Six Sigma and organisational performance: a …...Six Sigma and organisational performance 183 and...

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182 Int. J. Productivity and Quality Management, Vol. 6, No. 2, 2010 Copyright © 2010 Inderscience Enterprises Ltd. Six Sigma and organisational performance: a knowledge creation perspective Ang Boon Sin, Suhaiza Zailani* and T. Ramayah School of Management, Universiti Sains Malaysia, 11800, Penang, Malaysia, Fax: 04 6577448 E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: This study focuses on how Six Sigma practices can lead to organisational knowledge creation capability and how this capability can lead to the improvement of organisational performance. A survey was conducted to test the model linking knowledge creation process in Six Sigma practices to organisational performance through organisational knowledge creation. Out of the total 349 possible survey participants, 187 (53.6%) surveys were returned and received. The results indicate that Six Sigma practices allow knowledge to be created through knowledge creation processes (socialisation, externalisation, combination and internalisation). In turn, the knowledge created has positive impact on four different measures of organisation performance (customer, internal business process, employee learning and growth and financial). Specifically, knowledge creation has direct positive impact on customer, internal business process, employee learning and growth and indirect positive impact on financial. Overall, the study’s results show that process improvement through Six Sigma projects involves the creation of knowledge. Thus, the underlying basis for practices employed in Six Sigma projects is knowledge creation through the transformation of knowledge from tacit to explicit and vice versa and through the transfer of both types of knowledge from individuals to teams. Keywords: Six Sigma; knowledge creation; organisational performance; Malaysia. Reference to this paper should be made as follows: Sin, A.B., Zailani, S. and Ramayah, T. (2010) ‘Six Sigma and organisational performance: a knowledge creation perspective’, Int. J. Productivity and Quality Management, Vol. 6, No. 2, pp.182–212. Biographical notes: Ang Boon Sin is a Quality and Reliability Engineer. He is Six Sigma Black Belt and has over five years of experience in Six Sigma methodology application. He received his BSc (Hons) and MSc in Applied Statistical from University of Science, Malaysia. Suhaiza Zailani graduated from Lancaster University, Lancaster, UK with MSc in Operational Research and PhD in Management Science. Currently, she is an Associate Professor attached to the School of Management, Universiti Sains Malaysia, Penang. Her teaching areas are operations management, productivity

Transcript of Six Sigma and organisational performance: a …...Six Sigma and organisational performance 183 and...

Page 1: Six Sigma and organisational performance: a …...Six Sigma and organisational performance 183 and quality control and management science. Her current areas of interest are supply

182 Int. J. Productivity and Quality Management, Vol. 6, No. 2, 2010

Copyright © 2010 Inderscience Enterprises Ltd.

Six Sigma and organisational performance: a knowledge creation perspective

Ang Boon Sin, Suhaiza Zailani* and T. Ramayah School of Management, Universiti Sains Malaysia, 11800, Penang, Malaysia, Fax: 04 6577448 E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: This study focuses on how Six Sigma practices can lead to organisational knowledge creation capability and how this capability can lead to the improvement of organisational performance. A survey was conducted to test the model linking knowledge creation process in Six Sigma practices to organisational performance through organisational knowledge creation. Out of the total 349 possible survey participants, 187 (53.6%) surveys were returned and received. The results indicate that Six Sigma practices allow knowledge to be created through knowledge creation processes (socialisation, externalisation, combination and internalisation). In turn, the knowledge created has positive impact on four different measures of organisation performance (customer, internal business process, employee learning and growth and financial). Specifically, knowledge creation has direct positive impact on customer, internal business process, employee learning and growth and indirect positive impact on financial. Overall, the study’s results show that process improvement through Six Sigma projects involves the creation of knowledge. Thus, the underlying basis for practices employed in Six Sigma projects is knowledge creation through the transformation of knowledge from tacit to explicit and vice versa and through the transfer of both types of knowledge from individuals to teams.

Keywords: Six Sigma; knowledge creation; organisational performance; Malaysia.

Reference to this paper should be made as follows: Sin, A.B., Zailani, S. and Ramayah, T. (2010) ‘Six Sigma and organisational performance: a knowledge creation perspective’, Int. J. Productivity and Quality Management, Vol. 6, No. 2, pp.182–212.

Biographical notes: Ang Boon Sin is a Quality and Reliability Engineer. He is Six Sigma Black Belt and has over five years of experience in Six Sigma methodology application. He received his BSc (Hons) and MSc in Applied Statistical from University of Science, Malaysia.

Suhaiza Zailani graduated from Lancaster University, Lancaster, UK with MSc in Operational Research and PhD in Management Science. Currently, she is an Associate Professor attached to the School of Management, Universiti Sains Malaysia, Penang. Her teaching areas are operations management, productivity

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and quality control and management science. Her current areas of interest are supply chain management, benchmarking, new product development, and quality and productivity in organisations in general. She has also jointly produced many articles published in international journals such as International Journal of Information Management, Benchmarking, Supply Chain Management: An International Journal, International Journal of Productivity and Quality Management, International Journal of Value Chain Management, International Journal of Operations and Supply Chain Management, International Journal of Services and Operations Management, etc.

T. Ramayah holds an MBA from Universiti Sains Malaysia (USM). Currently, he is an Associate Professor at the School of Management in USM. He teaches main courses in research methodology and business statistics and has also conducted training courses for the local government (research methods for candidates departing overseas for higher degree, Jabatan Perkhidmatan Awam). Apart from teaching, he is an avid Researcher, especially in the areas of technology management and adoption in business and education. Thus far, he has published in several journals such as Information Development, Direct Marketing, WSEAS Transactions on Information Science & Applications, International Journal of Learning, the International Journal of Knowledge, Culture and Change Management, Asian Journal of Information Technology (AJIT), International Journal of Services and Technology Management (IJSTM), International Journal of Business Information Systems (IJBIS), etc.

1 Introduction

Six Sigma has emerged as one of the most successful business improvement model of the later 20th century. Six Sigma can be considered both a business strategy and a science that has the aim of manufacturing and service costs, and creating significant improvements in customer satisfaction and bottom-line savings through combining statistical and business process methodologies into an integrated model of process, product and improvement (Andrew et al. 2009). This is supported by Natarajan and Morse (2009) in which they have conducted study on the application of Six Sigma principles in a mid-sized organisation. Six Sigma process is a customer-oriented, structured, systematic, proactive and quantitative approach for continuous process improvement in the business processes of an organisation to ensure improved quality, low cost and fast delivery and drive out waste from business processes using statistical tools and techniques (Breyfogle et al., 2003; Harry and Schroeder, 2000).

Six Sigma has its roots in manufacturing and has been very successfully applied in manufacturing. Interestingly, today Six Sigma has proved to be successful in finance, healthcare, banking and many other business processes (Antony, 2004; Snee and Hoerl, 2003; Bruce, 2002; Pande et al., 2000, Natarajan and Morse, 2009). Most companies today view Six Sigma as a business strategy and methodology for improving process performance in such a way that customer satisfaction is increased and the bottom line is improved. The bottom line focus attracts the attention and support of corporate executives and financial analysts, and makes Six Sigma different from many earlier improvement approaches, such as statistical process control (SPC), total quality management (TQM) and ISO 9000, none of which has the explicit bottom line focus. Success stories of big corporations that have adopted Six Sigma, such as Motorola,

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General Electric (GE) and Allied Signal/Honeywell have been reported in various papers claiming that Six Sigma implementation results in high financial savings (Hendricks and Kelbaugh, 1998). There is growing evidence that Six Sigma has produced significant savings in bottom line of many companies. Based on the survey conducted by Antony and Darshak (2009), about 45% of the industries in India experienced financial gain more than $1 million (US) per annum. It is also reported that Six Sigma helps improve business process performance and customer satisfaction. Consider the following facts in Table 1. Table 1 Reported benefits and savings from Six Sigma companies

Company Metric/measures Benefit/savings

General Electric (1996) Financial $150 million General Electric (1997) Financial $700 million General Electric (1998) Financial $1 billion General Electric (1999) Financial $2 billion General Electric (2000) Financial $2.4 billion Motorola (1987–1997) Financial $14 billion Allied Signal (1992–1996) Financial $1.4 billion DuPont (1998–2002) Financial $1.6 billion Mount Carmel Health System (2004) Financial $2.4 million Motorola (1992) In-process defect levels 150 times reduction General Electric Turnaround time at

repair shops 62% reduction

Allied Signal (Honeywell) On-time delivery Increased to near 100% Motorola (1987–1997) Sales growth Achieve 20% per year Allied Signal (Honeywell) (1992–1997)

New product introduction time

Reduced product introduction time by 16%

Bank of America Customer satisfaction Reduced customer problem by 24%

Source: Kwak and Anbari (2004), Brue and Howes (2006), Pande et al. (2000), Rucker (2000) and Roberts (2004)

A couple of empirical studies examine the relationship between Six Sigma and their effects on organisational performance. They generally agree that Six Sigma has positive effects on organisational performance (Lee, 2002; Flora, 2003; Zu, 2005). For example, empirical research done by Lee (2002) indicates that Six Sigma has positive effects on operational performance and customer satisfaction. Flora (2003) studies the contrasting impact between Six Sigma and TQM on financial performance in US companies. The results suggest that the Six Sigma management philosophy is a more effective agent for financial success than the TQM philosophy. Zu (2005) examines the joint effects of Six Sigma and TQM practices on firms’ quality and business performance. The findings reveal that Six Sigma practices impact the organisation’s quality and financial performance by supporting traditional quality practices. In addition, Desai (2008) in his study on the small scale industry has proved that Six Sigma can help improving productivity as well as profitability. Nevertheless, all these studies mostly focus on single level impact analysis of organisational performance such as operating performance, customer satisfaction, quality performance and financial performance. However, it raises

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a specific question of whether or not these single levels of organisational performance impacts exhibited relevancy/effect between each other and as a whole contribute to overall organisational performance.

Six Sigma practices facilitate knowledge creation and support each of the knowledge creation processes from Nonaka’s (1994) framework of knowledge creation. Knowledge creation in Six Sigma has positive effect on four areas which can be categorised as financial, customer, internal business process and employee learning and growth. The exploratory investigation findings show that organisational performance impacts such as employee learning and growth, internal business process and customer exhibit relevancy between each other. These single levels of performance impact as a whole bring the results to overall financial impact. For example, Six Sigma training improves employees’ learning and growth and knowledgeable and skilful employees improve the capability of internal business processes by decreasing variation, which leads to reduction in defect and meeting customer requirement and finally an improvement in profit. Schroeder et al. (2008) argue that some organisations may find benefit from the Six Sigma approach because it fits their organisational needs better. Therefore, it is important to study the relationship between employee learning and growth, internal business process and customer and how these links relate to each other and has an effect for overall financial performance.

In addition, this study identifies that there is an opportunity to further understand the Six Sigma and organisational performance phenomenon from Nonaka’s knowledge creation perspective. Knowledge is a critical element to quality and has become the primary vehicle in driving organisational productivity, performance and profit. The spiral process of knowledge conversion as a whole at the heart of organisational knowledge creation and each pattern of the knowledge conversion process individually and independently also create new knowledge (Nonaka, 1994). Therefore, this study proposes that integrating Six Sigma practices with organisational knowledge creation process can provide new insight into how deployment of Six Sigma leads to organisational performance. It is necessary to gain a deeper understanding of the Six Sigma and knowledge creation in practice and in particular, how Six Sigma practices allow knowledge to be created and finally how the knowledge created can lead to improved organisational performance. The remainder of this paper is organised as follows. Section 2 discusses the literature review on the TQM and Six Sigma. It further elaborates on the linkages between Six Sigma with knowledge management and organisational performance. Following subsection develops the research hypotheses. Section 4 develops the methodology of the research. Section 5 presents the results of the study. Section 6 provides a discussion of the results. The paper ends with Section 7 which provides the conclusions to the study and directions for future research.

2 Literature review

2.1 TQM and Six Sigma

The emergence of TQM had been one of the major developments within quality movement in the 1980s. The focus on the development of TQM was in response to the intense global competition. Additionally, Six Sigma is grown out of traditional QM methods and many traditional QM practices are recognised as important for Six Sigma

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implementation (Zu et al., 2008). In other words, Six Sigma is in many ways a powerful renaissance of quality ideas and methods (Breyfogle et al., 2003), but it reveals a potential for success that goes beyond the levels of improvement achieved through the many TQM efforts (Pande et al., 2002; Zailani et al., 2007). When compared with TQM, Six Sigma has many differentiated characteristics: while TQM employee empowerment and self-managed teams, Six Sigma is driven by organisations champions; Six Sigma projects are more often cross-functional and in nature than TQM department-based projects; TQM training is generally restricted to simple tools and statistical techniques (e.g., the magnificent seven), while Six Sigma focuses on more advanced statistical methods (such as experimental design, fault analysis, simulation); and six sigma projects are much more accountable in terms of return on investment than TQM projects ever were (Snee, 2000; Senapati, 2004; Antony, 2006; Green, 2006). Table 2 provides a review of other major TQM omissions, as well as hints on how the Six Sigma system can keep them from derailing an organisation’s efforts (Breyfogle et al., 2003; Pande et al., 2002). Table 2 Six Sigma versus TQM

TQM limitation Six Sigma solution

Lack of integration Links to the business and personal ‘bottom line’ Leadership apathy Leadership at the vanguard A fuzzy concept A consistently repeated, simple message An unclear goal A no-non-sense, ambitious goal Purist attitudes and technical zealotry Tools and degree of rigor adapted to the circumstances Failure to break down internal barriers Priority on cross-functional process management Incremental vs. exponential change Incremental exponential change Ineffective training Black belts, green belts, master black belts Focus on product quality Attention to all business processes

2.1.1 Six Sigma concept

Six Sigma concepts was pioneered by Motorola in the 1980s and boosted by the efforts of GE, Allied Signal and others in the late 1990s. Motorola’s Six Sigma process was developed and implemented first in manufacturing and from 1990, the process was adapted to the non-manufacturing areas of the company (Dahlgaard-Park and Dahlgaard, 2006). Today, Six Sigma is one of the primary quality initiatives that have been billed as a critical business tool for the 21st century. In a fast changing business environment, companies have used Six Sigma initiatives to gain improvements in process and product quality. The success that companies have enjoyed results directly to improved customer satisfaction ratings and bottom line savings. Brue (2006) argues that Six Sigma has three meanings depending on the context. It is a level of quality; it is a problem solving methodology and it is a management philosophy. Sigma, a Greek letter (σ) is used as a statistical measure of variation in a process. A stated sigma level is used to describe how well the process variation meets the customer’s requirements. Achieving a Six Sigma level (6σ) of quality means that processes are producing only 3.4 defects per million opportunities (DPMO) with 1.5 σ allowable shift under normal distribution or practically it corresponds to 99.999660% yield. According to Shahabuddin (2008), even though 3.4

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defects per million is only a target, it is neither practical nor statistically-based on quality control charts.

Six Sigma is defined as a business improvement strategy used to improve profitability, to drive out waste, to reduce quality costs and improve the effectiveness and efficiency of all operations processes that meet or even exceed customers’ needs and expectations (Antony and Bañuelas, 2001). Harry and Schroeder (2000) view Six Sigma as a breakthrough strategy that combines improved metrics and a new management philosophy to significantly reduce defects, thereby strengthening a firm’s market position and improving the profit line. Similarly, Snee (1999) defines Six Sigma as a strategic business improvement approach that seeks to increase both customer satisfaction and an organisation’s financial health. These definitions view Six Sigma as a business strategy that can improve organisational financial health and customer satisfaction. For the purpose of this research, we adopt Zu’s (2005) Six Sigma definition.

“Six Sigma is defined as a structured management system to continuously improve organisational processes to achieve the strategic business goal of increasing bottom line benefits and enhancing customer satisfaction through collaboration of the organisation’s employees, customers and suppliers.”

2.1.2 Practices of Six Sigma

Practitioners and consultants publish a number of articles and books about Six Sigma. These literatures are often written by people who work in major companies using Six Sigma, such as GE, Motorola, Honeywell, etc. (Zu, 2005). For example, Pande et al.’s (2002) The Six Sigma Way is written based on the authors’ experiences in companies such as GE and Motorola which implement Six Sigma successfully. These literatures are rich with concepts, methodologies, background and evolution of Six Sigma and also provide the deployment procedure as well to industries for Six Sigma implementation. Various tools or techniques including statistical tools, analytical tools and managerial tools employed in Six Sigma are discussed as well. These literatures are valuable to researchers new to Six Sigma in providing giving evidence of the emerging importance of the Six Sigma philosophy. Since Six Sigma initiative started from industries, the practitioner literature provides a basic representation of the common Six Sigma program and practices in industries. Table 3 summarises the practices in Six Sigma organisation after reviewing these practitioner literatures.

2.2 Knowledge management

Knowledge is growing in importance as a key to a sustainable competitive advantage for all successful organisations (Davenport and Prusak, 2000; Nonaka and Takeuchi, 1995). The capability to create and utilise knowledge into business processes and product/services enable organisations to achieve superior performance (Teece, 1998). Multiple definitions of knowledge have been proposed in the literature. Zack (1999) defines knowledge simply as meaningful information which is accumulated through experience, communication or inference. In this study, Choo’s (2003) definition of knowledge is adopted which can refer to solution uniqueness, idea generation and improved understanding and capability in organisation. The focus is thus on knowledge that creates value in the organisation.

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Table 3 Summarises the practices in Six Sigma organisation

Six Sigma practices Pande et al.

(2002)

Breyfogle et al.

(2003)

Pyzdek (2003)

Brue and Howes (2006)

Park (2003)

Bertels (2003b)

Keller (2005)

Top management support and involvement

Yes Yes Yes Yes Yes Yes Yes

Customer focus Yes Yes Yes Yes Yes Yes Yes

Role structure Yes Yes Yes Yes Yes Yes Yes

Use metrics Yes Yes Yes Yes Yes Yes Yes

Project selection Yes Yes Yes Yes Yes Yes Yes

Tools and techniques

Yes Yes Yes Yes Yes Not in detail

Yes

Team Yes Yes Yes Yes Yes Yes Yes

DMAIC Yes Yes Yes Yes Yes Yes Yes

Design for Six Sigma

Yes Yes Yes Yes Yes Yes Not in detail

Process management

Yes Yes Yes Yes Yes Yes Yes

Training Yes Yes Yes Yes Yes Yes Yes

2.2.1 Classification of knowledge

Knowledge has been categorised into various classifications by several scholars. Blackler (1995) proposes five groups of knowledge: embrained knowledge, embodied knowledge, encultural knowledge, embedded knowledge and encoded knowledge. Wiig (1993) defines four types of knowledge: factual, conceptual, expectational and methodological. Conceptual knowledge involves systems, concepts and perspective (e.g., concept of track record). De Long and Liam (2000) argue that a major source of confusion in discussions about knowledge and knowledge management in organisations is the failure to recognise that there are at least three distinct types of knowledge; human knowledge, social knowledge and structured knowledge. Alavi and Leidner (2001), on the other hand, categorise knowledge as declarative, procedural, causal, conditional and relational. These types of knowledge are categories that can describe or explain events or things, where declarative knowledge is ‘know-about’ knowledge. There is another well-known classification of knowledge, which is proposed by Polanyi (1966) and further enhanced by Nonaka and Takeuchi (1995) into feasible perspective. Knowledge is viewed as comprising of two dimensions: tacit knowledge and explicit knowledge (Polanyi, 1967; Nonaka and Takeuchi, 1995). These two classifications of knowledge will be the main focus of this study. Tacit knowledge is personal, context-specific knowledge that resides in an individual and it relies on experience, hunches and insights (Nonaka and Takeuchi, 1995; Davenport and Prusak, 2000).

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2.3 Knowledge-based theory of the firm

The knowledge-based theory of the firm has emerged from the resource-based theory of the firm by focusing on intangible resources, rather than on physical assets. In this perspective, knowledge is the most important resource. The knowledge-based theory of the firm views knowledge as a key resource of the firm and a source of competitive advantage that improves firm performance (Davenport and Prusak, 1998; Grant, 1996; Kogut and Zander, 1996; Spender, 1996). According to this theoretical perspective, firms are viewed as distributed repositories of tacit and explicit knowledge whose heterogeneous knowledge bases are the key determinants of sustained competitive advantage (Kogut and Zander, 1996; Spender, 1996). Thus, capabilities to manage and create knowledge can provide sustainable competitive advantage (Argyris, 1999; De Geus, 1988; Prahalad and Hamel, 1990; Hatch and Dyer, 2004; Hayes et al., 1988). Researcher invokes the knowledge based theory because Six Sigma can contribute to competitive advantage by institutionalising continuous improvement of business processes (De Mast, 2006).

Nonaka and Takeuchi (1995) develop a ‘theory of organisational knowledge creation’, which lays the foundations of a general theory that describes the processes of knowledge creation in organisations. Further, Nonaka’s theory postulates that knowledge creation results from four knowledge conversion processes, referred to as SECI model: socialisation, externalisation, combination and internalisation. These processes take place within what the theory refers to as the spiral model in which knowledge is transformed from one form (explicit or tacit) to the other along the four ontological dimensions. Socialisation refers to the transfer of tacit knowledge among individuals. Externalisation is the process of transforming or translating tacit knowledge into explicit knowledge. Combination aggregates instances of explicit knowledge into newer, solidified forms of explicit knowledge. Internalisation represents the absorption of explicit knowledge as accepted, justified beliefs.

2.4 Six Sigma and knowledge management

Research in Six Sigma and knowledge management are only emerging. The study between the connection of Six Sigma and knowledge management is rather limited. Choo (2003) conducts an empirical study on the social and method mechanisms of knowledge creation to build up a better understanding of both the quality and knowledge management literature. The survey result reveals that two knowledge creation mechanisms involved in Six Sigma implementation have different effects on learning behaviour and knowledge created through those Six Sigma projects. Fugate et al. (2009) claimed that shared interpretation of knowledge among operational personnel mediates how knowledge is disseminated and used to design and implement a unified operational response to that knowledge. Following this, Dasgupta (2009) through his statistical knowledge has proposed a unified framework for achieving improvement and control of processes with categorical responses during implementation of Six Sigma. On the other hand, Stevens (2006) studies how knowledge management concepts such as mentoring, experts and expertise, and communities can be leveraged to impact a Six Sigma

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deployment. Of particular interest is the use of tacit knowledge in the execution of a Six Sigma deployment in a healthcare company. This research identifies that all three key knowledge management concepts clearly have a positive effect on the deployment and the use of tacit knowledge. Gopesh (2006) investigates how knowledge creation activities used in Six Sigma team project impact project performance. This study concentrates on knowledge creation at project level and restricting the scope of Six Sigma practices to application of both tools and technique for Six Sigma project success. The survey results indicate that socialisation and internalisation significantly impact the Six Sigma project success.

2.5 Balanced scorecards (BSC) perspective as an organisational performance

Organisations have struggled for many years with inadequacies of accounting performance measures. Ittner and Larcker (1998) cites various limitations of traditional financial measures. Specifically, financial measures are too historical and backward looking, lack predictive ability, not actionable and do not capture key business changes until too late. Because of these shortcomings, Kaplan and Norton (1992) argue that managers should not only focus on financial measures when taking decisions. Non-financial criteria have to be taken into account. The BSC was developed by Kaplan and Norton (1996) to complement traditional financial performance measures and provide a balanced view of both financial and non-financial measures. The BSC translates a company’s vision and strategy into real comprehensive objectives and measures across a balanced group of perspectives. The scorecard not only includes measures of desired results, but also the processes that will lead to the desired results in the future.

2.6 Six Sigma and organisational performance

In this study, a research framework is proposed by integrating Six Sigma practices with knowledge creation processes. This integration tends to produce a more complete understanding of the phenomenon of how Six Sigma improves organisation performance. A knowledge-based theory of the firm is used as a theoretical foundation for this framework. The theory can help to understand how the knowledge creation in Six Sigma can have substantial effects on the organisation performance (Davenport and Prusak, 1998; Grant, 1996; Kogut and Zander, 1996; Spender, 1996). The framework assumes that Six Sigma affect organisational performance through knowledge processes. Because many factors influence the determination of organisational performance, attempts to trace causality to any single factor like Six Sigma may be inappropriate. To avoid this issue and establish credibility between Six Sigma and organisational performance, intermediate variable such as knowledge is introduced. This incorporation may help confirm that Six Sigma ultimately create business value. In sum, this study proposes a research framework as shown in Figure 1.

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Figure 1 Research framework

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2.6.1 Six Sigma practices and knowledge creation process

a Six Sigma practices and socialisation: Six Sigma team consists of cross functional orientation which brings together a wide variety of knowledge, skills and ability. Socialisation practices such as face to face meeting and discussion in DMAIC projects enable individuals to express each other’s ideas in light of their knowledge and experiences (Stevens and Campion, 1994). Discussion and sharing among team members reveal different views of business processes and source of variation that exist causing business problem. Iterative interaction with customer and supplier allows knowledge creation and learning where customers or suppliers share and supplement knowledge, skill or experience that is not available in organisation. Socialisation between employees and customers provides a basic for learning (Bitner et al., 1990; Lengnick-Hall, 1996).

H1 Six Sigma practices that promote contact and interaction between or among intra-organisational members or inter-organisational members allow knowledge to be created through socialisation.

b Six Sigma practices and externalisation: Six Sigma design team successfully acquire the voice of customers (stated and unstated customer needs) and then serve as an idea for designing product with new features or new products to the market (Dean and Bowen, 1994). Tools such as quality function deployment (QFD), Plugh matrix and conjoint analysis are applied to link between customer tacit knowledge, design concept and prototype and then translate it in business and engineering language (Bertels, 2003a). This is how they make new products from conceptualising the voice of customer/tacit knowledge to product realisation in the market. In define phase of DMAIC model, Six Sigma team from different functions begin to generate ideas for process improvement by brainstorming (Gitlow et al., 1995).

H2 Six Sigma practices that help articulate improvement idea in the form of theories, concepts or cause and effect reasoning allow knowledge to be created through externalisation.

c Six Sigma practices and combination: Six Sigma is a fact and data driven methodology with emphasis on measurement and analysis of data. In Six Sigma organisation, business process, supplier and customer data are collected and combined to establish business metrics in order to monitor business process performance, customer satisfaction and financial health. The metrics does not only provide the information about business processes, but are also used to establish baseline and define explicit quality goal that will then guide to improve activities. Six Sigma methodologies use structured method and tools to combine and analyse data in order to drive business process performance (Pande et al., 2002).

H3 Six Sigma practices that promote data measurement and analysis allow knowledge to be created through combination.

d Six Sigma practices and internalisation: Organisations provide training programs for its employees at different stages of their work with the company. Training occurs in hands on fashion where instructors explain concepts followed by participants applying concepts to their improvement projects (Pyzdek, 2003). This training

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format ensures that participants not only understand the Six Sigma concept but also understand how to apply these concepts. Knowing how to apply Six Sigma concepts make certain that employees can handle challenging problems encountered in improvement project. Through training, employees internalise the tacit knowledge and try to create new knowledge after the internalisation process. Six Sigma’s focus on documentation allows the knowledge gained from improvement to be available to the entire organisation and facilitates the sharing of knowledge that occurs both formally (e.g., through searchable database) and informally (through communities of practice) (Bertels, 2003a). All this activities help organisations to internalise the knowledge gained from improvement activities.

H4 Six Sigma practices that promote learning by doing allow knowledge to be created through internalisation.

2.6.2 Knowledge creation and organisation performance

a Knowledge and customer: Six Sigma focuses on the expectations of customers. By using Six Sigma methodology, organisations conduct face to face meeting and listen to the voice of our customers; do customer survey; collect customer feedback and complaint data for analysis. All of these interaction help establish a foundation for shared experiences and mental modes between customer and organisational members, which will facilitate knowledge creation. Knowledge created enables organisation to identify top issues causing customer dissatisfaction and identify the appropriate Six Sigma projects to address those issues.

H5 Six Sigma practices that result in knowledge creation have positive effect on customer.

b Knowledge and internal business process: Internal business processes are a collection of interdependent activities or tasks organised to achieve specific business goals and it is often cut across multiple functional organisations. To improve internal business processes performance requires understanding both components and the interface/interaction between components. Researchers found that knowledge creation has positive link to quality (Mukherjee et al., 1998), innovation (Francisco and Guadamillas, 2002) and productivity (Lapre and Wassenhove, 2001). McKeen et al. (2006) reports that KM practices such as knowledge creation positively relates to efficient internal operation and product innovation. Accordingly, this is extremely important for competitive advantage in the marketplace (Chattopadhyay and Varma, 2009).

H6 Six Sigma practices that result in knowledge creation have positive effect on internal business process.

c Knowledge and employee learning and growth: Six Sigma organisations provide extensive training to the employee. The main purpose of training in Six Sigma program is towards preparing and equipping employees with skills and knowledge for participating in and leading projects. Training occurs in hands on fashion where instructors explain concepts followed by participants applying concepts to their improvement projects (Pyzdek, 2003). This training format ensures that participants not only understand the concepts in Six Sigma but also understand how to apply

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these concepts. Knowing how to apply Six Sigma concepts make certain that employees can handle challenging problems encountered in improvement project.

H7 Six Sigma practices that result in knowledge creation have positive effect on employee learning and growth.

2.6.3 Employee learning and growth, internal business process, customer and financial

a Employee learning and growth and internal business process: Six Sigma organisations provide intensive and differentiated training to the improvement teams (Pyzdek, 2003). There is a wide array of training, ranging from project management, change management, consensus and team building, to the tools and technique of measurement and process analysis (Paul, 2005). Knowledge gained through training broadens internal business processes perspective, sharpens problem-solving skills and facilitates learning. A well-trained employee has a better knowledge foundation for improving processes and products. In addition, through training and lessons learned, employee will be able to focus on the value of creating activities in the internal businesses that drive competitive advantage.

H8 Employee learning and growth has a positive effect on internal business processes.

b Employee learning and growth and customer: Customer satisfaction is a natural consequence of pride of workmanship. Pride of workmanship is determined by how capable the employee in delivering quality product and service to customer (Anderson et al., 1994). Six sigma organisations provide extensive training to the employee to ensure proper use and understand of the methodology and supplemented by process and product knowledge. Experienced employees are more knowledgeable about their organisation’s goal and the customer’s requirement (Anderson et al., 1994). Many service companies have also realised that there is a strong relationship between employee performance and customer satisfaction (Hartline and Ferrell, 1996).

H9 Employee learning and growth has a positive effect on customer satisfaction.

c Employee learning and growth and financial: Six Sigma organisations provide extensive training to its employee in which the employee can broaden their knowledge and skills for more efficient teamwork and achieve individual growth and development. Martensen and Gronholdt (2001) found that the development of competencies through various training programs has positive impact on employee satisfaction. Employee satisfaction is positively related to employee’s royalty to their company and negatively related to their intention to turnover (Brown and Peterson, 1993). Moreover, employee satisfaction and lower absenteeism rate have effect on financial performance.

H10 Employee learning and growth has a positive effect on financial performance.

d Internal business process and customer: The goal of Six Sigma is to achieve 3.4 DPMO in business processes. 3.4 DPMO corresponds to a 6σ quality level at 99.99966% yield. The production of high quality product and service at reasonable price has a direct effect on how external customer perceives them and purchase

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consumption experience (Gustafson and Johnson, 2002). This level of quality can be attained through DMAIC and DFSS, to improve business processes that reduce defect, cost (Rust et al., 2002), as well as cycle time and inventories (Napolitano, 2003), which eventually improve customer perception of quality and increase customer satisfaction (Rust et al., 2002). Anderson et al. (1998) find that the effect of operational performance on customer satisfaction is positive. In addition, Atkins et al. (2002) discovers a significant link between high quality and customer satisfaction.

H11 Internal business processes has a positive effect on customer.

e Internal business process and financial: In most companies today, the cost of poor quality represents 20% to 30% of total revenues (Pyzdek, 2003). The cost of poor quality is caused by process variation in product defects. The cost of poor quality is directly proportional to the level of sigma. Thus, if a company can improve its quality by one sigma level, its net income will increase hugely, approximately 10% net income improvement (Pyzdek, 2003). Organisational effectiveness measures such as reduced cycle times, higher quality and increased productivity are reported as contributing factors to financial performance (Tan et al., 1999).

H12 Internal business processes have a positive effect on financial performance.

f Customer and financial: Most of the previous studies examine the relationship between customer satisfaction and financial performance. Customer satisfaction occurs when product or services meet or exceed customer expectation. Customer satisfaction creates customer loyalty and retention which result in repeat purchase, growth in sales and increase in profits (Das et al., 2002). Buzzell and Weirsema (1981) find a positive relationship between customer satisfaction and growth in market share. Other studies report a significant positive relationship between customer satisfaction and revenue and profitability (Bernhardt et al., 2000).

H13 Customer satisfaction has a positive effect on financial performance.

3 Methodology

The target population of the survey study is the manufacturing firms that have implemented Six Sigma in Malaysia. The samples selected are the firms that implement Six Sigma program. The respondents are Six Sigma champion/black belt. All the industries/manufacturers or firms in Malaysia are registered with the Federation of Malaysia Manufacturers and the firms contacts can be obtained from the Federation of Malaysia Manufacturers (FMM) Directory 2006 Malaysia Industries 37th Edition. However, organisations that implement Six Sigma program are not reported publicly in this directory. Thus, we have to adopt an approach different from the conventional method of contacting Six Sigma implemented organisations. To conduct an efficient search for Six Sigma implemented organisations, three methods are adopted. First, the contact information is obtained for Six Sigma implementing organisations known as the Penang Skill Development Center (PSDC). Second, support is received from Six Sigma training consultancy firm in the form of referrals. Third, internet search and cold-call on those electrical and electronic companies listed on FMM Directory that has been informed as having implemented Six Sigma.

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The appropriateness of a sampling method depends on the goal of the study. If the goal is to generalise an entire population and to provide a statistical basis for asserting that the sample is representative, a probability sampling is appropriate. If the aim of the study, on the other hand, is to learn about individuals or cases for some purposes other than generalising a population, or if random selection is not feasible, then non-probability sampling is appropriate. The sampling design for this study is probability sampling using the simple random sampling technique. The reason is because the goal of the study is to examine the relationship between knowledge creation process and organisation performance in companies that have implemented Six Sigma. There are many techniques to determine the sample size. For this study, the technique used for determining the sample size is based on researchers’ recommendation in structural equation modelling analysis. Researchers in structural equation modelling commonly recommend a minimum sample size of 100 to 150 (Anderson and Gerbing, 1988; Ding et al., 1995). Thus, the aim for this quantitative study is to gather responses for a sample size of about 150 or more. To compensate for non-responses and poor responses, the total number of 375 potential surveys is administered, which is larger than the calculated number required ensuring good decision model.

The measurement instrument is created using an extensive review of the literature. A multi-item measure is used for constructs/variables which tend to be more reliable than single-item measures. Whenever possible, validated measures from extant research are used in operationalising the constructs in the theoretical framework. Most measures are adapted and modified to make them more suitable for this research setting, while other new measures are developed as needed. A 1–5 Likert scale is used for the items that measure the knowledge creation process construct, where 1 is very seldom and 5 is very often. Regarding knowledge creation and organisation performance constructs, a 1–5 Likert scale is used for the items, where 1 is strongly disagree and 5 is strongly agree. Likert-type scale uses numbers to assess objects on certain attributes and assume equal increments of the attribute being measured. This type of interval scale is desirable for most statistical operations as it is possible to compute an arithmetic mean from interval-scale measures (Aacker et al., 1995). In addition, Likert-type scales asking a relative assessment on a continuum have been commonly used for primary data collection in empirical strategy research and, more generally, in management and marketing research (Ward et al., 1998).

4 Analysis

A total of 180 responses to the study survey were received by the end of August 2008. Out of the total 349 possible survey participants, 187 (53.6%) surveys were returned and received by the end of August 2008. Seven (2.0%) firms were excluded from data analysis as they did not participate in the study or the surveys were returned incomplete. Five (1.43%) firms openly refused to participate in the survey. Among these firms, three (0.869%) firms’ personnel expressed that either they were not interested in the study or their firm had a no-survey’s policy. Another two (0.57%) firms expressed that the survey questions were irrelevant for their business practices at the moment as Six Sigma initiative is not ready to launch yet and it is under the roadmap of year 2009. Only two responses were returned incomplete. These two respondents expressed that part of the survey questions they were not ready to answer as they just launched the Six Sigma

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program only for a couple of months. Consequently, these responses were not included in the later data analysis. The overall quality of the responses was excellent. After subtracting the seven firms that would not be participating in the survey or incomplete survey, the study yielded a sample size of 180. By the end of the data collection, a total of 180 responses had been received, indicating a response rate of 51.6%. The survey respondent’s interest in this study was also clear. Fifty-six out of 180 (31.1%) respondents indicated that they would like to receive an advanced executive summary of the study findings. Additionally, some respondents provided information on the size of their companies, detailed information on their products, and gave special encouragement to the researcher for pursuing the study.

4.1 Characteristics of the survey respondents

The characteristics of the survey respondents’ businesses were assessed to provide a better understanding of their basic nature. The questions included primary business, industrial type, the number of employees, year of business operation and firm ownership. Table 4 reports the characteristics of the firms participating in the study survey. First, the primary business of the firm was asked for in order to understand the type of business/products that the study participants handled. Out of 180 respondents, 160 (88.9%) reported that their major businesses were related to electrical and electronics, ten (5.6%) chemical and chemical products, five (2.8%) IT and networking and both 2.8% for software application and paper related businesses. These statistics suggested that most of the Six Sigma initiatives were implemented by electrical and electronics companies.

Second, a large proportion of the respondents, 96.7%, represent the manufacturing industry. This is understandable, since Six Sigma started in manufacturing and is strongly embraced by this sector. The remaining 3.3% represent services industries. Third, the number of employees was requested to obtain an overview of the participant’s firm size in terms of the number of employees. Out of 180 respondents, 149 (82.8%) had more than 1,000 employees, 21 (11.7%) employed 501 to 1,000 people and ten (5.6%) respondents have less than 500 employees. This finding suggested that a significant portion of Six Sigma firms could be classified as large business operations and Six Sigma program implementation was mostly implemented by large businesses rather than small businesses. Fourth, the responses on the years of the firm’s operations revealed that over 36.1% of the participants had more than 15 years of operations, 25.6% with between 11–15 years of operation and 38.3% with less than ten years operation.

Table 5 summarises detailed information on the Six Sigma experiences of the study respondents. The length of time an organisation has been implementing Six Sigma is shown in Table 5. These results were expected since Six Sigma is a relatively new concept. This is evidence by the fact that over 75% of the organisations in the survey have been working with Six Sigma for three years or less. Out of 180 respondents, 160 (88.9%) reported that they were using DMAIC methodology and 20 (11.1%) reported employing DFSS methodology. These statistics suggested that most of the firms using Six Sigma initiative focus on existing business process improvement and there is a minority that focuses on product/business process design/redesign.

Six Sigma emphasises the financial results of a project. The firm’s annual cost savings per project were assessed. Out of 180 respondents, 64 (35.6%) reported

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that their annual cost savings per project between $100K–$150K, 36 (20%) between $50K–$100K or less than $50K respectively. 28 (15.6%) with $150K–$200K and only 8.9% with cost savings of more than $200K. Therefore, cost saving is one of the index to be used to determine the success or failure of Six Sigma initiatives. Finally, the title of the participant’s position within the firm was asked to ensure that the participant was qualified to respond to the survey questions as intended in the study design. Out of 180 respondents, 89 (49.4%) were champion, 46 (25.6%) were Six Sigma executive, 24 (13.3%) black belt, and 21 (11.7%) were master black belt.

Table 4 Business characteristics of the survey

Business characteristics Frequency Percentage

Primary business 180 100%

Electrical and electronics 160 88.9%

Chemical and chemical products 10 5.6%

IT and networking products 5 2.8%

Software applications 3 1.7%

Paper and paper products 2 1.1%

Food products and beverages 0 0.0%

Textiles and wearing apparels 0 0.0%

Rubber and plastic products 0 0.0%

Industrial type 180 100%

Manufacturing 174 96.7%

Services 6 3.3%

Healthcare 0 0.0%

Sales and marketing 0 0.0%

Education 0 0.0%

Government 0 0.0%

Number of employees 180 100%

Less than 100 0 0.0%

100–250 0 0.0%

251–500 10 5.6%

501–1,000 21 11.7%

More than 1,000 149 82.8%

Years of business operation 180 100%

< = 5 years 22 12.2%

6–10 years 47 26.1%

11–15 years 46 25.6%

>15 years 65 36.1%

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Table 5 Six Sigma experiences

Six Sigma experiences Frequency Percentage

Years of Six Sigma implementation 180 100% More than eight years 15 8.3% 5–8 years 27 15.0% 3–5 years 52 28.9% 2–3 years 86 47.8% Six Sigma methodology applied 180 100% DMAIC 160 88.9% DFSS 20 11.1% Average annual cost savings per project 180 100% Less than $50,000 36 20.0% $50,000–$100,000 36 20.0% $100,000–$150,000 64 35.6% $150,000–$200,000 28 15.6% More than $200,000 16 8.9% Position title 180 100% Six Sigma executive 46 25.6% Champion 89 49.4% Black belt 24 13.3% Master black belt 21 11.7%

4.2 Multicolinearity

The bivariate correlation matrix among variables included in model (Table 6) was examined. No correlation were greater than or equal to 0.85 (Kline, 2005; Tabachnick and Fidell, 2001), which was considered a cut-off value for bivariate multicolinearity. At the multivariate level, multicolinearity was examined while running path analysis using AMOS. There was no problem, which meant that the hypothesised model did not have a multicolinearity problem at the multivariate level. Table 6 Correlation matrix among variables

Construct SOC EXT COM INT KNO CUS IBP ELG FIN Socialisation (SOC) 1 Externalisation (EXT) 0.39 1 Combination (COM) 0.5 0.49 1 Internalisation (INT) 0.45 0.59 0.56 1 Knowledge creation (KNO)

0.67 0.47 0.69 0.43 1

Customer (CUS) 0.58 0.35 0.64 0.47 0.51 1 Internal business process (IBP)

0.37 0.32 0.42 0.51 0.66 0.61 1

Employee learning and growth (ELG)

0.45 0.58 0.52 0.38 0.56 0.31 0.33 1

Financial (FIN) 0.29 0.63 0.43 0.44 0.47 0.4 0.51 0.62 1

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Table 7 Measurement model

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4.3 Measurement model analysis results

The set of 48 measurement items representing nine factors were subjected to measurement model analysis (or confirmatory factor analysis), using maximum likelihood estimation and variance-covariance matrix, to verify the proposed factor structure. The relationships between the observed variables and their factors were specified in the measurement model. Each factor in the model was allowed to covary with other factor. Table 7 shows the results of the study’s measurement model analysis, including the construct names, observed variables, standardised factor loadings and Cronbach’s alpha.

4.4 Overall model fit

An overall model fit was examined with multiple goodness-of-fit measures. Among absolute fit indices, the chi-square value of the measurement model was 438.00, sample size of 180, and p-value less than .000. Although the chi-square statistic alone showed that significant differences may exist, other absolute fit indices suggested otherwise. RMSEA had a value of .098, falling just under the upper acceptable threshold of .10. The GFI value of .80 was also marginally acceptable as no absolute threshold levels for acceptability have been established for this measure (Hair et al., 2006). Additionally, among incremental fit indices, NNFI had a value of .95 and the CFI value was .96, indicating an excellent fit of the overall model. Thus, a mix of the various fit indices supported that the model was at least marginally acceptable and was sufficient to proceed to the next stage of data analysis.

4.5 Reliability and validity assessment

The measurement fit was evaluated by examining both the reliability and validity of the survey instrument. First, the reliability measures, Cronbach’s alpha, of all four independent variables, one mediating variable and four dependent variables ranging from 0.804 to 0.921, all exceeding well above the acceptable threshold level of 0.70 (Hair et al., 2006). The results suggested that the observed variables of each construct were highly inter-correlated, measuring the same latent construct. Second, convergent validity was assessed by examining the magnitude and sign of the factor loadings of observed variables onto the proposed latent (construct) variables. As seen in Table 7, all observed variables were statistically significant for the proposed constructs, given that all CR-values of the factor loadings from unstandardised solutions were higher than 1.96, indicating statistical significance at a 95% confidence level. Additionally, all factor loadings showed positive signs, suggesting the positive relationships between the observed variables and their respective constructs. Consequently, the data supported the study’s observed variables as effectively measuring the proposed constructs. Third, discriminant validity was addressed by the correlation between each possible pair of constructs. All of the inter-factor correlation coefficients were less than the value of 0.80. Thus, discriminant validity among the constructs in the model was considered to be supported. Table 8 displays the inter-factor correlation coefficients demonstrating discriminant validity of the study’s measurement model. Consequently, based on model fit, reliability and validity assessment, the study’s measurement model was concluded to

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be sufficiently adequate to move on to the next data analysis stage – structural model analysis.

4.6 Structural model analysis results

The structural model was designed to investigate the interrelationships of knowledge creation process in Six Sigma practices, knowledge creation and organisational performance, resulting in a total of 15 paths. Table 8 shows the results of the study’s structural model, including standardised parameter estimates, CR-values from unstandardised solutions, significance levels for the structural paths, and overall goodness-of-fit indices. Table 8 Structural model

Paths in the structural model Standardised

parameter estimate

Critical ratio

Probability <=

Socialisation ---> Knowledge creation 0.61 7.35 0.000

Externalisation ---> Knowledge creation 0.25 3.47 0.010

Combination ---> Knowledge creation 0.47 5.66 0.000

Internalisation ---> Knowledge creation 0.34 3.86 0.014

Knowledge creation ---> Customer 0.43 4.73 0.002

Knowledge creation ---> Internal business process 0.5 6.25 0.000 Knowledge creation ---> Employee learning and growth 0.39 4.19 0.009

Employee learning and growth ---> Customer 0.29 4.08 0.011 Employee learning and growth ---> Internal business process 0.33 3.88 0.080

Internal business process ---> Customer 0.51 5.80 0.000

Customer ---> Financial 0.36 3.83 0.018

Internal business process ---> Financial 0.28 3.29 0.012

Employee learning and growth ---> Financial 0.09 1.23 0.236

Goodness-of-fit statistics

X2 = 501.22, p < 0.000

CFI = 0.95

NFI = 0.94

RMSEA = 0.105

GFI = 0.78

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Overall, various fit indices for the structural model suggested a marginally acceptable fit to the data. Among the absolute fit indices, the chi-square statistic of 501.22 (N = 180, p-value <.000) suggested that there might be significant differences. However, the RMSEA had a value of 0.105, just around the upper acceptable threshold of 0.10, and the GFI value of 0.78 showed a marginally acceptable fit. Among the incremental fit indices, the NNFI value was .94 and the CFI value was .95, indicating an excellent fit of the overall structural model. Except for one path, all remaining paths proposed in the theoretical model were statistically significant at p < .05. The path that was found not to be statistically significant was the path connecting employee learning and growth to financial.

5 Discussion

Based on the results of the structural model, most of the study’s research hypotheses were supported. Table 8 shows the study’s structural model with unstandardised solutions and the research hypotheses. Each hypothesis is discussed in detail. Hypotheses H1 through H4 predicted that different Six Sigma practices allow knowledge to be created through knowledge creation processes. Hypothesis H1 predicted that Six Sigma practices that promote contact and interaction between or among intra-organisational members or inter-organisational members allow knowledge to be created through socialisation, was supported. The result of H1 was illustrated in the path from socialisation to knowledge creation, was positive and statistically significant at p < .05 (path coefficient = 0.61, CR-value = 7.35).

Hypothesis H2 predicted that Six Sigma practices that help articulate improvement ideal in the form of theories, concepts or cause-and-effect reasoning allow knowledge to be created through externalisation, was not supported. The result of H2 was illustrated in the path from externalisation to knowledge creation, was positive, however, not statistically significant at p < .05 (path coefficient = 0.25, CR-value = 3.47). Hypothesis H3 predicted that Six Sigma practices that promote data measurement and analysis allow knowledge to be created through combination, was supported. The result of H3 was illustrated in the path from combination to knowledge creation, was positive and statistically significant at p < .05 (path coefficient = 0.47, CR-value = 5.66).

Hypothesis H4 predicted that Six Sigma practices that promote learning by doing allow knowledge to be created through internalisation, was supported. The result of H2 was illustrated in the path from externalisation to knowledge creation, was positive and statistically significant at p < .05 (path coefficient = 0.34, CR-value = 3.86). As predicted, Six Sigma practices that promote contact and interaction between or among intra-organisational members or inter-organisational members allow knowledge to be created through socialisation was well-expected. As problem solving through Six Sigma teams are given a context for socialisation where feelings, emotions, experiences and mental models emerged primarily through team face-to-face meeting and discussions that require interaction between team members. These interactions are richer contextually as they are unedited, firsthand accounts of team members’ experiences. These practices are also commonly employed by employees in every Six Sigma organisations.

The result showed that Six Sigma practices that help articulate improvement ideal in the form of theories, concepts or cause-and-effect reasoning allow knowledge to be

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created through externalisation. Six Sigma team acquires the voice of customers and then serve as an idea process improvement or for designing product with new features or new products to the market (Dean and Bowen, 1994). Cross functional team was used to generate ideal for process improvement or product design by brainstorming (Gitlow et al., 1995). Application of Six Sigma tools such as cause and effect diagram and ‘five whys’ is used for organising the team’s ideal and establishes hypothesis and theories about the cause and effect relationship between defect and process characteristic (Keller, 2005). As a result, it facilitates externalisation by capturing tacit knowledge from team members/customers and externalises it in the form of idea or concept for product development and internal business improvement.

The data showed that Six Sigma practices that promote data measurement and analysis allow knowledge to be created through combination. This finding was intuitively plausible as one of the success factors of Six Sigma can improve organisation performance is the disciplined approach of DMAIC and DFSS methodologies. Each of the steps of DMAIC and DFSS is integrated with both statistical and non-statistical tool and techniques that is driven by the use of data and fact collection and analysis to decision-making. It enables Six Sigma team to gain an insight which resulted in new knowledge synthesised from multiple sources of information. This finding clearly supports Pande et al.’s (2002) argument that data collection and analysis in Six Sigma methodology allow knowledge creation and business process performance improvement.

Additionally, the data showed that Six Sigma practices that promote learning by doing allow knowledge to be created through internalisation. As Nonaka (1995) argues, learning by doing allows individuals to gains deep insight that enhances their sense of self-efficacy, and the act of doing that leads to internalisation usually involves an ongoing culmination and refinement of one’s knowledge through a series of reflection and action. In Six Sigma, the training occurs in hands on fashion where instructors explain concepts followed by participants applying concepts to the workplace through learning by doing (Pyzdek, 2003). Once Six Sigma improvement team identifies solution to business problems, the lessons learned and knowledge gained are documented and shared with the entire organisation. All these activities help organisation to internalise the knowledge gained.

Hypotheses H5 through H7 predicted that Six Sigma practices that result in knowledge creation have positive effect on customer, internal business process and employee learning and growth respectively, was well supported. The results of H5 is shown in the paths from knowledge creation to customer (path coefficient = 0.43, CR-value = 4.73). The path (H6) to internal business process (path coefficient = 0.50, CR-value = 6.25) and the path (H7) to employee learning and growth (path coefficient = 0.39, CR-value = 4.19) were statistically significant at p < .05 and all positive. The positive significant relationships between organisational knowledge creation and organisational performance of customers, internal business process and employee learning and growth were well-expected. The data revealed that Six Sigma practices allow knowledge to be created and have effect on customers. The findings clearly support McKeen et al.’s (2006) argument that knowledge creation positively relates to customer satisfaction and retention. Application of Six Sigma methodology, tools and techniques helps knowledge creation where root cause of the business process variation and inefficiency identified and correction action is put in place to eliminate the problem. This finding corroborated prior studies that knowledge creation has positive link

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to quality (Mukherjee et al., 1998), innovation (Francisco and Guadamillas, 2002) and productivity (Lapre and Wassenhove, 2001). The result also implied that knowledge creation is important for employee learning and growth. As Pande et al. (2000) argues, Six Sigma training and methodology is designed in such as way to ensure employees are really learned, knowledgeable, skilled and have capability in solving business process problems.

Hypotheses H8 and H9 predicted that employee learning and growth has positive effect on internal business process and customer respectively, were well-supported. The result of H8 was found in the paths from employee learning and growth to internal business processes (path coefficient = 0.29, CR-value = 4.08) and the paths (H9) to customer (path coefficient = 0.33, CR-value = 3.88) were statistically significant at p < .05 and both positive. Hypothesis H10 predicted that internal business process has positive effect on customer as supported (path coefficient = 0.51, CR-value = 5.80). Knowledge gained through Six Sigma training broadens internal business processes perspective, sharpens problem-solving skills and facilitates learning. A well-trained employee has a better knowledge foundation for improving process and products. The finding support Ryan et al. (1996) and Grandzol’s (1998) argument that trained or capable employees have positive impact on operational results measured by productivity, cycle time, scrap/waste, energy/efficiency and material usage.

The study finds that employee learning and growth has impact on customers. Six Sigma organisations provide extensive training to the employee to ensure they really learned, knowledgeable, skilled and has capability in solving business process problems. Experienced employees are more knowledgeable about their organisation’s goal and the customer’s requirement (Anderson et al., 1994). Customer satisfaction is a natural consequence of pride of workmanship. Pride of workmanship is determined by how capable the employee of delivering quality product and service to customer (Anderson et al., 1994). Support for this hypothesis closely parallel finding in previous studies (Hartline and Ferrell, 1996; Heskett et al., 1994; Rucci et al., 1998; Harrison and Freeman, 1999) where trained employee results in customer satisfaction and loyalty, and business performance.

The production of high quality product and service at reasonable price has a direct effect on how external customer perceives them and purchases consumption experience (Gustafson and Johnson, 2002). This level of quality can be attained through Six Sigma DMAIC and DFSS, to improve business processes that reduce defect, cost (Rust et al., 2002), as well as cycle time and inventories (Napolitano, 2003), which eventually improves customer’s perception of quality and increase customer satisfaction (Rust et al., 2002). The finding is consistent with Anderson et al.’s (1998) study that the effect of operational performance on customer satisfaction is positive. In addition, it is also in line with Atkins’ et al. (2002) finding that there is a significant link between high quality product and customer satisfaction.

Hypotheses H11 through H13 predicted that effect of customers, internal business processes and employee learning and growth on financial respectively. The result of H11 was illustrated in the paths from the path customers to financial (path coefficient = 0.36, CR-value = 3.83) and the path H12 from internal business process to financial (path coefficient = 0.28, CR-value = 3.29) were statistically significant at p < .05 and both positive. The path H13 from employee learning and growth was positive, however, not statistically significant at p < .05 (path coefficient = 0.09, CR-value = 1.23). Customer satisfaction occurs when product or services meet or exceed customer expectation.

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Customer satisfaction creates customer loyalty and retention which result in repeat purchase, growth in sales and increase profits. This finding was consistent with Das et al. (2002) and other studies (e.g., Buzzell and Weirsema, 1981; Bernhardt et al., 2000) that there was a positive relationship between customer satisfaction and revenue and profitability. Additionally, the data showed that the improvement on the internal business process has positive effect on financial performance. Six Sigma is used to reduce process variation and improve the design of product and service. One potential reason for why this data differed from what was predicted from earlier theory is that from sample respondents, 96.7% of respondents are from manufacturing industries where the skills and knowledge of the employee are contributed to improving business processed rather than direct impact to financial. Table 9 shows the summary of the research hypotheses tests. Out of 13 possible paths, 12 paths were supported by the statistical analysis. The remaining one path was not statistically supported for their significance. Table 9 Summary of the hypotheses tests

Hypotheses Results

H1 Six Sigma practices that promote contact and interaction between or among intra-organisational members or inter-organisational members allow knowledge to be created through socialisation

Supported

H2 Six Sigma practices that help articulate improvement ideal in the form of theories, concepts or cause-and-effect reasoning allow knowledge to be created through externalisation

Supported

H3 Six Sigma practices that promote data measurement and analysis allow knowledge to be created through combination

Supported

H4 Six Sigma practices, learning by doing allow knowledge to be created through internalisation

Supported

H5 Six Sigma practices that result in knowledge creation have positive effect on customer

Supported

H6 Six Sigma practices that result in knowledge creation have positive effect on internal business process

Supported

H7 Six Sigma practices that result in knowledge creation have positive effect on employee learning and growth

Supported

H8 Employee learning and growth has positive effect on internal business process

Supported

H9 Employee learning and growth has positive effect on customer Supported

H10 Internal business process has positive effect on customer Supported

H11 Customers has positive effect on financial Supported

H12 Internal business process has positive effect on financial Supported

H13 Employee learning and growth has positive effect on financial Not supported

6 Conclusions

This study focuses on the antecedents and outcomes of organisational knowledge creation in Six Sigma organisation. Specifically, the study focuses on how Six Sigma practices can lead to organisational knowledge creation capability and how this capability can lead

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to improved organisational performance. Based on a review of the literatures on Six Sigma, knowledge-based view of the firm, theory of organisational knowledge creation and field exploratory study, a prescriptive model was developed. A survey was conducted to test the model linking knowledge creation process in Six Sigma practices to organisational performance through mediating effect organisational knowledge creation.

Overall, the study’s results show that process improvement through Six Sigma projects involve the creation of knowledge. Thus, the underlying basis for practices employed in Six Sigma projects is knowledge creation through the transformation of knowledge from tacit to explicit and vice versa and through the transfer of both types of knowledge from individuals to teams. The transfer of tacit knowledge from individuals to teams through socialisation practices in Six Sigma projects allow knowledge to be created. Thus, it is important for black belts as project leaders to include social interactions among project-team members and people related to the process as part of project executions. Further, black belts must not only be trained in sophisticated analytical techniques, they should also gain expertise in practices for generating ideas and encouraging sharing among team members. Techniques such as brainstorming sessions and nominal group technique must not be overlooked. Knowledge creation is also significantly explained by practices for conversion of explicit knowledge to tacit knowledge (internalisation) pointing to the criticality of training of employees for transfer of knowledge gained from project execution to routine process implementation through learning by doing and also lesson learned sharing.

Further, the results clearly showed that organisational knowledge creation capability is related to firm performance; thus, this capability enables firms to adapt to rapidly changing competitive environments. Because new knowledge creation has a significant effect on firm performance and potentially on long-term survivability, it is important for us to better understand how to develop and leverage this capability. Specifically, knowledge creation has direct positive impact on customer, internal business process, employee learning and growth and indirect positive impact on financial. Overall, this study provides a more comprehensive understanding of how Six Sigma practices can lead to improved organisational performance and enhancing organisational capabilities.

Acknowledgements

The authors are grateful to anonymous reviewers of IJPQM for their constructive comments.

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