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The purpose of this paper is to develop a new ontology for knowledge management (KM) technologies, determining the relationships between these technologies and classification of them.

The study applies NOY methodology – named after Natalya F. Noy who initiated this methodology. Protégé software and ontology web language are used for building the ontology. The presented ontology is evaluated with abbreviation and consistency criteria and knowledge retrieval of KM technologies by experts.

All the main concepts in the scope of KM technologies are extracted from existing literature. There are 241 words, 49 out of them are domain concepts, eight terms are about taxonomic and non-taxonomic relations, one term relates to data property and 183 terms are instances. These terms are used to develop KM technologies’ ontology based on three factors: facilitating KM processes, supporting KM strategies and the position of technology in the KM technology stage model. The presented ontology is created a common understanding in the field of KM technologies.

Lack of specific documentary about logic behind decision making and prioritizing criteria in choosing KM technologies.

Uploading the presented ontology in the web environment provides a platform for knowledge sharing between experts from around the world. In addition, it helps to decide on the choice of KM technologies based on KM processes and KM strategy.

Among the many categories of KM technologies in literature, there is no classifying according to several criteria simultaneously. This paper contributes to filling this gap and considers KM processes, KM strategy and stages of growth for KM technologies simultaneously to choice the KM technologies and also there exists no formal ontology regarding KM technologies. This study has tried to propose a formal KM technologies’ ontology.

For organizations competing in volatile environments, strategic agility is the key for sustaining in the market. It is essential for such organizations to identify the main agility indicators that contribute to their strategic core. The purpose of this paper is to propose and test a systematic methodology that identifies key agility indicators through prioritization and establishing the intra- and inter-relationships among them.

The methodology consists of four phases. Phase I forms a pool of agility key performance indicators (KPIs). Phase II categorizes and ranks the KPIs based on their importance and the gap that exists between their current and desired level. Using interpretive structural modeling, phase III establishes the intra-relationships among the KPIs as well as agility attributes, agility enablers and improvement paths, collectively referred to as agility indicators. Finally, phase IV analyzes the inter-relationships among agility indicators using three consecutive houses of quality.

To demonstrate the capability of the proposed methodology, it was applied to a dairy food company operating in a competitive environment. The application could address the shortcomings of previous agility methodologies and helped the company to assign resources to the right agility indicators with the highest influence on strategic agility.

The methodology was applied to a single organization only. The application does not include long-term post-implementation observations and measurement of strategic agility.

Practitioners can benefit from the methodology to identify the right agility indicators of their organization and assign organizational resources for the improvement of such indicators. The methodology ensures selecting indicators that contribute to organization’s strategic agility, although ostensibly seem unrelated.

The paper contributes to the literature of strategic and organizational agility by proposing a systematic methodology that considers both intra- and inter-relationships among agility indicators. The methodology also makes a decent contribution in addressing organizational ambidexterity by analyzing mutually exclusive indicators pertaining to current and future opportunities for the organization.