Implementation Success Model in Government Agencies

2010 ◽  
Vol 2 (2) ◽  
pp. 19-32 ◽  
Author(s):  
Yair Levy ◽  
Theon L. Danet
Keyword(s):  
Self Efficacy ◽  
High Reliability ◽  
User Involvement ◽  
Structural Equations ◽  
Partial Least Square ◽  
Identification System ◽  
Government Agencies ◽  
Implementation Success ◽  
The Impact ◽  
Computer Self Efficacy

A recent presidential directive mandated that all U.S. government agencies establish a centralized identification system. This study investigated the impact of users’ involvement, resistance, and computer self-efficacy on the implementation success of a centralized identification system. Information System (IS) usage was the construct employed to measure IS implementation success. A survey instrument was developed based on existing measures from key IS literature. The results of this study indicated a strong reliability for the measures of all constructs (user involvement, computer self-efficacy, user’s resistance, and IS usage). Factor analysis was conducted using Principal Component Analysis (PCA) with Varimax rotation. Results of the PCA indicate that items of the constructs measured had high validity, while Cronbach’s Alpha for each factor demonstrates high reliability for all constructs measured. Additionally, results of a structural equations modeling analysis using Partial Least Square (PLS) indicate that computer self-efficacy and user involvement had positive significant impact on the implementation success. However, the results also demonstrated that user’s resistance had no significant impact on IS usage, while end user involvement had a strong negative impact on user’s resistance.

Implementation Success Model in Government Agencies

2012 ◽  
pp. 105-119
Author(s):  
Yair Levy ◽  
Theon L. Danet
Keyword(s):  
Self Efficacy ◽  
High Reliability ◽  
User Involvement ◽  
Principal Component ◽  
Least Square ◽  
Identification System ◽  
Government Agencies ◽  
Implementation Success ◽  
Is Implementation ◽  
Computer Self Efficacy

A recent presidential directive mandated that all U.S. government agencies establish a centralized identification system. This study investigated the impact of users’ involvement, resistance, and computer self-efficacy on the implementation success of a centralized identification system. Information System (IS) usage was the construct employed to measure IS implementation success. A survey instrument was developed based on existing measures from key IS literature. The results of this study indicated a strong reliability for the measures of all constructs (user involvement, computer self-efficacy, user’s resistance, and IS usage). Factor analysis was conducted using Principal Component Analysis (PCA) with Varimax rotation. Results of the PCA indicate that items of the constructs measured had high validity, while Cronbach’s Alpha for each factor demonstrates high reliability for all constructs measured. Additionally, results of a structural equations modeling analysis using Partial Least Square (PLS) indicate that computer self-efficacy and user involvement had positive significant impact on the implementation success. However, the results also demonstrated that user’s resistance had no significant impact on IS usage, while end user involvement had a strong negative impact on user’s resistance.

Learning Transfer of Computer Skills: Examining the Impact of Multilevel Computer Self-Efficacy Beliefs

2005 ◽  
Vol 04 (02) ◽  
pp. 125-132
Author(s):  
Bassam Hasan
Keyword(s):  
Transfer Learning ◽  
Self Efficacy ◽  
Positive Relationship ◽  
Computer Training ◽  
Far Transfer ◽  
Near Transfer ◽  
Computing Skills ◽  
The Impact ◽  
Training Practice ◽  
Computer Self Efficacy

The information systems literature has demonstrated a positive relationship between an individual's computer self-efficacy (CSE) and his/her ability to learn new computing skills. However, most past studies have overlooked the multilevel nature of the CSE construct and have not differentiated between near-transfer and far-transfer learning of computing skills. Accordingly, this study focuses on these two issues by investigating the impact of two levels of CSE (general and software specific) on two types of learning (near- and far-transfer) in computer training. The results of an experiment conducted to empirically test the relationships hypothesised among the study variables showed that software-specific CSE had significant effects on near-transfer and far-transfer learning and software-specific self-efficacy. In contrast, general CSE was found to have a significant effect on far-transfer learning only. The results provide valuable implications of computer training practice and research.

scholarly journals Scale Evaluating the Information Literacy Self-Efficacy of Medical Students Created and Tested in a Six-Year Belgian Medical Program

2019 ◽  
Vol 14 (2) ◽  
pp. 128-130
Author(s):  
Brittany Richardson
Keyword(s):  
Medical Students ◽  
Information Literacy ◽  
Self Efficacy ◽  
Medical Information ◽  
Medical Curriculum ◽  
High Reliability ◽  
Efficacy Beliefs ◽  
Medical Program ◽  
The Impact ◽  
Academic Year

A Review of: De Meulemeester, A., Buysse, H., & Peleman, R. (2018). Development and validation of an Information Literacy Self-Efficacy Scale for medical students. Journal of Information Literacy, 12(1), 27-47. Retrieved from https://ojs.lboro.ac.uk/JIL/article/view/PRA-V12-I1-2 Abstract Objective – To create and validate a scale evaluating the information literacy (IL) self-efficacy beliefs of medical students. Design – Scale development. Setting – Large, public research university in Belgium. Subjects – 1,252 medical students enrolled in a six-year medical program in the 2013-2014 academic year. Methods – Ten medical-specific IL self-efficacy questions were developed to expand a 28-item Information Literacy Self-Efficacy Scale (ILSES) (Kurbanoglu, Akkoyunlu, & Umay, 2006). Medical students in Years 1 – 5 completed the questionnaire (in English) in the first two weeks of the academic year, with students in Year 6 completing after final exams. Respondents rated their confidence with each item 0 (‘I do not feel confident at all’) to 100 (‘I feel 100% confident’). Principal Axis Factoring analysis was conducted on all 38 items to identify subscales. Responses were found suitable for factor analysis using Bartlett’s Test of Sphericity and the Kaiser-Meyer-Olkin measure (KMO). Factors were extracted using the Kaiser-Gutmann rule with Varimax rotation applied. Cronbach’s alpha was used to test the internal consistency of each identified subscale. Following a One-way-ANOVA testing for significant differences, a Tamhane T2 post-hoc test obtained a pairwise comparison between mean responses for each student year. Main Results – Five subscales with a total of 35 items were validated for inclusion in the Information Literacy Self-Efficacy Scale for Medicine (ILSES-M) and found to have a high reliability (Chronbach’s alpha scores greater than .70). Subscales were labelled by concept, including “Evaluating and Processing Information” (11 items), “Medical Information Literacy Skills” (10 items), “Searching and Finding Information” (6 items), “Using the Library” (4 items), and “Bibliography” (4 items). The factor loading of non-medical subscales closely reflected studies validating the original ILSES (Kurbanoglu, Akkoyunla, & Umay, 2006; Usluel, 2007), suggesting consistency in varying contexts and across time. Although overall subscale means were relatively low, immediate findings among medical students at Ghent University demonstrated an increase in the IL self-efficacy of students as they advance through the 6-year medical program. Students revealed the least confidence in “Using the Library.” Conclusions – The self-efficacy of individuals in approaching IL tasks has an impact on self-motivation and lifelong learning. The authors developed the ILSES-M as part of a longitudinal study protocol appraising the IL self-efficacy beliefs of students in a six-year medical curriculum (De Meulemeester, Peleman, & Buysse, 2018). The ILSES-M “…could give a clear idea about the evolution of perceived IL and the related need for support and training” (p. 43). Further research could evaluate the scale’s impact on curriculum and, conversely, the impact of curricular changes on ILSE. Qualitative research may afford additional context for scale interpretation. The scale may also provide opportunities to assess the confidence levels of incoming students throughout time. The authors suggested further research should apply the ILSES-M in diverse cultural and curricular settings.

scholarly journals Structural equation models - PLS in engineering sciences: a brief guide for researchers through a case applied to the industry

Athenea ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 5-18
Author(s):  
Juan Enrique Villalva A.
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Structural Equations ◽  
Partial Least Square ◽  
Equation Modeling ◽  
Engineering Sciences ◽  
The Impact

Modeling using structural equations, is a second generation statistical data analysis technique, it has been positioned as the methodological options most used by researchers in various fields of science. The best known method is the covariance-based approach, but it presents some limitations for its application in certain cases. Another alternative method is based on the variance structure, through the analysis of partial least squares, which is an appropriate option when the research involves the use of latent variables (for example, composite indicators) prepared by the researcher, and where it is necessary to explain and predict complex models. This article presents a brief summary of the structural equation modeling technique, with an example on the relationship of constructs, sustainability and competitiveness in iron mining, and is intended to be a brief guide for future researchers in the engineering sciences. Keywords: Competitiveness, Structural equations, Iron mining, Sustainability. References [1]J. Hair, G. Hult, C. Ringle and M. Sarstedt. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). California: United States. Sage, 2017. [2]H. Wold. Model Construction and Evaluation when Theoretical Knowledge Is Scarce: An Example of the Use of Partial Least Squares. Genève. Faculté des Sciences Économiques et Sociales, Université de Genève. 1979. [3]J. Henseler, G. Hubona & P. Ray. “Using PLS path modeling new technology research: updated guidelines”. Industrial Management & Data Systems, 116(1), 2-20. 2016. [4]G. Cepeda and Roldán J. “Aplicando en la Práctica la Técnica PLS en la Administración de Empresas”. Congreso de la ACEDE, Murcia, España, 2004. [5]D. Garson. Partial Least Squares. Regresión and Structural Equation Models. USA. Statistical Associates Publishing: 2016. [6]D. Barclay, C. Higgins & R. Thompson. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration”. Technology Studies. Special Issue on Research Methodology. (2:2), pp. 285-309. 1995. [7]J. Medina, N. Pedraza & M. Guerrero. “Modelado de Ecuaciones Estructurales. Un Enfoque de Partial Least Square Aplicado en las Ciencias Sociales y Administrativas”. XIV Congreso Internacional de la Academia de Ciencias Administrativas A.C. (ACACIA). EGADE – ITESM. Monterrey, México, 2010. [8]J. Medina & J. Chaparro. “The Impact of the Human Element in the Information Systems Quality for Decision Making and User Satisfaction”. Journal of Computer Information Systems. (48:2), pp. 44-52. 2008. [9]D. Leidner, S. Carlsson, J. Elam & M. Corrales. “Mexican and Swedish Managers’ Perceptions of the Impact of EIS on Organizational Intelligence, Decisión Making, and Structure”. Decision Science. (30:3), pp. 633-658. 1999.[10]W. Chin. “The partial least squares approach for structural equation modeling”. Chapter Ten, pp. 295-336 in Modern methods for business research. Edited by Macoulides, G. A., New Jersey: Lawrence Erlbaum Associates, 1998. [11]M. Höck & C. Ringle M. “Strategic networks in the software industry: An empirical analysis of the value continuum”. IFSAM VIIIth World Congress, Berlin 2006. [12]J. Henseler, Ch. Ringle & M. Sarstedt. Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer, 2012. [13]S. Daskalakis & J. Mantas. “Evaluating the impact of a service-oriented framework for healthcare interoperability”. Studies in Health Technology and Informatics. pp. 285-290. 2008. [14]C. Fornell & D. Larcker: “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, vol. 18, pp. 39-50. Februay 1981. [15]C. Fornell. A Second Generation of Multivariate Analysis: An Overview. Vol. 1. New York, U.S.A. Praeger Publishers: 1982. [16]R. Falk and N. Miller. A Primer for Soft Modeling. Ohio: The University of Akron. 1992. [17]M. Martínez. Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. Revista Iberoamericana para Investigación y el Desarrollo Educativo. Vol. 8, Núm. 16. 2018. [18]S. Geisser. “A predictive approach to the random effects model”. Biometrika, Vol. 61(1), pp. 101-107. 1974. [19]J. Cohen. Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. [20]GRI (2013). G4 Sustainability Reporting Guidelines. Global Reporting Initiative. Available: www.globalreporting.org

Job Satisfaction and Turnover Intentions during Technology Transition

2015 ◽  
Vol 28 (4) ◽  
pp. 38-51 ◽  
Author(s):  
Richard D. Johnson ◽  
Regina Yanson
Keyword(s):  
Job Satisfaction ◽  
Job Stress ◽  
Self Efficacy ◽  
Turnover Intentions ◽  
User Involvement ◽  
Intention To Leave ◽  
Scanning System ◽  
Using Data ◽  
Core Self ◽  
Computer Self Efficacy

Using data from 52 employees, this study investigated the relations among user involvement, core self-evaluations, computer self-efficacy, employee stress, job satisfaction, and intention to leave the organization during the implementation of a new barcode scanning system. The results revealed that core self-evaluations and user involvement were positively related to computer self-efficacy. In addition, core self-evaluations was negatively related to job stress, but user involvement was not. The results further showed that job stress was negatively related to job satisfaction. Finally, job stress and job satisfaction were each negatively related to intentions to leave the organization. Implications for theory and managers are discussed.

Does multitasking computer self-efficacy mitigate the impact of social media affordances on overload and fatigue among professionals?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
A.K.M. Najmul Islam ◽  
Eoin Whelan ◽  
Stoney Brooks
Keyword(s):  
Social Media ◽  
Self Efficacy ◽  
Information Overload ◽  
Positive Impact ◽  
Content Type ◽  
Study Results ◽  
Prior Literature ◽  
Information And Communication ◽  
The Impact ◽  
Computer Self Efficacy

PurposeThis paper investigates the moderating role of multitasking computer self-efficacy on the relationship between social media affordances and social media overload as well as its moderation between social media overload and social media fatigue.Design/methodology/approachThe authors hypothesize that social media affordances will have a positive impact on social media overload (i.e. information and communication overload). They also hypothesize that social media overload will affect social media fatigue. In addition, they hypothesize that multitasking computer self-efficacy will attenuate the effect of social media affordances on both information overload and communication overload. Similarly, they also hypothesize that multitasking computer self-efficacy will attenuate the effects of both information overload and communication overload on fatigue. The authors test this model by collecting two-wave data from 220 professionals using PLS techniques.FindingsSocial media affordances have significant impacts on information overload, but not on communication overload. In turn, information overload and communication overload significantly affect social media fatigue. Multitasking computer self-efficacy was found to attenuate the effect of social media affordances on both information overload and communication overload. Furthermore, the study results suggest that multitasking computer self-efficacy attenuates the effect of information overload and reinforces the effect of communication overload on social media fatigue.Originality/valueMost prior literature focused on students rather than on professionals. There is a lack of research that investigates how the affordances of social media relate to social media overload and fatigue. Furthermore, research that investigates mitigating mechanisms of social media fatigue has been rare. This paper fills these important research gaps.

Computer Anxiety in E-Learning: The Effect of Computer Self-Efficacy

10.28945/3386 ◽  
2009 ◽  
Author(s):  
Raafat Saade ◽  
Dennis Kira
Keyword(s):  
University Students ◽  
Self Efficacy ◽  
Management System ◽  
Computer Anxiety ◽  
Ease Of Use ◽  
Perceived Ease Of Use ◽  
E Learning ◽  
Computer Based ◽  
The Impact ◽  
Computer Self Efficacy

It has been reported that as many as fifty percent of adults, including first-year University students, have some sort of computer-related phobia. This report demonstrates that the use of computers still has some unpleasant side effects despite the Internet boom in the past decade. Past research shows that computer anxiety influences how users perceive ease of use of an information system. However, few have investigated the role of computer self-efficacy in mediating computer anxieties on perceived ease of use. Therefore, in this study we base our contribution on the variables of computer self-efficacy and computer anxieties. These two variables are believed to impact an individual’s use of computers and performance for computer-based tasks. Anxiety has been argued to impact computer-based learning by affecting levels of self-efficacy anchored in social learning and outcome expectation theories. Self-efficacy is determined by levels of anxiety such that reduced anxiety and increased experience improves performance indirectly by increasing levels of self-efficacy. In this study, we investigate the influence of computer anxiety on perceived ease of use and the mediating effect of computer self-efficacy on this relationship, within an e-learning context. A survey methodology approach was used in this study using 18 items for 3 constructs (perceived ease of use, anxiety, and self-efficacy). Survey data from 645 university students were analyzed. The psychometric properties of the items and constructs were validated followed by the assessment of mediation of computer self efficacy. Results from the use of a learning management system indicate that computer self-efficacy plays a significant role in mediating the impact of anxiety on perceived ease of use. This role is observed by computer selfefficacy (1) reducing the strength and significance of the impact of anxiety on perceived ease of use and (2) having a strong and significant relationship with computer anxiety. The findings demonstrate the importance of self-efficacy as a mediator between computer anxiety and perceived ease of use of a learning management system (LMS). With the continuous development of richer and more integrated interfaces, anxieties about learning to use the new interface and executing tasks effectively becomes of primary importance. Limitations and suggestions for future research are elaborated.

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