Abstract
PurposeThe purpose of this paper is to examine the importance of websites and social media platforms to find out how they contribute to the improvement of business performance. A new automated data collection method is developed to determine the technology maturity level of websites. These website quality indicators are linked to and compared against small and medium enterprise (SME) competitiveness data set to find competency pillars having significant impacts on the online presence, and to identify most important factors for online digital transformation. In this way, periodic analysis of websites can signal early warnings if competitiveness data of an SME is worth to refresh. Continuous maturity monitoring of competitors’ websites provides useful benchmark information for an enterprise as well.Design/methodology/approachA conceptual model was developed for the examination of the online presence and its effect on the competitiveness of small- and medium-sized businesses. An innovative, automatically generated WebIX indicator was developed through technical and content analysis of websites of 958 SMEs’ included in the Global Competitiveness Project (GCP) network data set. A series of ANOVA analysis was used for both data sources to determine the relationships between Web quality and competitiveness levels to define the online presence maturity categories.FindingsBoth the existence and the quality of the websites proved to have positive impact on the SME’s competitiveness. Different online presence maturity categories contribute to different competitiveness pillars; therefore, key factors of online digital transformation were identified. According to the findings, company websites are more related to marketing functions than information technology from the point of competitiveness.Originality/valueCompetency relationships were identified between online activity and competitiveness. The foundations of automated competitiveness measures were developed. The traditional survey based subjective data collection was combined with objective data collection methodology in a reproducible way.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call