Linking learning and knowledge creation to project success in Six Sigma projects: An empirical...

Int. J. Production Economics 141 (2013) 388–402

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Int. J. Production Economics

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Linking learning and knowledge creation to project success in Six Sigmaprojects: An empirical investigation

V. Arumugam a,n, Jiju Antony a, Maneesh Kumar b

a Strathclyde Institute for Operations Management, Department of DMEM, University of Strathclyde, Glasgow G1 1XJ, UKb Logistics & Operations Management Section, Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK

a r t i c l e i n f o

Article history:

Received 29 November 2011

Accepted 9 September 2012Available online 19 September 2012

Keywords:

Team learning

Six Sigma

Knowing-what

Knowing-how

Psychological safety

Project performance

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x.doi.org/10.1016/j.ijpe.2012.09.003

esponding author. Tel.: þ45 2568 0117.

ail addresses: [email protected] (V. Arumug

a b s t r a c t

This study investigates the impact of two organizational antecedents, (1) Six Sigma resources

(technical) and (2) team psychological safety (social), on learning behaviour and knowledge creation

and, in turn, on the success of Six Sigma process improvement projects. The paper proposes an

integrated model to explain process improvement implementation success through two learning

activities undertaken by Six Sigma project teams: Knowing-what and Knowing-how. The conceptualiza-

tion of these knowledge types in this research is different from usual conceptualization as it represents

the knowledge brought into projects through various phases of Six Sigma projects. The three

hypotheses proposed in the model were tested using the data collected from 52 Six Sigma project

teams from a single organization. Regression analysis showed psychological safety affects project

performance through knowing-how. Regression and bootstrapping analyses showed resources influ-

ence project performance through the combined mediation of knowing-what and knowing-how.

The paper provides an interdisciplinary treatment of knowledge management in process improve-

ment teams, and offers a research model demonstrating how Six Sigma project teams promote

deliberate organizational learning. By doing so, this study empirically establishes the notion that

technical and social supports jointly impact the success of operations management initiatives such as

Six Sigma through learning. The limitations of the study along with the future research directions are

highlighted.

& 2012 Elsevier B.V. All rights reserved.

1. Introduction

Teams are the fundamental learning units in organizations(Senge, 1990). Team learning is a process in which a team takesaction, obtains and reflects on feedback, and makes changes to adaptor improve (Edmondson, 1999; Argote et al., 2000). The literatureinvestigates the effects of various organizational antecedents onproject performance through team learning in project teams such asnew product development teams (Sarin and McDermott, 2003;Tatikonda and Montoya-Weiss, 2001), information technologydevelopment and implementation project teams (Lee and Choi,2003; Sabherwal et al., 2006), and best practice or new technologyimplementing project teams in hospitals (Edmondson et al., 2003;Tucker et al., 2007). However, there is a paucity of such research onprocess improvement teams.

Operations management scholars have found that processimprovement contributes to the competitive positions of organi-zations (Anand et al., 2009; Shah and Ward, 2003; Zu et al., 2008)

ll rights reserved.

am), [email protected] (J. A

and recognize the importance of knowledge management inprocess improvement (Choo et al., 2007; Linderman et al., 2004,2010; Molina et al., 2007; Llorens-Montes and Molina, 2006). Inspite of the importance of knowledge management within thefirm (Sutton and Hargadon, 1996), few studies investigate therelationship between knowledge management and quality man-agement, in general, and process management in particular (Chooet al., 2007; Zhang et al., 2008). This study empirically investi-gates learning and knowledge creation in Six Sigma processimprovement projects. The results and findings are equallyapplicable to any process improvement environment.

Invented by Motorola, Inc. in 1986 as a metric for measuringdefects and improving quality, Six Sigma evolved to a robustbusiness improvement strategy that focuses an organization oncustomer requirements (Antony, 2004; Harry and Schroeder,2000; Kumar et al., 2008; Schroeder et al., 2008). In Six Sigma,process improvement projects are identified, selected and prior-itized based on strategic importance to the organization, and

ntony), [email protected] (M. Kumar).

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V. Arumugam et al. / Int. J. Production Economics 141 (2013) 388–402 389

management commits resources for successful completion of theprojects. Each project team is led by a project leader well trainedin Six Sigma methodology, and the team carries out projectsfollowing a structured approach called DMAIC, which stands forDefine, Measure, Analyze, Improve and Control (Linderman et al.,2006; Pande et al., 2000; Schroeder et al., 2008).

Our review of the quality management literature suggests thatrecent studies on process improvement teams focused only on themechanisms of learning and knowledge creation (Anand et al.,2010; Mukherjee et al., 1998). Very few studies have focused oninfluencing variables and their effects on learning and knowledgecreation (Gutierrez et al., 2011; Linderman et al., 2010) and, inturn, project performance (Choo et al., 2007; Llorens-Montesand Molina, 2006). This is surprising since scholars havenoted technical and social components of quality/process man-agement lead to learning and knowledge creation (Hackman andWageman, 1995; Wruck and Jensen, 1994), and organizationalfactors, such as managerial actions, and contextual factors, suchas team composition, task conditions, learning goals, leaderbehaviour and socialization, influence learning in teams (Cohenand Levinthal, 1990; Van den Bosch et al., 1999; Edmondson,1999).

This study minimizes the gaps found in the literature byreporting an empirical investigation and understanding of theimpact of two organizational antecedents, (1) Six Sigma resources(technical) and (2) team psychological safety (social condition orsocial process practised within the team), on project performancein Six Sigma project teams through team learning behaviour, asthe implementation of Six Sigma requires both technical andprocess perspectives (McAdam and Lafferty 2004; Choo et al.,2007). By doing so, this study empirically supports the earlierresearch of Linderman et al. (2010) and Llorens-Montes andMolina (2006), and extends the research by Choo et al. (2007).

The paper proposes an integrated model to explain processimprovement implementation success through specific learningactivities undertaken by Six Sigma project teams. Drawing on twostreams of research, team learning and operations management,the model explains implementation success through two types oforganizational learning: (1) Knowing-what, facilitating the projectteam to understand the current process and its input factors(process characterization) and (2) Knowing-how, helping the teamidentify how these factors affect the process outcome and gen-erate optimal solutions by changing or modifying input factors forimproved process outcome (process optimization).

The rest of the paper is organized as follows. Section 2introduces Six Sigma resources, team psychological safety andthe two learning mechanisms and develops three theoreticalmodels and hypotheses. Section 3 presents research methodsincluding data collection and measures development. Section 4presents analysis and results. Section 5 includes a discussionabout theoretical and managerial implications, opportunities forfuture research and limitations of the research followed byconclusion in Section 6.

2. Theory development and hypotheses

Research shows that team-learning activities mediate therelationship between team inputs, such as composition, structureand context, and team outputs, such as innovation, efficiency andquality (Edmondson et al., 2007). Scholars focus on learningactivities in terms of learning behaviours or mechanisms adoptedby the team members, and investigate how various organizationalantecedents affect them. Table 1 displays recent studies with keyfindings, which reveal that team structures, such as team contextsand leader behaviour, and shared beliefs, such as team

psychological safety, shape team outcomes through learningbehaviours of the team.

Two things need to be elaborated regarding these studies.First, the relationship between learning behaviours and perfor-mance is not always positive, as it depends on the nature oflearning behaviour (Wong, 2004), and the current level of perfor-mance (Levinthal and March, 1993; March, 1991). It may bepossible for a team to compromise performance by overempha-sizing learning, particularly when they have been performing well(Bunderson and Sutcliffe, 2003). Furthermore, not all learningmay translate into organizational knowledge, as members mayfail to communicate with others for use (Ancona and Caldwell,1992). Thus, ‘‘organizational knowing’’ does not always translateinto ‘‘organizational doing’’ (Pfeffer and Sutton, 2000; Maier et al.,2001). For the purpose of this paper, however, we take theposition of many recent studies that learning results in improvedperformance (Edmondson, 1999, 2002; Edmondson et al., 2003;Sarin and McDermott, 2003; Tucker et al., 2007; Choo et al.,2007). Learning is a process of improving organizational actionsthrough better knowledge and understanding (Fiol and Lyles,1985). Huber (1991) asserts that organization learns if any of itsunits acquires knowledge that it recognizes as potentially usefulto the organization (Huber 1991, p. 89). Learning implies somekind of positive change in understanding, knowledge, ability/skill,process/routines, or systematic coordination that impacts perfor-mance (Edmondson et al., 2007). More specifically, problemsolving heuristics used in quality improvement projects helpteams use the knowledge collectively to identify and analyzeopportunities to improve quality (Hackman and Wageman, 1995).

Secondly, the conceptualization and operationalization ofmeasures of learning are not the same across all these studies(Table 1) and do not converge; therefore, theory building isproblematic (Edmondson et al., 2007). On the one hand, similarmeasures are used to represent conceptually different things,such as learning (Zellmer-Bruhn and Gibson, 2006), innovation(Wong, 2004) and experimentation (Gibson and Vermeulen,2003), and on the other hand, different conceptualizations suchas group interaction processes (Van der Vogt and Bunderson,2005), the extent to which a team creates new processes andpractices (Zellmer-Bruhn and Gibson, 2006), respondent’s percep-tions of their future learning behaviour in the team (Sarin andMcDermott, 2003), team activities such as reflection on groupprocesses and discussions with outsiders (Edmondson, 1999), andidentifying best practices and discovering the underlying rootcauses to implement new processes (Tucker et al., 2007) are usedto mean learning.

Six Sigma requires different treatment in comparison to otherteams, such as new product development, hospital improvementand information technology implementation teams. Six Sigmaproject teams are temporary, formed to improve a specificprocess, and have short project duration (generally 3 to 6 months)(Antony et al., 2007a; Pande et al., 2000; Pyzdek, 2003). Except forthe project leader, members contribute only a fraction of theirwork time. Therefore, social ties are not as close as in otherproject teams (Anand et al., 2010). Learning and knowledgetransfer, therefore, is through specific practices used by the SixSigma project team during the project such as DMAIC method,and project leaders’ knowledge-gathering behaviour in gatheringindividual knowledge and synthesizing into team-level knowl-edge to solve problems (Arumugam, 2011; Anand et al., 2010;Sarin and McDermott, 2003). Consequently, learning mechanisms,and the nature of social interaction such as psychological safetyand its effects on learning mechanisms, show greater variancethan that of other teams.

A psychologically safe atmosphere in a team makes a memberfeel safe for interpersonal risk taking without fear of negative

Table 1Summary of recent studies on project performance using learning and knowledge creation as mediators.

Authors Data and method Predictors Mediators Dependentvariables

Key findings

Edmondson (1999) 53 Work teams from a

manufacturing firm;

survey, interviewsand

observations.

Psychological

safety

Learning

behaviours

Team performance

(satisfying

customer needs

and expectataions)

Team design and

leadership-


Team efficacy Psychological safety-

Learning behaviours-

Leader coaching Team performance

Context support

Sarin and McDermott (2003) 52 New product teams

(229 members) from

6 high tech companies;

survey.

Leader

characteristics

Team learning Team performance: Team leadershp-Team

learning-

Level of innovation Team performance

Speed to market

Gibson, Vermeulen (2003) 156 teams from

medical and

pharmaceutical firms;

survey and interviews.

Sub group strength Learning

behaviours

Performance Performance

Manageemnt-Subgroup

strength

Team

empowerment

Team empowerment-

Subgroup strength

KM system. Knowledge

Management-Subgroup

strength

Sub group strength-

Learning behaviour

(nonlinear)

Bunderson and Sutcliffe (2003) 44 management teams

from business units of a

large consumer

products firm; survey.

Past performance Team learning

orientation

Performance Trelatiponship between

team learning orientation

and efficiency based

performance would be

Nonmonotonic and

moderated by past

performance

Wong (2004) Team managers and

members of 73 teams

from 4 organizations;

survey.

Group cohesion

(strength of

intrateamsupport

and relationship)

Task condition

Local learning Group efficiency Local learning-Group

effeciency

Distal learning Group

innovativeness

Distal learning -Group

innovativeness

Group cohesion-Distal

learning

Group cohesion-(–)

Local learning

Van der Vegt and Bunderson

(2005)

Members and

supervisors of 57

teams; survey.

Team identity Learning

behaviours

Performance Team identification

moderates the effects of

expertise diversity on

learning behaviour and

performance

Zellmer-Bruhn and Gibson (2006) 115 teams and

supervisors from five

multinational

pharmaceutical and

medical firms; survey

and interviews.

Team autonomy Learning

behaviours

The extent to

which the team

created new

processes and

practices

Team autonomy by

organizational contexts

has both positive and

negative influence on

team learning.

Team learning-Task

performance

Team leaning-

Interpersonal relations

Choo et al. (2007) 206 Six Sigma project

teams in a

manufacturing firm;

survey.

Adherance to

Method

Learning

behaviours

Project

performance

Method- Learning

behaviours-

Performance

Psychological

safety

Knowledge

creation

Psychological safety-

Knowledge created-

Performance

Learning behaviours-

Performance

Tucker et al. (2007) 23 Hospital

Improvement project

teams; survey and

interviews.

Psychological

safety Level of

evidence

supporting new

practices

Learning

activities:

Learn-what and

Learn-how

Implementation

success of new

practices

Level of evidence-

Implementation success

Psychological safety-

Learn-how-

Implementation

success

V. Arumugam et al. / Int. J. Production Economics 141 (2013) 388–402390


consequences to self-image, status or career (Kahn, 1990;Edmondson, 1999). This psychological experience shapes theprocesses of people engaging in knowledge transfer among theteam members (Edmondson, 1999; Tucker et al., 2007). Earlierresearch by Kahn (1990) has shown that the relationship amongpeople representing different hierarchical echelons is potentiallymore stifling and threatening than relations with peers (Kahn,1990). In Six Sigma project team, members generally are trainedin various tools and techniques in problem solving. Moreover,each individual participating in a project team is an expert in hisown field/function and hence they will have more confidence.Prior research has shown that the higher the confidence, thehigher will be the motivation to share their knowledge, but at ahigher level of confidence, psychological safety will have a lowereffect on motivation (Siemsen et al., 2009). Therefor learningbehaviour of the individual may not be influenced by psycholo-gical safety. However, knowledge sharing and the consequentnew team level knowledge creation that may take place inmeetings including discussions or brainstorming sessions arelikely to be influenced by psychological safety. To sum up, theeffect of psychological safety on learning and knowledge transferin a Six Sigma team may differ from other teams.

Another important influencing factor in Six Sigma projectteams is the project resources provided by the management.Resource availability is associated positively with learning, inno-vation and performance (Cohen and Levinthal, 1990; Lawrenceand Dyer, 1983). Only a very few studies (Zhang et al., 2008;Linderman et al., 2010) have focused on the effects of resourceson project performance in process improvement project teams tosupport the claim. Zhang et al. (2008), for example, with plantlevel as unit of analysis, established empirically that projectmanagement infrastructure for process improvement pro-grammes such as Six Sigma and Lean have a significant relation-ship with operational performance. Linderman and his team,using a grounded theory approach have developed a frameworklinking tools and techniques with infrastructure to knowledgecreation (Linderman et al., 2010). The researchers have notempirically tested the model. Hence there is a need for empiricalinvestigation on the impact of resources on learning and in turnon performance at a micro-level analysis such as project level, onwhich the present study focuses.

2.1. Six Sigma, learning and knowledge creation

In Six Sigma project, team brings in different knowledge domainsand the cross-functional team facilitates the flow of information andknowledge across functional boundaries (Gupta and Govindarajan,2000), and develops team-level knowledge. Learning behaviours ofthe Six Sigma project team include discussions within and outsidethe team (Pande et al., 2000; Pyzdek, 2003; Arumugam, 2011;Anand et al., 2010), seeking information and knowledge fromexternal experts in related fields (Arumugam, 2011), critical obser-vation of the process (Arumugam et al., 2012), seeking informationand knowledge from customers and suppliers about the process(Nair et al., 2011; Chakravorty, 2009). Interaction with outsidepeople creates interpretation of knowledge through common cog-nitive schemas and frameworks (Weick, 1979), metaphor andanalogy (Nonaka and Takeuchi, 1995), and stories and narratives(Brown and Duguid, 1991). Research shows that local learning(within the team) enhances team efficiency, and distal learning(learning from outside sources) improves team innovativeness(Wong, 2004); teams that practise both perform better (Anconaand Caldwell, 1992).

In Six Sigma projects, the team questions the fundamentalassumptions and the relationships among variables until the teamestablishes and verifies causal relationships scientifically. Learning

mechanism in such projects may be termed as action learning(Revans, 1985) and double-loop learning (Argyris and Schon, 1978).

Six Sigma promotes deliberate learning (Zollo and Winter,2002) through projects. Deliberate learning refers to learningresulting out of deliberate activities to create, acquire and transferknowledge (Zollo and Winter, 2002), as opposed to experientiallearning which occurs as a result of employees’ accumulation ofexperience through task repetition. Experiential learning is alsoknown as ‘‘first order learning’’ and ‘‘learning-by-doing’’ (Adlerand Clark, 1991). Deliberate learning is known as ‘‘second orderlearning’’ (Adler and Clark, 1991) or ‘‘planned learning’’ and‘‘induced learning’’ (Levy, 1965).

In Six Sigma context, learning is induced by a series ofdeliberate learning activities by the Six Sigma programme. Mem-bers of the project team are given extensive training in problem-solving approaches. The project team brings in different knowl-edge domains and individual members learn from each other andknowledge is shared widely among the members through varioustechniques adopted in DMAIC phases. The challenging improve-ment goals (stretch strategy) for projects motivate organizationalmembers to engage in intentional learning activities that createknowledge and make improvements (Locke and Latham, 1990;Linderman et al., 2003, 2006; Choo, 2011).

The DMAIC approach is an enabling mechanism in organiza-tional learning that fosters learning among members at a fasterrate than they would otherwise because of the short duration ofthe project. The learning and knowledge created are also trans-ferred to other areas where the organization has similar processes.Team members also carry with them their new skills and knowl-edge to other areas where they are assigned with either newprojects or a new job (Hoerl, 2001). Thus, the deployment of SixSigma provides a positive and conducive environment wheredeliberate learning is induced in improvement teams whichimproves members’ ways of using knowledge (Arumugam, 2011;Llorens-Montes and Molina, 2006; Wiklund and Wiklund, 2002;Pande et al., 2000; Linderman et al., 2010).

Our conceptualization of learning and knowledge creation inthis research is different from usual conceptualizations found inearlier research, as it represents the knowledge brought intoprojects through Define and Measure, and Analyze and Improvephases in Six Sigma project. Our conceptualization of knowing-what and knowing-how thus refer to ‘‘input’’ knowledge (learningthat goes into project execution) as opposed to ‘‘outcome’’ knowl-edge (learning that comes out of projects). Past research, forexample, conceptualizes knowledge as an outcome such as newideas, improved understanding and enhanced capability of theproject teams for future work (Choo et al., 2007; Choo, 2011).Similarly, know-what and know-how (and know-why) refer tooutcome from the projects (Kim, 1993; Leonard-Barton, 1990;Mukherjee et al., 1998)

Given that learning behaviours vary depending upon the type ofteam, the nature of the task and the context of the team, earlierresearch in team learning literature conceptualizes different learn-ing mechanisms, such as radical and incremental learning inorganizational teams (Edmondson, 2002), learning-what andlearning-how in hospital teams (Edmondson et al., 2003), localand distal learning in functional and cross-functional teams (Wong,2004), and learn-what and learn-how in hospital process imple-mentation teams (Tucker et al., 2007). Learning that helps anorganization to explore and develop new capabilities is radicallearning, while learning that helps to execute and improve existingcapabilities is known as incremental learning (Edmondson, 2002).For example, learning activities that help in creating new strategies(by top management teams) or developing new products can beradical learning. Learning that helps to execute and improveexisting operations by modifying processes through the DMAIC


method is an example of incremental learning. Incremental learn-ing will result in improved efficiency and reduced cost, and thusimprovement of existing organizational capabilities. Our concep-tualisation of knowing-what and knowing-how falls into incre-mental learning activity category. This conceptualisation is similarto Tucker et al. (2007) study, where learn-what (activities thatidentify current best practices) and learn-how (activities thatoperationalize practices) learning mechanisms were used in thecontext of healthcare process improvement team.

Scholars state that knowing is much more than knowledge. In SixSigma project, the project team converts individual knowledge intocollective team knowledge and uses that new knowledge in actionand as a tool for further knowing. That is, the team uses its new-found knowledge as tools in interaction with the process underinvestigation and one another in an instance of productive inquiry.Improving a process, for example, is not the result of simplyacquiring new or more knowledge by the project team, but theresult of developing innovative ways of using the knowledge(solving problems). This, in the problem-solving context, generatesnew knowledge and new forms of knowing. Here, knowledge refersto what people possess, and knowing is something that is part ofhuman action, referring to the ways team members deploy knowl-edge in their interactions with contexts, including what they do aswell as what they possess (Cook and Brown, 1999). Thus knowing-in-perspective aims to connect both individual and collectivedimensions (Gherardi, 2006; Bouty and Gomez, 2010). Further,knowing is part of dynamic human action and takes place in thedynamic interaction with the world, and hence knowing is adynamic creation (Gherardi, 2006; Nicolini et al., 2003). Ourconceptualization of the two learning behaviours incorporatesinquiry, such as discussions among selves and with knowledgeablepeople, and reflection and taking action. These behaviours make theteam use knowledge in new, innovative and more productive ways.

2.2. Project performance

Six Sigma projects will have different objectives, such as cycletime reduction, cost reduction, efficiency improvement, processcapability enhancement in terms of Sigma level improvement,customer satisfaction improvement, and rejection level reduction.We take an objective-oriented definition for Six Sigma projectssuch as ‘‘to deliver beneficial objectives of change’’ (Turner andMuller, 2003), emphasizing a project’s temporariness and unique-ness (Yu et al., 2005). We define a project as successful if what itcreates adds value to the customer. A beneficial objective in a SixSigma context will be customer satisfaction, cost benefit andstrategic impact for the organization (Pande et al., 2000;Schroeder et al., 2008; Antony and Banuelas, 2002). DMAICfocuses on improving the process from a current level to a new

Table 2Knowing-what activities and outcome.

Activities Outcome

Seek information from customers and

suppliers

Know more about the process, incoming

variations and quality requirements of

the product/services coming out of the

processes

Talk to people with similar project

experience

Gain project experience and learn what

works well in projects

Study similar projects Gain more knowledge on problem

solving approach and use of tools and

techniques

Seek information/knowledge from

outside experts (internal and external

to the organization), get convincing

narratives and histories

Get more process knowledge from

success stories and learn from mistakes

higher level of performance (Goh, 2002; Pande et al., 2000). Thiswill result in the improved quality of the products, enhancedproductivity and cost reduction to the organization. In general,the improvement results in customer satisfaction. Since allprojects are selected based on their impact on the strategicobjectives of the organization (Pande et al., 2000; Pyzdek, 2003),the successes of these projects collectively impact on organiza-tional effectiveness.

2.3. Knowing-what, knowing-how and project performance

By sharing known and acquired knowledge about the processunder investigation, team members deduce unknown conse-quences and come to a broad and collective understanding ofthe entire process, its interfaces, and various influencing factorsthat affect process output. We refer to this learning behaviourthat takes place during the Define and Measure phases of DMAICas knowing-what, parallel to operational learning (Kim, 1993).Various learning behaviours and their effects are shownin Table 2. Teams draw different forms of knowledge from varioussources. In summary, the team attains skills in problem solvingand gains basic understanding of the process under investigation.With knowing-what knowledge, teams explore further to getmore process knowledge with data collection, analysis and dis-cussions during the subsequent phases of DMAIC.

Knowing-what is knowledge about the state of the world anddealing with facts and concepts. Knowing-how is the competenceknowledge that deals with skills, procedures and processes(Kogut and Zander, 1992). Knowing-what is what Deming callsscience of the process, the process task knowledge, and knowing-how is Deming’s profound knowledge, a fundamental knowledgethat is scientific and contributes to the methodological knowledgenecessary for conducting process inquiry that enhances organiza-tional learning (Deming, 1986).

Knowing-how is a broader set of knowledge that the Six Sigmateam acquires during the Analyze, Improve and Control phases ofDMAIC. We define this learning behaviour or capability as acollective learning behaviour of the team in knowing and imple-menting far-reaching adaptations, including modification of pro-cesses for improved outcome. Thus, it includes both the learningbehaviour and the knowledge created by the team. Variousactivities the team undertakes, along with the outcome andoverall effects of those activities and the key references aredisplayed in Table 3.

Knowing-how is effective if the team is capable of carrying outthe project beyond the Measure phase, having acquired a basicknowledge of the process and attained skills in problem solvingthrough knowing-what learning. Thus, knowing-what whichrefers to the knowledge brought through Define and Measure

Overall effects References

Problem-solving skills

improved; more

knowledge of the process

under investigation

Zu et al. (2008); Chakravorty (2009); Anand et al.

(2010); Arumugam (2011); Linderman et al.

(2010); Breyfoglie (1999); Voelpel et al. (2005);

Nair et al. (2011); Gutierrez et al. (2011)

Knowing-how

Performance

Knowing-what

Fig. 1. Mediation model of knowing-what, knowing-how and performance.

Table 3Knowing-how activities and outcome.

Activities Outcome Overall effects References

Carry out critical observation Gain more insight and

understanding of the

process

Creation of team level knowledge on process.

Enhanced process capability and improved

quality of product/service; Increased team

capability; Improved organizational learning.

Pande et al., 2000); Arumugam et al. (2012);

Anand et al. (2010); Harry and Schroeder,

(2000); Breyfogle (1999); Evans and Lindsay

(2005); Henderson and Evans (2000); Brun

(2011); Hoerl, 2001; Chakravorty (2009); Bohn

(1994); Edmondson (1999); Nadler et al.

(2003);Nair et al. (2011)

Use statistical tools to analyze data

and use scientific principles to

understand process behaviour and

relationships among variables

Deduce causal

relationships of all

variables

Discuss and brainstorm to gain more

understanding

Synthesize individual

knowledge into team-

level knowledge

Reflection and action cycle Identify improved

combinations of

variables for enhanced

output and implement

changes


phase (exploitative) affects the project outcome throughknowing-how which is the knowledge gathered through thesubsequent phases-Analyze, Improve and Control (explorative)-of the project. This implies that knowing-how mediates therelationship between knowing-what and performance (Baronand Kenny, 1986). Mediation refers to an indirect effect ofknowing-what on performance through knowing-how. AsLevinthal and March (1993) argue, organizations need to managea balance between explorative and exploitative learning. Simul-taneous application of these components results in internalinnovation (Tushman and O’Reilly, 1996).

Scholars note that shared interpretation of knowledge med-iates the effect of knowledge dissemination on the design ofprocess improvement (Huber, 1982; Nonaka and Takeuchi, 1995;Hult et al., 2004), since shared meaning provides a basis for acommon and focused effort among organizational members(Weick, 1979; Daft and Weick, 1984). Research suggests thatshared interpretation of knowledge among operational personnelmediates how knowledge is disseminated and used to design andimplement a unified operational response to that knowledge(Fugate et al., 2009).

This implies that without the effectiveness of knowing-what,knowing-how cannot include full value creation. The reverse isalso true, and the complementary nature of the two learningmechanisms is evident. An increase in one knowledge processmay not have a positive effect on project performance indepen-dent of the other.

Based on the arguments above, the effects of knowing-what onknowing-how, knowing-what on performance, and knowing-howon performance should be equally positive. Hence:

H1. Knowing-how mediates the relationship between knowing-what and project performance.

Fig. 1 displays our first research framework, showing knowing-how mediating the effect of knowing-what on projectperformance.

2.4. Six Sigma resources

Six Sigma resources include a belt system adopted by organi-zations, rigorous training provided to project leaders and mem-bers, information technology support through Six Sigmainformation systems where project details are documented fortracking and monitoring, and continued support provided by topmanagement teams in executing projects (Antony et al., 2007b;Antony and Banuelas, 2002; Linderman et al., 2010; Pande et al.,

2000; Pyzdek, 2003; Schroeder et al., 2008). The belt systemincludes various levels of specialists depending on the level oftraining they undergo, including Master Black Belts, Black Beltsand Green Belts. Past research into Six Sigma context has shownthat the coaching expertise of Black Belts in a Six Sigma projectteam is positively related to the project performance (Hagen,2010; Arumugam, 2011). This role structure in Six Sigma facil-itates a hierarchical coordination mechanism of work for qualityimprovement across multiple organizational levels, ensuringbetter work design and coordination capability (Sinha and Vande Ven, 2005; Zu et al., 2008; Van den Bosch et al., 1999; Kogutand Zander, 1992). This infrastructure provides an organizationalcontext that enables and ensures organization toward system-atically improving processes (Anand et al., 2009). On the otherhand, lack of coordination and support result in slow progress,and subsequently lead to project failure (Choo et al., 2007; Wruckand Jensen, 1998).

Research shows that differential training at differentlevels helps facilitate improved effectiveness in project teamsdue to knowledge creation (Linderman et al., 2003; Lee andEbrahimpour, 1985; Lapre and Van Wassenhove. (2001). Zu andFredendall (2009) found that employee involvement, training andperformance and recognition have a positive effect on DMAIC. Inrelated research, Zu et al. (2008) found that the Six Sigma rolestructure supports DMAIC, adherence to which promotes activ-ities that meet the project goals, especially challenging goals, andhelp achieve project success (Chakravorty, 2009; Linderman et al.,2006). Further, past research has shown that the adherence tomethods such as DMAIC positively influences learning behaviourin the team (Choo et al., 2007; Anand et al., 2010). Projectdatabase helps the team with many suggestions and ideas forsuccessful execution of their projects and thus knowing-what andknowing-how knowledge (Linderman et al., 2010).

Performance

Knowing-what

Six Sigma resources

Knowing-how

Fig. 2. Mediation model of Six Sigma resources, knowing-what, knowing-how and

Performance.

Knowing-how

Performance


Fig. 3. Mediation model of psychological safety, knowing-how and performance.


Research into the R&D environment shows that resource suchas facilities, equipment and access to information enhance crea-tivity (Amabile and Gryskiewicz, 1987) and project success. Theaforementioned discussion indicated that resource availability/provision is critical for the success of a Six Sigma project usingDMAIC methodology (i.e., knowing-what in the Define andMeasure phases and Knowing-how in the Analyze, Improve andControl phases). Thus resources are seen as indirectly impactingon performance through learning.

Based on these arguments, the effects of resources on bothknowing-what and performance should be equally positive.Furthermore, because of H1, which posits that knowing-whathas a positive influence on performance through knowing-how,we offer:

H2. Knowing-what and knowing-how mediate the effect ofresources on project performance (Fig. 2).

2.5. Psychological safety

There is increasing evidence that social interactions influencethe knowledge process in organizations (Brown and Duguid,1991; Garvin, 1993; Nonaka, 1994; Weick, 1979; Yanow, 2000).Nonaka (1994) emphasizes the importance of socialization whereteam members create new ideas through dialogue. Socializationcapabilities are related to organizational psychology (Bartlett andGhoshal, 1989). If a conducive and comfortable team climatecharacterized by interpersonal trust and mutual respect in whichpeople are comfortable being themselves exists, team membersfeel psychologically safe. This sense of safety, called ‘‘teampsychological safety’’ (Edmondson, 1999), is defined as a sharedbelief that the team is safe for interpersonal risk taking. Whensuch an environment is present in the team, each team memberfeels a sense of confidence and he or she feels the team will notembarrass, reject or punish him or her for speaking up. In suchenvironments, members show interest in exchanging knowledgewith fellow team members, and this enhances the conversion ofindividual knowledge into team-level knowledge. Thus, teampsychological safety influences team learning behaviour. Psycho-logical safety influences the team’s cognitive ability to gain moreinsights and a collective understanding of the problems andissues with the process, enhancing knowledge creation (Chooet al., 2007) and, hence, knowing-how.

Past research has shown that team psychological safety hasimproved team learning behaviour, such as seeking feedback andhelp and speaking up about mistakes in work teams, and in turnteam performance (Edmondson, 1999). In Six Sigma context, Chooet al. (2007) found that psychological safety influences knowledge

creation and, in turn, performance in a project team. A recentstudy by Tucker et al. (2007) undertaken in hospital improvementteams investigated the effect of psychological safety on learn-howactivities that operationalize practices. They found that psycho-logical safety enables learn-how, which leads to concrete changesin work practices.

Edmondson (1999) found that learning behaviour mediatesthe effects of psychological safety on team performance, conclud-ing that team learning behaviour translates effective team designand leadership into team performance. This psychologically safeatmosphere of risk taking encourages exploration and creativity(Amabile et al., 1996); it shows influence on knowing-how, whichwe operationalize to represent knowledge creation, innovativesolution creation and implementation.

We posit that psychological safety influences knowing-howpositively and, in turn, project performance. Thus:

H3. Knowing-how mediates the relationship between psycholo-gical safety and project performance (Fig. 3).

3. Research methods

We conducted a survey and collected data to test the hypoth-eses proposed. This type of research is suggested when knowl-edge of a phenomenon has been articulated in a theoretical formincorporating well-defined concepts, models and propositions(Fowler, 2009; Saunders et al., 2009). In this case, data collectionwas carried out with the specific purpose of testing the adequacyof the concepts developed in relation to the phenomenon ortesting the hypothesized linkages among concepts (Forza, 2002;Malhotra and Grover, 1998).

Our unit of analysis is Six Sigma project teams. Data for thisstudy were collected from a Six Sigma firm, a high-tech engineer-ing company that we call EMFG. EMFG is a Fortune 500 companywith over 40,000 employees worldwide. EMFG has had Six Sigmadeployment for more than four years. EMFG employs DMAICmethodology for operational and transactional projects andDesign for Six Sigma (DfSS) for design-related projects. Weselected all 110 completed projects carried out through theDMAIC process during the last two years in one of the company’smanufacturing business units. Recent projects have minimizedmeasurement error due to the recollection effect. Respondents areproject leaders and at least two team members from each project.Multiple informants minimize common method bias and increasetriangulation and instrument validity.

The three hypotheses were tested using data collected fromSix Sigma project teams from EMFG. Testing in a single firmallows for controls in confounding factors such as variations inproducts, market, customers, technology and organizational cul-ture. These projects carry wide variations in goals, the nature ofthe problems, composition of teams, and application of tools and

Table 4Factor analysis.

Variables Resources Knowing-what

Knowing-how

Availability of Belt System 0.59 �0.11 0.53

Sponsor supporting projects 0.68 0.08 0.53

Database for project documentation 0.74 0.04 0.27

Resources for project execution 0.89 0.29 �0.04

Removal of barriers of project

execution

0.85 0.35 0.03

Information from outside team 0.01 0.87 0.06

Discussion with customers and

suppliers

0.13 0.61 0.37

Talked to Past Six Sigma teams to

learn more

0.19 0.79 0.20

Reflection on process knowledge 0.26 0.69 0.24

Carried out observation 0.34 0.28 0.76Conducted group discussions and

brainstorming

0.43 0.18 0.59

Able to specify the causal variables 0.12 0.28 0.72Change implemented on process

variables

�0.06 0.21 0.79

Variance explained 3.31 2.93 2.65

% Variance explained 25.45 22.56 20.41

Cronbach a 0.87 0.79 0.81

Principal component analysis. Varimax rotation with Kaiser normalization.

Rotation converged in 7 iterations.


techniques; they were expected to show wide variations inlearning and knowledge creation. It is expected that each projectcontained variations in the support and resources received frommanagement, since the type, nature and complexity of eachproject were different.

3.1. Data collection

Data were collected between September and November 2010.A Web-based survey found in similar studies (Anand et al., 2010;Choo et al., 2007) was used. The Six Sigma Deployment Championof EMFG sent personalized e-mails to all members and leaders ofthe 110 project teams along with the survey questionnaire links.Reminder and thank-you e-mails were sent every week until wereceived a satisfactory response rate. Excluding incompleteresponses, we obtained usable replies from 52 projects, a 47.3%response rate. This rate is comparable to similar studies in thequality management literature. In total, 158 people participatedin the survey. Black Belts led 23 projects, and the rest were led byGreen Belts. The number of operational-and transactional-relatedSix Sigma projects conducted were 37 and 15 respectively. In eachproject, the team varied in size from three to nine members. Anestimate of non-response bias was calculated by testing thedifference in variables between early and late respondents(Lambert and Harrington, 1990). A two-sample t-test was per-formed on the average score of all variables. No differences werefound between early and late respondents.

Responses on dimensions of resources, knowing-what,knowing-how and performance were collected from the projectleaders, and responses on psychological safety – a team-levelconstruct – were collected from both project leaders and teammembers (minimum of two team members for each project).Project leaders are trained Black Belts or Green Belts who haveexpertise in tools and techniques and problem-solving methodssuch as DMAIC. Leaders use various practices to gather andsynthesize the knowledge of team members toward achievingproject outcomes (Arumugam, 2011; Anand et al., 2010); they arein a good position to provide information about knowledgecreation practices used by the team during execution of theproject, resources made available and project outcomes. Thestudy looked into the completed projects only.

3.2. Measures

There are differences between learning in new product devel-opment (R&D) environments and process-improvement projectactivity, the focus of the present study. Most of the scalesavailable on learning were created for the former and nothingwas found suitable for this study. In addition, existing scales weredeveloped for organization-level studies where the focus isbroader knowledge management environments, and not suitablefor studies where the focus is team-level activities or processes.Our review found no suitable measures available for Six Sigmaresources, knowing-what and knowing-how dimensions. There-fore, we developed new scales for knowing-what, knowing-howand Six Sigma resources.

Similarly to Tucker et al. (2007) and Mukherjee et al. (1998), wedeveloped all measurement scale items for the two learning beha-viours based on activities that a Six Sigma team performs to learnand create knowledge in process improvement project execution.While developing the scales, we extensively referred to Six Sigma,quality management, and team-learning literature and relatedknowledge management scales developed in extant research.

Academicians and practitioners reviewed the scales for refine-ment, resulting in 14 items for a pilot survey. Thus, content validity –the extent to which a measure spans the domain of the construct’s

theoretical definition – was ensured (Rungtusanatham, 1998;Churchill, 1979).

A pilot survey was conducted with 15 Six Sigma Black Beltsand 5 project team members from 5 Six Sigma organizations,different from the survey organization. These leaders had wideexperience in carrying out Six Sigma projects and participated inSix Sigma deployment in their respective organizations; hence,they were knowledgeable. Based on the pilot study, some itemswere modified for better clarity. We conducted principal compo-nent analysis (PCA) with varimax rotation for the 14 items onresources, knowing-what and knowing-how. This resulted in 13items for these dimensions after removing 1 item based on thecommonly used guidelines of 0.50 for loadings (Hair et al., 1998).Table 4 shows the final items for the three constructs thatcaptured the extent to which the respondents agree using a7-point scale from strongly disagree to strongly agree. Weestimated internal consistency using Cronbach’s alpha for eachscale and found them to be within the acceptable limit (Nunnallyand Bernstein, 1994).

All items for team psychological safety and performance areadapted from validated measures from past studies. The scale forpsychological safety measured the climate of the team character-ized by members’ feeling safe and comfortable with each other.The three items used were adapted from Edmondson’s teampsychological safety construct (1999). These items captured teaminteractions characterized by information and knowledge sharing,open discussions and valuing individual skills and knowledge.

This study investigates the differential success of Six Sigmaprocess improvement projects. Different project objectives war-rant different approaches to defining success criteria. This posesproblems for comparisons across projects. To measure and com-pare project performance across all project types, we used thelevels (between 1 and 7) at which teams achieved the desiredresults against the objectives of the project. These items mea-sured the improvement results from each project. The scalemeasures the extent to which customer satisfaction, cost benefitand strategic impact on the organization were achieved in eachproject. Similar operationalization was used in earlier studies in


quality management literature (Anand et al., 2010; Choo et al.,2007; Linderman et al., 2006). All the three scale items are fromLinderman et al. (2006). Final scale items of all measures aregiven in Appendix A.

Since data on all independent and dependent variables, exceptpsychological safety, were collected from a single respondent(project leader), Harman’s single-factor test was conducted todetect the presence of mono-method bias. In this test, an un-rotated factor analysis is performed on all variable scores (bothdependent and independent) collected from a single respondent.If a single factor emerges or if one general factor explains most ofthe covariance in the independent and dependent variables, it isreasonable to conclude that a significant common or mono-method variance is present. Many studies in operations manage-ment literature use this method (Anand et al., 2010). We carriedout an exploratory factor analysis (EFA) without specifying thenumber of factors on the 16 items. The resulting un-rotatedsolution identified three factors with eigenvalues greater thanone (variance extracted: 42.57%, 13.5% and 10.73%), suggestingthat any mono-method bias that exists is not likely to beproblematic (Podsakoff and Organ, 1986).

4. Analysis and results

We controlled for the size of the team, project duration and SixSigma project experience of the team leader in the analysis sincethese have been found to affect project success in extant research.Since the data on the project experience of the leaders werehighly skewed, they were log transformed for analysis (Anandet al., 2010) and the transformed data were found normal.

We used regression to test all hypotheses. The mean score foreach variable was calculated by averaging the scores of the itemsbelonging to each construct. High correlations among somevariables raised concerns of potential multicollinearity issues, sowe assessed variance inflation factor (VIF) scores. The highest VIFscore within the model was 2.80, well within the threshold valueof 10.0 (Kutner et al., 2005). Table 5 displays descriptive statisticsof all variables used in the study.

Table 6 displays the results of the regression analyses. Allcontrol variables were entered first, followed by other studyvariables for analysis. The mediating role of knowing-how onthe relationship between knowing-what and performance (H1)was tested using Baron and Kenny’s (1986) three-step method:(1) the proposed mediator predicts the dependent variable,(2) the independent variable predicts the mediator, and (3) thecontribution of the independent variable drops substantially forpartial mediation and becomes insignificant for full mediation. Inthis analysis, knowing-what was associated positively with pro-ject success (b¼0.346, std. error¼0.151, po0.05), knowing-whatpredicted knowing-how (mediator) (b¼0.494, std. error¼0.106,

Table 5Descriptive statistics of variables.

Alpha Mean Std. dev 1

1. Team size n/a 5.769 1.516

2. Project duration n/a 6.510 2.336 0.225

3. Leader experience (log) n/a 0.170 0.282 0.162

4. Resources 0.81 5.881 1.062 �0.010

5. Psychological safety 0.82 5.679 0.979 �0.117

6. Knowing-what 0.79 5.154 1.142 0.259

7. Knowing-how 0.78 5.591 0.919 0.047

8. Project performance 0.86 5.635 1.185 0.087

n po0.05.nn po0.01 Sample size 52.

po0.001), knowing-how influenced project success (b¼0.719,std. error¼0.137, po0.001) and the contribution of knowing-what on performance became non-significant (b¼�0. 012, std.error¼0.131, non-significant) when entered into the regressionmodel with knowing-how, which remained significant (b¼0.725,std. error¼0.157, po0.001). These results satisfy the conditionsfor the complete mediation of knowing-what by knowing-how onits effect on performance. Therefore, H1 was supported, but nosignificant direct effect of knowing-what on performance wasfound, which is contrary to our prediction.

Going by the reported adjusted R2 values for M4 and M5 (0.494and 0.483) and R-square values that remain unchanged, it appearsthat knowing-how, by itself (in M4), is a more powerful predictorfor performance than both knowing-what and knowing-how,together (in M5). It is, however, to be noted that the result shouldnot be interpreted as knowing-what being redundant. Rather, thisresult should be seen in the light of our conceptualization of thevariables, with knowing-what being the necessary antecedent toknowing-how.

The second panel (H2) and M2 in Table 6 test hypothesis 2.Resources were associated positively with project success(b¼0.373, std. error¼ .149, po0.01), resources predictedknowing-what (mediator) (b¼0.348, std. error¼ .137, po0.01),knowing-what influenced project success (column M2: b¼0.346,std. error¼0.151, po0.05), and the contribution of resourcesbecame less significant (b¼0.293, std. error¼0.158, po0.05)when entered into the regression model with knowing-what,which remained non-significant (b¼0.230, std. error¼0.157,n.s). These results, in view of the third step of Baron and Kenny’sprocedure (1986), do not satisfy the conditions for the mediationeffect of knowing-what on the relationship between resourcesand performance. However, the last step in the analysis has apositive increase of 0.038 in R2 value (from 0.209 for M6 to 0.247for M8), meaning an increase of 3.8% variance being explained bythe introduction of knowing-what. In view of our conceptualiza-tion of knowing-what affecting the project outcome throughknowing-how, we carried out additional regression analysis totest the combined mediation effect of knowing-what andknowing-how on the effect of resources on performance. In theregression analysis, we used all predictors (resources, knowing-what and knowing-how) without any control variables (to gainmore degrees of freedom). Results are shown in Table 7.

In the model M15, resources and knowing-what were found tobe non-significant (b values were �0.011 and 0.014, respectively)with knowing-how predicting performance (b¼0.727, std.error¼ .165, po0.001); This model was found to be highlysignificant, (F¼18.107, po0.001), with R2 value changedfrom 0.216 (resources and knowing-what as predictors) to0.531(resources, knowing-what and knowing-how as predictors).The results of M12, M13 and M14 (Baron and Kenny, 1986)together with the results of M15 provide support for the

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M12 M13 M14 M15

K-what Per Per Per

Resources .396(.140)nn .393(.145)nn .285(.154)n � .011(.133)

Knowing-what .271(.143)a .014(.121)

Knowing-how .727(.165)nnn

R2 .157 .154 .216 .531

Adj R2 .140 .137 .184 .502

F 9.303nn 9.110nn 6.753nn 18.107nnn

All values are standardized betas; Standard errors are given within parentheses.a po0.1.n po0.05.nn po0.01.nnn po0.001.


mediation effect of both knowing-what and knowing-how.Furthermore, we evaluated the significance of the total indirecteffect of resources on performance by using the approach pro-posed by Preacher and Hayes (2004, 2008) and Hayes (2009),using bootstrap sampling. Brief details and findings are given inAppendix B. We found that the mediation effect by knowing-whatand knowing-how on the relationship between resources andperformance is highly significant (po0.005). Therefore, hypoth-esis H2, which posits that resources influence performancethrough knowing-what and knowing-how, was supported.

The results of H3 are reported in panel 3 (H3) showingknowing-how mediated the effect of psychological safety onproject performance (Table 6). Psychological safety was asso-ciated positively with project success (b¼0.396, std. error¼0.160,po0.01), psychological safety predicted knowing-how (mediator)(b¼0.607, std. error¼0.103, po 0.001), knowing-how influencedproject success (column M4: b¼0.719, std. error¼0.137, po0.001), and the contribution of psychological safety on perfor-mance became non-significant (b¼�0.066, std. error¼0.161,non-significant) when entered into the regression model withknowing-how, which remained significant (b¼0.762, std.error¼0.177, po0.001). These results satisfy the conditions forcomplete mediation of psychological safety by knowing-how onits effect on performance. Hence, H3 was supported.

The control variables (team strength, project duration andleader experience) had no influence on performance.

This study demonstrates that Six Sigma resources are relatedpositively to knowing-what, and psychological safety is related toknowing-how, which, in turn, mediates the effect of knowing-what on project performance.

5. Discussion and implications

This study contributes to both operations management andknowledge management literature by advancing research in teamlearning; it examines the effects of organizational and socialantecedents in process improvement environments through SixSigma perspective. By incorporating organizational behaviour intooperations management contexts, this study responds to calls formultidisciplinary research (Boudreau et al., 2003; Lindermanet al., 2006). Our research builds on earlier theorizing of teamlearning that promotes organizational learning (Senge, 1990;Edmondson, 1999) and extends findings to process improvementteams (Mukherjee et al., 1998; Choo et al., 2007). The findingsprovide an important complement to existing work on perfor-mance capabilities due to team learning such as producing newproducts (Sarin and McDermott, 2003), hospital improvement


processes (Tucker et al., 2007; Pisano et al., 2001; Nembhard andTucker, 2011) and process improvement in quality management(Choo et al., 2007).

The study offers empirical support to prior research in SixSigma process improvement by Linderman et al. (2010) andLlorens-Montes and Molina (2006). Linderman et al.’s (2010)research framework proposes that leadership establishes anorganizational design and culture that provides a foundation tothe improvement infrastructure; technical support and socialsupport collectively lead to knowledge creation. Our studyempirically supports the view that technical (resources) andsocial (in terms of psychological safety) support lead to knowl-edge creation. Quality management practices involve both coreprocesses, focusing on tools and techniques, and infrastructureprocesses, focusing on how the improvement processes arepractised (Flynn et al., 1995). Infrastructure practices such asmanagement support, commitment and motivation create anenvironment supportive of core practices. Existing quality man-agement theory supports the notion that to produce better out-comes from quality management strategies, both core andinfrastructure practices are necessary. Though recent studiessuggest infrastructure practices work through core aspects toproduce improvement (Flynn et al., 1995; Kaynak, 2003; Ho et al.,2001; Rahman and Bullock, 2005; Zu, 2009), others fail to supportthis view (Powel, 1995; Samson and Terziovski, 1999). Themajority of research reports conflicting results on how thesetwo practices affect performance (Sousa and Voss, 2002). Thereason could be an insufficient understanding of the interplaybetween these two practices in determining performance and,more specifically, the mechanisms through which they affectperformance. This study empirically clarifies the notion thattechnical and social practices influence the learning behaviourof people and promote knowledge creation in process improve-ment project teams jointly, which, in turn, enhances performance.

Llorens-Montes and Molina (2006) propose that learning willhave a positive effect on Six Sigma team performance, and ourstudy empirically supports this view by confirming that learningbehaviour in Six Sigma teams does indeed enhance teamperformance.

Leader experience (log) influenced knowing-how (b¼0.240,std. error¼0.356, po0.05) but not knowing-what. This relation-ship was found significant only when psychological safety wasalso present and had a significant relationship (b¼0.607, std.error¼0.103, po0.001) with knowing-how (Table 6, H3). This isconsistent with earlier research that suggests that a project leaderhas influence on knowledge creation through facilitating andcoordinating activities in gathering and synthesizing individualknowledge into team-level knowledge for generating improve-ment solutions in Six Sigma projects (Arumugam, 2011), and forinnovativeness and speed to market in new product developmentprojects (Sarin and McDermott, 2003). This result also supportsthe earlier finding that psychological safety is influenced byleader behaviour (Edmondson, 1999).

This study offers a unique model of how process improvementteams such as Six Sigma promote deliberate organizationallearning through practice. We posit that Six Sigma resourcesfacilitate a learning mechanism that we call knowing-what, andteam psychological safety present in the team facilitates socialinteractions, which, in turn, facilitate another learning mechan-ism that we call knowing-how. Our study demonstrates thatknowing-what enables knowing-how, which leads to betterchanges in work processes by identifying cause and effectrelationships among variables. These mechanisms jointly facil-itate knowledge creation, and enhance team performance. Thisstudy also establishes the empirical distinctions between the twolearning mechanisms. Future research in knowledge management

in process improvement environments should take note of thedistinction. The research model and the measures developed hereare equally applicable to any project teams focusing on processimprovement.

Our study establishes that both resources and psychologicalsafety are important factors for the success of projects. This studyalso finds that their effects are mediated by distinct learningprocesses. The finding that resources influence performancethrough learning processes provides empirical support to earlierresearch that theorized that resources were critical to perfor-mance (Cohen and Levinthal, 1990; Lawrence and Dyer, 1983).The mediation effect found in this study refines the commonbelief that resources in process improvement projects have adirect effect on project performance, by demonstrating thatresources impact on performance indirectly through learningand knowledge creation. We have empirically supported earlierresearch (Linderman et al., 2010) that resources are a factor whichinfluence project performance through learning and knowledgecreation.

Team psychological safety was found to influence knowing-how but not knowing-what. This result is consistent with theearlier finding of Choo et al. (2007) that psychological safetyinfluences knowledge created and not learning behaviour. As ourknowing-how scale captured knowledge creation, innovativesolution creation and implementation, our study also extendsEdmondson’s (1999) and Tucker et al. (2007) findings thatinterpersonal climate influences learning and changes efforts.

Tucker et al. (2007), in their study on hospital improvementteams, did not include the mediation effect of learn-how on thelink between learn-what and performance, even though learn-what construct was conceptualized as an antecedent to learn-howconstruct. We have conceptualized knowing-what as an antece-dent to knowing-how, and our analysis has shown that knowing-how mediates the relationship between knowing-what and per-formance. This suggests that similar conceptualization may behelpful in future studies on hospital process improvement.

Process knowledge is more procedural than declarative,context-specific and embodied in actions and skills (Singley andAnderson, 1989) and hence both knowing-what and knowing-how include tacit dimensions. Though our measurement scales onlearning include items that captured tacit dimensions, our studydid not investigate the interplay between tacit and explicitknowledge (Anand et al., 2010; Linderman et al., 2004) and howthey are affected by resources and psychological safety, whichmay be a potential area for future research. Another interestingarea of research would be the effect of project types and projectcomplexity on the two learning behaviours and performance.

5.1. Managerial implications

The results of the study have broader implications for man-agers and Six Sigma practitioners. Organizations deploy qualityimprovement programs such as Six Sigma with a hope that theseprograms enhance performance and outweigh the investmentmade. The challenge faced by the organization in deploying theseprograms is to ensure that knowledge possessed by the indivi-duals are gathered, synthesized and the new knowledge is createdwhich is applied appropriately for improved performance(Arumugam, 2011; Anand et al., 2009). Our study points out thatboth social support in terms of team psychological safety andresources affect knowledge creation in teams and in turn perfor-mance. Managers, therefore need to ensure better team climate inaddition to project resources to project teams that help knowl-edge sharing and create organizational knowledge which is theprime source of competitive advantage.

Knowing-how


Knowing-what

Six Sigma resources

Performance

Fig. 4. Overall research model.


Prior research has shown that psychological safety is influ-enced by leader behaviour and team composition (Edmondson,1999). Managers therefore must provide proper team composi-tion and an effective project leader to promote better team socialinteraction that will help to enhance performance. Further, ourresearch points out providing resources toward training and otherinfrastructure without providing suitable mechanisms for mana-ging and maintaining knowledge transfer among team memberswill not result in the expected project success. This finding rejectsthe notion that investing in more resources for projects enhancesproject success rate.

5.2. Limitation

Despite the interesting results of this study, several limitationsneed to be mentioned. First, findings cannot be generalized sinceour sample size is only 52 projects, coming from a singleorganization. Although testing the theory in a single organizationdoes help control for confounding factors such as variation incorporate culture and products, it lacks external generalizability.Study of Six Sigma project teams in a single organization and thattoo in one of its business units may have restricted the range ofvariability on variables under study. It is likely, for example, thatthese sample teams emphasize learning more than many otherteams elsewhere given the nature of the projects (i.e., technologyof processes specific to the business unit and complexity of theprocesses). Future studies with samples drawn from diverseorganizations would be desirable. Second, in testing the doublemediation hypothesis (H2), we used the results of additionalregression analyses supported by bootstrapping analysis. Thoughthese analyses helped to logically interpret the findings, morerobust method such as Structural Equation Modelling (SEM) thatcan assess simultaneous effects of all variables, would be appro-priate. Third, a longitudinal investigation would provide furtherinsights into the dynamics of the effects of learning on projectsuccess, since learning and the resulting knowledge creation aretime dependent. Fourth, to prevent the analysis from being toocomplex, we have not included other factors that affect projectsuccess through learning. Future research should include thesefactors, such as structure, leader behaviour, team compositionand learning orientation among others. We have also not includedany unsuccessful projects, the inclusion of which would havegiven us some insight into the effect of project failure on learningmechanisms. This could be an interesting extension for futureresearch.

We have investigated the effects of antecedents and theperformance consequences of learning behaviours using threeseparate mediation models in this research. All these threemodels, however, can be combined into a single model as shownin Fig. 4.

With a large sample of data, future work can replicate thisresearch with other antecedents and contextual factors men-tioned above by using SEM.

6. Conclusion

Six Sigma is an evolving phenomenon that has attracted bothacademics and practitioners. It was the aim of this paper throughempirical investigation to bring further insights on Six Sigmathrough learning and knowledge creation in project teams, con-tribute to theory development, and offer implications for practi-tioners. First, this study extends theory and research onorganizational learning in quality improvement projects byexamining the contribution of deliberate learning in Six Sigmaprojects. Second, the study provides empirical support to the

existing literature that focuses knowledge creation in Six Sigmateams. Finally, by investigating the effect of two antecedents,resources and team psychological safety on learning and knowl-edge creation in Six Sigma project teams, the present studyempirically clarifies to the managers that technical support tothe project team and social practices in the team jointly promoteknowledge creation in project teams and in turn impact onproject performance. The findings enrich our understanding ofteam learning in Six Sigma project teams.

Acknowledgements

We are most grateful to two anonymous reviewers for theirconstructive and insightful suggestions and comments at variousstages of this manuscript which helped to improve the presenta-tion of the paper considerably. We appreciate the contributions ofthe company whose participation made this research possible.

Appendix A. Measurement scales

All responses range from 1(strongly disagree) to 7(stronglyagree).

Six Sigma resources (a¼0.87)

1.
The Belt system was available for consultation 2. Our project sponsor was supportive throughout the project 3. We have systems/databases where we documented our pro-
ject details
4. Management team provided necessary support for project
execution
5. Management team helped to remove any barriers in project
execution
6. We have a hierarchy of expertise like MBB, BB, GB or equivalent
Knowing-what (a¼0.79)

1.
We collected information from industry friends outsideour team
2.
We talked with customers and suppliers about our processes 3. We talked to people having similar experiences in Six Sigma
projects to find out what has worked well
4. We reflected on our understanding of the process


Knowing-how (a¼0.81)

1.
We carried out observations to understand the process better 2. We had a number of group discussions to get new information
and new ideas
3. We were able to specify the impact of causal variables on
project outcome
4. Degree of change implemented was greater on process
variables

Psychological safety (a¼0.82), adapted from Edmondson (1999)

1.
Members of the team were able to discuss problems and toughissues openly
2.
Members of the team accepted each other’s differences 3. Working with members of this team, my unique skills and
talents were valued and utilized

Project performance (a¼0.86), from Linderman et al. (2006)

1.
We met or exceeded customers’ expectations in this project 2. The cost savings or strategic impact of the project were
significant
3. The team had superb results with the project
Note: Italicized item was removed based on the results of scalevalidity analysis

Appendix B. Bootstrapping

Simulation research has shown that bootstrapping is one of themost powerful methods for testing mediating effects, and growingliterature recommends the use of bootstrapping for assessing indirecteffects (Preacher and Hayes, 2008; MacKinnon et al., 2004; Williamsand MacKinnon, 2008). In bootstrapping, the initial sample isconceptualized as a pseudo-population representing the populationfrom which the sample was derived. By making use of numeroussamples (usually 1,000 or 5,000 resamplings are recommended)

Knowing-what

Knowing-how

Six Sigma resources

Performance

c’

b1

a3

a1 a2

b2

Fig. B1. Multiple mediation model.

Table B1Mediation of the effect of resources on project performance through kn

Mediator Point estimate S.E.

Knowing-what 0.1204 0.077

Knowing-what and knowing-how 0.1640 0.082

S.E: Standard error; BC: Bias corrected; CI: Confidence interval ; 1,000

drawn from the initial sample, the bootstrapping method estimatestest statistics such as standard error and confidence interval for thesample. An empirical approximation of the sampling distribution ofan indirect effect is built and used to estimate the confidence interval(CI) for the indirect effect. Using bootstrapping, no assumptions aboutthe shape of the sampling distribution of the statistics (such asnormality) are necessary while making any inferences (Stine, 1989).

Fig. B1 shows a schematic representation of the doublemediation effect of resources on performance by knowing-whatand knowing-how. The figure shows the direct effect of resourceson performance (path c’) and the specific indirect effects ofresources on performance via the two mediators. In the figure,a1 refers to the (unstandardized) slope coefficient of knowing-what regressed on resources, a2 refers to the slope coefficient ofknowing-how regressed on resources, and a3 refers to the slopecoefficient of knowing-how regressed on knowing-what; b1 andb2 refer to the conditional coefficients of performance regressedon knowing-what and knowing-how respectively, when bothare included as simultaneous predictors of project perfor-mance. The specific indirect effect of resources on performancethrough knowing-what is quantified as (a1n b1), and the specificindirect effect of knowing-what and knowing-how is quantifiedas (a1n a3n b2).

Using regression analyses, regression coefficients (a1, a2, a3, b1 andb2) are found. Then 1,000 bootstrapping estimates are found for thesecoefficients using SPSS syntax. Indirect effects through knowing-what(a1n b1) are calculated for these 1,000 resamplings. The distribution ofthese 1,000 estimates serves as empirical, non-parametric approx-imations of the sampling distributions of the indirect effect ofknowing-what. An inference is made about the size of the indirecteffect in the population by using these estimates to generate a CI%confidence interval. This is done by sorting the 1,000 values of theindirect effects from the lowest to the highest. In this ordered set, fora 95% confidence interval, for example, the lower bound of confidenceinterval is defined as the value of (a1n b1) in the 1,000(.5-CI/200)th,which is the 25th position, and the upper bound is the value in the1þ1000(.5þCI/200)th, which is the 976th position. This procedureyields a percentile bootstrap confidence interval. The percentile boot-strap CI can be improved by calculating bias-corrected (BC) intervals,where the end points are adjusted to obtain a bias-correctedconfidence interval (Efron, 1987; Stine, 1989). A similar procedureis used to estimate the CI for the indirect effects of knowing-what andknowing-how (a1n a3n b2).

If zero is not found between the lower and upper bound, thenthe indirect effect is not zero with CI% confidence. That isrejecting the null hypothesis that the true indirect effect is zeroat the 100-CI% level of significance.

Table B1 displays the bootstrapping output. Estimates for thespecific indirect effects of knowing-what, (a1n b1) ¼0.1204, andthose of knowing-what and knowing-how, (a1n a3nb2) ¼ 0.1640,are given along with the percentile CI and bias-corrected CI. Fromthe table it is evident that the combined mediation throughknowing-what and knowing-how is significant at po0.005.

owing-what and knowing-how.

Bootstrapping

Percentile 99.5% CI BC 99.5% CI

Lower Upper Lower Upper

7 �0.0135 0.3945 �0.0112 0.4174

3 0.0157 0.4409 0.0253 0.4829

Bootstrapping samples.


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Linking learning and knowledge creation to project success in Six Sigma projects: An empirical...

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