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Enregistrement W2808149732

Knowledge management and dynamic capabilities: four essays on their relationship and influence on green innovation performance.

2017· dissertation· en· W2808149732 sur OpenAlexfundno aff
Gema Albort-Morant

Notice bibliographique

RevueidUS (Universidad de Sevilla) · 2017
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueBusiness, Innovation, and Economy
Établissements canadiensnon disponible
Organismes subventionnairesIndigenous and Northern Affairs Canada
Mots-clésDynamic capabilitiesKnowledge managementEngineeringComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The environmental impact has become a growing global concern in the current economic and social spheres, including consumers, companies, politicians and organizations of various kinds. Therefore, to survive within the current situation, marked by highly dynamic, volatile and hyper-competitive scenarios, companies should adopt strategies aimed at encouraging sustainability and innovation. The growing social concern and regulatory environment is significantly contributing to the development of green innovation strategies by a large number of companies, which is allowing them to ensure their long-term survival and to increase performance (LaForet, 2009). Companies must therefore make an effort to keep up-to-date of changes, fluctuations and trends emerging in the market gradually. This implies to be oriented to the demands of their main customers and stakeholders, developing a strategy of orientation toward sustainability and green innovation. The increasing demands of society in environmental matters is forcing companies to integrate sustainability issues into its regular activity so that the company achieve its social, environmental and economic objectives. There are two main driving forces that promote environmental management (Chen, 2008): (i) the international set of rules and regulations concerning the protection of the environment, and (ii) the ecological awareness of consumers (Chen, Lai and Wen, 2006). Anyone that are the objectives that lead companies to carry out environmental management, the integration of environmental sustainability issues in business strategy and the ecological orientation of the innovation process is becoming a strategic opportunity for firms (Porter & Reinhardt, 2007). Therefore, as indicated by several studies, environmental management and green practices are closely linked to business innovation (Aragon-Correa, 1998; Perez-Valls et al, 2015). In this sense, those companies that are pioneers in the generation of strategies of green innovation will be able to maintain competitive advantages. Therefore, the success of green innovation performance (GIP) helps companies to achieve greater efficiency, as well as to establish and strengthen their core competencies and to improve their green image. Consequently, all this will allow businesses to achieve superior performance and greater profitability (Chen, 2008). The literature focused on the study of organizational capabilities highlights dynamic capabilities approach (DC) as a valuable source of generation of sustainable competitive advantages, and as a key component for access, the achievement and development of new knowledge to improve innovation outcomes. Several authors have defined dynamic capabilities as a set of processes that are necessary for the reconfiguration of the routines and resources of a company (Cepeda and Vera, 2007). In addition, the latest literature in the field of knowledge management highlights the concepts of relationship learning (RL), absorptive capacity (ACAP) and knowledge base (KB), as fundamental drivers of firm innovativeness and performance. Therefore, the main objective of this thesis is to try to understand in depth the concept of green innovation performance, as well as to ascertain the impact exerted by the distinct drivers of green innovation –dynamic capabilities, knowledge base, relationship learning and absorptive capacity–. Accordingly, we focus our study with the target of answering the following research questions, which we will group into four blocks: Question 1: What are the conceptual roots of the green innovation variable? Question 2: To what extent do the existing internal capabilities of firms and their interaction with external knowledge sources —relationship learning enhancement —affect the level of green innovation performance? Question 3: How does the presence of relationship learning actually affect the link between a firm’s knowledge base and green innovation performance? Question 4: How does a firm’s potential absorptive capacity influence realized absorptive capacity? How does a firm’s potential absorptive capacity influence the creation of green innovation through processes and products? How does a firm’s realized absorptive capacity influence the creation of green innovation through processes and products? Within the introduction and development of the four main chapters, together with the theoretical background gathered in Chapter 2, we have proposed to respond to the key research questions and to empirically test the relations postulated. The first research question is dealt with in Chapter 3. The second question is considered in Chapter 4. Chapter 5 assesses the third research question. Lastly, Chapter 6 evaluates the fourth research question. The first conclusion of this thesis is that the companies’ base of resources and capabilities is essential for the development of green innovations, since it allows them to obtain a competitive advantage in dynamic and turbulent environments. Organizations are different from each other. For this reason, companies have to work hard to acquire and develop the combination of resources and capabilities that allow them to adapt to the environmental needs identified in the market and society in general, and acquire a favorable position in relation to their competitors. In this vein, the most valuable resource within companies these days is its knowledge base. A firm’s knowledge base establishes the possibility and ability to understand and employ novel knowledge for problem solving, decision making or innovations development (Ahuja and Katila, 2001). For this reason, companies should have a deep and broad knowledge base, since it enables its preparation for catalyzing new ideas that might lead to launching innovative products and services successfully. However, it is necessary for companies to develop and keep absorptive capacity to improve their outcomes and spread competitive advantages, given that this absorptive capacity can strengthen, reorient or supplement the previous organizational knowledge base, as well as interpret and exploit the new knowledge acquired. In addition, dynamic capabilities allow firms to develop particularly unique, reliable and satisfactory combinations of internal and external resources that may lead to creating value for the customers and other stakeholders, whose needs are continually evolving. Therefore, green innovation performance requires firms to implement changes on the basis of dynamic capabilities development and effective knowledge absorption and management processes. The second conclusion of this study is the importance of the companies’ creation of relationship learning ties with their stakeholders in order to learn about new environmental requirements and in this way adapt their resources and capabilities to obtain green innovation performance. Relationship learning is a joint activity between the firm and one or more parts –supplier, customer, partner, competitors, etc.–, whose objective is to share information and strenghthen the knowledge base (Leal-Rodriguez et al., 2014). For example, it is important that the companies build and maintain relationship learning mechanisms with their suppliers to negotiate the type of raw material that they need or the packaging design. Consequently, companies should share, combine and integrate information about environmental topics to improve their yields. The last conclusion of the present thesis is that there are not significant differences between green product and process innovation. Green product innovation deals with the introduction of new products or services characterized by waste recycling, energy-saving or reducing the use of polluting resources, whereas green process innovation is connected with the development of production which satisfies the requirements of environmental protection. These two types of innovation are interrelated as developing a green product innovation adapts the processes that operators take into account of the environmental requirements which should be followed. As a result, green process innovations are worked out with the idea of creating eco-friendly products that respect and care for the environment, or modify existing products in a firm’s portfolio with the intention of adapting them to the new needs of the market. The empirical results extracted reveal that potential and realized absorptive capacity are positively linked to both green product and process innovation performance.

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.

Comment cette classification a été obtenuedéplier

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,639
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
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 Opus0,029
Tête enseignante GPT0,233
Écart entre enseignants0,204 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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