Task-Technology Fit and Process Virtualization Theory: An Integrated Model and Empirical Test

Author(s):  
Eric M. Overby ◽  
Benn Konsynski
2006 ◽  
Vol 91 (1) ◽  
pp. 97-108 ◽  
Author(s):  
John E. Mathieu ◽  
Lucy L. Gilson ◽  
Thomas M. Ruddy

2017 ◽  
Vol 8 ◽  
Author(s):  
Antonio Chirumbolo ◽  
Flavio Urbini ◽  
Antonino Callea ◽  
Alessandro Lo Presti ◽  
Alessandra Talamo

Author(s):  
Hemlata Gangwar

The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.


2013 ◽  
Vol 20 (4) ◽  
pp. 124-128 ◽  
Author(s):  
Angela Barber

Spelling is a window into a student's individual language system and, therefore, canprovide clues into the student's understanding, use, and integration of underlyinglinguistic skills. Speech-language pathologists (SLPs) should be involved in improvingstudents' literacy skills, including spelling, though frequently available measures ofspelling do not provide adequate information regarding critical underlying linguistic skillsthat contribute to spelling. This paper outlines a multilinguistic, integrated model of wordstudy (Masterson & Apel, 2007) that highlights the important influences of phonemicawareness, orthographic pattern awareness, semantic awareness, morphologicalawareness and mental graphemic representations on spelling. An SLP can analyze anindividual's misspellings to identify impairments in specific linguistic components andthen develop an individualized, appropriate intervention plan tailored to a child's uniquelinguistic profile, thus maximizing intervention success.


2010 ◽  
Author(s):  
Irwin J. Jose ◽  
Rustin D. Meyer ◽  
Richard Hermida ◽  
Vivek Khare ◽  
Reeshad S. Dalal

Sign in / Sign up

Export Citation Format

Share Document