Quality management practices, knowledge management and key business results in SMEs and large organizations: a multi-group analysis

Arturo Calvo-Mora, Manuel Rey-Moreno, Antonio Navarro-García, Rafael Periáñez-Cristóbal

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Following the Total Quality Management philosophy and the knowledge management (KM) approach, this contribution aims to study the influence of process management methodology (PMM) and partner management (PM) on KM, and the relationships between this variable and key business results. The conceptual model is tested on a sample of 225 Spanish companies. PLS-SEM approach was used to test the research model. In order to assess the moderating effects of organisational size, a multi-group approach was adopted using two subsamples with large companies and small and medium-sized enterprises (SMEs). The findings indicate that the use of PMM and partner involvement are key factors for KM to have a significant impact on the key business results (KBR). Moreover, the organisational size is determinant when analysing the effect of PMM and PM on KM.
Original languageEnglish
Title of host publicationProceedings of the 2nd International Symposium on Partial Least Squares Path Modeling
Subtitle of host publicationThe Conference for PLS Users
EditorsJörg Henseler, Christian Ringle, José Roldán, Gabriel Cepeda
Place of PublicationEnschede
PublisherUnivesity of Twente
Number of pages13
ISBN (Print)9789036540568
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event2015 PLS User Conference: 2nd International Symposium on Partial Least Squares Path Modeling - The Conference for PLS Users - Seville, Spain
Duration: 16 Jun 201519 Jun 2015

Conference

Conference2015 PLS User Conference
CountrySpain
CitySeville
Period16/06/1519/06/15

Fingerprint

Dive into the research topics of 'Quality management practices, knowledge management and key business results in SMEs and large organizations: a multi-group analysis'. Together they form a unique fingerprint.

Cite this