Dynamic interaction in knowledge based systems: An exploratory investigation and empirical evaluation

Beemer, Brandon A.; Gregg, Dawn G.

doi:10.1016/j.dss.2010.04.007

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…TAM proposes perceived usefulness (PU) [4,12,95] and perceived ease of use (PEOU) determine intention to accept a technology [21,47]. Empirical evidence has shown that PEOU does have an effect on intention to accept not only directly but also indirectly through PU [23,32,86].…”

Section: The Impact Of Tam Variables On Behavioural Intention To Accementioning

confidence: 99%

Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context

Miltgen

Popovič

Oliveira

2013

Decision Support Systems

336

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International audienceThe information systems (IS) literature has long emphasized the importance of user acceptance of computer-based IS. Evaluating the determinants of acceptance of information technology (IT) is vital to address the problem of underutilization and leverage the benefits of IT investments, especially for more radical technologies. This study examines individual acceptance of biometric identification techniques in a voluntary environment, measuring the intention to accept and further recommend the technology resulting from a carefully selected set of variables. Drawing on elements of technology acceptance model (TAM), diffusion of innovations (DOI) and unified theory of acceptance and use of technology (UTAUT) along with the trust-privacy research field, we propose an integrated approach that is both theoretically and empirically grounded. By testing some of the most relevant and well-tested elements from previous models along with new antecedents to biometric system adoption, this study produces results which are both sturdy and innovative. We first confirm the influence of renowned technology acceptance variables such as compatibility, perceived usefulness, facilitating conditions on biometrics systems acceptance and further recommendation. Second, prior factors such as concern for privacy, trust in the technology, and innovativeness also prove to have an influence. Third, unless innovativeness, the most important drivers to explain biometrics acceptance and recommendation are not from the traditional adoption models (TAM, DOI, and UTAUT) but from the trust and privacy literature (trust in technology and perceived risk)

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Section: The Impact Of Tam Variables On Behavioural Intention To Accementioning

confidence: 99%

Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context

Miltgen

Popovič

Oliveira

2013

Decision Support Systems

336

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“…In the literature, it is suggested that PU determines the intent to use a technology (Miltgen et al , 2013; Beemer and Gregg, 2010; Cheng et al , 2006; Wu et al , 2011; Davis, 1989; Kim et al , 2007). This hypothesis is also confirmed by our model, since, as we mentioned above, individuals are aware of the need for a BBCA system, so they understand its usefulness.…”

Section: Discussionmentioning

confidence: 99%

“…The constructs variables are presented in Appendix 1. In the literature, it is suggested that PU (Beemer and Gregg, 2010;Cheng et al, 2006;Wu et al, 2011) and perceived ease of use (PEOU) determine the intent of adopting a technology (Davis, 1989;Kim et al, 2007). Also, it was shown by empirical evidence that the ease of use has an impact on the user's intention to use (Dishaw and Strong, 1999;Gefen and Straub, 2000;Venkatesh and Davis, 2000).…”

Section: Modified Technology Acceptance Modelmentioning

confidence: 99%

Key factors driving the adoption of behavioral biometrics and continuous authentication technology: an empirical research

Stylios

Kokolakis

Thanou

et al. 2022

ICS

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Purpose For the success of future investments in the implementation of continuous authentication systems, we should explore the key factors that influence technology adoption. The authors investigate the effect of various factors of behavioral intention through the new incorporation of a modified technology acceptance model (TAM) and diffusion of innovation theory (DOI). Also, the authors have created a new theoretical framework with constructs such as security and privacy risks (SPR), biometrics privacy concerns (BPC) and perceived risk of using the technology (PROU). In this paper, the authors conducted a structural equation modeling empirical research. This research is designed in such a way to respond to the trade-off between users’ concern for the protection of their biometrics privacy and their protection from risks. Design/methodology/approach The authors provide an extensive conceptual framework for both existing models (TAM and DOI) and the new constructs that the authors have added to the model. In addition, this research explores external factors, such as trust in technology (TT) and innovativeness (Innov). In addition, the authors have introduced significant constructs, to overcome the limitations of the TAM and to adapt it to the needs of the present research. The new theoretical framework the authors introduce in the present research concerns the constructs SPR, BPC and PROU. Findings The authors found that the main facilitators of behavioral intention to adopt the technology (BI) are TT, followed by compatibility (COMP), perceived usefulness (PU) and Innov. This research also shows that individuals are less interested in the ease of use of the technology and are willing to sacrifice it to achieve greater security. COMP and Innov also play a significant role. Individuals who believe that the usage of the behavioral biometrics continuous authentication (BBCA) technology would fit into their lifestyle and would like to experiment with new technologies have a positive intention to adopt the BBCA technology. The new constructs the authors have added are SPR, BPC and PROU. The authors’ results support the hypotheses that SPR is a facilitator to PU and PU acts as a facilitator to BI. Consequently, the hypothesis that individuals do not feel adequately protected by classical methods will consider the usefulness of the BBCA as a technology for their extra protection against risks is confirmed by the model. Also, with the constructs BPC and PROU, the authors examined if individuals’ concerns regarding their biometrics privacy act as inhibitors in the BI. The authors concluded that individuals consider that the benefits of using BBCA technology are much more important than the risks for their biometrics privacy since the hypothesis that the major inhibitor of BI is PROU is not supported by the model. Originality/value To the best of the authors’ knowledge, this research is among the first in the field that examines the factors that influence the individuals’ decision to adopt BBCA technology.

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“…Knowledge-based systems (KBS) have long been used to assist business decisions [27][28][29][30]. However, the application of KBS in the domain of supplier selection is quite limited [14,31].…”

Section: Case-based Reasoning (Cbr)mentioning

confidence: 99%

Hybrid Knowledge-Based Flexible Supplier Selection

Wang

2014

2014 International Conference on Management of E-Commerce and E-Government

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Supplier selection no longer relies on cost only. Multi-criteria should be considered in making the selection. Subject to environmental changes, original suppliers may not be feasible or effective ones. Selection has to be flexible reacting to new market requirements. In this paper, a hybrid knowledge-based flexible supplier selection system (HKBF-SSS) is proposed incorporating a case-based reasoning (CBR) module and a mathematical programming module comprised of a proposed linear programming (LP) model. The system enables the company to select capable and most cost-effective supplier(s).

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Dynamic interaction in knowledge based systems: An exploratory investigation and empirical evaluation

Cited by 10 publications

References 27 publications

Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context

Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context

Key factors driving the adoption of behavioral biometrics and continuous authentication technology: an empirical research

Hybrid Knowledge-Based Flexible Supplier Selection

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