PAPERS
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 21
\nINTRODUCTION \u25a0
\nT
\nhis article considers the impact of program alignment and related factors
\nthat contribute to successful program delivery. The study
\nattempted to disclose the key underlying assumptions that connect
\nprogram management with related theories of strategic, organizational,
\nand project ( help with nursing paper writing from experts with MSN & DNP degrees) management. Exposing the hidden management ideology
\nand practices that actually inform structure and content requires an
\nunderstanding of program success and failure factors (Lycett, Rassau, &
\nDanson, 2004). The identification of an interaction structure and management
\npractice that supports continuous alignment may thus provide significant
\npotential for reducing the unacceptable rate of program failures
\n(Kotter, 1995; Morris, Crawford, Hodgson, Shepherd, & Thomas, 2006). The
\nstudy should be of relevance to professionals who retain or exercise influence
\nover the formation and execution of programs.
\nBusiness strategy is complex and intertwined with all the processes and
\nsystems that are required to effectively manage an organization. Program
\nmanagement may be considered an effective building block and umbrella
\nframework in the operationalization of business strategy. The links between
\nbusiness strategy and program management reside within the alignment of
\nthe strategic processes of formulation and implementation. Strategic alignment
\nwill be unique to a particular organization and will involve a dynamic
\nand iterative process of mutual adjustment and reshaping (Beer, Voelpel,
\nLeibold, & Tekie, 2005). Strategic implementation in many companies is an
\nenigma due to misaligned project ( help with nursing paper writing from experts with MSN & DNP degrees)s and a lack of a systemic approach to business
\nstrategies.
\nUnderstanding the potential contribution of program alignment may
\nthus further contribute to the improvement of the effectiveness and efficiency
\nof the delivery of strategic objectives (Burdett, 1994; Chorn, 1991;
\nStrassmann, 1998). This study has empirically explored implementing strategy
\nthrough programs and the need to continually manage program context.
\nProgram management environments are complex, and the uncertainty
\nthat arises from multiple combinations of unique personalities, stakeholder
\nexpectations, assumptions, constraints, changing environments, and human
\nsocial systems can provide the impetus for failure (Lehtonen & Martinsuo,
\n2009). The messy, complex, and multifaceted environment of program management
\nproduces a need to continually realign the program and related
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees)s to changing environmental and corporate objectives (Thiry, 2004).
\nPrevious research has recommended new directions that focus attention on
\ncausality and complexity in context (Ivory & Alderman, 2005; Morris & Pinto,
\n2007; Pollack, 2007). The inherent complexity involved in applying structured
\nprogram management frameworks to organizational contexts thus warrants
\nfurther serious consideration (Pellegrinelli, Partington, Hemingway,
\nMohdzain, & Shah, 2007). This study responds to this by applying a dynamic
\nSuccessful Programs Wanted:
\nExploring the Impact of Alignment
\nGraeme Ritson, Northumbria University, Newcastle upon Tyne, United Kingdom
\nEric Johansen, Northumbria University, Newcastle upon Tyne, United Kingdom
\nAllan Osborne, Northumbria University, Newcastle upon Tyne, United Kingdom
\nABSTRACT \u25a0
\nAlignment between formulation and implementation
\nof business strategy can be important for
\nachieving successful programs. The authors
\nhave explored the development of a program
\nmanagement alignment theory. Statistical testing
\nshowed that interaction between the study
\nmodel variables was found to be multidimensional,
\ncomplex, and subtle in influence. Thus,
\nthe authors conclude that programs have both
\ndeliberate and emergent strategies requiring
\ndesign and management to be organized as
\ncomplex adaptive systems. Program life-cycle
\nphases of design and transition were often
\nformed from an unclear and confusing strategic
\npicture at the outset, which can make those
\nphases difficult to control. Learning was established
\nas an underlying challenge. The study
\nmodel demonstrated continuous alignment as
\nan essential attribute contributing toward successful
\ndelivery. This requires program design
\nand structure to adopt an adaptive posture.
\nKEYWORDS: corporate strategy; program
\nmanagement; governance; continuous alignment;
\ndeliberate and emerging strategies
\nProject Management Journal, Vol. 43, No. 1, 21\u201336
\n\u00a9 2011 by the Project Management Institute
\nPublished online in Wiley Online Library
\n(wileyonlinelibrary.com). DOI: 10.1002\/pmj.20273
\n22 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\nsystems perspective to program management.
\nThis may improve the usefulness
\nand practical application of existing
\ngood practice frameworks (Office of
\nGovernment Commerce [OGC], 2007;
\nProject Management Institute [PMI],
\n2009).
\nTheoretical Background
\nand Model
\nThe study was operationalized through
\nthe core concepts of systems, governance,
\ninnovation and learning, corporate
\nstrategy, environmental factors,
\ncontinuous alignment, and successful
\ndelivery (Figure 1). This viewed success
\nas a multidimensional construct\u2014
\nprogram management success, program
\nsuccess, achieving business objectives,
\nstrategic orientation, and business success
\n(Shenhar, Dvir, Levy, & Maltz,
\n2001). These dimensions consider success
\nfrom a business, corporate, and
\neconomic level, respectively. Appropriate
\nhypotheses were advanced to
\nformulate a reasonable prediction
\nabout the relationship of the variables
\ncontained in the model.
\nCorporate Strategy, Project, and
\nProgram Management
\nProgram management is strategic in
\norientation through delivering outcomes
\nand benefits related to the organization\u2019s
\nstrategic goals (Association
\nfor Project Management [APM], 2007a;
\nOGC, 2007; PMI, 2009). This will require
\nthe program and interrelated project ( help with nursing paper writing from experts with MSN & DNP degrees)s
\nto have their objectives and strategies
\naligned with corporate strategy to create
\nan iterative hierarchy that develops
\ninto business operations (Dietrich &
\nLehtonen, 2005). Some organizations
\nmay be adopting program management
\nas they develop their strategic
\nmanagement capability. There will be a
\nmultiplicity of options available in
\nachieving strategic objectives through
\nprogram-driven approaches requiring
\nintelligent program design. Context will
\nbe crucial in determining appropriate
\nprogram formation (Pellegrinelli, 2002;
\nPellegrinelli et al., 2007). International,
\ngovernment, societal, industrial, commercial,
\nand business programs will
\ndiffer in focus and predictability of outcome.
\nClearly articulated corporate strategy
\nwill support the prioritization and
\nexecution of the right programs and
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees)s. This alone will not guarantee
\nprogram success. Ill-conceived business
\nstrategy will not necessarily be
\nredeemed by program and project ( help with nursing paper writing from experts with MSN & DNP degrees)
\nmanagement. This will make reliable
\nprioritization and consistent allocation
\nof resources based on the greatest
\nstrategic contribution more difficult
\n(Hrebiniak, 2006). Programs may also
\nbe compromised by strategic business
\ncase misrepresentation. This will result
\nin misalignment with strategic objectives.
\nManaging programs will involve
\nthe task of remaining aligned with corporate
\nstrategy (OGC, 2007).
\nSome organizations have welldeveloped
\nprogram management
\nmaturity and organizational management
\nsystems that support continuous
\nalignment. Well-aligned organizations
\nwill be able to prioritize both activities
\nand how identified work gets executed.
\nThe greater the alignment between the
\noperating environment, strategy, structure,
\nand processes, the more positive
\nH1. Corporate strategy
\nH6. Continuous
\nalignment
\nH2. Internal and external
\nenvironments
\nH4. Systems, subsystems,
\nand processes H3. Successful delivery
\nH5. Program
\ngovernance
\nH7. Learning and
\ninnovation
\nFigure 1:Theoretical model and hypotheses.
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 23
\neffects this will have on performance
\n(Middleton & Harper, 2004). Alignment
\nwill be essential for strategic success,
\nalthough nonalignment may exist for
\ntemporary periods through significant
\norganizational and industry-sector
\nchange. Strategy formulation and
\nimplementation will thus require organizational
\nalignment with its resource
\ncapability (Engwall & Jerbrant, 2003).
\nShenhar et al. (2001) also suggested
\nthat project ( help with nursing paper writing from experts with MSN & DNP degrees) success will be strongly
\nlinked to an organization\u2019s business
\neffectiveness.
\nResearch indicates that project ( help with nursing paper writing from experts with MSN & DNP degrees) failure
\nis distinctly linked to factors at the
\nfront end of a project ( help with nursing paper writing from experts with MSN & DNP degrees) through misalignment
\nwith an organization\u2019s key strategic
\npriorities (Pinto & Slevin, 1998). Hyvari
\n(2006) and Engwall (2003) supported
\nthis in concluding that organizational
\ncontext will be an important factor
\nin determining success or failure.
\nPrograms should be selected and
\nformed from organizational strategy by
\naligning and coordinating related project ( help with nursing paper writing from experts with MSN & DNP degrees)s
\n(Morris & Jamieson, 2005). Program
\nformation and structure may be unclear
\nat the outset, requiring flexible
\nstrategic implementation planning and
\nmodular phased project ( help with nursing paper writing from experts with MSN & DNP degrees)s. This need for
\na dynamic interface provides a clear
\nbusiness case for organizations to
\nimprove their capability in the management
\nof programs of project ( help with nursing paper writing from experts with MSN & DNP degrees)s and to
\nensure that structure follows business
\nstrategy. These and other contextdependent
\nand decision-oriented
\nissues lead to the following hypothesis:
\nH1: Corporate strategy leads to a
\nvision and stakeholder strategy that
\ntakes account of the organization,
\nmarket, and sector in which it operates.
\nEnvironmental Factors
\nModern organizations are constantly
\nanalyzing their business activities and
\nindustry sector, searching for business
\nopportunities (Venkatraman, 1989).
\nThis may result in constant change of
\nbusiness priorities and plans. De Wit
\nand Meyer (2005) argued that the most
\ncommon cause of corporate failure is
\nmisalignment between the organization
\nand its environment. Sheppard
\nand Chowdhury (2005) strengthened
\nthis argument and emphasize that a
\nfundamental failure of management
\nwill exist if they do not properly evaluate
\nthe environment. Globalization and
\ntechnological innovation create dynamic
\nand complex environments for many
\ncurrent businesses where change is
\na constant factor. Managing major
\nchanges successfully may require an
\norganization-wide approach, and this
\nwill impact at both the operational and
\nstrategic levels (Carnall, 2007). The size
\nand scale of some organizations make it
\nimpracticable for a radical change from
\nexisting practices to be orchestrated at
\nthe same time. Strategic change decisions
\nmust be oriented in the most
\nappropriate sequence to increase the
\nlikelihood of execution success (Bruch,
\nGerber, & Maier, 2005). The OGC (2008)
\nrecommended the adoption of program
\nand project ( help with nursing paper writing from experts with MSN & DNP degrees) prioritization categories to
\nensure alignment between business
\npriorities, current capability, and
\ncapacity to deliver.
\nWell-constructed and well-managed
\nprograms should provide confidence
\nthat the right project ( help with nursing paper writing from experts with MSN & DNP degrees)s are being sponsored
\nand that the desired benefits will
\nbe achieved. Managing a program
\nwill be a complex undertaking requiring
\nboth project ( help with nursing paper writing from experts with MSN & DNP degrees) management acumen
\nand the capability of a business leader
\n(Pellegrinelli, 1997). Every program will
\nbe unique to its contextual environment.
\nRapidly changing and chaotic
\nenvironmental factors will create a high
\nlevel of task and organizational complexity.
\nThis will require the impetus to
\nmonitor and challenge program and
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees) performance. Increasing program
\ncomplexity may provide an ever-present
\nthreat of failure. Solving one unyielding
\nproblem may create unexpected drawbacks
\nelsewhere. The more complex the
\nprogram, the greater interaction risk and
\ninterdependence with the internal
\nand external environment (Verma &
\nSinha, 2002). This will necessitate a focus
\non the tension between strategic direction,
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees) delivery, operational effectiveness,
\nand external influences. The
\norganization is unlikely to be in total
\nalignment and will have different
\nchange capabilities. An open outlook
\nand sense of cooperation would be
\nideal but this is seldom realized in practice
\n(Kotter & Schlesinger, 2008).
\nOrganizational change resistance will
\nbe inherent (Ford & Ford, 2009). The
\nprogram will more than likely be differentiated
\nand gradual rather than radical
\nand coordinated.
\nKnowledge of the current and future
\nenvironment will influence the choice of
\nstrategic objectives and strategies
\nemployed. Changes to the organizational
\nand business environment may lead
\nto significant alterations to the program
\nscope and priorities. The time horizon
\nand long-term nature of some programs
\ncan have a significant impact on manageability.
\nThe speed of technical evolution
\nand communication technology
\nmay require adjustment or even cancellation
\nof tight-time-frame programs.
\nThe program\u2019s mission should provide a
\nfocus for an integrated continuous decision
\nalignment framework (Scherpereel,
\n2006). Major strategic reviews may be
\nrequired at different times in the
\nprogram\u2019s life cycle to coordinate alignment.
\nThis will be an indicator of organizational
\nstrategic maturity (APM,
\n2007b). Unpredictable environmental
\nfactors are thus cross-linked to managing
\nprograms and will require a dynamic,
\nflexible, and adaptive temporary
\norganization. Program management in
\npractice will involve top-down strategic
\nimplementation linked with bottom-up
\nemerging management strategy through
\nsuccessful project ( help with nursing paper writing from experts with MSN & DNP degrees) integration in the
\nhost organization (Srivannaboon &
\nMilosevic, 2006). These emergentshaping
\nconjectures lead to the following
\nresearch hypothesis:
\nH2: The organization\u2019s strategy is
\ninfluenced and reshaped from both
\nthe internal and the external environments.
\n24 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\nSystemic Factors
\nProgram management is a management
\nstrategy informed by complexity
\nthinking, which increases manageability
\nand coordination. Deliberate and
\nemergent business strategies will
\nrequire flexibility in the program design
\n(Elizabeth & Ysanne, 2007; Mintzberg,
\n1994; Mintzberg, Ahlstrand, & Lampel,
\n1998; Mintzberg & Waters, 1985).
\nEmergence describes a dynamic
\nprocess that is the product of ongoing
\nsystem interactions. This refers to the
\ncoexistence and impact of program
\nmanagement, project ( help with nursing paper writing from experts with MSN & DNP degrees) management,
\nbusiness-as-usual activities, environmental
\nfactors, and corporate governance.
\nThe emergent and coevolutionary
\ndynamic of program management will
\nrequire open systems. Open system
\nrefers to the uncontrollable variable of
\nthe environment and the self-organizing
\ntendency if left unmanaged. This introduces
\nnonlinear interaction, unpredictability,
\nand feedback loops in support
\nof organizational learning theory.
\nOpen systems theory considers the
\norganization as a number of interdependent
\nsubsystems that are open to
\nand connected with their environment.
\nThis provides the potential for the
\nsystem to take on a new form in
\nresponse to environmental factors
\nrequiring the facilitation of informationdriven
\nactivities.
\nProgram management provides an
\nintegration solution for strategic business
\nmanagement in dealing with
\ncomplexity and chaos in multiproject ( help with nursing paper writing from experts with MSN & DNP degrees)
\nenvironments (Pourdehnad, 2007).
\nEstablishing systemic alignment
\nbetween people, processes, and technology
\nwill provide benefits. Emerging
\ntechnologies can be adopted to enhance
\norganizational alignment capability and
\nmaturity (Gaddie, 2003). The program
\nsystem may be radically unpredictable
\nbeyond its immediate future, requiring a
\ndynamic approach of emergent planning
\n(Kash & Rycoft, 2000). The capability
\nof an organization and the coordinated
\npresence of critical program elements
\nwill influence integration success.
\nCritical program elements refer to the
\ncontextual program design or blueprint.
\nEstablished program processes
\nwill need to continuously integrate
\nadaptive decision making through
\nlearning processes (Lindkvist, 2008).
\nThis will require a focus on complex
\ninteractions, interdependency, processes,
\nand the coevolution of business
\nsystems.
\nUnderstanding what management
\npractices are required for any given program
\nwill be an important challenge. It
\nwill be fundamentally important that
\nthe distinguishing features of the program
\nare understood, as this should influence
\nprogram design (Meyer, Loch, &
\nPich, 2002). Uncertainty (structural,
\ntechnical, directional, and temporal)
\nwill be inevitable and a basic feature of
\nthis complex system. Leading a program
\nwill thus be multifaceted, situational,
\nand transient (Uhl-Bien, 2006).
\nContextual uncertainty may materialize
\nin the form of an opportunity or risk.
\nHigh uncertainty and complexity will
\nrequire a holistic approach in designing
\nthe program (Maylor, Brady, CookeDavies,
\n& Hodgson, 2006). Influences
\nfrom the external environment may be
\nfrequent, accidental, and unpredictable,
\nwith the internal environment
\nbeing equally as dynamic (Rybakov,
\n2001). Drawing together these system
\ndynamic principles leads to the following
\nresearch hypotheses:
\nH3: The program mission and objectives
\nsupport the creation of worthwhile
\nbusiness benefits and the
\nsuccessful delivery of the program.
\nH4: Programs and project ( help with nursing paper writing from experts with MSN & DNP degrees)s are managed
\nthrough a set of interdependent
\ncritical processes and subsystems
\nthat support strategic alignment and
\nrealignment.
\nGovernance
\nCorporate governance provides the
\nstructure for initiating and determining
\nthe objectives of an organization and the
\nmeans of monitoring, evaluating,
\nand influencing performance. Effective
\nprogram governance will be a major
\nstrategic factor and cannot be confined
\nto a narrow static model that ignores
\ndynamic complexity. This will require
\nemphasis toward flexibility with the
\norganization-program-governance interface
\n(Rycroft & Kash, 2004). The sponsoring
\ngroup will be pivotal for success.
\nA further critical aspect will be the
\ndetermination of structures and control
\nmeasures to ensure alignment with
\nthe unique organizational and contextual
\nenvironments. The success of the
\nprogram will require a flexible governance
\nstructure that can be identified
\nfrom contextual design criteria to
\nensure that it is fit for purpose. The
\nprogram mission will be a critical reference
\npoint for aligning structures,
\npolicies, procedures, behaviors, and
\ndecision making.
\nMultiowned program governance
\nwill require a strong focus on alignment
\n(APM, 2007b). This will be derived from
\nintertwining multiple perspectives of
\ngovernance in establishing a self-organizing
\ncomplex adaptive system (White,
\n2001). Various alignment strategies may
\nbe needed in response to stakeholder
\nobjections and agenda-setting behaviors.
\nEmergent and ill-defined programs
\nwill need to make greater use of alignment
\nmechanisms and tools. Welltimed,
\naccurate, and focused reporting
\nwill be central to integrating both performance
\nand learning loops. These
\nattributes provide the essential platform
\nfor configuration activities in the
\nprocess of actively shaping and reactively
\nadapting to the shifting contextual
\nenvironment. These issue and problembased
\nsuppositions lead to the following
\nresearch hypotheses:
\nH5: Program governance provides
\nthe control framework through
\nwhich the objectives are delivered
\nwhile remaining within corporate
\nvisibility and control.
\nH6: A continual process of realignment
\nensures that programs and project ( help with nursing paper writing from experts with MSN & DNP degrees)s
\nremain linked to corporate objectives
\nand environmental influences.
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 25
\nInnovation and Learning
\nOrganizational learning is essentially
\nabout an organization increasing its
\nability to explore opportunities and
\nundertake effective action (Carlile,
\n2004). This may lead to far-reaching
\nchanges and the formulation of new
\norganizational strategies. Learning and
\ncontinuous improvement is attributed
\nas the highest level of management
\nmaturity. There must be defined roles,
\nfunctions, and procedures for learning
\nto become organizational (Lipshitz,
\nPopper, & Friedman, 2002). The various
\nbodies of knowledge incorporate the
\nneed to learn from project ( help with nursing paper writing from experts with MSN & DNP degrees)s. Learning
\nthrough programs and project ( help with nursing paper writing from experts with MSN & DNP degrees)s is thus a
\nsubset of organizational learning
\n(Brady & Davis, 2004). This will need
\nthe systemic integration of data, information,
\nand knowledge. Sense (2007)
\nsuggested that project ( help with nursing paper writing from experts with MSN & DNP degrees)s are an embryonic
\nstructure that develops a new
\ncommunity of practice through situational
\nlearning and negotiation of
\nemergent opportunities. This provides
\nan experientially constructed temporary
\nsystem for solving problems and
\nknowledge transfer.
\nLearning from both success and
\nfailure will thus be essential in a program
\nof project ( help with nursing paper writing from experts with MSN & DNP degrees)s. Programs provide
\nenhanced opportunity for learning
\nthrough the batching of related project ( help with nursing paper writing from experts with MSN & DNP degrees)s,
\ninterdependency, and the
\nincreased socialization from resource
\nsharing. Project management practices
\nwill differ between industry sectors and
\norganizations, providing a potential
\ngulf in language and learning that must
\nbe considered. The ill-defined requirements
\nof some programs will challenge
\nthe linear stage-gate process of innovation
\nthrough inherent iteration (Smith &
\nWinter, 2005). Programs may require
\ndiffering distinctive leadership styles at
\ndifferent junctures in the program life
\ncycle. Multidisciplinary learning will
\nemerge through a process of activity
\nand alignment decision making (Fong,
\n2003). The following research hypothesis
\ncaptures this strong learning relationship:
\nH7: Within the life cycle, program
\nand project ( help with nursing paper writing from experts with MSN & DNP degrees) processes embrace
\nchange and realignment by using
\nlearning to create innovation and
\nimprovement opportunities to support
\nthe successful delivery of the
\nprogram.
\nMethod
\nThe study was designed to bring to the
\nforefront critical issues that supported
\nthe advancement of alignment theory
\nfor program management. The need for
\na rigorous theory-building process led
\nto the selection of a mixed-method
\nstudy design that involved both statistical
\nand text analysis.
\nSample and Data Collection
\nThe participants were selected from
\na population profile that was established
\nfrom the Rethinking Project
\nManagement Network, accredited project ( help with nursing paper writing from experts with MSN & DNP degrees)
\nmanagement training and consulting
\norganizations, the e-mail list for the
\nAssociation for Project Management
\nProgramme Management Specific
\nInterest Group, and specific e-mail
\ngroups from a UK-wide service-led
\norganization. The Rethinking Project
\nManagement Network was a UK government-funded
\nresearch initiative that
\naimed to develop, extend, and enrich
\nmainstream project ( help with nursing paper writing from experts with MSN & DNP degrees) management ideas
\nin relation to developing practice. This
\nincluded leading academics and practitioners
\nin the field of project ( help with nursing paper writing from experts with MSN & DNP degrees) management.
\nThe program concluded with five
\ndirections established for future
\nresearch, which were outlined in a 2006
\nspecial issue of the International Journal
\nof Project Management. These themes
\ncomplemented this study through a
\ngrowing emphasis on programs and
\nmanaging collections of project ( help with nursing paper writing from experts with MSN & DNP degrees)s.
\nAssociation for Project Management
\nAccredited Project Management training
\nproviders specialize in the delivery of
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees)-based training that is aligned to
\nAPM qualifications. These companies
\ninfluence developing practice and project ( help with nursing paper writing from experts with MSN & DNP degrees)
\nprofessionals through their consultancy
\npractice. The APM Programme
\nManagement Specific Interest Group
\naims to be the leading internationally
\nrecognized group for program management.
\nThis study contributed to their
\nmission to promote the science and discipline
\nof program management. The
\nspecific e-mail groups from the serviceled
\norganization consisted of those
\ninvolved in both transformational
\nchange and information system programs.
\nAll respondents were further
\nclassified by their role within programs
\nand the context of their practice-related
\nexperience. This targeted professional
\ngroups who were classified as consultants
\nor experts, senior managers that are
\nactively involved in programs, program
\nmanagers, project ( help with nursing paper writing from experts with MSN & DNP degrees) managers, and those
\nholding project ( help with nursing paper writing from experts with MSN & DNP degrees)-related job functions.
\nThis ensured the respondents were representative,
\nknowledgeable, and appropriate
\nto the study.
\nData collection was by means of a
\nstandardized questionnaire and semistructured
\ninterviews. The quantitative
\ndata was collected through a multimode
\nadministration method primarily
\nfrom an e-mail-driven strategy supported
\nby a web survey. The web survey
\nmirrored the self-administered questionnaire.
\nThis did not include advance
\nnotification, as it was administered
\nthrough the Programme Management
\nSignificant Influence Group monthly
\nnewsletter. The e-mail-driven list consisted
\nof 264 subjects, while the web
\nsurvey provided 2,005 additional subjects.
\nSix volunteer informants were
\nrandomly selected for interviews that
\nclassified themselves as either program
\nconsultants or experts and program
\nmanagers to ensure absolute knowledge
\nof the dynamics of the study model.
\nEach interview volunteer further represented
\na different program management
\ncontext\u2014information technology,
\norganizational change, new product
\ndevelopment, civil engineering, and
\nsomeone who had diverse experience
\nwith different types of programs. These
\ninterviews further explored the causal
\nrelationships of the research model to
\nunderstand how participants actually
\nconstructed theory and determined
\n26 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\nemerging phenomena in relation to the
\nstudy. This provided the opportunity for
\nthe interviewee to introduce issues that
\nthey conceived as important. The quantitative
\nand qualitative phases were
\nintegrated by an iterative process, with
\neach influencing the other accordingly.
\nMeasures
\nThe questionnaire design was structured
\nto gather information and understanding
\nabout organizational, environmental,
\nprogram, and project ( help with nursing paper writing from experts with MSN & DNP degrees) management
\nalignment. Respondents were requested
\nto respond based on their practicerelated
\nexperience and expertise. This
\nrequired questions to be answered by
\nrespondent experience and insight.
\nClosed-ended questions were used to
\nclassify the professional orientation
\n(program management consultant\/
\nexpert; senior manager involved in programs;
\nprogram manager\/director;
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees) manager involved in programs;
\nother\u2014please specify) and program
\nmanagement context of participants
\n(IT\/software development; organizational
\nor management change; new
\nproduct development; construction\/
\ncivil engineering; generalized). These
\ndiffering characteristics and attributes
\nwere coded by a five-category nominal
\nlevel of measurement. The model variables
\nand hypotheses were included in
\nthe questionnaire as closed-ended
\nquestions to validate the respondent\u2019s
\nopinion of the statements. These were
\nmeasured on a four-point Likert scale
\nranging from 1 (never) to 4 (always).
\nThis was a deliberate decision in
\nremoving the availability of a middle
\nalternative to ensure respondents indicated
\nthe direction of their viewpoint.
\nThe model variables and hypotheses
\nneeded to be constructed into a
\nstructural equation model to test and
\nconfirm proposed relationships. This
\ninvolved translating the proposed
\nalignment theory into a structural
\nmodel. Learning and innovation, program
\ngovernance, and systemic factors
\nwere classified as independent variables
\nin the study model. Successful
\ndelivery was the dependent variable,
\nwhich was hypothesized to be influenced
\nby the independent variables
\nand intervening variables of environment,
\ncontinuous alignment, and corporate
\nstrategy. The general sample
\ncharacteristics and size of the study
\ndata determined the measurement and
\ninterpretation of the statistical analysis.
\nThe selection of structural equation
\nmodeling ensured that measurement
\nerror was taken into account in the procedures
\n(Schumacker & Lomax, 2004).
\nThe model was identified by including
\nan error parameter for each variable
\nthat fixed the factor loading to 1.
\nThis multivariate statistical approach
\ncombined the application of both
\npath and confirmatory factor models in
\nanalyzing the causal model and study
\ndata. Analysis of Moment Structures
\n(AMOS) is an add-on module for SPSS
\nthat allows structural equation models
\nto be specified by using a simple dragand-drop
\ndrawing tool to test proposed
\ncausal relationships. The specified
\nstructural model follows standard
\ndrawing conventions to show the causeand-effect
\nrelationships (Figure 2). The
\nvariables measured in the study model
\nare depicted by enclosed rectangles.
\nUnobserved variables or model measurement
\nerrors are denoted by circles.
\nThis ensured that measurement errors
\nwere explicitly considered in statistical
\ncalculations. Straight-line singleheaded
\narrows from one variable to
\nanother indicate a direct influence
\nfrom that variable to the other. Zero rating
\nvalues would indicate that there
\nwas no direct impact. The absence of a
\nstraight-line single-headed arrow
\nbetween variables indicates that there
\nare no direct effects hypothesized.
\nDouble-headed curved lines between
\nvariables indicate a covariance. These
\ncoefficients detect and measure the
\nrelationship between two variables
\nthrough an index range, with zero indicating
\nno relation and 1.0 suggesting a
\nperfect relationship. The strength and
\nimpact of each model parameter estimate
\nis illustrated by the numerical output
\nbeside an arrowhead or variable in
\nthe study model. Problems in specifying
\nStudy Variable
\nStudy Variable
\nParameter value Error
\nindicating no relation
\nParameter value
\nindicating a perfect relationship
\n1.0
\n0.0
\nCircle used to draw
\nthe unobserved
\nvariables\/model
\nmeasurement error
\nStudy Variable
\nSingle-headed arrows
\nused to draw the causeeffect
\nrelationship
\nbetween variables
\nRectangle used to draw
\nthe study variables
\nDouble-headed arrow
\nused to draw the
\ncovariance between
\nvariables
\nError
\nError
\nFigure 2: Structural equation model drawing conventions.
\nNote. Model measurement error factor loading fixed to 1.0.
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 27
\nthe drawn model structure are highlighted
\nby an error message or by the
\nAMOS text output not calculating.
\nMeasurement and study model modification
\ninvolved identification with and
\nlinking of qualitative data.
\nReliability and Validity
\nThe mixed-method study design combined
\nthe different criteria used for
\nvalidity and reliability in both qualitative
\nand quantitative research. There
\nwere a number of prerequisites that
\nneeded to be satisfied before a multivariate
\nanalysis could be undertaken.
\nExploratory data analysis validated the
\nappropriateness of statistical methods
\nand techniques (Table 1). Kurtosis outputs
\nestablished that outliers in the
\ndata sample were not problematic, as
\nthis can provide misleading values with
\nstatistical methods and techniques. The
\npresence of outliers would affect structural
\nequation model fit significance tests.
\nThe Pearson\u2019s correlation coefficient
\nand two-tailed significance-level tests
\nindicated that all variables were significantly
\ncorrelated. Correlation is a
\nmeasure of linear dependence between
\nvariables. The stronger the correlations,
\nthe more power structural equation
\nmodeling has to detect an incorrect
\nfitting model. Reliability testing
\nprovided an indication of the general
\nquality of the study data. The
\nCronbach\u2019s alpha statistic (1951) was
\nadopted as a measure of the internal
\nconsistency and reliability of the study
\ndata. This statistical output is from any
\nvalue less than or equal to 1 and provides
\nan unbiased estimate of the ability
\nof the data to be generalized. The
\nvalue of above 0.70 is recommended,
\nalthough values exceeding 0.80 are
\ndesirable for higher reliability test studies.
\nThe Cronbach\u2019s alpha statistic output
\nof 0.831 thus validated that the
\ninternal consistency reliability of this
\nanalysis was good. The exploratory
\ndata analysis procedure concluded that
\nthe data were approximately multivariate
\nnormal in distribution and suitable
\nfor application to structural equation
\nmodeling. This classification was
\nessential, as small deviations from
\nmultivariate normality can lead to a
\nlarge difference in the Chi-square test.
\nAMOS labels this test global model fit
\n(CMIN). This label will now be adopted
\nthroughout the remainder of this article
\nwhen referring to the Chi-square test.
\nMultiple pilot-testing methods
\nwere used to refine the standardized
\nquestionnaire and qualitative interview
\nstructure to validate that the designs
\nwere clear, simple, and elicited the
\nappropriate responses. Missing data
\nwere eliminated with participants of
\nthe online survey by adopting a computer
\nquestionnaire, which needed a
\nresponse before completing the survey.
\nThe quality and completeness of the
\nreturned e-mail questionnaires was
\nextremely high. The only incidence of
\nmissing data was clarified by a follow-up
\n0 2 3 4 5 6 7 Mean SD
\nStrategy Correlation 0.407** 0.402** 0.366** 0.360** 0.441** 0.386** 2.89 0.695
\nSig. (two-tailed) 0.000 0.000 0.000 0.000 0.000 0.000
\nEnvironment Correlation 0.297** 0.309** 0.301** 0.403** 0.336** 3.23 0.673
\nSig. (two-tailed) 0.002 0.001 0.001 0.000 0.000
\nSuccessful Delivery Correlation 0.503** 0.485** 0.442** 0.454** 2.93 0.726
\nSig. (two-tailed) 0.000 0.000 0.000 0.000
\nSystemic Factors Correlation 0.490** 0.453** 0.406** 2.89 0.782
\nSig. (two-tailed) 0.000 0.000 0.000
\nGovernance Correlation 0.531** 0.354** 2.89 0.758
\nSig. (two-tailed) 0.000 0.000
\nContinuous Alignment Correlation 0.522** 2.70 0.761
\nSig. (two-tailed) 0.000
\nLearning and Innovation Correlation 2.50 0.751
\nSig. (two-tailed)
\nCronbach\u2019s Alpha Reliability Statistics Based on Standardized Items 0.831
\n**Pearson\u2019s correlation is significant at the 0.01 level (two-tailed).
\nTable 1: Pearson\u2019s correlation and Cronbach\u2019s alpha reliability tests.
\n28 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\ne-mail. Structural equation modeling
\nused interval data from the questionnaire
\nfor testing purposes. Randomized
\nselection was adopted for the interview
\ninformants to address the amount of
\ndiversity bias evident from the quantitative
\ndata phase. This ensured that a
\nrepresentative sample could be generalized
\nacross the wider program management
\ncommunity. The semistructured
\ninterviews focused on rigorous subjectivity
\nby respondent validation of the
\nquantitative research findings. This
\ninvolved asking questions such as
\n\u201cDoes the model structure make
\nsense?\u201d and \u201cAre the relationship paths
\nrepresentative of your experience?\u201d
\nRespondents were then encouraged to
\njustify opinions and provide alternative
\nexplanations. This facilitated understanding
\nand interpretation of relationships
\nbetween study variables.
\nStructural equation modeling validation
\nessentially involved statistical
\nand theoretical Assessment of model fit.
\nThe selection of appropriate statistical
\nfit measures considered issues such as
\nsample size and overall complexity of
\nthe model. These tests are based on the
\nassumption that the correct and complete
\nrelevant data have been modelled.
\nThe study sample size adopted a
\nlower limit of 100 respondents as proposed
\nby some authors (Chen, Bollan,
\nPaxton, Curran, & Kirby, 2001; Gagne &
\nHancock, 2006). Unobserved error variables
\nwere included to explicitly depict
\nthe unreliability of measurement in the
\nmodel. This allows the structural relations
\nbetween variables to be accurately
\nestimated. Criteria for study model fit
\nand testing were determined from the
\nsize of the study data and sample multivariate
\ncharacteristics. Model validation
\nwas determined through global
\nmodel fit (CMIN), root mean square
\nerror of approximation (RMSEA), goodness-of-fit
\nindex (GFI), and other normalized
\nmodel fit measures. CMIN of
\nzero illustrates a perfect model fit,
\nalthough it is generally accepted that
\nthis is impractical in reality. For reasonable
\nsample sizes, a difference enough
\nto produce a CMIN in the region of the
\ndegrees of freedom (DF) would suggest
\na close model fit. RMSEA was adopted
\nbecause it provides an output that
\ndoes not penalize model complexity.
\nModification index results following a
\nspecification search illustrated the reliability
\nof the relation paths drawn in a
\nspecified structural equation model.
\nData Analysis
\nStudy model modification followed an
\niterative process between the structural
\nequation modeling statistical analysis
\n(Arbuckle, 2007) and model structure
\ntheoretical validation through semistructured
\ninterviews (Silverman,
\n2006). The best-fitting model that was
\nalso consistent with theory was selected.
\nStructural equation models combine
\nmeasurement models (e.g., reliability
\ntests) with structural models (e.g.,
\nregression weights). This is based on
\ndata-driven model fitting. AMOS provided
\nautomated modification indices
\nas an alternative to manual model
\nbuilding and model trimming. Model fit
\nwas first measured on the closeness of
\nthe study-sample variance-covariance
\nmatrix. Modification needed to satisfy
\nthese measurement criteria and also
\nmeet the need for theoretical meaningfulness.
\nStatistical model cross-validation
\nthrough semistructured interviews
\nestablished problems in the structure of
\nthe model. This resulted in the model
\nstructure being redefined with the variables
\narranged differently, affecting
\npath relations (Figure 1).
\nThe model estimates were then
\nrecalculated. This involved the AMOS
\nautomated modification function, indicating
\nthat all the model correlations
\nand direct relations from the independent
\nvariables to the intervening variables
\nwere optional. This supported the
\npotential of further model refinement by
\nremoving poorly weighted relationship
\nparameters following the specification
\nsearch. This provided a multiplicity of
\nother models that fitted the data and
\nidentified potential adjustments that
\ncould be made to the model. The AMOS
\ntext output indicated the estimated
\nchange and reliability in the new path
\ncoefficient for each alternative model
\nproposed. Improvement in model fit
\nwas measured by a reduction in CMIN.
\nThe statistical model output indicated
\nthat overall model fit was adequate and
\ncould not be further statistically
\nimproved (Table 2 and Figure 2). The
\nmodel structure was then further
\nrefined to provide a theoretically validated
\nmodel. This introduced a new
\nvariable that could not be statistically
\nmeasured.
\nResults
\nThe e-mail survey provided a response
\nrate of 31% (81). The web survey had a
\nlarger population but significantly
\nlower response rate of 1% (29), reducing
\nthe overall study response rate to 5 percent
\n(110). This provided some concern
\nregarding statistical significance.
\nVarious studies have concluded that a
\nlower response rate does not necessarily
\ndifferentiate reliably between accurate
\nand inaccurate data (Keeter,
\nKennedy, Dimock, Best, & Craighill,
\n2006; Visser, Krosnick, Marquette, &
\nCurtin, 1996). The findings of these
\nstudies found that much lower
\nresponse rates were only minimally less
\naccurate. The 29 web survey respondents
\nwere included, as they provided a
\nrich source of expert data. More importantly,
\ntheir responses also enhanced
\nthe sample size to improve statistical
\nmodel significance. Survey respondents
\nwere reasonably dispersed over
\nfour program management practicerelated
\ngroups\u2014experts (25%), senior
\nmanagers (27%), program managers
\n(21%), and project ( help with nursing paper writing from experts with MSN & DNP degrees)-related roles (27%).
\nAn Assessment of Hoelter\u2019s (1983) critical
\nN from the model output suggests
\nthat the largest model sample size
\nrequired at a significance level of 0.05 is
\na threshold of 37. This provided reasonable
\nconfidence of sample-size adequacy
\n(N 110) against concerns of statistical
\nsignificance.
\nThe statistically modified model
\nis illustrated in Figure 3 following
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 29
\nParameter Standardized
\nDescription of Path Estimate SE CR P Estimate
\nEnvironment d Governance 0.134 0.091 1.473 0.141 0.151
\nEnvironment d Systems 0.124 0.090 1.378 0.168 0.144
\nEnvironment d Learning 0.201 0.087 2.293 0.022 0.224
\nAlignment d Systems 0.122 0.084 1.457 0.145 0.125
\nAlignment d Environment 0.191 0.088 2.171 0.030 0.169
\nAlignmentd Governance 0.312 0.084 3.705 *** 0.311
\nAlignment d Learning 0.308 0.082 3.741 *** 0.304
\nStrategy d Environment 0.233 0.093 2.515 0.012 0.226
\nStrategy d Alignment 0.160 0.099 1.621 0.105 0.175
\nStrategy d Governance 0.086 0.092 0.932 0.352 0.094
\nStrategy d Systems 0.101 0.087 1.167 0.243 0.114
\nStrategy d Learning 0.129 0.090 1.436 0.151 0.140
\nSuccessful delivery d Environment 0.084 0.102 0.826 0.409 0.078
\nSuccessful delivery d Alignment 0.293 0.091 3.201 0.001 0.307
\nSuccessful delivery d Strategy 0.246 0.100 2.454 0.014 0.236
\nTable 2: Regression weights and standardized regression weights.
\nProgram governance
\nSystemic factors
\nLearning and innovation
\nEnvironment
\nContinuous alignment
\nCorporate strategy
\n0.57
\ne1
\n1
\n0.61
\ne2
\n1
\n0.56
\ne3
\n1
\nSuccessful delivery
\n0.37 e4
\n0.32
\ne5
\ne6 0.34
\n1
\n1
\n1
\n0.12
\n0.23
\n0.19
\n0.16
\n0.08
\n0.29
\n0.25
\n0.13
\n0.31
\n0.12
\n0.10
\n0.20
\n0.13
\n0.31
\ne7 0.39
\n1
\n0.24
\n0.29
\n0.20
\nProgram Management Alignment Theory
\nChi-square 23.156 (3 df)
\np 0.000
\n0.09
\nFigure 3: Statistically modified structural equation model.
\n30 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\nmeasurement model reliability testing.
\nAn analysis of the number of distinct
\nparameters (NPAR 25) and the number
\nof degrees of freedom (DF 3)
\ndetermined that the model was complex.
\nThis outcome will provide conflicting
\nresults with some model fit
\nmeasures that attempt to balance parsimony
\nor simplicity against model
\ncomplexity. RMSEA provides an output
\nthat does not penalize model complexity.
\nThe modified model has an output
\nof 0.248 that exceeds the reasonable
\nerror approximation of 0.1 suggested by
\nBrowne and Cudeck (1989). This suggests
\npoor model fit, although RMSEA
\ncan be misleading when the minimum
\nsample discrepancy function (CMIN\/
\nDF) is small and sample size is not large
\n( 200). Figure 3 illustrates the modified
\nmodel, which provides an adequate
\nCMIN of 23.156, with the minimum
\nsample discrepancy function
\nbeing satisfactory (CMIN\/DF 7.719).
\nThe minimum sample discrepancy
\nfunction (CMIN\/DF) attempts to make
\nCMIN less dependent on sample size.
\nThe minimum sample discrepancy
\nfunction (CMIN\/DF) should be close to
\n1 for perfect fitting models. CMIN for
\nmodels involving 75 to 200 cases are a
\nreasonable measure of model fitness.
\nHowever, complex models are more
\nlikely to have a good CMIN. The p values
\nfor CMIN for sample sizes less than
\n200 are also useful as this measure is a
\nfunction of sample size (p 0.000).
\nOther measures of model fit were
\nconsidered in addition to the CMIN
\nstandard. The objective was to find the
\nmost parsimonious model, which was
\nwell fitting by a selection of GFI tests.
\nComparison against a baseline model
\nallows a further Assessment against the
\nsaturated (e.g., guaranteed to fit any set
\nof data perfectly) and independence
\n(e.g., severely constrained to provide a
\npoor fit) models through a number of
\nindices. These fitness measures are normalized
\nto fall between 0 and 1, with an
\noutput close to 1 indicating a good fit.
\nJ\u00f6reskog and S\u00f6rbom\u2019s (1984) GFI supports
\nthis outcome of good model fit
\n(GFI 0.948). The Bentler-Bonett
\n(1980) normed fit index (NFI) further
\nsuggests a good model fit (NFI 0.900),
\nalthough results less than this value
\nindicate that substantial improvement
\nis required. Bollen\u2019s (1989) incremental
\nfit index (IFI) and Bentler and Weeks\u2019
\n(1980) comparative fit index (CFI) both
\nindicated a very good model fit (IFI
\n0.912 and CFI 0.904).
\nParsimony-adjusted measures provide
\nan estimate of the required parameters
\nto achieve a specific level of model
\nfit. This rewards parsimonious models
\nwith relatively few parameters to estimate
\nin relation to the number of variables
\nand relationships in the model.
\nSome researchers oppose penalizing
\nmodels with more parameters. There is
\nno commonly agreed-upon cut-off
\nvalue for an acceptable model,
\nalthough some authors use above 0.50
\nand others 0.60 (Preacher, 2006). The
\nJames, Mulaik, and Brett (1982) parsimonious
\nnormed index (PNFI 0.129)
\nand parsimonious comparative fit
\n(PCFI 0.129) suggest poor model fit.
\nThese results are influenced by model
\ncomplexity. This measure of fit thus
\noffers little in contributing to the selection
\nof the best-fitting model other than
\nfor assessment after consideration of
\ngoodness-of-fit measures among proposed
\ncompeting models.
\nPath correlation coefficients in the
\nmodel were interpreted after a well-fitting
\nmodel had been accepted.
\nRegression weight significance tests for
\neach parameter relationship in the
\nstructural model are given in Table 2.
\nThe first column is labelled Parameter
\nEstimate (PE), with the next column
\nindicating the Standard Error (SE) for
\neach parameter. The Critical Ratio (CR)
\nis the parameter estimate divided by
\nthe SE. Any critical ratio that exceeds
\n1.96 in size would be identified as significant,
\nusing a significance level of
\n0.05. The SE is only an approximation
\nand therefore may not be the best
\napproach in determining parameter
\nsignificance, and caution was used in
\nits interpretation. Individual parameter
\nvalues can also be affected by sample
\nsize, with Anderson (1984) recommending
\nthat sample sizes exceed 150
\nfor reasonable and stable parameter
\nrelationship estimates. Structural path
\ncoefficients are the effect sizes calculated
\nby AMOS. These are displayed above
\ntheir respective arrows in the structural
\ndrawing diagram (0.8: high, 0.5: moderate,
\nless than 0.2: low).
\nAll correlation coefficients for the
\nvariables that represent the critical program
\nelements are positive in direction
\nand have moderate strength (0.20 to
\n0.29). The CRs for each of these are statistically
\nsignificant (CR 3.927, 4.595,
\nand 3.487). This indicates that there is a
\nclosely defined relationship between
\nthe critical program variables, suggesting
\na finely balanced direct correlated
\neffect on the intervening variables. The
\ncontinuous alignment intervening variable
\nis directly influenced the greatest
\nfrom the collective strength of all the
\nindependent variables (0.31, 0.12, and
\n0.31). Both the governance and learning
\nand innovation variables are
\ndeemed statistically significant (CR
\n3.705 and 3.741, respectively), although
\nthe systemic variable is insignificant
\n(CR 1.457, p 0.145). The dominant
\nindependent program variable in relation
\nto strength impact is learning and
\ninnovation (0.13, 0.31, and 0.20)
\nThe relationship paths from the
\nenvironment variable to continuous
\nalignment and strategy are both statistically
\nsignificant (CR 2.171, p 0.030
\nand 2.515, p 0.012, respectively).
\nThe environment variable has a direct
\n(CR 0.826, p 0.409) and indirect
\neffect on the successful delivery dependent
\nvariable through both the continuous
\nalignment and corporate strategy
\nintervening variables. Continuous
\nalignment also has a direct (CR 3.201,
\np 0.001) and indirect effect on the
\nsuccessful delivery dependent variable
\nthrough corporate strategy. Continuous
\nalignment and corporate strategy have a
\nmoderate direct individual effect but a
\nstrong collective influence on successful
\ndelivery (0.29 and 0.25, respectively).
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 31
\nThe environment variable has a low
\ndirect impact on successful delivery
\n(0.08) but moderately contributes indirectly
\nthrough two other intervening
\nvariables (0.19 and 0.23). Table 3 further
\nsummarizes the testing of hypothesized
\nrelation pathways in the accepted
\nstudy model (CR 1.96, significant at
\nthe p 0.05 level).
\nThe findings of the statistical investigation
\nsuggest that the independent
\nvariables are predicators of successful
\nprogram delivery through the complex
\ninteraction of the intervening variables.
\nThe outcome of the analysis illustrates
\nthat the model is complex, with relationships
\namong indicators precariously
\nbalanced. The modified model offers
\nan empirical explanation of the critical
\nrelationships involved for continuous
\nalignment and successful delivery. The
\nresults of this mathematical maximization
\nprocedure are sample-specific and
\ncan only be generalized to the study
\npopulation. This provides a causal
\nmodel that articulates but does not
\nconclude causal assumptions. The
\nsemistructured interviews further validated
\nthe relationship and importance
\nof the statistical analysis impact
\nweighting for each parameter. An
\nunderlying theme that emerged from
\neach interview was the need to make
\nmore clearly explicit the activity of program
\ndesign. This provided a strong
\njustification for the importance and
\ninclusion of program design in the
\nstudy model. The inclusion and visibility
\nof this variable were further supported
\nby expanding the necessary dynamic
\nfeedback from corporate strategy.
\nThe model structure was revised
\naccordingly (Figure 4). Quantitative
\ndata had already been gathered, so the
\nmodified and theoretically validated
\nPath Coefficients Critical
\nDescription of Relationship Path (Estimate) Ratio (CR) p-value Result
\nH1 Successful delivery d Strategy 0.246 (Moderate) 2.454 0.014 Significant
\nDesign d Strategy No data No data No data Untested
\nH2 Alignment d Environment 0.191 (Low) 2.171 0.030 Significant
\nStrategy d Environment 0.233 (Moderate) 2.515 0.012 Significant
\nSuccessful delivery d Environment 0.084 (Low) 0.826 0.409 Insignificant
\nH3 Successful delivery d Strategy 0.246 (Moderate) 2.454 0.014 Significant
\nSuccessful delivery d Environment 0.084 (Low) 0.826 0.409 Insignificant
\nSuccessful delivery d Alignment 0.293 (Moderate) 3.201 0.001 Significant
\nH4 Environment d Systems 0.124 (Low) 1.378 0.168 Insignificant
\nAlignment d Systems 0.122 (Low) 1.457 0.145 Insignificant
\nStrategy d Systems 0.101 (Low) 1.167 0.243 Insignificant
\nH5 Environment d Governance 0.134 (Low) 1.473 0.141 Insignificant
\nAlignment d Governance 0.312 (Moderate) 3.705 *** Significant
\nStrategy d Governance 0.086 (Low) 0.932 0.352 Insignificant
\nH6 Strategy d Alignment 0.160 (Low) 1.621 0.105 Insignificant
\nSuccessful delivery d Alignment 0.293 (Moderate) 3.201 0.001 Significant
\nH7 Environment d Learning 0.201 (Moderate) 2.293 0.022 Significant
\nAlignment d Learning 0.308 (Moderate) 3.741 *** Significant
\nStrategy d Learning 0.129 (Low) 1.436 0.151 Insignificant
\nH8 Learning d Design No data No data No data Untested
\nSystems d Design No data No data No data Untested
\nGovernance d Design No data No data No data Untested
\nTable 3:Tests of hypothesized relationship pathways (p-value 0.05).
\n32 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\nmodel could not be statistically tested
\nfurther.
\nDiscussion
\nThe study was designed to advance the
\ndevelopment of an alignment theory for
\nprogram management through a rigorous
\ntheory-building process. Structural
\nequation modeling was selected as a
\ntechnique that was used to estimate,
\nanalyze, and test the study model that
\nspecified relationships among variables.
\nThis allowed testing and validation
\nof already constructed theories
\ninvolving an Assessment of structure and
\nmodel fit. Semistructured interviews
\nwere discovery-focused, which uncovered
\ncontradictions and new ways of
\nthinking in the study model. This resulted
\nin the respecification of the structural
\nmodel variables and validation of
\nthe accepted conceptual model. The
\nstrength of structural coefficient paths
\nin the model needed assessment, as
\ngoodness-of-fit measures do not provide
\nan absolute guarantee that each
\nparticular part of the model fits well.
\nThe structural model was evaluated by
\nmodification indices that report the
\nimprovement in fit that results from
\nadding or deleting an additional path to
\nthe model. This introduced competing
\nmodels and the Assessment of individual
\nparameters. Modification indices results
\nsuggested that model adjustments
\nwould make no further improvements
\nto CMIN. Modifications also needed to
\nhave substantive sense and theoretical
\nvalidation.
\nModel Assessment is one of the most
\ndisputed and difficult issues connected
\nwith structural modeling (Arbuckle,
\n2007). Structural equation models are
\ngenerally considered a good fit if the
\nvalue of the global model fit (CMIN
\n23.156, p 0.000) and badness of fit
\nindex (RMSEA 0.248) test is adequate,
\nand at least one incremental fit index
\n(GFI 0.948) and one baseline fit measure
\n(NFI 0.900; IFI 0.912 and CFI
\n0.904) meet the predetermined criteria.
\nThe study model satisfies and in some
\ncases exceeds this convention with the
\nexception of badness of fit index
\n(RMSEA). The minimum sample discrepancy
\nfunction (CMIN\/DF) and
\nstudy sample size make RMSEA difficult
\nto interpret. This may be deceptive and
\nnot necessarily indicate a poorly fitting
\nmodel. The structural model could be
\nconsidered adequate against prescribed
\nmeasures of fit providing a model that
\nconveys causal assumptions. The meaning
\nof \u201ccausal,\u201d in this study, should be
\ninterpreted with care, as structural equation
\nmodeling does not confirm that
\nan accepted model produces validated
\ncausal conclusions. The research
\nframework provided convergence and
\nProgram governance
\nSystemic factors
\nLearning and innovation
\nEnvironment
\nContinuous alignment
\nCorporate strategy
\ne1
\n1
\ne2
\n1
\ne3
\n1
\nSuccessful delivery
\ne4
\ne5
\ne6
\n1
\n1
\ne7
\n1
\n1
\nProgram design
\ne8
\n1
\nFigure 4: Statistically modified and theoretically validated model.
\nFebruary 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 33
\ncorroboration of findings resulting in
\nthe statistically modified and theoretically
\nvalidated model. This was responsive
\nto changes in the unfolding of the
\nstudy. The study model was only partly
\nstatistically tested due to insufficient
\ndata, which revealed weaknesses in the
\nresearch design and methodology. These
\nwere addressed to validate the structural
\nmodel and hypotheses before administrating
\nthe test instrument to the wider
\nstudy population.
\nThe qualitative research aspects of
\nthe study design offered a richness and
\ndepth of understanding unlikely to be
\nachieved with a stand-alone quantitative
\napproach. Some interesting underlying
\nissues, most notably relationships
\namong strategy, learning, and program
\ndesign, were exposed. There was reasonably
\nclear demonstration that
\nstrategic vision was being translated
\ninto programs. Program life-cycle phases
\nof design and transition were found
\nto be particularly problematic in practice
\nby interview respondents. This
\nemphasized that strategy was a rather
\nambiguous phenomenon in practice.
\nThe creation of strategy was seen to be
\nan easier process than implementation.
\nThis reinforced that organizations were
\ncomplex systems. Strategic management
\nwas principally perceived by
\ninterview respondents as providing
\nrequired organizational direction in
\ndealing with success and failure from a
\nbusiness context. There was recognition
\nthat program success would not be
\nguaranteed even when a clearly articulated
\nbusiness strategy was apparent
\nfrom the outset. Strategy was generally
\nseen to be emergent, affecting the program
\nas it moves down the organization.
\nThere was recognition that
\nabsolute organizational alignment may
\nbe difficult and unrealistic as a consequence.
\nInterview respondents confirmed
\nthat this made it necessary to
\nview programs as dynamic and evolving
\nstructures.
\nThe front-end of programs were
\nidentified to be frequently ill defined,
\nwith low levels of formation constraining
\nthe early definition of success. This suggested
\nthat program design and structure
\nwas a dynamic process that needed
\nto be continually assessed from program
\nformation through to program
\nclose. Interview respondents emphasized
\nthat programs of project ( help with nursing paper writing from experts with MSN & DNP degrees)s should
\ncontinually use the best knowledge
\nobtainable to inform a systems view. In
\ntheir view, this was necessary to align
\npractice with stated goals. Other viewpoints
\nemphasized the need to move
\naway from the linear, milestone-based
\nprocesses of some business activities,
\nbecause integration was seen to go
\nhand-in-hand with experienced complexity.
\nThe different practice-related
\nviews of the interview respondents
\ndemonstrated that in contextual detail
\nevery program will be unique. This further
\nconfirmed the presence of a high
\nlevel of execution-complexity with a
\nhigh level of organizational and environmental
\ncomplexity, since a wide
\nvariety of variables need to be considered.
\nThere was general consensus that
\nmany organizations were not designed
\nfor project ( help with nursing paper writing from experts with MSN & DNP degrees) management. Program
\ndesign was stated to be much more
\ncomplex than a static process. Practical
\nchallenges were identified when the
\nhost organization did not have the requisite
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees) management capability.
\nThis further emphasized the highly
\ncomplex nature of effectively designing
\nprograms of project ( help with nursing paper writing from experts with MSN & DNP degrees)s. Interview respondents
\nstated that program design was a
\nsignificantly important pre-implementation
\nactivity. This led to its greater
\nprominence in the study model, as
\ninappropriate setup was seen as something
\nthat would negatively impact
\nimplementation and management of
\nthe program. Interview respondents
\nsuggested that programs need to be
\ndesigned to acknowledge the complexity
\nand emergent details of the program.
\nThe study further exposed that program
\nculture was often underscored by
\nlearning and innovation in responding
\nto inherent program complexity. This
\nstrong underlying profile for learning
\nand knowledge-sharing practices was
\noccasionally underrated in the interview
\nphase of the study. There was
\nsome evidence of systemic learning,
\ndriven by a project ( help with nursing paper writing from experts with MSN & DNP degrees) management
\napproach, with people who had similar
\nlevels of knowledge. Nonetheless, there
\nwas a general tendency for learning to
\nbe classified as a low priority. However,
\nthe statistical model findings strongly
\nsuggest that learning and innovation in
\nprograms are fundamentally important
\nfor success. The different types of learning
\nthat emerged related less to structured
\napproaches and more to satisficing
\nand improvisational outcomes. There
\nwas some recognition that increasing
\nprogram complexity will make organizational
\nlearning a primary measure of
\nprogram management effectiveness.
\nExamples were given where program
\nlearning had been effective but had
\nnot been transferred to the wider
\norganization.
\nConclusions
\nThe selected model provides a conceptual
\nframework to support the understanding
\nof programs. The strength of
\nthe model is in the illustration of the
\nsystemic characteristics that will make
\nprograms particularly challenging to
\nunderstand and manage. The hypothesized
\nstatements can be conceived to be
\na plausible set of interconnected narratives
\nthat describe the relationships that
\nsupport the conceptualization of the
\nstudy model. This needs to include
\nthe program design variable to allow
\nrecursive feedback in the model. The
\nstudy model does highlight the importance
\nof effective program design and
\ntransition management. The model
\nsuggests that successful program delivery
\nwill be an elusive concept in practice
\nthat requires flexibility for strategic and
\nenvironmental adaptation.
\nThe findings of the study conclude
\nthat programs have both deliberate and
\nemergent strategies, requiring program
\ndesign and management to be organized
\nas complex adaptive systems. This
\nintegrates theoretical concepts from
\n34 February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj
\nSuccessful Programs Wanted
\nPAPERS
\nsystems thinking, organizational, and
\nproject ( help with nursing paper writing from experts with MSN & DNP degrees) management theory. Complex
\nadaptive systems are often illustrated
\nby unclear strategies from the outset
\nand influential constant changes.
\nInterviewee knowledge of the concept
\nof complex adaptive systems was limited,
\nalthough descriptions and viewpoints
\nsupported this system dynamic. Senior
\nmanagers and program managers need
\nto recognize the importance of all the
\nstudy model variables, how they align,
\nand the program capability required in
\nsuccessfully delivering business strategy.
\nThe adoption of program management
\nshould thus be a well-considered
\nstrategic decision.
\nThis study contributes to program
\nmanagement by the understanding of
\ncomplex adaptive systems and its application
\nto the project ( help with nursing paper writing from experts with MSN & DNP degrees) management field
\nin many ways. First, the study identified
\nseveral high-level variables that should
\nbe considered to ensure the successful
\nalignment of programs. These variables
\ncan be used to analyze project ( help with nursing paper writing from experts with MSN & DNP degrees) and program
\nfailures contributing to organizational
\nlearning. Second, the exploration
\nof these variables has led to the development
\nof a model that reveals an interaction
\nstructure depicting program formation
\nand implementation in practice.
\nFinally, the results of validating the
\nstudy model have identified some of
\nthe managerial problems that should be
\nconsidered when designing and managing
\nprograms of project ( help with nursing paper writing from experts with MSN & DNP degrees)s.
\nRecommendations for Further
\nResearch
\nBased on the literature review, study
\nresults and emergent issues identified in
\nthe study, there were some insights that
\nprovide direction for future research. The
\nemergent reality of program management
\nrequires a clearer understanding
\non the impact of structured, incremental,
\nand contextual learning. Learning
\nwithin programs is also an identified gap
\nwithin the published literature. The
\nstudy also identified a significant need
\nto identify the effective practices and
\napproaches that support successful
\nprogram design. Research in this field
\nshould consider how organizations
\neffectively apply an adaptive posture to
\nenvironmental factors.
\nLimitations
\nThe study has a number of limitations.
\nStructural equation modeling cannot
\ntest directionality in relationships. The
\ndirections of arrows in the accepted
\nstructural equation model represent the
\nresearcher\u2019s hypotheses of causality
\nwithin a program management system.
\nThis is limited to the choice of selected
\nvariables and hypothesized relation
\npathways. Increasing the sample size
\nwould improve the statistical model
\nconvergence and parameter estimate
\naccuracy, providing greater confidence
\nin the model outcome. This may directly
\naffect the model path regression
\nweightings. The findings of the statistical
\nmodel are also influenced by the
\nresearcher\u2019s organization, which was
\nundergoing a significant organizationwide
\nchange program. This potential
\nbias was adequately accounted for in
\nthe selection strategy for the semistructured
\ninterviews. Change programs are
\nvision-led and emergent. This puts
\ngreater emphasis on culture change and
\norganizational readiness, which may
\nhave enhanced the model path relationship
\nregression weightings for learning.
\nEvery program classification will have
\nan inherent need to remain aligned with
\nbusiness strategy, regardless of issues
\nrelating to program context. \u25a0
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\nscience. Systems Research and
\nBehavioral Science, 18(3), 241\u2013257.
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\nCopyright of Project Management Journal is the property of John Wiley & Sons, Inc. and its content may not be
\ncopied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written
\npermission. However, users may print, download, or email articles for individual use.<\/p>\n","protected":false},"excerpt":{"rendered":"
PAPERS February 2012 \u25a0 Project Management Journal \u25a0 DOI: 10.1002\/pmj 21 INTRODUCTION \u25a0 T his article considers the impact of program alignment and related factors\u2026<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8827,10099,10540,10536,10892,10538,10539,10537,10891],"tags":[10549,10616,10615,7687,10627,8857,10552,10295,10548,10622,8907],"class_list":["post-130438","post","type-post","status-publish","format-standard","hentry","category-australia-essays","category-buy-essay","category-cheap-essay-writers","category-get-assignments-done","category-page-paper","category-phd-essay-writers","category-research-paper-writing-service-for-any-topic","category-study-bay-essay","category-write-my-assignment-ireland","tag-ai-plagiarism-free-essay-writing-tool","tag-australian-best-tutors","tag-best-trans-tutors","tag-buy-essay-uk","tag-cheap-dissertation-writer","tag-help-with-writing-an-essay","tag-help-write-my-paper-ai-free","tag-homework-for-you","tag-online-essay-writers","tag-phd-essays","tag-write-my-essay-for-me"],"_links":{"self":[{"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/posts\/130438","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/comments?post=130438"}],"version-history":[{"count":0,"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/posts\/130438\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/media?parent=130438"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/categories?post=130438"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.studyproessays.com\/help\/wp-json\/wp\/v2\/tags?post=130438"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}