bjom12038_relationships between design characteristics and outcomes of strategy workshops

Off to Plan or Out to Lunch Relationshipsbetween Design Characteristics andOutcomes of Strategy Workshops

Mark P Healey Gerard P Hodgkinson1 Richard Whittington2 andGerry Johnson3

Manchester Business School University of Manchester Manchester M13 9QH UK 1Warwick BusinessSchool University of Warwick Coventry CV4 7AL UK 2Saiumld Business School Park End Street

Oxford OX1 1HP UK and 3Lancaster University Management School Lancaster UniversityLancaster LA1 4YX UK

Email markhealeymbsacuk

Strategy workshops also known as away days strategy retreats and strategic lsquooff-sitesrsquohave become widespread in organizations However there is a shortage of theory andevidence concerning the outcomes of these events and the factors that contribute to theireffectiveness Adopting a design science approach in this paper we propose and test amultidimensional model that differentiates the effects of strategy workshops in terms oforganizational interpersonal and cognitive outcomes Analysing survey data on over 650workshops we demonstrate that varying combinations of four basic design characteris-tics ndash clarity of goals and purpose routinization stakeholder involvement and cognitiveeffort ndash predict differentially these three distinct types of outcomes Calling into questionconventional wisdom on the design of workshops we discuss the implications of ourfindings for integrating further the strategy process strategy-as-practice and strategiccognition literatures to enrich understanding of the factors that shape the nature andinfluence of contemporary strategic planning activities more generally

Introduction

Strategy workshops ndash also known as strategyaway-days strategy retreats and strategic lsquooff-sitesrsquo ndash are a common practice in organizations Inthe UK nearly four out of five organizations useworkshops for strategizing (Hodgkinson et al2006) and they are part of the executive calendarin both the USA (Frisch and Chandler 2006) andmainland Europe (Mezias Grinyer and Guth2001) Carrying high expectations for influencingstrategy formulation and implementation theyrepresent significant resource investments

In response to calls to reinvigorate research intothe activities and practices of contemporary strat-egy making of all forms (Jarzabkowski 2003Jarzabkowski and Balogun 2009 Johnson Melin

Earlier versions of this paper were presented at the 2010Annual Meeting of the Academy of Management (Mon-treal Canada) and the 2008 Strategy Practices andOrganizations Strategy-as-Practice Symposium (AstonBusiness School Aston UK) We gratefully acknowl-edge the financial support of the UK ESRCEPSRCAdvanced Institute of Management Research undergrant numbers RES-331-25-0028 (Hodgkinson) andRES-331-25-0015 (Johnson) and the Millman ResearchFund (Whittington) We are also grateful to the Char-tered Management Institute for their assistance with thedata collectionA free Video Abstract to accompany this article is avail-able at httponlinelibrarywileycomjournal101111(ISSN)1467-8551Correction Note This article was first published onlineon the 9th of September 2013 under a subscription pub-lication licence The article has since been made Online-Open and the copyright line and licence statement wastherefore updated in June 2014

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British Journal of Management Vol bullbull bullbullndashbullbull (2013)DOI 1011111467-855112038

copy 2013 The Authors British Journal of Management published by John Wiley amp Sons Ltd on behalf of BritishAcademy of Management Published by John Wiley amp Sons Ltd 9600 Garsington Road Oxford OX4 2DQ UK and350 Main Street Malden MA 02148 USAThis is an open access article under the terms of the Creative Commons Attribution License which permits usedistribution and reproduction in any medium provided the original work is properly cited

and Whittington 2003 Johnson et al 2007Whittington 1996) workshops are receivingincreased attention from management scholars(Jarratt and Stiles 2010 Jarzabkowski and Spee2009 Jarzabkowski Balogun and Seidl 2007Johnson et al 2010 MacIntosh MacLean andSeidl 2010 Whittington et al 2006) Descriptivedata show that workshops are seen as integral tothe strategic planning process are largely the pre-serve of top-level managers and are undertakenfor various purposes from creating space toreflect on current strategies to stimulating debatesabout the future and tackling organizationaldevelopment needs (Hodgkinson et al 2006)

From a theoretical standpoint workshops areof particular interest because they represent animportant type of lsquostrategic episodersquo (Hendry andSeidl 2003) That is these events provide a rareopportunity to suspend normal structures toreflect on current policies and engage in new stra-tegic conversations Currently however there islittle systematic theory or evidence linking thestructure and conduct of workshops to their effec-tiveness Accordingly in this paper we examinethe critical success factors associated with work-shops introducing a design-based theory of strat-egy workshop effectiveness to understand betterhow these events impact upon organizations Inso doing we address three issues in the growingliterature on workshops as a key type of strategicepisode

First the few empirical studies of workshopspublished to date have tended to use small-scalecase-based methods (Bowman 1995 MacIntoshMacLean and Seidl 2010 Mezias Grinyer andGuth 2001 Whittington et al 2006) Forinstance Hodgkinson and Wrightrsquos (2002) caseanalysis of scenario planning practices centred ona single workshop-based intervention SimilarlyJohnson et alrsquos (2010) study of workshops asritual was based on cases in just four organiza-tions As Huff Neyer and Moslein (2010) haveobserved of research on strategy practices ingeneral a heavy reliance on small-scale ethno-graphic methods has produced a somewhatnarrow evidence base These authors suggestedanalysing larger data sets to widen the breadth ofinformation on strategy practices and increase thegeneralizability of findings Heeding this advicewe report results from a large-scale field survey ofover 650 workshops conducted across a range ofsettings

Second studies of workshops to date construethe outcomes of these events in a largely undif-ferentiated manner evaluating effectiveness interms of the eventrsquos contribution to strategic con-tinuity andor strategic change (Jarzabkowski2003 Whittington et al 2006) Although suchorganizational-level outcomes are an importantpart of the effects of workshops they do not tellthe whole story In this paper we extend strategyprocess research (Grant 2003 Ketokivi andCastaner 2004 Mintzberg 1994) to suggest thatthe benefits of workshops also lie in people-related or interpersonal outcomes Additionallywe posit that there is an important cognitivedimension to workshop outcomes given the roleof intervention techniques in enhancing strategicthinking (Bowman 1995 Grinyer 2000Hodgkinson and Healey 2008) Hence we offer amore nuanced view of the impact of workshops bydistinguishing theoretically and empiricallybetween three distinct types of outcome minus organi-zational interpersonal and cognitive

Third the extant literature considers only anarrow range of factors that influence workshopeffectiveness often restricted to the behaviours offacilitators or influential individuals (Hodgkinsonand Wright 2002 Whittington et al 2006) Hith-erto no study has examined comprehensivelyhow basic design features relate to workshop out-comes although there have been calls for suchwork (Hendry and Seidl 2003 Jarzabkowski andSpee 2009) Studies that have looked explicitly atdesign issues have focused on a limited set of fea-tures (Johnson et al 2010 MacIntosh MacLeanand Seidl 2010) Extending this line of inquiry weadopt a design science approach to develop andtest a series of hypotheses that link systematicallya range of workshop design characteristics (egthe extent of preparation the variety of stake-holders involved the analytical tools adopted) tothe various outcomes alluded to above

Although design characteristics influence theeffectiveness of all workgroup practices includingthose in the upper echelons of organizations(Cohen and Bailey 1997) the embryonic litera-ture on workshops (and indeed strategic episodesmore generally) provides little detailed guidanceon which design features are important or howthey are important Accordingly we turn tovarious additional literatures to posit multiplegenerative mechanisms that contribute to work-shop (in)effectiveness

2 M P Healey et al

copy 2013 The Authors British Journal of Management published by John Wiley amp Sons Ltd on behalf of BritishAcademy of Management

In design science research it is appropriate anddesirable to draw on a range of theories to gener-ate and test hypotheses that substantiate designprinciples (van Aken 2004 2005 Hodgkinsonand Healey 2008 Hodgkinson and Starkey 20112012 Pandza and Thorpe 2010) Groundingdesign propositions in generative mechanismsunderpinned by robust social science theoryincreases their efficacy (Dunbar and Starbuck2006 Romme and Endenburg 2006 Simon1969) Based on this logic we ground our hypoth-eses in various bodies of theory from work moti-vation and ritual theory to managerial andorganizational cognition

Adopting a design approach enables us tospeak directly to outstanding questions posed byHendry and Seidlrsquos (2003 p 194) social systemstheory concerning the effectiveness of a given stra-tegic episode such as lsquowho should participate whether or by whom it should be facilitated orwhat provision should be made in advance foraddressing its outcomesrsquo Given the limitedcurrent evidence base identifying the design char-acteristics that yield positive outcomes (egchanges to the business plan enhanced interper-sonal relations improved strategic understand-ing) and mitigate negative ones (eg interpersonalconflict strategic inertia) should provide firmerfoundations for future design activity (cfChristensen 1997 Frisch and Chandler 2006)Figure 1 provides a visual representation of ourhypotheses linking various design characteristicsto workshop outcomes next we explain our con-

ceptualization of outcomes before presenting thearguments underpinning the hypotheses

Conceptualizing the outcomes ofstrategy workshops

Workshops are often criticized because of a basicconfusion about what these events are tryingto achieve (Johnson et al 2010 MacIntoshMacLean and Seidl 2010) In this section weposit that workshop outcomes fall into the threetypes enumerated above We derive this three-foldclassification from a conceptual analysis of theliterature on workshops and related strategic epi-sodes supplementing this where necessary withinsights from strategy process and strategic cog-nition research

Organizational outcomes

We define organizational outcomes as the impactof workshops on the organizationrsquos strategicdirection including its vision values espousedstrategy business plan and attendant businessprocesses Hence in this context organizationaloutcomes concern actual changes to the organiza-tion and its direction that are distinct fromfinancial performance outcomes This definitionfits with evidence that workshops and relatedpractices can either bolster strategic continuityor alternatively stimulate strategic change(Jarzabkowski 2003 Whittington et al 2006)Indeed attaining such ends is the espousedpurpose of many workshops (Johnson et al2010) For example Lorsch and Clark (2008)observed how board retreats at Philips Electron-ics helped directors decide to forgo their dwin-dling position in the semiconductor market andconcentrate on the growing health technologymarket Other accounts suggest that outcomes ofthis magnitude are exceptional many off-sitesleave little lasting impression on the organization(Bourque and Johnson 2008 Frisch andChandler 2006 Mintzberg 1994)

Anecdotal evidence suggests that where work-shops do influence firmsrsquo strategic direction this isbecause the formal event provides a rare forumfor examining and changing strategy content ndash forexample refining the organizationrsquos goals ormission adjusting its strategic plan or communi-cating a new vision (Campbell Liteman andSugar 2003 Fahey and Christensen 1986 Ready

Figure 1 Theorized model of strategy workshop design charac-teristics and outcomes

Design Characteristics and Outcomes of Strategy Workshops 3


and Conger 2008) Returning to the Philipsexample it was lsquoopen and frank discussionsrsquo con-cerning the lsquolong-term logicrsquo of the business thatstimulated the decision to switch strategic focus(Lorsch and Clark 2008 p 110) At other timesthe aim is to bolster commitment to the status quoor maintain an existing imperative For exampleWhittington and colleagues (2006) observed howthe chief executive of a large charity used work-shops to bolster support for her plan to centralizecontrol Although workshops may fail to influ-ence wider organizational strategizing the extentto which they do exert such influence as reflectedin noticeable impact on strategy content is thus akey indicator of effectiveness

Interpersonal outcomes

Strategy workshops are often instigated withpeople-related outcomes in mind such asteam-building and organizational development(Campbell Liteman and Sugar 2003 Frisch andChandler 2006 Hodgkinson et al 2006) Henceour second indicator of workshop effectivenessconcerns the interpersonal outcomes obtainedwhich we define as potential impact on relationsamong key actors We maintain that workshopscan exert a direct impact on relations among thoseexecutives managers and employees involved inthe formal proceedings

First bringing together individuals to collabo-rate on common issues facilitates interpersonalcontact building a shared sense of purpose andidentity that fosters cohesion (Anson Bostromand Wynne 1995 Hodgkinson and Healey 2008Hogg and Terry 2000) conversely managers maysuffer disengagement if a workshop brings to lightirreconcilable differences within the executiveteam (Hodgkinson and Wright 2002)

Second involvement in planning can instil ashared feeling of organizational appreciation(Ketokivi and Castaner 2004) which fostersbehavioural integration (see also Kim andMauborgne 1993 Wooldridge Schmid andFloyd 2008) From both a processual(Hutzschenreuter and Kleindienst 2006) andstrategy-as-practice (Johnson et al 2010) perspec-tive the benefits of planning reside as much in suchlsquosoftrsquo outcomes as in performance-focused out-comes Highly ritualized workshops in particularpromote lsquocommunitasrsquo or group bonding at leastwithin the workshop event (Johnson et al 2010)

Cognitive outcomes

The third type of workshop outcome we identifyconcerns the potential impact on participantsrsquounderstanding of strategic issues which we termcognitive outcomes This includes understandingof the organizationrsquos strategic position and direc-tion the strategic issues it faces and the widerbusiness environment

Workshops are commonly viewed as a way oftaking decision makers beyond their day-to-dayconcerns to participate in higher-level debates thegoal being to stimulate creativity and enhancelsquoblue skiesrsquo thinking (Hodgkinson and Healey2008 Hodgkinson et al 2006) According toBowman (1995 p 6) the goal of many workshopsis to lsquosurface the intuitive core of beliefs which isframing and constraining strategic debatersquo Simi-larly Grinyer (2000) outlines how firms use work-shops to reveal and challenge top managersrsquoimplicit assumptions ndash embedded in schemasdominant logics and other knowledge structures ndashthereby overcoming cognitive inertia the over-reliance on outmoded mental models of thefirmrsquos strategic situation (Barr Stimpert andHuff 1992 Hodgkinson and Wright 2002)Through formal analysis externalization andinformation exchange workshops can help refineparticipantsrsquo understanding of key strategic issuessuch as who the organizationrsquos competitors arehow products and services are contributing tocompetitiveness and the robustness of futureplans to industry prospects (Frisch and Chandler2006 van der Heijden 1996)

Although it is plausible that cognitive andorganizational outcomes are related we assumehere that they constitute distinct effects Forinstance a workshop might influence how man-agers think about their strategy (a cognitiveoutcome) but not produce direct changes to thestrategic plan or business activities (organiza-tional outcomes) Moreover organizational out-comes concern effects on realized strategy thatmay only be noticeable some time after theformal event In contrast both cognitive andinterpersonal outcomes constitute more immedi-ate effects ie those experienced within or soonafter the event Having delineated the differenttypes of outcomes the next section providesthe theoretical rationale for the hypothesizedlinks with the design characteristics shown inFigure 1

4 M P Healey et al


Hypothesized design characteristics aspredictors of workshop outcomesClarity of goals and purpose

Goal clarity Anecdotal evidence suggests thatoff-sites frequently fail because designers do notunderstand the required outcomes they thusneglect to restrict the scope of discussions whichleaves participants unclear about what to focusupon or how to progress (Frisch and Chandler2006 Hendry and Seidl 2003 Johnson et al2010) Goal setting theory (Locke and Latham1990) one of the most extensively validated theo-ries of work motivation emphasizes that havingclear goals at the outset of any group task is vitalfor focusing effort on desired outcomes energizingparticipants and maintaining persistence (for areview see Latham and Pinder 2005) At the grouplevel setting clear goals improves performance bydeveloping collective identity building cohesionand facilitating constructive debate and consensus(Kerr and Tindale 2004 OrsquoLeary-KellyMartocchio and Frink 1994) Clear goals arelikely to be particularly critical in strategy work-shops where the presence of individuals withdiverse backgrounds and interests can militateagainst focus Grinyer (2000) for instance high-lights the importance of lsquosetting the framersquo ndash com-municating the goals rules and boundaryconditions ndash for attaining cognitive outcomes fromstrategic interventions Although the motivationaleffects of goal clarity are well understood in thewider literature validating their specific influenceon workshop outcomes is important for establish-ing robust design propositions in the context ofstrategizing Given the powerful evidence baseconcerning goal clarity we predict

H1 The clearer the workshop objectives themore positive the perceived organizational out-comes interpersonal outcomes and cognitiveoutcomes

Purpose and type of workshops As notedabove organizations undertake workshops for avariety of espoused purposes but mainly to facili-tate strategy formulation or implementation(Hodgkinson et al 2006) Although workshopsmay also serve implicit purposes such as legitima-tion (Langley 1989) it is reasonable to expectthat events convened to formulate strategy willentail different approaches to and yield different

outcomes from those convened for purposes ofimplementation

Consider first workshops designed for for-mulation These events often entail the use ofthought-provoking exercises and analytical toolsndash analysing industry trends brainstorming prob-lems stimulating lsquoblue skiesrsquo thinking (Johnsonet al 2010) ndash designed to help attendees makesense of particular strategic issues or generate newideas (ie cognitive outcomes) When seeking tostimulate such open debate designers typicallyinvolve attendees from varied backgrounds withdiverse perspectives the ultimate goal being toenrich their mental models (Grinyer 2000 vander Heijden et al 2002) In contrast the purposeof workshops designed for implementation pur-poses is often to close down debate and keepparticipants grounded (Johnson et al 2010)focusing activities on delivering actual changestoward a particular strategic direction (ie organi-zational outcomes) Such events are designed tobuild strategic consensus and commitment to spe-cific courses of action (Whittington et al 2006)In this sense broadened thinking is the antithesisof implementation workshops Related evidenceshows that interventions designed for consensusbuilding yield inferior decision outcomes relativeto those designed to stimulate debate (SchweigerSandberg and Ragan 1986 Schweiger Sandbergand Rechner 1989) Hence

H2a Workshops undertaken for the purposesof strategy formulation will be associated withcognitive outcomes that are perceived morepositively relative to workshops undertaken forimplementation purposes

A potential difficulty concerning formulation-focused workshops is that participants may seethem as having failed to deliver tangible benefits(Hodgkinson et al 2006 Johnson et al 2010)Specifically because such events focus on abstractcognitive outcomes such as broadening partici-pantsrsquo assumptions they may fall short of directlyinfluencing the organizationrsquos formal strategy oractual strategic routines Workshops designed spe-cifically for implementation however may stand agreater chance of attaining tangible outcomes Tothe extent that such workshops are more actionoriented they are more likely to yield substantiveorganizational effects than events arranged simplyto generate ideas Convening groups specificallyfor implementation provides members with a com-



pelling mission which sustains energy toward con-crete goals (Higgins Weiner and Young 2012)Consistent with this logic Whittington et al(2006) observed that in workshops designed toachieve buy-in facilitators controlled debate andbuilt consensus around particular courses ofaction thereby encouraging participants to acceptand respond to specific strategic imperatives Ifparticipants in implementation-oriented work-shops internalize and act upon the imperatives athand such events are more likely to influencewider strategizing Therefore we predict

H2b Workshops undertaken for the purposesof strategy implementation will be associatedwith organizational outcomes that are per-ceived more positively relative to those under-taken for formulation purposes

Routinization

Removal Commentators often emphasize theimportance of workshop design features thatfoster innovation by breaking away from the con-fines of everyday organizational routines (egEden and Ackermann 1998 van der Heijdenet al 2002) Such devices include using externalfacilitators staging events away from the regularworkplace disengaging from standard operatingprocedures and using novel analytical tech-niques These hallmarks of the strategy workshopmake sense in the light of Doz and Prahaladrsquos(1987 p 75) view that strategic change requireslsquostepping out of the existing management processndash since these processes are set to sustain the ldquooldrdquocognitive perspectiversquo Creativity research simi-larly suggests that being away from routine workto engage in new experiences in an environmentremoved from the prevailing pressures can restorecognitive capacity (Elsbach and Hargadon 2006Kaplan and Kaplan 1982)

Johnson and colleagues (Bourque and Johnson2008 Johnson et al 2010) however provide adifferent view of removal Adopting an anthropo-logical perspective they maintain that the abovedesign features engender a form of lsquoprivilegedremovalrsquo characteristic of social rituals more gen-erally a key element of which is the separation ofthe event from everyday practices From this per-spective removal features enhance the uniquenessof workshops imbuing them with ritualisticmeaning in their own right Hence although

removal devices might help to open up strategicthinking they might also create difficulties whenseeking to reconnect with the practical realitiesconfronting the organization at the end of theformal proceedings (see also Hendry and Seidl2003) thus reducing the likelihood of attainingsubstantive organizational outcomes (eg changesto the enduring strategic plan) We thus propose

H3a The greater the degree of workshopremoval the more positive the perceived cogni-tive outcomes

H3b The greater the degree of workshopremoval the less positive the perceived organi-zational outcomes

Serialization If removal fosters disconnectionthen integrating workshops with regular strategyprocesses should help realize tangible outcomesRemoval is likely to be particularly high forstand-alone events which may appear as merenovelties For instance the lsquoannual strategyretreatrsquo is typically a highly ritualized event thatexhibits mass displacement from the social struc-tures underpinning routine strategizing (Bourqueand Johnson 2008 Johnson et al 2010) Forsuch one-off events ideas and agreements formedwithin the confines of the workshop often fail totranslate into subsequent action In contrast weargue that serialization ndash returning to ideas andcommitments over a series of episodes ndash is likelyto embed understanding more deeply in manag-ersrsquo collective consciousness Evidence shows thatrepeating analytical activities and revisitingdebates enhances the amount of time and energyfocused on strategic issues which increases thelikelihood of learning and builds momentumtowards chosen courses of action (Fiol 1994Lant and Hewlin 2002) Furthermore socialsystems theory suggests that strategic episodesthat are more frequent acquire their own struc-tures and legitimacy thus becoming recognizedmeans of lsquogetting strategy work donersquo (Hendryand Seidl 2003 MacIntosh MacLean and Seidl2010) We thus predict

H3c Workshops organized as part of a series ofevents will be associated with cognitive out-comes that are perceived more positively rela-tive to workshops held as one-off events

H3d Workshops organized as part of a series ofevents will be associated with organizational

6 M P Healey et al


outcomes that are perceived more positivelyrelative to workshops held as one-off events

Stakeholder involvement and participation

The opportunity for diverse stakeholders toaddress collectively strategic issues ndash as observedoften in workshops ndash may be rare in organizationallife (Johnson et al 2010 Lorsch and Clark 2008)Building on evidence regarding the benefits ofinclusiveness in strategy processes (Floyd andLane 2000 Westley 1990 Wooldridge and Floyd1990) we theorize that workshops designed forwider participation will yield positive interper-sonal outcomes for three reasons First researchshows that involving stakeholders other than topmanagement (eg middle managers) in strategiz-ing creates a collective sense of ownership fairnessand commitment (Kim and Mauborgne 1993Korsgaard Sapienza and Schweiger 2002)whereas their omission can cause alienation andconflict (Wooldridge Schmid and Floyd 2008)Second bringing together disparate stakeholdersin a forum designed to develop collective solutionsresults in shared identities in turn fostering socialcohesion (Gaertner et al 1990 Hodgkinson andHealey 2008 van Knippenberg De Dreu andHoman 2004) Third events that enable diverseparticipants to understand and empathize witheach otherrsquos views a process known as perspectivetaking strengthens social bonds (Galinsky Kuand Wang 2005) Hence

H4a The greater the range of stakeholdergroups involved in workshops the more posi-tive the perceived interpersonal outcomes

Notwithstanding the above arguments it islikely that as the overall number of individualworkshop participants exceeds an optimum pointthe quality of debate and information exchangewill deteriorate With increased numbers of par-ticipants seeking to contribute to group activitiesthe diversity of perspectives and agendas airedbecomes unmanageable heightening task andinterpersonal conflict (Amason 1996 Amasonand Sapienza 1997 De Dreu and Weingart2003) We thus predict

H4b There is a curvilinear relationship betweenthe size of the workshop group and perceivedinterpersonal outcomes such that interpersonal

outcomes will be more positive for groups ofintermediate size relative to small and largegroups

Cognitive effort

A common goal in workshops directed towardstimulating change is to challenge andor enrichdecision makersrsquo understanding of strategic issues(Eden and Ackermann 1998 Grinyer 2000 vander Heijden et al 2002 Mezias Grinyer andGuth 2001) From a cognitive standpoint work-shops seek to move participants out of routinemodes of thinking and into more effortful delib-erations thereby challenging prevailing mentalmodels (Hodgkinson and Clarke 2007 Reger andPalmer 1996) In particular engaging more fullywith focal strategic issues prior to the workshopshould enable deeper and broader informationprocessing during the event In addition work-shops of greater duration allowing for a greaterrange of activities more detailed discussion andgreater information sharing are likely to fosterricher debate which in turn should yield greaterunderstanding (van Knippenberg De Dreu andHoman 2004 Schweiger Sandberg and Ragan1986) Although workshops of extreme durationmight lead to fatigue thereby undermining posi-tive outcomes events of such length are rare(Hodgkinson et al 2006) Hence

H5a The greater the degree of preparation forworkshops the more positive the perceived cog-nitive outcomes

H5b The greater the duration of workshopsthe more positive the perceived cognitiveoutcomes

Several writers suggest that the analytical toolsemployed in workshops can help to update man-agersrsquo mental models of the strategic situation(Eden and Ackermann 1998 Hodgkinson andHealey 2008 Jarratt and Stiles 2010 MeziasGrinyer and Guth 2001) Such tools provide ameans of organizing complex information con-cerning the organization (eg core competencesanalysis) and its external environment (eg fiveforces analysis) We theorize that employing arange of analytical tools should improve cognitiveoutcomes through two mechanisms First using adiversity of tools can help participants to synthe-size information from multiple perspectives



thereby enhancing the quality of deliberation(Schweiger Sandberg and Ragan 1986Schweiger Sandberg and Rechner 1989 WrightParoutis and Blettner 2013) Second expandingthe range of tools should augment the degree ofcognitive effort expended enabling participants toelaborate their understanding of strategic issuesHence

H5c The greater the range of analytical toolsdeployed in workshops the more positive theperceived cognitive outcomes

In seeking to enhance cognitive outcomes thenature of the tools used is a further considerationIn line with dual-process models of cognition (egLouis and Sutton 1991) certain strategy tools canexert pronounced cognitive effects by shiftingusers out of automatic thinking and into moreeffortful forms of information processing(Hodgkinson and Maule 2002 Hodgkinson et al1999 2002 Maule Hodgkinson and Bown 2003)Based on this logic tools deployed to challengemanagersrsquo assumptions about their organizationand its environment might be particularly valuable(Eden and Ackermann 1998 van der Heijdenet al 2002 Mezias Grinyer and Guth 2001) Forinstance research suggests that scenario planningif designed appropriately can induce changesin strategistsrsquo mental models (Healey andHodgkinson 2008 van der Heijden et al 2002Hodgkinson and Healey 2008) Considering mul-tiple hypothetical futures induces effortful mentalsimulations that can stretch individual and collec-tive thinking (Schoemaker 1993) In contrastsome forms of analytical tool such as the tradi-tional SWOT analysis may be more familiarrequiring participants to articulate existing knowl-edge rather than have their assumptions chal-lenged actively Hence we predict

H5d Workshops involving analytical tech-niques directed specifically toward stimulatingcognitive challenge will be associated with per-ceived cognitive outcomes that are more posi-tive than workshops that do not involve suchtechniques

MethodsSample and procedure

We tested our hypotheses by means of a question-naire survey distributed to a stratified random

sample of 8000 members of the UKrsquos CharteredManagement Institute (CMI) CMI membershipspans all levels of management from trainee tosenior executive across a range of sectors andthus constitutes a suitably wide cross-section ofUK managers for testing the hypotheses Thesurvey instrument assessed the design featuresand outcomes pertaining to the most recent work-shop in which respondents had participated aswell as background questions about the hostorganization and a number of questions beyondthe scope of the present hypotheses1 By request-ing factual responses regarding the design featuresof a specific target event we sought to reducepotential response bias thus improving dataaccuracy (Mezias and Starbuck 2003 Starbuckand Mezias 1996)

We received 1337 returns (a response rate of1671) We removed from further analysisrespondents who had not participated in a work-shop in their current organization (34) becauseindividuals who had since departed the organiza-tion that hosted the workshop may have been lessable to provide accurate data Excluding theserespondents yielded a total of 846 valid responseswhich we retained for further analysis Respond-entsrsquo organizations varied in size from small andmedium-sized enterprises to large multinationalsoperating in a range of industries Sixty-seven percent were service organizations the remainderwere manufacturing firms The average timeelapsed since the focal workshop took place was89 months (standard deviation of 108) To checkfor response bias we compared responses includedand excluded from the final sample Multivariateanalysis of variance adopting Wilksrsquo lambdarevealed no statistically significant differencesin workshop outcomes between those includedand excluded (F(1775) = 117 ns) Furthermorealthough executives dominated the sample (seniormanagers 48 company directors 39) relative tomiddle managers (13) the former did not per-ceive workshop outcomes significantly differentlyfrom the latter (F(2711) = 191 ns)

Measures

We used perceptual self-report measures to assessour dependent variables concerning the focal

1Interested readers can obtain a copy of the surveyinstrument by contacting the authors

8 M P Healey et al


workshop outcomes for two reasons First thereare no independent objective measures of work-shop outcomes currently available Second objec-tive measures might not capture the relevantoutcomes of strategic planning activities (PearceFreeman and Robinson 1987) a highly likely sce-nario in the present case given the specificity ofthe outcomes we posited (eg impact of the focalworkshop on the business plan improvements ininterpersonal relations and influence on under-standing of strategic issues)2

In contrast we used objective self-report indi-cators for the majority of our independent vari-ables (eg workshop duration in days whether anexternal or internal facilitator led the event) Byusing respectively factual and perceptual indica-tors of design characteristics and workshopoutcomes we sought to foster psychological sepa-ration between predictors and dependent vari-

ables thereby minimizing potential problems dueto common method variance (CMV) (Podsakoffet al 2003)

Workshop outcomes In line with our theoriz-ing we assessed three types of outcome organi-zational interpersonal and cognitive Table 1shows the item wordings which emphasized thedistinction between the three sets of outcomes

Four items measured organizational outcomesSpecifically we asked participants to rate theimpact of the focal workshop upon the followingaspects of their organization the business planstrategy visionmission statement corporatevalues and business processes These aspects con-stitute key organizational dimensions of strategicplanning (Brews and Hunt 1999 Brews andPurohit 2007 Grant 2003) Items were scored ona five-point bipolar impact scale (1 = lsquovery nega-tiversquo to 5 = lsquovery positiversquo)

We used four items to measure interpersonaloutcomes Respondents rated the impact of thefocal workshop from a personal perspective ontheir relationships with senior managers col-leagues junior managers and lower-level employ-

2A number of strategy process studies have adopted self-report instruments to assess dependent variables similarto ours such as perceived strategic planning benefits(Gerbing Hamilton and Freeman 1994) and satisfactionwith strategic decisions (Kim and Mauborgne 1993)

Table 1 Exploratory factor analysis of dependent variables (workshop outcomes)

Items Factor loadings

Organizationaloutcomes

Interpersonaloutcomes

Cognitiveoutcomes

lsquoWhat impact did the strategy workshop have upon the following aspects of yourorganizationrsquoa

Corporate values 078Visionmission statement 076Business planstrategy 065Business processes 057

lsquoFrom a personal perspective what impact did the workshop have upon your relationshipswith the followingrsquoa

Colleagues 079Junior managers 075Senior managers 073Employees 059

lsquoHow far do you agree that attending the workshop improved your own understanding ofthe followingrsquob

Products and services 079Competitor activity 075Other departments 062Organizationrsquos future plans 050

Eigenvalue 245 241 198Percentage variance explained 2039 2011 1651Cumulative variance explained 2039 4050 5701Cronbachrsquos alpha 072 077 071

aScale 1 = very negative 2 = negative 3 = no impact 4 = positive 5 = very positivebScale 1 = strongly disagree 2 = disagree 3 = neither agree nor disagree 4 = agree 5 = strongly agree



ees Field observations suggest that workshopscan and often do affect relationships betweenthese internal groups (van der Heijden 1996Hodgkinson and Wright 2002 Johnson et al2010) The relevant items were scored on the samefive-point scale used to assess organizationaloutcomes

Finally we measured cognitive outcomes withfour items that asked participants to reflect onhow the focal workshop had affected their ownunderstanding of key strategic issues Respond-ents indicated whether the event had improvedtheir understanding of the organizationrsquos futureplans products and services other departmentsrsquoactivities and competitor activity These issuesare central to managersrsquo understanding of strat-egy and they are often the focus of strategic plan-ning exercises (Brews and Purohit 2007 Porter1985 Powell 1992) Items were scored on a five-point Likert scale (1 = lsquostrongly disagreersquo to5 = lsquostrongly agreersquo) As shown in Table 1 reli-abilities for all three scales were adequate givenNunnallyrsquos (1978 p 245) advice that for lsquohypoth-esized measures of a construct reliabilities of70 or higher will sufficersquo

Goal clarity Goal clarity was measured using afive-point Likert scale on which respondentsreported the extent to which the objectives ofthe focal workshop were clearly communicatedbeforehand (1 = lsquostrongly disagreersquo to 5 =lsquostrongly agreersquo)

Purpose We assessed the purpose of the focalworkshop using two dummy variables codingwhether it was undertaken primarily for thepurposes of formulation (1 = formulation 0 =implementationother) or implementation (1 =implementation 0 = formulationother) based ona list of ten reasons for holding the workshop (eglsquoto formulate new strategyrsquo lsquoto plan strategyimplementationrsquo lsquoto examine blockages to strat-egy implementationrsquo)

Removal To measure the focal workshoprsquosdegree of removal from everyday organizationalactivities we derived a summated scale from threeitems assessing the workshoprsquos relatedness tothe organizationrsquos strategic planning system(0 = related 1 = unrelated) the location at whichthe workshop was held (0 = in-house 1 = off-site)and who led it (0 = internal leader 1 = external

leader) Summing responses to these three itemsgave each workshop a removal score rangingfrom 0 to 3 where 0 = low removal and 3 = highremoval By way of illustration a highly removedworkshop was unrelated to the strategic planningsystem held off-site and led by someone externalto the organization

Serialization To measure serialization iewhether the focal workshop was part of a con-certed effort rather than a stand-alone event werecorded the number of workshops in the series (1= lsquoone-off eventrsquo 2 = lsquo2minus3rsquo 3 = lsquo4minus5rsquo 4 = lsquo6 ormorersquo)

Involvement We assessed stakeholder involve-ment by summing the total number of stakeholdergroups involved in the focal workshop (rangingfrom 0 to 9 lsquoemployeesrsquo lsquoline managersrsquo lsquomiddlemanagersrsquo lsquosenior managersrsquo lsquoexecutive direc-torsrsquo lsquonon-executive directorsrsquo lsquoconsultantsrsquolsquosuppliersrsquo lsquocustomersrsquo)

Participants To measure group size werecorded the number of participants in the event(1 = lsquo1minus3rsquo to 6 = lsquo26 or morersquo) and computed thesquared term of this number to examine thehypothesized curvilinear effects

Cognitive effort Four indicators served asproxies for the degree of cognitive effort expendedon the focal workshop The first two indicatorscaptured the amount of preparation undertaken(1 = lsquononersquo to 6 = lsquoone weekrsquo) and the total dura-tion of the workshop (1 = lsquohalf a dayrsquo to 6 = lsquooverfive daysrsquo) while the third assessed the totalnumber of strategy tools employed in the eventParticipants selected applicable tools from a menuof 11 of the most common techniques3 The fourthindicator devised to test Hypothesis 5d assessedthe nature of the tools deployed For this purposegiven its theorized role in stimulating learning(Healey and Hodgkinson 2008 van der Heijden

3The complete list of tools assessed was as follows Por-terrsquos five forces SWOT analysis BCG matrix scenarioplanning competences analysis cultural web McKin-seyrsquos 7Ss stakeholder analysis market segmentationvalue chain analysis and PEST(EL) analysis Recentstudies show that these are among the most commonlydeployed strategic analysis tools (Jarratt and Stiles 2010Jarzabkowski et al 2013 Wright Paroutis and Blettner2013)

10 M P Healey et al


et al 2002 Hodgkinson and Healey 2008Schoemaker 1993) we dummy coded usage ofscenario planning as a proxy for cognitively chal-lenging tool use

Control variables We included several controlvariables that might affect the hypothesized rela-tionships To control for potential industry andsector differences in strategic planning (Powell1992) we coded whether the host organizationwas a manufacturing or service organization(industry) and whether it was in the public orprivate sector (sector) To account for potentialdifferences in strategic planning between largeand small firms (Miller and Cardinal 1994) wealso controlled for organizational size (combinedstandardized mean for number of employees andfinancial turnover) To partial out the potentialhalo effects of inferring workshop success fromthe organizationrsquos general status (Feldman 1986)we controlled for organizationsrsquo perceived stateof development with two dummy variablesgrowing = 1 (versus 0 = stableshrinking) andstable = 1 (versus 0 = growingshrinking) Weused two individual-level controls First to checkfor position bias (Ketokivi and Castaner 2004)we controlled for respondentsrsquo managerial level adummy variable coded as director = 1 (versus0 = seniormiddle manager) and manager = 1(versus 0 = directormiddle manager) Second weincluded respondentsrsquo role in the event (0 = par-ticipant 1 = facilitator) to control for potentialself-serving bias (Clapham and Schwenk 1991)among those leading workshops

Results

Our analysis involved two major steps First weconducted exploratory and confirmatory factor

analyses to establish the dimensionality of thedependent variables and verify the three-factormeasurement model Second we employed hier-archical multiple regression to examine thehypothesized relationships (Cohen et al 2003)deleting list-wise cases with missing values

Factor analysis

We evaluated the structure and robustness of ourdependent variables via the split-sample valida-tion procedure (Fabrigar et al 1999 Gerbingand Hamilton 1996 Mosier 1951) This involved(i) splitting the sample into two randomly deter-mined sub-samples of equal size (N = 423) (ii)conducting an exploratory factor analysis ndash prin-cipal components analysis (PCA) with Varimaxrotation ndash on the first sub-sample to specify themodel pertaining to the 12 dependent variableitems and (iii) fitting the model obtained from theexploratory PCA to the data from the second sub-sample via confirmatory factor analysis

As Table 1 shows the PCA produced threeinterpretable factors with eigenvalues greater thanunity which together accounted for 57 of thetotal variance4 The resulting factor structure isin line with the tripartite model of workshopoutcomes enumerated above Factor 1 reflectsorganizational outcomes Factor 2 reflects inter-personal outcomes and Factor 3 reflects cognitiveoutcomes

Next we used the hold-out sub-sample to vali-date the fit of the three-factor model via confirma-

4Preliminary testing confirmed that the data were suit-able for factoring Bartlettrsquos test for sphericity wassignificant (χ2 = 98154 p lt 0001) while the Kaiser-minusMeyerminusOlkin measure of sampling adequacy (= 0909)was well above the 060 threshold advocated byTabachnick and Fidell (2007)

Table 2 Summary statistics of confirmatory factor analysis of dependent variables

Goodness of fit indices Hypothesized Alternative Alternativethree-factor model one-factor model two-factor model

DELTA-2 (IFI) 093 084 088RNI 093 084 087CFI 093 084 087RMSEA 007 010 009χ2 15138 27494 22192df 51 54 53χ2df 297 509 419Δχ2

(df) minus 12356(3) 7054(2)



tory factor analysis Table 2 shows the results Thebasic means of assessing model fit is via the overallchi-squared goodness of fit index Howeverbecause the chi-squared is notoriously over-sensitive (ie it detects any small misspecificationin a model if the sample is large enough see Hu andBentler 1998) researchers more typically rely onthe ratio of the chi-squared to the degrees offreedom Researchers have recommended ratios aslow as 2 and as high as 5 to indicate reasonable fit(Marsh and Hocevar 1985) Hence the observedχ2df ratio of 297 for the hypothesized model iswithin the acceptable range Of the various otherindices available for evaluating model fit Gerbingand Anderson (1992) advocate three specificindices on the basis of Monte Carlo evidence theincremental fit index (IFI) (also known as theDELTA-2 index) the relative non-centrality index(RNI) and the comparative fit index (CFI) In thepresent case the IFI RNI and CFI values (all 093)were above the traditional threshold of 090(Bentler and Bonett 1980 Marsh Hau and Wen2004) again indicating acceptable model fitMoreover the root mean square error of approxi-mation (RMSEA) of 007 was within the accept-able range of le 008 suggested by Browne andCudeck (1993) a further indication of acceptablefit for the hypothesized model

We also evaluated the comparative fit of thehypothesized three-factor model against two com-paratively parsimonious alternatives a basicmodel in which all items loaded on a single factorand a two-factor model in which four lsquopeople-relatedrsquo items loaded on one factor and theremaining eight items loaded on a lsquostrategyrsquofactor The respective fit indices and chi-squareddifference scores reported in Table 2 show thatthe hypothesized model provided a significantlybetter fit than its rivals

Regression analysis

Table 3 reports descriptive statistics and inter-correlations and Table 4 reports the regressionresults Following Schwab et al (2011) weinclude confidence intervals to provide preciseestimates and report both unstandardized andstandardized regression coefficients to conveyeffect size information

Supporting Hypothesis 1 clarity of workshopobjectives proved to be the single most importantpredictor of workshop effectiveness and was asso-

ciated positively with all three workshop outcomesat the p lt 0001 level (all βs ge 024) Hypothesis 2ain contrast was not supported workshops under-taken for purposes of formulation as opposed toimplementation were not significantly more likelyto be associated with positive cognitive outcomes(β = 001 ns) Supporting Hypothesis 2bhowever workshops undertaken for purposes ofimplementation as opposed to formulation weresignificantly more likely to be associated with posi-tive organizational outcomes (β = 008 p lt 005)As Table 4 shows the results generally support ourarguments regarding the effects of routinizationOnly Hypothesis 3a which predicted a positiverelationship between removal and cognitive out-comes was not supported (β = minus005 ns) In con-trast the relationship between removal andorganizational outcomes was negative and signifi-cant (β = minus008 p = 005) supporting Hypothesis3b Hypotheses 3c and 3d which predicted respec-tively that serialization would be associated posi-tively with cognitive (β = 012 p lt 001) andorganizational (β = 010 p lt 001) outcomes werealso supported

Our results also support Hypothesis 4a whichpredicted that wider stakeholder involvementwould be associated positively with interpersonaloutcomes (β = 009 p lt 005) In terms of theeffects of group size although a significant curvi-linear relationship between group size and inter-personal outcomes is evident (β = 011 p lt 005)it is in the opposite direction to that theorizedSpecifically small workshops (1minus3 and 4minus6persons) and large workshops (26 or morepersons) were associated with superior outcomescompared with intermediate-sized workshops(7minus10 11minus15 and 16minus25 persons) Hence Hypoth-esis 4b is not supported5

5Closer inspection of the data suggested that a moreappropriate way to describe the effect of group size is interms of the contrast between small and large workshopsAccordingly we ran a one-way analysis of covariance inwhich we entered the full set of control variables as covari-ates The independent variable comprised a two-levelgroup size factor formed by recoding the original groupsizes such that small groups (1minus3 4minus6 7minus10 persons) werecoded as 0 and large groups (11minus15 16minus25 and 26+persons) as 1 The results showed that small workshopsproduced significantly more positive interpersonal out-comes (F(1781) = minus497 p lt 005) This finding is consistentwith the idea that small group exercises are less prone toconflict and are thus more beneficial to interpersonalrelations (Amason and Sapienza 1997)

12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al


In terms of the effects of cognitive effort astheorized both preparation (β = 010 p lt 001)and duration (β = 009 p lt 005) were associatedpositively with cognitive outcomes supportingHypotheses 5a and 5b respectively Although thenumber of tools deployed (Hypothesis 5c) wasnot significantly related to cognitive outcomes(β = 003 ns) the type of tools deployed doesseem to matter Specifically the use of scenarioplanning was associated positively and signifi-cantly with the attainment of cognitive outcomes(β = 012 p lt 001) thus supporting Hypothesis5d6

Robustness checks

Following the guidelines of Lindell and Whitney(2001) and Podsakoff et al (2003) we employedthree statistical procedures to test for CMV Firstwe used Harmanrsquos one-factor test The resultsshowed that no single common method factoradequately represented the data rather numer-ous factors were required to account for themajority of the covariance7 Second followingPodsakoff et al (2003) we re-ran each of ourthree substantive regression models partiallingout the effects of the putative method factor byentering scale scores for the first common factoras a covariate in the regression analyses Theresults showed that including this control variableexerted no significant influence on the pattern ofrelations among the predictor and dependentvariables8 Third we used the marker variabletechnique advocated by Lindell and Whitney(2001) for controlling for CMV in cross-sectional

research designs We selected a marker variabletheorized to be unrelated to the predictor anddependent variables namely an item assessingrespondentsrsquo job tenure (all r values le 007 ns)Next we re-ran each of the regression modelsentering the marker variable as a control to makethe partial correlation adjustment All of the betacoefficients that were significant in the originalanalyses remained significant after controlling forthe marker variable Overall the results of thesechecks confirm that the relations observedbetween our predictor and outcome variablescannot simply be attributed to CMV

Discussion

Many organizations view strategy workshops as ameans of stepping back from the daily grind toconsider wider issues critical to their future(Campbell Liteman and Sugar 2003 Frisch andChandler 2006 Hodgkinson et al 2006) Despitetheir popularity we know little about the out-comes of these events or the factors that influencethose outcomes Most prior studies have beensmall-scale adopted an undifferentiated view ofworkshop outcomes andor have focused on alimited subset of success factors (Hodgkinson andWright 2002 Johnson et al 2010 Whittingtonet al 2006) The present study addressed theselimitations by (i) providing evidence from a largesample (ii) presenting a new more nuancedmodel of workshop outcomes and (iii) confirminga systematic series of hypothesized relationshipsbetween basic design characteristics and thevarious outcomes identified Below we discuss theimplications of our findings for understandingthe generative mechanisms of workshop effective-ness (and possibly other types of strategic episode)and suggest ideas for further research

Re-conceptualizing the outcomes of strategyworkshops

Research to date has equated the success of work-shops and related strategic episodes with directcontributions to the organizationrsquos overridingstrategic direction (Hendry and Seidl 2003

6To check whether other analytical tools were similarlyassociated with positive cognitive outcomes we tested anadditional regression model incorporating the two mostcommonly reported analytical tools namely SWOTanalysis and stakeholder analysis as dummy variablepredictors Inserting these predictors ahead of scenarioplanning neither variable was associated significantlywith cognitive outcomes (βs lt 006 ns) while the effectof scenario planning remained statistically significant(β = 011 p lt 005)7The unrotated factor solution of all variables revealedeight factors with eigenvalues greater than unity Thefirst factor accounted for only 13 of the total variancewith subsequent factors accounting for monotonicallydecreasing proportions of variance (second factor 11third 9 eighth 5 the eight factors combinedexplained 62 of total variance)8For the three models tested the overall effect of partial-ling out the common method factor was to reduce neg-

ligibly the size of the beta coefficients for the predictors(the average change across all coefficients was a mere001)



Hodgkinson and Wright 2002 Jarzabkowski2003 Whittington et al 2006) be that initiatingstrategic change or bolstering strategic continuity(cf Johnson et al 2010) Based on a conceptualanalysis of the literature supported by factoranalyses of our data set we have articulated athree-dimensional model that can reveal nuancesof workshop effectiveness missed by unitary con-ceptions While our findings corroborate thatinfluencing organizationsrsquo overriding missionand business planstrategy is a key indicator ofeffectiveness we have identified two additionalindicators namely interpersonal and cognitiveoutcomes Building on recent advances in thestudy of strategic episodes (Jarzabkowski andSeidl 2008 Jarzabkowski Balogun and Seidl2007 Maitlis and Lawrence 2003) futureresearch should explore the extent to which ourtripartite model captures the outcomes of othertypes of episode such as management meetingsboardroom decisions and group strategy projects

The claim that workshops leave few lastingeffects may be accurate if one is looking for majororganizational change as a direct result suchoutcomes may well be exceptional (cf Frisch andChandler 2006 Lorsch and Clark 2008)However our findings indicate that the absence ofsuch impact does not necessarily equate withfailure Before reaching such a conclusion it isessential to consider softer outcomes in particularthe effects on interpersonal relations and strategicunderstanding This more fine-grained view ofworkshop outcomes should help inform futureresearch analysing how this particular type ofepisode contributes to the ongoing socio-cognitive processes of strategizing

Antecedents of workshop effectiveness

Whereas previous research focuses on a relativelynarrow set of workshop success factors(Hodgkinson and Wright 2002 Johnson et al2010 Whittington et al 2006) we adopted adesign science approach to identify a wider set ofantecedent variables and attendant generativemechanisms In so doing our study has shed newlight on some of the key questions about this par-ticular type of strategic episode raised by strategy-as-practice theorists regarding who gets involvedin these events and what provision should bemade in advance to maximize productive out-comes (Hendry and Seidl 2003 Jarzabkowski

2003 Johnson Melin and Whittington 2003Whittington 2006a) In particular our findingsdemonstrate that four design characteristics ndashgoal clarity routinization stakeholder involve-ment and the degree of cognitive effort induced ndasheach play important complementary roles

Our finding that the clear communication ofobjectives beforehand constitutes the most impor-tant predictor of all three outcomes extends goalsetting theory (eg Latham and Pinder 2005Locke and Latham 1990) to the context of work-shops providing important clues regarding a keygenerative mechanism for producing effectiveevents Although previous studies focus on thebenefits of sharing strategic goals for widerorganizational functioning (Jarzabkowski andBalogun 2009 Ketokivi and Castaner 2004) ourresults extend this principle to a more micro-levelby demonstrating that communicating clearobjectives is equally important for success in spe-cific strategizing episodes (Hendry and Seidl2003)

We also observed the effects of goals in terms ofthe basic purpose of workshops Specificallyworkshops undertaken with the explicit intentionof aiding strategy implementation were morelikely to yield noticeable organizational out-comes In contrast our findings suggest that forworkshops designed to help formulate strategythere is a high risk of losing the intangible insightsand solutions produced (cf Grinyer 2000 vander Heijden et al 2002 Mezias Grinyer andGuth 2001) Given the difficulty of transferringthe outcomes of strategic episodes into widerorganizational action (Bourque and Johnson2008 Hendry and Seidl 2003 Johnson et al2010) there appears a particular need to under-stand how to capture the outcomes offormulation-focused workshops A related need isto develop better measures of the cognitive out-comes of formulation-related episodes so thatthese events are not judged automatically asunsuccessful when they lsquofailrsquo to translate intodirect organizational action (cf Hodgkinson andWright 2002 2006 Whittington 2006a 2006b)More generally given that formulation andimplementation workshops appear to producedifferent outcomes our findings suggest that it isimportant not only to communicate objectivesclearly but also to explain the differing objectivesfor formulation and implementation events sothat participants can prepare effectively and

16 M P Healey et al


understand what to expect in terms of intendedoutcomes

A defining characteristic of strategy workshopsndash evident in their depiction as lsquoaway-daysrsquo orlsquostrategy retreatsrsquo ndash is the set of design featuresused to remove proceedings from everydayorganizational routines A common view is thatthese features free managers from the habitualstrictures that hinder authentic strategic debatethereby stimulating innovative thinking Crea-tivity research (Elsbach and Hargadon 2006Kaplan and Kaplan 1982) and the practitionerliterature (eg Campbell Liteman and Sugar2003 Frisch and Chandler 2006 van der Heijdenet al 2002) support this view However conceiv-ing workshops as a form of ritual suggests thatthese features distance the outputs of creativeendeavours from the practical realities imposedby everyday routines and practices (Bourque andJohnson 2008 Johnson et al 2010) thus reduc-ing the likelihood of workshop decisions reachingback into organizational life On balance ourresults support an unfavourable view of removalIn line with ritual theory we observed a negativeassociation between removal and organizationaloutcomes Hence there is a clear need to identifymeans of integrating valuable workshop out-comes into the wider organization Although wefailed to find the theorized positive cognitiveeffects of removal we leave open the possibilitythat more sophisticated instruments might detectthese putative benefits

Given the apparently restrictive role of removalfeatures that strengthen the links between work-shops and the fabric of the organization shouldprove beneficial Our findings support this notionSpecifically and in line with MacIntosh MacLeanand Seidl (2010) we found that organizing work-shops as a series of events increases the likelihoodof attaining positive organizational and cognitiveoutcomes This finding supports the idea that lsquoseri-alizationrsquo amplifies the time and energy focused onparticular strategic issues increasing the likeli-hood of learning while providing the requisitespace to build commitment to new ideas Hence animportant implication is that if the goal is toachieve high levels of cognitive challenge a seriesof workshops is likely to be more effective than asingle event Future research might examine theoptimum design for series of workshops (eg start-ing small to formulate ideas and then using largegroups to implement or starting large to generate

ideas and then using small groups to refine them)Understanding potential spill-over effects fromone episode to another in series of workshops isanother important objective for future research

Our findings also contribute to the literature onwidening participation in key strategic episodessuch as workshops Consistent with this growingline of inquiry (Hutzschenreuter and Kleindienst2006 Ketokivi and Castaner 2004 KorsgaardSapienza and Schweiger 2002 WooldridgeSchmid and Floyd 2008) we found a positiveassociation between the breadth of stakeholderinvolvement and improved interpersonal relationsamong workshop participants The importance ofstakeholder involvement supports the idea thatbuilding social cohesion among decision makers isan essential function of workshops Our resultsshow that small group workshops appear particu-larly effective as a bonding mechanism Howeveras Jarzabkowski and Balogun (2009) observesimply bringing people together may not auto-matically produce harmony Hence furtherresearch is needed to uncover the social dynamicsresponsible for the effects found here One prom-ising avenue is to undertake in-depth qualitativeanalyses of how users overcome subgroup conflictand foster cohesion within strategic episodes(Hodgkinson and Healey 2008)

The present findings show that workshopsdesigned to stimulate higher levels of cognitiveeffort ndash as indicated by the amount of prepara-tion time dedicated to the focal event and the useof cognitively challenging analytical techniques ndashwere associated with perceived improvements inthe understanding of strategic issues These find-ings fit with the idea that effortful informationprocessing is an important mechanism underpin-ning strategic learning (Barr Stimpert and Huff1992 Hodgkinson et al 1999 2002 Louis andSutton 1991 Reger and Palmer 1996) Our find-ings suggest that choosing the lsquorightrsquo analyticaltechniques is also important Of the popular tech-niques deployed only scenario planning was asso-ciated with positive cognitive outcomes Althoughadvocates have long claimed that scenarios yieldunique learning effects (Schoemaker 1993 vander Heijden et al 2002) this is the first large-scalefield study to lend empirical support to thoseclaims Going forward since our measure of tooluse was relatively crude being restricted to thenumber and type of tools deployed futureresearch might adopt detailed field methods (cf



Jarratt and Stiles 2010 Jarzabkowski and Seidl2008 Langley 1989 Whittington et al 2006) toexplore exactly how and why certain tools areused

Implications for practice

Although we demonstrated various statisticallysignificant relationships between workshop designcharacteristics and outcomes the effects of indi-vidual predictors were generally small in magni-tude By way of illustration decreasing the degreeof workshop removal by one unit would improveorganizational outcomes by 004 units on a five-point scale (see Table 4) In contrast howeverclarity of objectives was the predictor of greatestpractical significance for which a one-unitincrease would improve organizational outcomesby 023 units on a five-point scale which isapproximately one quarter of the differencebetween having no impact and having a positiveimpact Because individual variables tended toexert small effects designers should attend tomultiple features For instance we highlightedfour predictors of organizational outcomes thattogether explain 16 of the variance in the per-ceived impact on firmsrsquo strategic plans and busi-ness processes

Given that the effects we found are small anddifficult to interpret in practical terms we call fortwo things in future work First researchersshould consider objective measures of workshopoutcomes that are practically meaningful (eg thenumber of tangible new initiatives resulting froma given workshop changes in communication fre-quency among participants) Second operation-alizing more precisely the constructs outlined heremight help uncover stronger relationships (ielarger effects) between design characteristics andoutcomes For instance to assess better the effectsof cognitive effort future research might measurethe amount of time spent on challenging strategicanalyses or employ direct measures of the extentof divergent thinking

Limitations

We cannot rule out the possibility that the use ofself-report measures in our study may have over-or under-estimated the actual workshop outcomesrealized Therefore future studies should adoptobjective indicators particularly of harder out-

comes such as tangible changes in the organiza-tionrsquos strategic direction Although it is moredifficult to measure objectively the softer outcomeswe identified a combination of behavioural meas-ures for the assessment of interpersonal outcomes(eg pre- versus post-event changes in communi-cation among attendees) and factual knowledgetests for the assessment of cognitive outcomeswould represent a significant step forward In addi-tion since directors and managers comprised themajority of our sample it remains to be seenwhether lower-level employees view workshopoutcomes similarly Finally given our studyrsquoscross-sectional design we were unable to drawvalid inferences about causal relations amongworkshop characteristics and outcomes Futurework using longitudinal designs would permitmore robust claims One promising option wouldbe to measure design features prior to and duringthe workshop and assess outcomes with objectivemeasures at a later point in time

Conclusion

Our study demonstrates the value of distinguish-ing the often-overlooked interpersonal and cogni-tive outcomes of strategy workshops from theirimpact on the organizationrsquos strategic directionMoreover it provides evidence that four basicworkshop design characteristics are importantdifferentially to the three types of outcomes iden-tified Although clear goals are important to alltypes of outcome attaining organizational out-comes depends more on design characteristicsconcerning routinization whereas interpersonaloutcomes rely on those concerning involvementand cognitive outcomes depend on those concern-ing cognitive effort We hope our findings providethe signposts required to guide much-neededfuture studies of these widespread strategicepisodes

References

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18 M P Healey et al


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Hodgkinson G P and I Clarke (2007) lsquoExploring the cogni-tive significance of organizational strategizing a dual-processframework and research agendarsquo Human Relations 60 pp243minus255

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Hodgkinson G P and A J Maule (2002) lsquoThe individual inthe strategy process insights from behavioural decisionresearch and cognitive mappingrsquo In A S Huff and MJenkins (eds) Mapping Strategic Knowledge pp 196minus219London Sage

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Hodgkinson G P R Whittington G Johnson and MSchwarz (2006) lsquoThe role of strategy workshops in strategydevelopment processes formality communication coordina-tion and inclusionrsquo Long Range Planning 39 pp 479minus496

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Johnson G A Langley L Melin and R Whittington (2007)Strategy as Practice Research Directions and ResourcesCambridge Cambridge University Press

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Mark P Healey is Lecturer in Strategic Management at Manchester Business School University ofManchester UK He received his PhD in Management Sciences from the University of ManchesterInstitute of Science and Technology His research focuses on cognition in organizations particularlyapplied to strategic decision making and the wider strategic management process His research hasappeared in leading scholarly journals including Annual Review of Psychology Organization Studiesand Strategic Management Journal

Gerard P Hodgkinson is Professor of Strategic Management and Behavioural Science and AssociateDean (Strategy) at Warwick Business School University of Warwick UK His research interestscentre on the psychological foundations of strategic management applied psychological measure-ment and the nature and significance of management and organizational research for academia andwider publics From 1999 to 2006 he was the Editor-in-Chief of the British Journal of Management

Richard Whittington is Professor of Strategic Management at the Saiumld Business School and MillmanFellow at New College University of Oxford His main current research interest is developing a moremacro perspective on strategy-as-practice He is author or co-author of nine books includingEuropersquos best-selling strategy textbook Exploring Strategy (9th edn 2011) He currently serves on theboard of the Strategic Management Society

Gerry Johnson is Emeritus Professor of Strategic Management at Lancaster University ManagementSchool and a Senior Fellow of the UK Advanced Institute of Management Research His primaryresearch interest is strategic management practice in particular with regard to strategy developmentand change in organizations His research has been published in leading international journals suchas the Academy of Management Review Academy of Management Journal Strategic ManagementJournal and Journal of Management Studies

22 M P Healey et al


and Whittington 2003 Johnson et al 2007Whittington 1996) workshops are receivingincreased attention from management scholars(Jarratt and Stiles 2010 Jarzabkowski and Spee2009 Jarzabkowski Balogun and Seidl 2007Johnson et al 2010 MacIntosh MacLean andSeidl 2010 Whittington et al 2006) Descriptivedata show that workshops are seen as integral tothe strategic planning process are largely the pre-serve of top-level managers and are undertakenfor various purposes from creating space toreflect on current strategies to stimulating debatesabout the future and tackling organizationaldevelopment needs (Hodgkinson et al 2006)

From a theoretical standpoint workshops areof particular interest because they represent animportant type of lsquostrategic episodersquo (Hendry andSeidl 2003) That is these events provide a rareopportunity to suspend normal structures toreflect on current policies and engage in new stra-tegic conversations Currently however there islittle systematic theory or evidence linking thestructure and conduct of workshops to their effec-tiveness Accordingly in this paper we examinethe critical success factors associated with work-shops introducing a design-based theory of strat-egy workshop effectiveness to understand betterhow these events impact upon organizations Inso doing we address three issues in the growingliterature on workshops as a key type of strategicepisode

First the few empirical studies of workshopspublished to date have tended to use small-scalecase-based methods (Bowman 1995 MacIntoshMacLean and Seidl 2010 Mezias Grinyer andGuth 2001 Whittington et al 2006) Forinstance Hodgkinson and Wrightrsquos (2002) caseanalysis of scenario planning practices centred ona single workshop-based intervention SimilarlyJohnson et alrsquos (2010) study of workshops asritual was based on cases in just four organiza-tions As Huff Neyer and Moslein (2010) haveobserved of research on strategy practices ingeneral a heavy reliance on small-scale ethno-graphic methods has produced a somewhatnarrow evidence base These authors suggestedanalysing larger data sets to widen the breadth ofinformation on strategy practices and increase thegeneralizability of findings Heeding this advicewe report results from a large-scale field survey ofover 650 workshops conducted across a range ofsettings

Second studies of workshops to date construethe outcomes of these events in a largely undif-ferentiated manner evaluating effectiveness interms of the eventrsquos contribution to strategic con-tinuity andor strategic change (Jarzabkowski2003 Whittington et al 2006) Although suchorganizational-level outcomes are an importantpart of the effects of workshops they do not tellthe whole story In this paper we extend strategyprocess research (Grant 2003 Ketokivi andCastaner 2004 Mintzberg 1994) to suggest thatthe benefits of workshops also lie in people-related or interpersonal outcomes Additionallywe posit that there is an important cognitivedimension to workshop outcomes given the roleof intervention techniques in enhancing strategicthinking (Bowman 1995 Grinyer 2000Hodgkinson and Healey 2008) Hence we offer amore nuanced view of the impact of workshops bydistinguishing theoretically and empiricallybetween three distinct types of outcome minus organi-zational interpersonal and cognitive

Third the extant literature considers only anarrow range of factors that influence workshopeffectiveness often restricted to the behaviours offacilitators or influential individuals (Hodgkinsonand Wright 2002 Whittington et al 2006) Hith-erto no study has examined comprehensivelyhow basic design features relate to workshop out-comes although there have been calls for suchwork (Hendry and Seidl 2003 Jarzabkowski andSpee 2009) Studies that have looked explicitly atdesign issues have focused on a limited set of fea-tures (Johnson et al 2010 MacIntosh MacLeanand Seidl 2010) Extending this line of inquiry weadopt a design science approach to develop andtest a series of hypotheses that link systematicallya range of workshop design characteristics (egthe extent of preparation the variety of stake-holders involved the analytical tools adopted) tothe various outcomes alluded to above

Although design characteristics influence theeffectiveness of all workgroup practices includingthose in the upper echelons of organizations(Cohen and Bailey 1997) the embryonic litera-ture on workshops (and indeed strategic episodesmore generally) provides little detailed guidanceon which design features are important or howthey are important Accordingly we turn tovarious additional literatures to posit multiplegenerative mechanisms that contribute to work-shop (in)effectiveness

2 M P Healey et al


















Cognitive outcomes




4 M P Healey et al














Routinization









6 M P Healey et al









Cognitive effort















Measures



8 M P Healey et al













Cognitiveoutcomes






















10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al


















Cognitive outcomes




4 M P Healey et al














Routinization









6 M P Healey et al









Cognitive effort















Measures



8 M P Healey et al













Cognitiveoutcomes






















10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al














Routinization









6 M P Healey et al









Cognitive effort















Measures



8 M P Healey et al













Cognitiveoutcomes






















10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al









Cognitive effort















Measures



8 M P Healey et al













Cognitiveoutcomes






















10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al










Measures



8 M P Healey et al













Cognitiveoutcomes






















10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al













Cognitiveoutcomes






















10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al













10 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al




Results



Factor analysis








(df) minus 12356(3) 7054(2)





Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al




Regression analysis






12 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al


Tab

le3

Mea

nss

tand

ard

devi

atio

nsa

ndco

rrel

atio

nsof

the

stud

yva

riab

les

Mea

nSD

12

34

56

78

910

1112

1314

1516

1718

1920

2122

1In

dust

ry0

960

572

Ow

ners

hip

054

050

minus02

53

Org

aniz

atio

nal

size

827

371

minus00

7minus0

18

4D

evel

opm

ent

grow

ing

061

049

001

008

minus01

25

Dev

elop

men

tst

able

026

044

000

000

minus00

2minus0

74

6R

espo

nden

tdi

rect

or0

380

490

050

09minus0

37

011

minus00

27

Res

pond

ent

man

ager

049

050

minus00

3minus0

04

023

minus00

80

02minus0

77

8R

espo

nden

tro

le0

190

390

000

03minus0

16

005

001

014

minus00

99

Goa

lcl

arity

394

086

003

004

minus00

80

15minus0

09

016

minus01

50

1310

Fo

rmul

atio

n0

880

330

030

01minus0

06

001

004

011

minus00

90

00minus0

03

11

Impl

emen

tatio

n0

650

480

00minus0

03

005

004

minus00

5minus0

09

008

004

009

minus01

812

R

emov

al1

150

82minus0

05

008

minus01

5minus0

04

006

009

minus00

5minus0

03

minus01

0minus0

05

minus00

313

Se

rial

izat

ion

099

110

minus00

6minus0

01

013

011

minus01

00

02minus0

01

004

010

001

011

minus00

114

St

akeh

olde

rs2

821

460

02minus0

12

013

minus00

30

04minus0

19

009

000

003

004

012

001

010

15

Part

icip

ants

364

152

006

minus02

90

44minus0

06

003

minus02

50

14minus0

07

minus00

1minus0

10

012

005

011

037

16

Part

icip

ants

squa

red

155

211

28

minus00

30

15minus0

25

010

minus00

40

10minus0

08

003

009

minus00

10

010

02minus0

03

000

001

17

Prep

arat

ion

294

136

minus00

90

12minus0

01

009

minus00

40

09minus0

04

029

021

011

006

minus00

50

170

08minus0

04

minus00

418

D

urat

ion

214

096

minus00

30

060

20minus0

05

005

minus00

80

08minus0

03

006

005

007

022

015

012

023

minus01

00

2619

N

umbe

rof

tool

s2

251

800

000

090

000

06minus0

02

008

minus00

60

030

120

130

080

020

080

11minus0

06

minus00

20

240

2120

Sc

enar

ios

028

045

003

001

006

002

001

007

minus00

9minus0

01

010

007

005

minus00

10

030

070

080

030

100

100

4821

O

rgan

izat

iona

lou

tcom

es3

770

510

07minus0

06

minus01

10

21minus0

12

016

minus01

40

100

410

020

11minus0

11

015

008

000

008

014

004

018

010

22

Inte

rper

sona

lou

tcom

es3

860

670

080

07minus0

18

022

minus00

50

17minus0

12

016

039

004

007

minus00

10

060

03minus0

14

017

016

minus00

10

130

090

54

23

Cog

nitiv

eou

tcom

es3

700

580

070

05minus0

13

019

minus01

00

11minus0

10

009

030

005

000

minus00

60

150

02minus0

08

010

019

008

014

016

047

044

Cor

rela

tion

sr

ge0

08ar

esi

gnifi

cant

atp

lt0

05r

ge0

12ar

esi

gnifi

cant

atp

lt0

01r

ge0

22ar

esi

gnifi

cant

atp

lt0

001



Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al


Tab

le4

Res

ults

ofth

ere

gres

sion

anal

yses

Pred

icto

rsO

rgan

izat

iona

lou

tcom

esn

=67

2In

terp

erso

nal

outc

omes

n=

713

Cog

nitiv

eou

tcom

esn

=69

0

Bβ

SE95

C

IB

βSE

95

CI

Bβ

SE95

C

I

Con

trol

vari

able

sIn

dust

rya

002

003

003

minus00

40

090

070

060

03minus0

01

012

008

007

0

040

000

15

Ow

ners

hipb

minus00

8minus0

08

003

minus01

5minus0

01

minus00

10

000

04minus0

07

007

003

003

004

minus00

50

10O

rgan

izat

iona

lsi

zeminus0

01

minus00

80

04minus0

16

minus00

10

00minus0

01

004

minus00

90

07minus0

02

minus01

3

004

minus02

2minus0

05

Dev

elop

men

tgr

owin

g0

130

13

005

002

02

30

460

33

0

050

230

43

015

013

0

050

020

24

Dev

elop

men

tst

able

001

001

005

minus00

90

110

390

26

0

050

160

36

003

002

005

minus00

90

12R

espo

nden

tdi

rect

or0

060

050

06minus0

05

016

010

007

006

minus00

40

18minus0

09

minus00

80

06minus0

19

004

Res

pond

ent

man

ager

minus00

4minus0

04

005

minus01

50

060

030

020

05minus0

08

012

minus01

1minus0

09

006

minus02

00

02R

espo

nden

tro

lec

005

004

003

minus00

30

100

140

08

003

002

01

50

010

010

04minus0

06

008

Inde

pend

ent

vari

able

sG

oals

and

purp

ose

Goa

lcl

arity

023

039

002

032

04

60

260

34

0

030

270

40

016

024

004

016

03

1Fo

rmul

atio

nd0

020

010

04minus0

06

008

Impl

emen

tatio

ne0

070

08

003

001

01

4R

outin

izat

ion

Rem

oval

minus00

4minus0

08

003

minus01

3minus0

01

minus00

3minus0

05

004

minus01

20

02Se

rial

izat

ion

005

010

0

030

040

17

006

012

0

040

040

18

Invo

lvem

ent

Stak

ehol

ders

004

009

0

020

020

16

Part

icip

ants

minus00

6minus0

13

0

01minus0

21

minus00

6Pa

rtic

ipan

tssq

uare

d0

030

11

001

004

01

8C

ogni

tive

effo

rtPr

epar

atio

n0

040

10

002

002

01

8D

urat

ion

003

009

0

030

030

12

Num

ber

ofto

ols

001

003

001

minus00

50

12Sc

enar

ios

015

012

0

050

040

19

R2

028

027

019

Adj

uste

dR

20

260

260

18F

209

1

214

3

101

3

df12

1216

Not

en

vari

esdu

eto

mis

sing

valu

es

B=

unst

anda

rdiz

edbe

taco

effic

ient

β=

stan

dard

ized

beta

coef

ficie

ntS

E=

stan

dard

erro

rof

β95

C

I=

25

low

eran

d97

5

uppe

rlim

its

of95

co

nfide

nce

inte

rval

a Se

rvic

eot

her

firm

=0

man

ufac

turi

ngfir

m=

1b P

ublic

sect

or=

0pr

ivat

ese

ctor

=1

c Par

tici

pant

=0

faci

litat

or=

1d F

orm

ulat

ion

=1

impl

emen

tati

ono

ther

=0

e Impl

emen

tati

on=

1fo

rmul

atio

not

her

=0

p

lt0

05

plt

001

plt

000

1

14 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al



Robustness checks



Discussion















16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al









16 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al














Limitations



Conclusion


References



18 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al














































































20 M P Healey et al












































22 M P Healey et al












































22 M P Healey et al


bjom12038_relationships between design characteristics and outcomes of strategy workshops

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