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Enregistrement W7010049739

Filling the Gap: Developing Knowledge Management (KM) Maturity Assessment Capability in OPM3 for IT Organizations in Pakistan

2015· dissertation· en· W7010049739 sur OpenAlex

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Notice bibliographique

RevueHEC National Digital Library · 2015
Typedissertation
Langueen
DomaineDecision Sciences
ThématiqueConstruction Project Management and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMaturity (psychological)Competitive advantageOPM3Capability Maturity ModelInformation technologyProject managementOrganizational learningProject management triangle
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Applications of knowledge management in project management is an active area of research. There are 
\nat least three important reasons for this: re-use of knowledge can substantiate success rates of the 
\nprojects significantly, projects can provide a sustainable competitive advantage to the organizations, 
\nemployee turnover rates are climbing (Statistics, 2013) to the new heights due to globalizations and 
\nadvancements in information and communication technologies. 
\nThis research was initiated in the belief that successful completion of projects plays a vital role in 
\nmaintaining sustainable competitive advantage for the organizations; which in turn relies on the 
\nefficient exploitation of 'intangible' assets of the organizations (Grant, 1991; Jugdev, Mathur, & Fung, 
\n2007b; Jugdev & Thomas, 2002). Successful completion of projects is of more importance when we 
\ntalk about Information Technology (IT) organizations because IT organizations are unique in a way that 
\nthese are totally dependent on projects. Projects, whether in IT organizations or in any other 
\norganization, are accomplished by implementing practices and processes of project management and 
\ncombining various organizational assets and resources in some unique way. That is why assessment of 
\nthe extent to which organizations are practicing such project management capabilities is considered 
\nimportant. To fulfill this need, researchers and management consultancy organizations around the world 
\ndeveloped various project management maturity assessment models over the past three decades. These 
\nmodels assess various aspects of the organizations but lack in the assessment of the extent to which 
\norganizations are exploiting successfully their 'intangible' assets. The Organizational Project 
\nManagement Maturity Model (OPM3®) is one of the leading models (PMI, 2011) developed by Project 
\nManagement Institute (PMI®) to assess organizational project management maturity. This model, 
\nalthough the most comprehensive models of its kind, still lacks the capability to assess 'intangible' 
\nassets of the organizations. Therefore, the objective of my research is to bridge this deficiency and 
\nenhance the capability of OPM3® by making it capable of assessing the extent to which organizations 
\nare managing their 'intangible' assets. Organizations possess a breadth of 'intangible' assets and some 
\nof these assets are not directly measurable while others are difficult to measure. One of such 'intangible' 
\n9 
\nassets is 'knowledge' which is possessed and created by the organizations of all types. Careful 
\nassessment and management of that knowledge is of critical importance for the organizations. This 
\nknowledge lies in organizations at different places and in various forms such as in their processes, 
\npractices, documents, culture, human capital, etc. This study will not only help the IT organizations in 
\nPakistan but also to the organizations worldwide by creating awareness of the best practices to follow 
\nfor managing their knowledge efficiently. 
\nThe researcher divided this study in two major phases for data collection and its analysis. In the first 
\nphase, open-ended qualitative interviews were conducted with senior project managers of IT 
\norganizations in two major cities of Pakistan in medium to large organizations to solicit and gather their 
\nopinions about best practices for knowledge management (KM). After performing qualitative data 
\nanalysis on this data, we identified major themes and their respective best practices for KM. Based on 
\nthese best practices, we developed hypotheses and collected data again from various organizations from 
\nIT sector, both in-country and out-of-country, to validate the results and verify the applicability of best 
\npractices in different industrial sectors and in four countries: Pakistan, UAE, Canada and USA. Various 
\nstatistical tests were conducted on these data to look for correlations and variances among groups of 
\nrespondents to finally suggest the best practices which are of real worth. 
\nThe output of the study is a collection of globally and cross-industries validated knowledge management 
\nbest practices capable of guiding organizations 'what to do' if they want to harness one of their 
\nintangible assets i.e. knowledge. We recommend that these best practices should be incorporated in 
\nOPM3® as they have been statistically tested to have applicability in the organizations worldwide.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,859
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0000,000
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,089
Tête enseignante GPT0,427
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle