Investigating use continuance of data mining tools

Huang, Tony Cheng-Kui; Wu, Ing-Long; Chou, C.Y.

doi:10.1016/j.ijinfomgt.2013.05.007

Cited by 51 publications

(48 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hsieh and Wang (2007) found perceived usefulness significant in predicting both satisfaction and extended use among enterprise resource planning users. Huang, Wu, and Chou (2013) found perceived usefulness had a strong effect on both satisfaction and continuance intention among data-mining tool users. Recently, Cheng (2014) confirmed these associations among blended e-learning users.…”

Section: H1mentioning

confidence: 93%

Towards a Decomposed Expectation-Confirmation Model of IT Continuance: The Role of Usability

Islam¹,

Mäntymäki²,

Bhattacherjee³

2017

CAIS

View full text Add to dashboard Cite

Abstract:We propose a decomposed expectation confirmation model of IT continuance by 1) decomposing user expectation and confirmation into two dimensions of usefulness and usability, 2) conceptualizing the role of usability in IT continuance research, and 3) exploring changes in user perceptions of usability and usefulness over time and their impacts on user satisfaction and continuance intention. We tested the proposed research model using longitudinal data that we collected in two points in time six months apart from 125 users of the LinkedIn professional social networking site (pSNS). The results show that 1) perceived usability, usability confirmation, perceived usefulness, and usefulness confirmation determined user satisfaction with pSNS and 2) perceived usability along with satisfaction predicted continuance intention, whereas perceived usefulness had no effect. This study contributes IT continuance research by 1) proposing and empirically validating a decomposed model of IT continuance and 2) by bringing in usability as a core construct of interest for IT continuance research.

show abstract

Section: H1mentioning

confidence: 93%

Towards a Decomposed Expectation-Confirmation Model of IT Continuance: The Role of Usability

Islam¹,

Mäntymäki²,

Bhattacherjee³

2017

CAIS

View full text Add to dashboard Cite

show abstract

“…The technology adoption literature in the post-disconfirmation d o m a i n m o s t l y u s e s t h e p e r s p e c t i v e s o f expectation-(dis)confirmation (Bhattacherjee, 2001, Bhattacherjee and Premkumar, 2004, Oliver, 1980 and innovation diffusion (Rogers, 1995, Huang et al, 2013 to explain the outcomes of technology utilisation. The Expectation Confirmation Model (ECM) is widely used for studying IS users' continuance intention.…”

Section: Technology Adoptionmentioning

confidence: 99%

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

2020

View full text Add to dashboard Cite

This study aims to address a research gap related to the outcomes of the use of technology when the performance falls short of initial expectations, and the coping mechanisms that users may deploy in such circumstances. By adopting Cognitive Dissonance Theory, the objectives of the study are a) to examine how dissonance, caused by the negative disconfirmation of expectations, may translate into a positive outcome and b) study how negative emotions, such as anger, guilt and regret, determine the selection of the mechanism to reduce dissonance. The theorised model was tested using a cross-sectional research design and a sample of 387 smart home users. The focus on smart home users fitted the objectives of the study due to the high expectations that users form and the challenges that the utilisation of technology sometimes causes. The collected data was analysed using structural equation modelling. Findings indicate that post-disconfirmation dissonance induces feelings of anger, guilt and regret, correlating with dissonance reduction mechanisms, which in turn have a distinctive effect on satisfaction and wellbeing. The findings of the study contribute to the discussion on expectation-disconfirmation and cognitive dissonance, by illustrating the interrelationship between emotional, cognitive and behavioural factors following the evaluation of technology performance and confirming that negative disconfirmation may result in satisfaction.

show abstract

“…The databases examined were those of the academic and financial management that included a certain amount of data from the LMS [51]. These data were requested from the admissions department and from the academic management of the registry of the higher education institution participating in this study.…”

Section: Case Studymentioning

confidence: 99%

A Business Intelligence Framework for Analyzing Educational Data

2020

View full text Add to dashboard Cite

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university.

show abstract

Investigating use continuance of data mining tools

Cited by 51 publications

References 53 publications

Towards a Decomposed Expectation-Confirmation Model of IT Continuance: The Role of Usability

Towards a Decomposed Expectation-Confirmation Model of IT Continuance: The Role of Usability

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

A Business Intelligence Framework for Analyzing Educational Data

Contact Info

Product

Resources

About