Combining design science with data analytics to forecast user intention to adopt customer relationship management systems

Abstract

Customer relationship management (CRM) has become one of the most popular enterprise information systems (EIS) because it is widely adopted in service sectors. Although the technology-acceptance-model (TAM) has been well applied to study the adoption of EIS, most previous research focused on behavior science rather than considering the necessity of design features. This research aims to accomplish the following goals: (1) identifying the causalities between design features and behavioral intention, (2) deriving the priorities of design features, and (3) forecasting user intention to adopt CRM systems. Specifically, exploratory features and analytic features, respectively, characterize perceived ease-of-use (PEOU) and perceived usefulness (PU). Experimental results show that the firms owing prior implementation experiences of EIS focus on PU while the inexperienced firms focus on PEOU. The expert users knowledgeable in statistics and database are more interested in PU. These findings provide a basis of market segmentation for CRM vendors.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.