what is enough planning

462 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 62, NO. 4, NOVEMBER 2015

What is Enough Planning? Results From a GlobalQuantitative Study

Pedro Serrador and Rodney Turner

Abstract—Project planning is widely thought to be an importantcontributor to project success. However, there is a little research toaffirm its impact and give guidance as to how much effort shouldbe spent on planning to achieve best results. We aim to rectifythis omission. Data was collected on 1386 projects from 859 re-spondents via a global survey. A significant relationship was foundbetween the quality of the planning deliverables and success. De-tailed analysis of the data collected revealed an inverted-U relation-ship between the percentage of effort spent on planning and projectsuccess. After correcting for key moderator effects, a significant re-lationship with an R2 of 0.15 was revealed. Further analysis showedthat the fraction of planning effort that maximized the project suc-cess was 25% of project effort. This was substantially more thanthe 15% mean value reported by respondents. The greatest impactwas found to be on the broad success measures with a lesser ef-fect on project efficiency: time; budget; and scope. The inverted-Urelationship between effort spent on planning and project successindicates that projects can spend too much time in planning, aswell as too little. But we found that projects are spending less timein planning than the optimum to achieve best results.

Index Terms—Efficiency, planning, project, success.

I. INTRODUCTION

IN THIS paper, we investigate the impact of project planningand project plans on project success. Does better project

planning lead to more successful outcomes on projects? Tradi-tional project management is based to a large extent on con-jecture, with little empirical evidence in support of some of thememes [1]. Project planning is one such meme. Received wis-dom is that planning is very important and the more effort thatis put into the planning process, the better the project plans andthe more successful will be the project [2], [3]. Time spent onplanning activities will reduce risk and improve success. On theother hand, inadequate planning will lead to a failed project,[4], [5]. If poor planning has led to failed projects, then perhapstrillions of dollars have been needlessly lost [6]. Our survey ofthe literature suggests that there is a relationship between theamount of project planning and the quality of project plans,and between both of those and project success. But is there anoptimum amount of planning and how much is too much? Webelieve this relationship needs to be clarified. This leads to ourresearch question:

Manuscript received March 24, 2014; revised October 31, 2014, April 26,2015, and May 26, 2015; accepted June 12, 2015. Date of publication July 23,2015; date of current version October 16, 2015. Review of this manuscript wasarranged by Department Editor P. ED Love.

P. Serrador is with Serrador Project Management, Mississauga, ON L5E 3G3,Canada (e-mail: [email protected]).

R. Turner is with SKEMA Business School, LSMRC Univ Lille Nord deFrance, Lille F59777, France (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEM.2015.2448059

RQ: What is the impact of the amount of planning effort onproject success?

II. LITERATURE REVIEW

A. Project Success

Before we can discuss the impact of the project planningphase on success, we need to define what we mean by projectsuccess. Unfortunately, as Pinto and Slevin [7, p. 67] note “Thereare few topics in the field of project management that are sofrequently discussed and yet so rarely agreed upon as the notionof project success.” Shenhar and Dvir [8] suggest five measuresof project success: 1) project efficiency, 2) impact on the team,3) impact on the customer, 4) business success, and 5) preparingfor the future.

In this paper, we refer to the following.1) Project efficiency: completing the desired scope of work

on time and within budget, while meeting scope goals.2) Project success: meeting wider business, strategic and

enterprise goals.We follow Cooke-Davies [12] who says that project manage-

ment success is achieving the project efficiency goals and projectsuccess is achieving business and enterprise goals. Ultimately,whether or not, the latter achieved is a subjective judgment bykey stakeholders [9]. Thomas et al. [5, p. 106] state that “Ex-amples abound where the original objectives of the project arenot met, but the client was highly satisfied,” as well as the re-verse. Zwikael and Globerson [10] and Dvir et al. [2] suggestthat project efficiency and project success are often correlated.Serrador and Turner have shown that this correlation is 0.60 [9].While the measure of project success in the past has focused ontangibles [11], current thinking is that ultimately project suc-cess can best be judged by the primary sponsor [12] and will bebased on how well they judge that the project meets the widerbusiness and enterprise goals.

B. Project Planning

Mintzberg describes planning as the effort to formalizingdecision making activities through decomposition, articulation,and rationalization [13]. In construction, preproject planning isdefined as the phase after business planning where a deal isinitiated and prior to project execution [14]. Another definitionof planning is “what comes before action” [15]. For the purposeof this paper, we will use these definitions.

1) Planning phase: the phases and associated effort thatcomes before execution in a project.

2) Planning effort: the amount of effort in work hours ex-pended in planning.

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SERRADOR AND TURNER: WHAT IS ENOUGH PLANNING? RESULTS FROM A GLOBAL QUANTITATIVE STUDY 463

C. Planning in Construction

Project management has a long history in the constructionindustry and there have been a number of studies on the rela-tionship between planning and project success. Hamilton andGibson [16] found that the top third of projects from a planningcompleteness perspective had an 82% chance of meeting theirbudget goals compared to only 66% of projects in the lowerthird. Similar results are seen for schedule and design goals.Shehu and Akintoye [17] found in a study of construction pro-grams that effective planning had the highest criticality index of0.870 of all the critical success factors studied.

The project definition rating index (PDRI) is a widely adoptedmethod for industrial projects to measure completeness ofproject planning [14]. By filling a questionnaire, the complete-ness of project planning can be assessed. No planning is indi-cated by a PDRI score of 1000 while a score of 200 or lessis good planning [3]. Gibson, Wang, Cho, and Pappas showthat effective preproject planning using PDRI leads to improvedperformance in terms of cost, schedule, and operational char-acteristics. They found that scores under 200 were associatedwith cost and schedule performance 3% below budget, whereasPDRI scores above 200 were associated with costs 13% overbudget, 21% behind schedule, and twice as many change orders[18]. (Please note, PDRI is a measure of the completeness ofproject plans, not the amount of effort that has gone into theplanning process which differs from our research question.)

In addition, Gibson and Pappas note a marked difference inempirical measurements of project success based on the PDRIscore [19]. In the construction industry, project success is closelylinked to project efficiency so this can apply to efficiency andsuccess [20].

D. Planning in the Information Technology industry

The reports of high failure rates for software projects are wellknown [6], [21]. Some studies in this area have tried to quantifyhow much planning should be done for software projects. Poston[22] states that in software development projects, testing was43% of overall project effort for the projects studied, whereasplanning and requirements accounted for only 6% of effort. Healso notes that the earlier defects are identified such as in theplanning/design phase, the less they cost to fix.

Muller and Turner reported a correlation betweenpostcontract signing planning and project schedule variance[23]. Also, Tausworthe notes the importance of the work break-down structure (WBS), a planning artifact, on software projectsuccess [24]. Deephouse et al. showed that project planning wasconsistently associated with success more than other practices[25, p. 198]. The dependence for successful planning was 0.791for meeting targets and 0.228 for quality. However, they do qual-ify their findings by noting that respondents may have thoughtthat if “the project was late, clearly the plan was not realistic.”

E. Planning and Success in the General Project ManagementLiterature

Thomas et al. [5, p. 105] state, “the most effective team cannotovercome a poor project plan” and projects which started down

the wrong path can lead to the most spectacular project failures.Morris [4, p. 5] similarly argues that “The decisions made atthe early definition stages set the strategic framework: Get itwrong here, and the project will be wrong for a long time.”Munns and Bjeirmi [26] state that for a project which is flawedfrom the start, successful execution may matter to only to theproject team, while the wider organization will see the projectas a failure. Thus, there is a recurring theme that planning isinherently important to project success or one could argue thatwithout it project management would not exist. However, inthese works it is just conjecture.

Pinto and Prescott [27] found that a schedule or plan had acorrelation of 0.47 with project success, while technical taskshad a correlation of 0.57 and mission definition a correlation of0.70. Pinto and Prescott [27] again found that planning factorsdominated throughout the project lifecycle. Planning was foundto have the greatest impact on the following success criteria:perceived value of the project (R2 = 0.35); and client satisfaction(R2 = 0.39).

Shenhar [28] notes that better planning is the norm inhigh- and superhigh-technology projects. This was found toapply consistently to the deliverables normally produced in theplanning phase. Dvir and Lechler [29] found that the quality ofplanning had a +0.35 impact on R2 for efficiency and a +0.39impact on R2 for customer satisfaction. Dvir et al. [2] noted thecorrelation between aspects of the planning phase and projectsuccess. The planning procedures effort was found to be lessimportant to project success than defining functional and tech-nical requirements of the project. The correlation was 0.297 forfunctional requirements and 0.256 for technical requirements.Zwikael and Globerson [10, p. 694] noted the following, “or-ganizations, which scored the highest on project success, alsoobtained the highest score on quality of planning.” What appearsto be clear is that activities we defined as a part of the planningphase: requirements definition, scope definition, and technicalanalyses are important to project success [30].

It is clear that activities occurring prior to execution andalong with planning are important to project success [2]. Turnerand Muller note that “There is growing evidence that compe-tence in the traditional areas of the project management bodyof knowledge are essential entry tickets to the game of projectmanagement, but they do not lead to superior performance [31,p. 6]. They are hygiene factors, necessary conditions for projectmanagement performance.”

F. Reasons not to Plan

Andersen [32, p. 89] questions the assumption that projectplanning is beneficial from a conceptual standpoint. He asks,“How can it be that project planners are able to make a detailedproject plan, when either activities cannot be foreseen or theydepend on the outcomes of earlier activities?” Bart [33] makesthe point that in research and development projects too muchplanning can limit creativity.

Collyer et al. [20, p. 109] describe examples of failed projectssuch as the Australian submarine and the Iridium satelliteprojects. They say, “While useful as a guide, excessive detail inthe early stages of a project may be problematic and misleading


in a dynamic environment.” Collyer and Warren suggest that indynamic environments, creating detailed long-term plans canwaste time and resources and lead to false expectations [34].Aubrey et al. [35] note that for one project management of-fice (PMOs) they studied, overly rigorous planning processesresulted in an impediment to rapidity. Flyvbjerg et al. [36] high-light that senior management can choose not to use the estimatesfrom the planning phase.

Zwikael and Globerson [10] note that even though there isa high quality of planning in software and communicationsorganizations, these projects still have low ratings on success.Chatzoglou and Macaulay [37] note that any extra planning willresult in a chain reaction delay in the next phases of the project.Thomas et al. [5] write that for most projects there are pressuresto reduce the time and effort spent on the planning phase. Also,Chatzoglou and Macaulay [37, p. 174] consider why planningis sometimes shortened or eliminated because managers think,“It is better to skip the planning and to start developing therequested system. However, experience shows that none of theabove arguments are valid.” The literature does not support theconclusion that planning should not be done in projects thoughsome caveats are highlighted.

G. How Much to Plan?

Surprisingly, little research has been done on how muchplanning should be done in projects. We have looked at plan-ning quality and now we will look at the impact of the amount ofeffort spent planning. Daly stated, without presenting evidence,that schedule planning should be 2%, specifications 10%, andfinal design 40% of the total cost [38]. Now much of this designis done during execution. Similarly, Poston states that planningand requirements should be 6% of project cost, product designshould be 16%, and detailed design should be 25% [22]. Empir-ical guidance on how much time should be spent in planning hasbecome less common over time. Whether this is because thisguidance was found not to be effective, the diversity of technol-ogy projects increased or it simply fell out of favor is not clear.Nobelius and Trygg [39] found front-end activities made up atleast 20% of the project time. Similarly, Wideman [40] statesthat the typical effort spent in the planning phase in constructionprojects is approximately 20% of the total work hours.

Chatzoglou and Macaulay [37, p. 183] outline a rule of thumbfor planning effort for IS/IT projects, the three times program-ming rule and the lifecycle stage model: “one estimates howlong it would take to program the system and then multiply bythree” to get the total effort. Software testing is estimated totake roughly an equal amount of effort as development [41].This leaves one third of total effort for the planning phase andother miscellaneous tasks.

However, all of the above are just observations of how muchtime people spend on projects planning. There is no indicationof what is the appropriate amount of planning. Choma and Bhat[42, p. 5, 7] found that “the projects with the worst resultswere those that were missing important planning components.”However, they also found that “the projects in this sample thattook longer in planning had the worst results.” Their analysis

points to that either too much planning can be negative to projectsuccess or that a planning phase that lasts too long can be anindicator of a problem project.

Similarly, Choo reported that there is a U-shaped relationshipbetween problem definition time and project duration [43] ina study of 1558 projects in a global computer manufacturingfirm. He reported a clear relationship between problem defini-tion time, which shows similarity to the planning phase, andone measure of success, project duration. In this firm, it wascorrelated with project savings which he inferred was relatedto project success. In his final model, he reported a R2 of 14.8between problem definition time and project duration and anoptimal problem definition time between 0.20 and 0.30 of theoverall project time for the cases studied, which were just ITprojects in one company.

H. Conclusion

Dvir et al. [2, p. 94] state that “With the advancement incomputerized planning tools and the blooming in project man-agement training, a certain level of planning is done in allprojects, even in those that eventually turn out to be unsuc-cessful projects. Hence, when a certain level of planning is donein all types of projects, a significant statistical correlation cannotbe found in the data.” This is an important point. The questionof whether some planning versus no planning is correlated withproject success may be a moot. The benefits of planning havebeen confirmed through the practice of project management. Ithas, thus, become an expected part of all projects. It has be-come, as suggested by Turner and Muller [31], and as a part ofall project management books of knowledge, a hygiene factorfor successful projects. The question now is how much planningleads to the greatest success.

Table I summarizes our literature review above. From thistable, we can see that the preponderance of the literature suggeststhat planning is important for project success. Some of this isbased on empirical evidence, some just on conjecture. A smallernumber of authors suggest that there is a negative correlation,but one of these is based on conjecture, and this paper suggeststhat you can do too much planning, but it is also beneficial upto a point.

Table II summarizes the empirical results from theliterature review. A metaanalysis using weighting was consid-ered as described in Hwang et al. [44] but we did not considerthis valid, given the varied nature of the source documents: dif-ferent industries, different methodologies, and different types ofcross-functional projects. A high-level metaanalysis reviewingthe means was completed instead. These studies used differentmethodologies and even different definitions of planning andsuccess. If we compare this to the approximately 20–33% effortspent on planning reported by Nobelius, Trygg, and Wideman,there appears to a clear return on this investment in terms ofproject success [39], [40].

Thus, from the literature review, we can get a preliminaryanswer to our research question: project planning effort hasbeen found important for project success. However, what is


TABLE ISUMMARY OF THE REVIEWED LITERATURE ON RELATIONSHIP BETWEEN PLANNING AND SUCCESS

Positive empiricalrelationship betweenplanning and success

Conceptual positiverelationship betweenplanning and success

No relationship betweenplanning and success

Conceptual negativerelationship betweenplanning and success

Empirical negativerelationship betweenplanning and success

Pinto and Prescott [27] Tausworthe [24] Flyvbjerg et al. [36] Bart [33] Choma and Bhat [42]Hamilton and Gibson [16] Chatzoglou and Macaulay

[37]Andersen [32]

Deephouse et al. [25] Munns and Bjeirmi [26] Zwikael and Globerson [10]Muller and Turner [23] Morris [4] Collyer et al. [20]Shenhar et al. [30] Shenhar [47]Dvir et al. [2] Shenhar et al. [47]Gibson and Pappas [19] Ceschi [55]Dvir and Lechler [29] Zwikael and Globerson [10]Gibson et al. [18] Thomas et al. [5]Zwikael and Globerson [10] Shehu and Akintoye [17]Salomo et al. (2007) Blomquist et al. (2010)Wang and Gibson [3] Collyer et al. [20]Choma and Bhat [42]

TABLE IISUMMARY OF EMPIRICAL RESULTS AFTER SERRADOR [46]

Study Empirical Relationship Impact of planning on success, normalized to R2

Aggregate Efficiency Overall Success

Pinto and Prescott [27] Planning found to have thegreatest impact on success factors

perceived

R2 = 0.35 R2 = 0.35 R2 = 0.39

value of the project R2 = 0.39client satisfaction Average R2 = 0.37

Deephouse et al. [25] The dependence for successfulplanning was 0.791 for meeting

targets and 0.228 for quality.

R2 = 0.625 R2 = 0.34

R2 = 0.052Average R2 = 0.34

Dvir et al. [2] Meeting the planning goals iscorrelated 0.570 to overall project

success measures.

R2 = 0.32 R2 = 0.32

Dvir and Lechler [29] Quality of planning had a +0.35impact on R2 for efficiency and a+0.39 impact on R2 for customer

satisfaction.

R2 = 0.35 R2 = 0.35 R2 = 0.39

R2 = 0.39Average R2 = 0.37

Zwikael and Globerson [10] Planning quality correlates asfollows:

R = 0.52 for cost R2 = 0.27 R2 = 0.28 R2 = 0.29R = 0.53 schedule R2 = 0.28

R = 0.57 technical performance R2 = 0.32R = 0.51 customer satisfaction R2 = 0.26

Average R2 = 0.28Gibson et al. [18] R2 = 0.42 Correlation between

planning completeness andproject success

R2 = 0.42 R2 = 0.42

Salomo et al. (2007) Project planning/risk planningand innovation success

R2 = 0.33 R2 = 0.30

Goal clarity/process formality andinnovation success

R2 = 0.27

Average R2 = 0.30Wang and Gibson [3] PDRI score of a building

construction project is related toproject cost and schedule success

(R = 0.475)

R2 = 0.23 R2 = 0.23

Overall Average R2 = 0.33 R2 = 0.33 R2 = 0.34


the relationship? We, therefore, will investigate the followinghypotheses.

H1: There exists a relationship between planning qualityand project success.H2: There exists a relationship between planning effort andproject success.H3: There is an optimum amount of planning effort.

III. RESEARCH METHODOLOGY

We undertook a research to build on the existing literature andtest our hypotheses. To do this, we took a post positivist viewthat a relationship can be found between the amount of projectplanning and project success. Postpositivism falls between pos-itivism, where a completely objective solution can be found toa research question, and phenomenology, where all experiencesare subjective [45]. Because perception and observation are atleast partially based on subjective opinion, our results cannot befully objective. Some concepts such as project success may notbe fully quantifiable and are impacted by subjective judgment ofthe participants and sponsors. Postpositivism understands thatthough positivism cannot tell the whole truth in business re-search, its insights are nonetheless useful. We used inductiveanalysis to examine these relationships. We gathered data onquantities such as effort of planning phase, effort of overallproject, and percentage of project effort which was dedicated tothe planning phase. This information could be gathered usinga quantitative approach employing techniques such as surveys,a qualitative technique such as interviews, or a mixed methodsapproach. The research question being examined here is welldefined, so a quantitative approach was taken.

A. Survey

Data were collected from practitioners who are members ofProject Management Institute (PMI) or members of LinkedInproject management groups. Invitations to fill out a question-naire (an on-line questionnaire using surveymonkey.com) wereposted on discussion boards of PMI communities of practice(CoPs) as well as a number of LinkedIn groups. A notice wasalso included in some groups’ mailings. We sought to gather alarge dataset over as wide range as possible of different typesof projects. Identifying the overall population sample pool wasnot possible. Though the membership numbers for the LinkedIngroups are available (typically in the 1000s) and membershipnumbers in the PMI CoPs are also available (membership upto the 10 000s), memberships in each of these groups are notmutually exclusive. There is also no way to know how manymembers read group postings.

Respondents were asked to think of projects they had beeninvolved with and select two: one “more successful” and onethat they defined as “less successful.” The survey was targeted atproject managers but was not restricted to people who managedthe projects. The majority of respondents identified themselvesas project managers or senior project managers. Participantswere also asked about aspects of the project which we used asthe 12 moderators in our analysis, see Table VIII for a full listand appendix for the survey questions.

It is the case that with most studies of project success thatuse questionnaires or interviews, the results rely on participantsstating how successful a project was. This is subjective by na-ture. One could argue that there may be ways to measure successin an objective way; however, this likely only applies to projectefficiency. Therefore, this paper will largely be concerned withperceived project success as reported by participants. To mea-sure this factor, questions in the survey were largely based on acombination of the success dimensions defined by Muller andTurner [23] and Shenhar et al. [47]. Survey questions, in general,used a 5 or 7 point Likert-like numeric scale [48]. Pure Likertscales were not used as there were several questions where nu-merical responses were appropriate. The varying scale was usedpartially to follow the scales from the existing literature: using 7point scales to allow optimum ordinal value for numeric rangesand 5 point scales for subjective ratings. Since a variety of scaleswas used, this ensured that item context effects as per Podsakoffet al. [49] were not an issue. Monosource bias and other re-sponse biases can occur in self-rated performance measures asdiscussed by Podsakoff et al. [49]. By targeting project man-agers, we intended to receive information from the individualwho would have the best overall view of the project. The par-ticipants were asked to rate how other stakeholders viewed thesuccess of the project. Some monosource bias was, therefore,inevitable. However, to reduce the impact and for privacy rea-sons, anonymity was allowed in the survey and company nameswere not captured.

Projects can vary extensively and their need for planning canalso be variable [34]. The goal of the research was to gathera large enough dataset to study the importance of planning ingeneral over a wide range of projects. A total of 865 peoplestarted the survey with 859 completing at least the first portionof it which requested information on one successful project.Although each participant was asked to provide data on twoprojects, not all participants entered data for two projects; there-fore, the total number of projects was 1539. After removal ofoutliers and bad data, the usable total available for study was1386 projects. Projects which reported planning efforts over 2×standard deviation (SD) were considered outliers or abandonedprojects and fell outside the scope of this research. Projectswhich reported no planning time were removed as bad data.The remaining projects were reviewed for normality over thesuccess factors and were found to have a normal distribution.

People from over 60 countries answered the survey. Thelargest numbers came from the USA, 313 (36.5%), India, 59(6.9%), Canada, 57 (6.6%), and Australia, 19 (2.2%). Some 183(21.3%) chose not to answer the question. Although there wasa preponderance of responses from North America, there wasgood representation from the whole world.

B. Approach

Inductive analysis was used to find the relationship betweenplanning and success. In general, the simplest relationships weretested first and then testing continued using progressively moreinvolved techniques. The typical progression is to use correla-tion analysis to understand if there is a relationship followedby linear regression to see if there is a dependent relationship.


TABLE IIISUMMARY OF INDICES AND FACTORS

Indices and factors Description

Planning Effort Index Ratio of planning phase effort (in hours) compared to overall project effort.Efficiency Factor Summated scale of project time, budget, and scope (1–7).Success Factor Summated scale of the success of the project from the point of view of sponsors, clients, team, and end users (as reported by respondents) (1–5).Overall success Factor Summated scale of project success including efficiency variables, success variables, and respondents’ overall assessment (1–5).

TABLE IVCRONBACH ALPHA ANALYSIS OF SUCCESS MEASURES

Summary for scale: Mean = 30.776; SD = 8.45; Valid N: 1378; Cronbach alpha: 0.905; Standardized alpha: 0.922; Average interitem corr.: 0.632Mean if deleted Var. if deleted StDv. if deleted Itm-Totl- Correl. Squared-Multp. R Alpha if deleted

Project time goals 26.496 51.906 7.205 0.640 0.516 0.903Project budget goals 26.045 55.008 7.417 0.539 0.416 0.912Scope and requirements goals 25.831 54.238 7.365 0.637 0.421 0.900Project sponsors success rating 27.398 55.604 7.457 0.821 0.840 0.884Project team’s satisfaction 27.437 57.063 7.554 0.791 0.725 0.888Client’s satisfaction 27.366 55.901 7.477 0.827 0.851 0.884End users’ satisfaction 27.411 57.228 7.565 0.767 0.744 0.889Overall project success rating 27.446 55.770 7.468 0.814 0.783 0.885

TABLE VDESCRIPTIVES BY INDUSTRY WITH ANOVA RESULTS

Planning effort index Success Factor Project success rating Efficiency Factor Overall Success Factor Valid N

Construction 0.146 3.486 3.528 4.630 3.660 41Financial services 0.133 3.328 3.355 4.618 3.354 257Utilities 0.145 3.349 3.455 4.535 3.553 42Government 0.126 3.382 3.423 4.731 3.438 152Education 0.132 3.410 3.480 5.080 3.530 42Other 0.140 3.284 3.231 4.455 3.233 157High technology 0.123 3.401 3.477 4.784 3.538 223Telecommunications 0.170 3.419 3.393 4.805 3.458 133Manufacturing 0.132 3.214 3.286 4.298 3.295 122Health care 0.145 3.408 3.303 4.895 3.408 113Professional services 0.139 3.328 3.352 4.685 3.292 69Retail 0.173 3.151 2.933 4.367 3.000 35All Groups 0.153 3.347 3.361 4.656 3.397 1386p(F) 0.010 0.689 0.882 0.397 0.496

This is followed by a nonlinear regression if a significant linearrelationship is not discovered. Finally, moderated hierarchicalregression analysis (MHRA) was used to understand how thisrelationship is impacted by moderating variables [50].

IV. RESULTS AND ANALYSIS

Respondents were asked to provide project person hours spenton both planning and on the project as a whole. To facilitate theanalysis, we created some indexes and factors (see Table III).Success factors were calculated using a summated scale, that is,each term was normalized, summed together, and then the sumwas again normalized to a 1–5 or 1–7 range.

After a confirmatory factor analysis using normalized vari-max rotation was completed on the success factor, a Cronbachalpha analysis was performed (see Table IV). In general, analpha value of 0.9 is required for practical decision making sit-uations. while a value of 0.7 is considered to be sufficient for

research purposes [51]. The average was greater than 0.8 in allcases, and alpha would not greatly improve by deleting any ofthe survey questions (see appendix). The results of Cronbach’salpha analysis supported the initial assumptions that the ele-ments identified for measuring success were valid measures ofsuccess for this survey and accurately measured the judgmentsof respondents [2], [10], [47]. Projects came from a wide varietyof industries (see Table V). The analysis of variance (ANOVA)results show a significant p value for planning effort index. Thisshows that planning varies with industry. Success does not varysignificantly by industry; there are successful projects in allindustries.

A. Planning Quality Versus Success

After performing a factor analysis on the 12 moderatorscollected (see Table VIII), it became clear that four of themwere connected and described an underlying planning quality


TABLE VIREGRESSION ANALYSIS FOR PLANNING QUALITY FACTOR VERSUS THE SUCCESS MEASURE

Regression Summary for Dependent Variable: Success Factor R = 0.515; R2 = 0.265; Adjusted R2 = 0.265; p < 0.0001

Beta B p-levelIntercept 5.259 0.000Planning Quality Factor –0.515 –0.891 0.000

factor. We, therefore, named it the planning quality factor. Anormalized varimax rotation was selected to achieve the highestloadings and best model fit. The other factors were not found tobe significant.

Planning Quality Factor = mean of the following four re-sponses: 1) quality of the WBS; 2) quality of goals/vision; 3)stakeholder engagement level; and 4) experience level of team.

Some components can be clearly seen to be the result of athorough analysis and planning exercise: Quality of the WBSand quality of goals/vision. Another component can be seenas an important input to a good planning effort: Stakeholderengagement level. Experience level of the team does not imme-diately come to mind as a planning related variable. However,based on the factor analysis, it is related to planning quality. Onecan speculate that this may be because a better planning cyclemay allow the selection of a more effective team or that moreexperienced teams complete more effective planning.

Now that the meaning of the factor has been defined, we nextcompleted a regression of success versus the planning qualityfactor (see Table VI). This shows a statistically significant re-lationship with a low p < 0.0001 between the planning qualityfactor and overall success. In addition, there is a strong R2 of0.265. This result is in broad agreement with the average R2

reported in the literature of 0.34. The planning quality factorcalculated here is a measure of planning quality similar to whatwas studied in the previous research. However, it is not as com-prehensive, so a lower R2 is to be expected. This result is inkeeping with previous research and validated the methodologyof this research. Therefore, hypothesis H1 is supported.

H1: There exists a relationship between planning quality andproject success.

B. Planning Effort Versus Success

To start the effort impact analysis, we examined therelationship between planning effort index and project suc-cess rating. Note that the rating was the single measure ofoverall success reported by participants. The rating was usedrather than the success factors because it is easier to graph forillustration purposes. We found that in general the planning in-dex increases within the success category. The exception is thefailure category that showed the highest mean planning effortindex of any group. The ANOVA analysis did not show a sta-tistically significant relationship. By looking at these means, itappeared that a simple linear relationship did not exist.

These data were now plotted to get a visual picture of therelationship (see Fig. 1). Looking at this graph, we can see thelowest amount of effort was typically spent on projects deemed

Fig. 1. Mean plot of planning effort index by project success rating with errorbars (where 5 is a highly successful project).

not fully successful. In this case, one can hypothesize that in-adequate planning impacted project success. Projects deemedoutright failures reported the mean highest percentages of up-front project planning. This is an interesting finding in keepingwith Choma and Bhat [42].

Based on Fig. 1, it was decided to review the data with anassumption that the relationship between the effort index andproject success is not linear but could be polynomial in nature(see Fig. 2). There is clearly a quadratic relationship betweenthe planning effort index and the overall success factor. Thisfits with position that if a project spends too much effort in theplanning phase, too much of the overall budget and time will bespent before execution [37]. This would make the project lesssuccessful overall. Also, complex or challenging projects with alow probability of success may have very long planning phases.Conversely, a project that spends too little upfront time planningwill also be less successful [2]. Therefore, an inverted-U curvefits with the proposition and the findings of the literature review.

Table VII shows a more detailed analysis based on a nonlinearregression. The overall was p < 0.0059 which shows statisti-cal significance of the polynomial model specification. The fitof this relationship is quite low with R2 less than 0.01. Thissuggests a small causal relationship indicating that less than1% of project success can be attributable to the amount of ef-fort spent planning. This is counterintuitive and deserved furtheranalysis. The residuals were examined to confirm normality andhomoscedasticity and results were acceptable. Twelve variableswere examined to find their impact on the relationship between


TABLE VIINONLINEAR REGRESSION ANALYSIS OF PLANNING EFFORT INDEX VERSUS OVERALL SUCCESS FACTOR

Regression Summary for Dependent Variable: Overall Success Factor R = 0.086; R2 = 0.007; Adjusted R2 = 0.006, p < 0.0059Beta B p-level

Intercept 3.191 0.000Planning effort index 0.255 2.026 0.001Planning effort index ∗∗2 −0.239 −4.063 0.003

Fig. 2. Scatterplot and curve fitting for overall success factor versus planningeffort index.

TABLE VIIISUMMARY OF MODERATOR FINDINGS FOR DEPENDENT VARIABLE SUCCESS

AND INDEPENDENT VARIABLE PLANNING EFFORT INDEX

Moderator Role Versus Project Success

Quality of WBS Independent variable and moderatorQuality of the goals/vision Independent variableStakeholder engagement level Independent variableExperience level of team Independent variable and moderatorInternal versus Vendor based ModeratorMethodology type (traditional versusagile)

Independent variable and potentialmoderator

Novelty to organization Independent variableTechnology level of the project No relationshipProject length No relationshipProject complexity No relationshipNew product versus Maintenance No relationshipTeam size No relationship

planning and success, initially in the factor analysis and then asmoderators (see Table VIII).

When we completed an MHRA using these interaction rela-tionships, we get the results shown inTable IX. For the MHRAanalysis, we will be trying to discover the underlying relation-ship between dependent and independent variables and under-stand how it is impacted by moderating variables as per Sharmaet al. [50]. MHRA analysis in SPSS enables us to explore theserelationships in more detail. We can see that through the moder-ator analysis, a more significant relationship between planning

TABLE IXMHRA ANALYSIS FOR SIGNIFICANT MODERATORS IN THE PLANNING EFFORT

INDEX VERSUS OVERALL SUCCESS FACTOR RELATIONSHIP

Variables entered Step 1 Step 2 Step 3

Main EffectsPlanning effort index 1.972∗∗ 2.030∗∗ 13.007∗∗∗

Planning effort index∗∗2 −4.044∗∗ −4.103∗∗ −25.064∗∗∗

ModeratorsInternal vs vendor based 0.028 0.010Interaction TermsWBS∗Planning effort index −2.927∗∗∗

WBS∗Planning effort index∗∗2 4.662∗∗∗

Experience∗Planning effort index −3.965∗∗∗

Experience∗Planning effort index∗∗2 8.944∗∗∗

Internal∗Planning effort index 0.619+Internal∗Planning effort index∗∗2 −1.330+F for Regression 5.404∗∗ 8.510∗∗∗ 26.851∗∗∗

R2 0.006 0.016 0.145

+ p < 0.10; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

effort and project success has been uncovered, with R2 = 0.145,p < 0.001.

In order to confirm the final model, we completed a generalregression analysis with the interaction terms (see Table X). Theresult of this model is both a very good p value <0.001 and arelatively strong R2 = 0.145. Tests of residuals for this modelalso showed good results. Normal probability plots, p–p plotsand homoscedasticity plots were good. This model was alsoregressed against the success factor and efficiency factor. Thesuccess factor produced very similar results, while regressionagainst the efficiency factor had a good p value but a lower R2

of 0.079.We can now graph the resulting curve (see Fig. 3). However,

since there are three moderators impacting the shape of thiscurve, we need to make assumptions on their values. If weassume that these moderators have maximum impact on therelationship, a project is potentially impacted by 1.5 successfactor levels out of 5 if planning time is too low.

One can note from this graph that the y intercept indicates thatfor zero upfront planning projects still show average success.However, these curves refer to averages of variable projects withnumerous moderators in play. Many projects also do substantialplanning during execution. The point we must take away is thatfor the average project, doing no upfront planning will reduceits success rating by 0.5 to 1 success levels. That is, turn asuccessful project to an average project, or an average project toan unsuccessful one. The impact on projects that have an overlylong planning phase is even more severe.


TABLE XMULTIPLE REGRESSION OF FINAL MODEL AGAINST OVERALL SUCCESS FACTOR WITH MODERATOR INTERACTION TERMS

Regression Summary for Dependent Variable: Overall success factor R = 0.387; R2 = 0.150; Adjusted R2 = 0.145; p < 0.000Beta B p-level

Intercept 3.222 0.000Planning effort index −0.898 12.648 0.000Planning effort index ∗∗2 0.629 −24.405 0.000WBS∗Planning effort index −0.984 −2.924 0.000WBS∗Planning effort index∗∗2 0.966 4.653 0.000experience∗Planning effort index 0.405 −3.960 0.000experience∗∗Planning effort index∗∗2 −0.358 8.928 0.000internal∗Planning effort index 0.405 0.713 0.000internal∗Planning effort index∗∗2 −0.358 −1.495 0.005

TABLE XICALCULATED OPTIMUM PLANNING INDEX VALUES FOR RELATIONSHIP BETWEEN PLANNING INDEXES AND SUCCESS FACTORS

Overall success Project success Project Efficiency Mean Reported planning index average

Planning Effort index 0.255 0.248 0.250 0.251 0.153

Fig. 3. Planning effort index versus success for maximum moderator values.

Our second hypothesis is therefore supported.H2: There exists a relationship between planning effort and

project success.

C. Optimum Planning Effort

Since there is a quadratic relationship between planningeffort and success factors, it was possible to calculate a maxi-mum to the resulting quadratic curve [52]. The results are givenin Table XI. We can see from this table that the optimum plan-ning amounts are relatively consistent between the three successfactors. In addition, optimum planning values were calculatedon various subsets of the data.

The mean project planning effort reported by respondentswas substantially lower at 15.3% of total effort. This confirms aview that should not be surprising to practitioners; not enoughplanning is being done and that if longer planning phases werethe norm, there would be higher overall project success.

These results are interesting from a number of viewpoints.They are in line with the approximately 20–33% effort spenton planning identified in the literature review [37], [39], [40].Second, this result is lower than the R2 = 0.33 with efficiencyand R2 = 0.34 with success reported from the literature reviewmetaanalysis (see Table II) implying that there can still be a re-turn on investment from spending 25% of effort on the planningphase. The three results are also within 0.01 of each other, which

helps to validate the research methodology. Finally, the resultsare higher than the averages found in this survey as reported inTable V.

Finally, our third hypothesis is supported.H3: There is an optimum amount of planning effort.

V. SUMMARY

Our hypotheses were as follows.H1: There exists a relationship between planning qualityand project success.H2: There exists a relationship between planning effort andproject success.H3: There is an optimum amount of planning effort.

These three hypotheses were all supported. This research didconfirm the relationship between planning effort and projectsuccess and that a quadratic relationship exists between thepercentage of effort spent planning and project success. Thisrelationship showed statistical significance with a low p valuebut also had a low R2 value, which showed a relatively weakrelationship. After completing moderator analysis, a model wasderived that showed that this relationship had an R2 of 0.15,which is a notable relationship for factors in the study of projectmanagement. Projects are often large, complex efforts and anyone factor that can account for 15% of their success is important.This satisfactorily answered our research question: What is theimpact of the amount of planning effort on project success.

Table XII gives a summary of the findings. We can see thatwith the moderator analysis complete, we have both a modelwith a better fit as well as higher statistical significance. Thisshows even more statistical significance for the full model in-cluding moderators. The planning effort has a stronger link withoverall success than with project efficiency. This may indicatethat shortening planning cycles impact projects by reducingtheir final value to the company and stakeholders even thoughmanagers may still be able to deliver them on time and budget.


TABLE XIISUMMARY OF MAIN FINDINGS OF BINOMIAL RELATIONSHIP BETWEEN PLANNING INDEXES AND SUCCESS FACTORS

Base Moderators includedOverall success factor (R2) Success Factor (R2) Efficiency Factor (R2) Overall success factor (R2) Success Factor (R2) Efficiency Factor (R2)

Planning Effort index 0.006∗∗ 0.006∗∗ 0.003∗ 0.145∗∗∗ 0.142∗∗∗∗ 0.079∗∗∗

p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001

TABLE XIIISUMMARY OF SUBGROUPS ANALYSIS FOR OPTIMUM PLANNING LEVELS

B-Intercept B-Planningeffort index

B-Planningeffort index∗∗2

P Valid N Averageplanning effort

Optimumplanning level

Region— NorthAmerica

3.278 2.559 −5.656 0.031 756 0.151 0.226

Team type—International

3.156 3.035 −7.032 0.03 442 0.149 0.216

Industry—ProfessionalServices

3.065 4.313 −8.575 0.087 54 0.139 0.251

Industry-Education

3.009 6.631 −15.779 0.118 42 0.132 0.210

Industry—Government

3.346 1.517 −5.167 0.105 152 0.126 0.147

Industry—Retail 2.421 5.303 −5.208 0.108 30 0.173 0.509

Our findings and methodology are supported by those ofChoo [43]. He adopted a similar methodology and foundR2 = 0.148 between problem definition duration and projectduration/savings though our findings are more general in defini-tion and breadth [43]. In addition, our optimum planning indexfindings fall within Choo’s range of optimum problem definitiontimes of between 0.20 and 0.30 of total project time. The veryclose concordance of these results from two unrelated studies,speak to the robustness of the results.

A. Limitation of the Study and Robustness

Monosource bias is a limitation of this study. Data on eachproject were collected from only one individual. This is, un-fortunately, is somewhat unavoidable in a global, open, on-linesurvey. Steps were taken to reduce this impact by requestinga successful and an unsuccessful project from each participantand by granting both company and personal anonymity to par-ticipants [49]. As well, the results of this research are in concor-dance with other research such as Choo [43] where monosourcebias was not an issue. Future research could, however, mea-sure this effect through interviews with a wider range of projectstakeholders either in a smaller, more targeted survey or usingqualitative techniques.

The initial low R2 value was a concern area in this research.Low R values with low p values can still be significant, thoughsome questions the importance of the relationship [48]. A varietyof investigations were undertaken to confirm the robustness ofthis result when the initial R2 was lower than expected.

1) Analysis of polynomials with higher powers: This didnot yield solutions with better p or higher R. p valuesincreased p = 0.08 for power 3 models and p = 0.15 formodels with powers of 4 rendering them not statisticallysignificant.

2) Linear analysis of subsets of the data from planning ef-fort index of 0.01 to 0.24: This did not produce a morestatistically significant result while R2 fell to 0.0047.

3) Analysis using log of effort index: This did not produce astatistically significant result: p increased to 0.15

4) Regression of the hours spent planning rather than theindex value: This did not yield solutions with better p orhigher R.

5) Alternative measurement specifications for project suc-cess were investigated and produced results were compa-rable to the reported ones.

None of these attempts yielded results that had both anacceptable p value and higher R values. As well, subsets ofthe data were examined in the analysis of some subgroups suchas industries and regions. For those groups where there were ad-equate data for statistical significance, R2 results were similarlylow. They typically fall in the 0.005–0.02 range. Subsets withp values in the 0.10 range, which are out of statistical rangefor this research, also showed similar low R characteristics.This supports the validity of the research methodology. Withthe moderator impact accounted for, higher R2 values becameapparent.

Subgroups were also analyzed to confirm optimum planninglevels (see Table XIII). Below is a summary of some of thoseanalyses. The statistical significance level (p ≤ 0.05) wasrelaxed to p˜0.10 to allow a broader view. Note that thesesmaller groups do not have the very good p values of the otherparts of this paper. Subgroups not noted below had even higherp values and were not considered. This is likely due to the issuethat several hundred projects are required for a statistically validsample and in most cases, this volume of data was not availablefor those groups. The results show some of the variationbetween industries. Retail, for example, shows a very high


SURVEY QUESTIONS

Category Question Response ranges Reference

Project effort What was the total project effort (in persondays).

Numerical—person days [53]

Planning phase effort What was the total effort expended on theplanning phase (person days)? Planning:

everything before execution.

Numerical—person days [53]

Project success rating—overall How do you rate the overall success of theproject?

5 point scale—failure, not fully successful,mixed, successful, very successful

[47]

Success: Sponsor feedback How did the project sponsors andstakeholders rate the success of the

project?

5 point scale—see above [23], [47]

Success: Meeting budget goals How successful was the project in meetingproject budget goals?

7 point scale—>60% over, 45–59% over,30–44% over, 15–29% over, 1–14% over,

on budget, under budget

[2], [10], [47]

Success: Meeting timeline goals How successful was the project in meetingproject time goals?


Success: Meeting scope/requirements How successful was the project in meetingscope and requirements goals?

7 point scale—see above [2]

Success: Team’s view How do you rate the project team’ssatisfaction with project?


Success: Client’s view How do you rate the client’s satisfactionwith the project’s results?


Success: Users’ view How do you rate end users’ satisfactionwith project results?


Project team size How large was the project team (full timestaff equivalent)

7 point scale [53]

Project complexity Rate the complexity of the project 3 point scale—Low, Medium, High [54]Project length What was the project length (full lifecycle) 3 point scale—< 1 year 1 to 3 years, >

3 years[12]

WBS Rate the detail level of the WBS used theproject

4 point scale—Excellent, Good, Poor,Very Poor/Not used

[24]

Goals/Vision Statement Rate the applicability/quality of the visionstatement or project goal definition for the

project.


Novelty to the organization How new is this type of project to theorganization?


Internal versus vendor What percentage of project was completedby vendors?

6 point scale [53]

Local versus remote team Where were the team members located?Choose the option that best fits the

majority of team members.

3 point scale [53]

Level of use of technology Low tech indicates none or very maturetechnology where super high-tech

indicates the use or development ofcompletely new technology.

4 point scale [47]

New product versus maintenance Does this project involve developing a newproduct, installation or system or is it

related to maintenance of what alreadyexists?

3 point scale [47]

Experience level of team How experienced was the project team? 3 point scale [5]Degree of stakeholder engagement How engaged were the key stakeholders

for the project?4 point scale [9]

Methodology type How much of the project was done usingagile or iterative techniques? (100 = fullyagile, 0 = fully waterfall, 50 = an equalmix of agile and waterfall techniques.)

6 point scale—80–100%, 60–79%,40–59%, 20–39%, 1–19%, 0%

[55]

planning optimum perhaps because for retail projects, the ma-jority of work is really in the planning. There is a little to buildin execution compared to construction and IT, for example. Thisis an interesting result with only 30 datapoints. Interestingly,the optimum planning level for government projects was quitelow. One can speculate that they are less dependent on upfrontplanning and perhaps more dependent on other success driverssuch as change management or stakeholder management. Onecan argue this is in keeping with the work of Flyvbjerg et al.[36]. In general, however, the subgroups analysis is in the rangeof and validates the overall results.

B. Summary of Recommendations

Planning is important to project success as numerous authorshave previously written [4], [5], [22]. It is clear from this re-search that the average project is not spending enough time onupfront planning to maximize success. This should not be sur-prising to researchers or practitioners; it appears that in industry,not enough planning is being done and that if longer planningphases were the norm, there would be higher overall projectsuccess. The inverted-U-shaped relationship between planningeffort and success is significant and should be considered infuture research.


The planning phase effort does not impact all aspects of suc-cess equally. The planning phase effort has the strongest rela-tionship with overall project success. Reducing the effort spenton the planning phase may impact projects by reducing theirfinal value to customers, stakeholders, and the company. Thismay be the case even though managers may still be able todeliver them on time and within budget.

The phenomenon that projects may not be planning ade-quately could be a factor in the high project failure rates reportedin the literature [6], [21]. It is recommended that projects con-sider doing more planning upfront both for traditional projectsand for agile projects. However, projects with a too longplanning phase were also found to have lower success ratings.Projects that schedule more than 25% effort on the upfrontplanning phase should be reviewed for progress and risk fac-tors. Overplanning could be a symptom of a project that is toocomplex to deliver successfully, a lack of firm requirements orof a team that is not experienced enough in this project area: allof which could potentially lead to a failed project.

C. Areas for Future Research

This research does present some results that warrant furtherinvestigation.

1) Industry differences: Further research should considerfocusing on specific industries, consolidating industrygroups or collecting a larger volume of data.

2) Regional differences: This may be potential for researchon regional differences from a variety of viewpoints: busi-ness environment, culture, infrastructure, and tradition.

3) Planning phase time: A research effort, perhaps qualita-tive, looking specifically at the factors that define the timespent on the planning phase could be considered.

4) Planning expertise: The impact of planning expertise,planning experience, or planning phase training on therequired planning time and impact on project success is afactor that could be further examined.

5) Cost/Benefit of planning: The cost/benefit of additionalplanning for organizations that deliver projects is an areafor potential future investigation.

C. APPENDIX

See Appendix Table in previous page.

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Pedro Serrador received the B.Sc.(Hons.) degree inphysics and computer science from the University ofWaterloo, Waterloo, ON, Canada; the MBA degreefrom Heriot-Watt University, Edinburgh, Scotland;and the Ph.D. degree in strategy and programme andproject management from SKEMA Business School(Ecole Suprieure de Commerce de Lille), Euralille,France.

He is a Writer and a Researcher on project man-agement topics and the Owner of Serrador ProjectManagement, a consultancy in Toronto, Canada. He

is also an Adjunct Professor at Humber College, Toronto, and the Univer-sity of Toronto, Toronto. He specializes in technically complex and high riskprojects, vendor management engagements, and tailoring and implementingproject management methodologies; he has worked on projects in the financial,telecommunications, utility, medical imaging, and simulations sectors for someof Canada’s largest companies. His areas of research interest are project success,planning, and agile, and he has presented a number of peer-reviewed papers onthese topics at academic conferences. He is an author of books and articles onproject management, and is also a regular speaker at PMI global congresses.

Dr. Serrador received the PMI 2012 James R. Snyder International StudentPaper of the Year Award and the Major de Promotion Award for best Ph.D.Thesis 2012–2013 from SKEMA Business School.

Rodney Turner is a Professor of Project Manage-ment at SKEMA Business School, Lille, France,where he is the Scientific Director for the Ph.D.in Project and Programme Management, and is theSAIPEM Professor of Project Management at thePolitecnico di Milano, Milano, Italy. He is also anAdjunct Professor at the University of TechnologySydney, Sydney, Australia. He is the author or editorof 18 books. His research areas cover project man-agement in small to medium enterprises, the manage-ment of complex projects, the governance of project

management including ethics and trust, project leadership, and human resourcemanagement in the project-oriented firm.

Mr. Turner is an editor of the International Journal of Project Management.He is the Vice-President and an Honorary Fellow of the United Kingdom’sAssociation for Project Management, and an Honorary Fellow and former Pres-ident and Chairman of the International Project Management Association.