of the collected data for all variables.

Table 6 - Descriptive analysis

Variables

Descriptive Statistics

Mean

Median

Standard Deviation

5% Trimmed Mean

Skewness

Statistic

Std. Error

Critical Success Factors

CSF1

4.2

0.1

4.2

0.57

4.19

-0.664

CSF2

3.9

0.1

4.0

0.62

3.88

-0.557

CSF3

4.2

0.1

4.3

0.54

4.20

-0.601

CSF4

3.7

0.1

4.0

0.65

3.68

0.145

CSF5

3.6

0.1

3.7

0.60

3.62

-0.451

CSF6

4.6

0.1

5.0

0.54

4.65

-0.980

Six-Sigma Practices

Six-Sigma role structure

2.53

0.067

2.67

0.60

2.53

-0.084

Six-Sigma structured improvement procedure

3.24

0.063

3.33

0.56

3.25

-0.682

Six-Sigma focus on matrices

3.57

0.080

3.62

0.72

3.57

-0.229

Stepwise Regression analysis

The stepwise selection procedure was employed to ascertain the relationship between the identified new independent variables and implementation of Six-Sigma practices (dependent variables).

Hypothesis (H1) testing

Investigating the relationship between the identified new independent variables and "Six-Sigma role structure" (dependent variable # 1) illustrated that the overall model was significant as p-value = 0.009 (Healey, 2009), F = 7.227, R-Square = 8.38% (Table 7). An assessment of individual variables significance and the associated estimated regression parameters are displayed in Table 7. The stepwise analysis has identified the best model with one variable (CSF5; Resource Management) to explain the amount of variation in "Six-Sigma role structure". The estimated regression equation is characterized as follows:

Six-Sigma role structure = 3.574 - 0.288*CSF5 (1)

Table 7 - Model (I) Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

0.2895

0.0838

0.0722

0.57988

1.906

Model

Sum of Squares

df

Mean Square

F

p-value

Regression

2.430

1

2.430

7.227

0.009

Residual

26.565

79

0.336

Total

28.995

80

* Predictors: (Constant), CSF5

Model

Unstandardized Coefficients

Standardized Coefficients

t

p-value

B

Std. Error

Beta

(Constant)

3.574

0.393

9.088

0.000

CSF5

-0.288

0.107

-0.290

-2.688

0.009

Durbin-Watson analysis (see Table 7) illustrated that computed value (1.906) is higher than the tabulated upper limit value at 5% significance (1.66) (Freund, et al., 2006). This implies that residuals were actually independent from each other (no autocorrelation problem). Normal P-P plot of regression standardized residuals (Figure 1) confirms that residuals are normally distributed.

Figure 1 - Role Structure Normal P-P Plot of Regression Standardized Residuals

Beta coefficient (-0.288) reveals that the higher the adoption of CSF5 (Resource Management), the lower the level of implementing Six-Sigma role structure practices. The analysis was aiming to test the relationship regardless of its direction. Therefore, the mentioned above analysis reveals hypothesis (H1) acceptance.

Hypothesis (H2) testing

Investigating the relationship between the identified new independent variables and "Six-Sigma structured improvement procedure" (dependent variable # 2) illustrated that the overall model was significant as p-value = 0.001 (Healey, 2009), F = 7.552, R-Square = 16.2% (Table 8). An assessment of individual variables significance and the associated estimated regression parameters are displayed in Table 8. The stepwise analysis has identified the best model with two variables (CSF1; Foundations and CSF3; Alignment) to explain the amount of variation in "Six-Sigma structured improvement procedure". The estimated regression equation is characterized as follows:

Six-Sigma structured improvement procedure=2.546+0.531*CSF1-0.363*CSF3 (2)

Table 8 - Model (II) Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

0.403

0.162

0.141

0.522

1.974

Model

Sum of Squares

df

Mean Square

F

p-value

Regression

4.111

2

2.056

7.552

0.001

Residual

21.230

78

0.272

Total

25.341

80

* Predictors: (Constant), CSF1, CSF3

Model

Unstandardized Coefficients

Standardized Coefficients

t

p-value

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

(Constant)

2.546

0.489

5.209

0.000

CSF1

0.531

0.137

0.534

3.886

0.000

0.569

1.758

CSF3

-0.363

0.143

-0.348

-2.535

0.013

0.569

1.758

Durbin-Watson analysis (see Table 8) illustrated that computed value (1.974) is higher than the tabulated upper limit value at 5% significance (1.69) (Freund, et al., 2006). This implies that residuals were actually independent from each other (no autocorrelation problem). Normal P-P plot of regression standardized residuals (Figure 2) confirms that residuals are normally distributed.

Figure 2 - Structured Improvement Procedure Normal P-P Plot of Regression Standardized Residuals

Variance Inflation Factor (VIF) revealed that independent variables (CSF1 and CSF3) are not inter-correlated among themselves as VIF < 5 (see Table 8). This indicates that multicollinearity problem is not exist (Montgomery, et al., 2006 and Navidi, 2006).

Beta coefficient for CSF3 (-0.363) reveals that the higher the adoption of CSF3 (Alignment), the lower the level of implementing Six-Sigma structured improvement procedure practices. However, Beta coefficient for CSF1 (0.531) reveals that the higher the adoption of CSF1 (Foundations), the higher the level of implementing Six-Sigma structured improvement procedure practices. The analysis was aiming to test the relationship regardless of its direction. Therefore, the mentioned above analysis reveals hypothesis (H2) acceptance.

Hypothesis (H3) testing

Investigating the relationship between the identified new independent variables and "Six-Sigma focus on matrices" (dependent variable # 3) illustrated that the overall model was significant as p-value = 0.000 (Healey, 2009), F = 14.074, R-Square = 15.1% (Table 9). An assessment of individual variables significance and the associated estimated regression parameters are displayed in Table 9. The stepwise analysis has identified the best model with one variable (CSF2; Communication and Support) to explain the amount of variation in "Six-Sigma focus on matrices". The estimated regression equation is characterized as follows:

Six-Sigma focus on matrices = 1.823 + 0.451*CSF2 (3)

Table 9 - Model (III) Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

0.389

0.151

0.140

0.667

1.586

Model

Sum of Squares

df

Mean Square

F

p-value

Regression

6.269

1

6.269

14.074

0.000

Residual

35.190

79

0.445

Total

41.459

80

* Predictors: (Constant), CSF2

Model

Unstandardized Coefficients

Standardized Coefficients

t

p-value

B

Std. Error

Beta

(Constant)

1.823

0.472

3.865

0.000

CSF2

0.451

0.120

0.389

3.752

0.000

Durbin-Watson analysis (see Table 9) illustrated that computed value (1.586) is less than the tabulated lower limit value at 5% significance (1.61) (Freund, et al., 2006). This implies that residuals were actually independent from each other (no autocorrelation problem). Normal P-P plot of regression standardized residuals (Figure 3) confirms that residuals are normally distributed.

Figure 3 - Focus on Matrices Normal P-P Plot of Regression Standardized Residuals

Beta coefficient for CSF2 (0.451) reveals that the higher the adoption of CSF2 (Communication and Support), the higher the level of implementing Six-Sigma structured improvement procedure practices. This indicates that hypothesis (H3) is accepted.

Discussion and Conclusions

Previous literature showed relationship between CSFs and successful implementation of Six-Sigma (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011). However, most of the empirical evidence failed to; provide consensus about specific CSFs that should be adopted; and expected influence of these factors on successful implementation of Six-Sigma. Moreover, most of empirical evidence comes from developed countries context. This research expanded the knowledge through; identifying CSFs commonly used; categorize these factors under specific variables; test the influence of these variables on successful implementation of Six-Sigma in developing countries context.

The empirical evidence of this research identified four variables significantly affect successful implementation of Six-Sigma namely; Foundations (CSF1), Communication and Support (CSF2), Alignment (CSF3), and Resource Management (CSF5). This result confirms empirically (Brun, 2011; Mehrjerdi, 2011; Antony et al., 2005; Kwak and Anbari, 2006; Antony, 2006; Chakrabarty and Tan, 2007; Chung et al., 2008; Yusr et al., 2011) argument that proper/improper identification of CSFs will affect Six-Sigma programs success or failure. Moreover, the research findings succeeded to pinpoint the direction of the relationship between significant CSFs and successful implementation of Six-Sigma programs. Foundations (CSF1) and Communication and Support (CSF2) showed that the higher the adoption of these variables, the higher the level of implementing Six-Sigma practices. However, Alignment (CSF3), and Resource Management (CSF5) showed that the higher the adoption of these variables, the lower the level of implementing Six-Sigma practices. These results confirm (Raisinghani et al., 2005) argument that many companies have chosen not to go the Six-Sigma route; and (Mehrjerdi, 2011) who illustrated that some organizations that adopted Six-Sigma had to scrape their entire Six-Sigma program after spending significant amount of money.

Overall, this research provides a new perspective through highlighting the need of identifying proper CSFs before implementing Six-Sigma programs. It reveals also that adopting improper CSFs may affect Six-Sigma projects outcomes negatively. Neglecting proper CSFs may reduce the organization ability to achieve desired results. The research findings identified several interesting areas that could be explored in further research. Further research may be valuable to investigate reasons behind negative effect that may exist due to applying improper CSFs. Another interesting research area is to test the CSFs among different industries and identify whether significant differences do exist or not. Further researches may be held to replicate same methodology with larger sample as one of the limitations existing in this research was sample size.

The practical implication of this research is that it provides organizations aiming to apply Six-Sigma with a list of CSFs that should be considered before applying Six-Sigma programs. Moreover, it provides guide for core variables that may affect programs success or failure.

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