A Structural Equation Model Of End-User Satisfaction With A Computer-Based Medical Information System

Henry, John W.; Stone, Robert W.

doi:10.4018/irmj.1994070102

Cited by 184 publications

(102 citation statements)

References 47 publications

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“…A growing body of research suggests that computer users' prior experience with technology affects their attitudes about technology in general (Gefen, Karahanna & Straub, 2003;Henry & Stone, 1994;Martins & Kellermanns, 2004;Stoel & Lee, 2003;Wober & Gretzel, 2000). The greater amount of experience users have with technology the higher the levels of users' satisfaction in learning to use new technology (Simmers & Anandarajan, 2001;Volery & Lord, 2000).…”

Section: Discussionmentioning

confidence: 99%

Students’ Perceptions of Online Learning: A Comparative Study

Smart¹,

Cappel²

2006

JITE:Research

258

114

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Executive SummaryIn search of better, more cost effective ways to deliver instruction and training, universities and corporations have expanded their use of e-learning. Although several studies suggest that online education and blended instruction (a "blend" of online and traditional approaches) can be as effective as traditional classroom models, few studies have focused on learner satisfaction with online instruction, particularly in the transition to online learning from traditional approaches. This study examines students' perceptions of integrating online components in two undergraduate business courses where students completed online learning modules prior to class discussion. The results indicate that participants in an elective course rated the online modules significantly better than those in a required course. Overall, participants in the elective course rated the online modules marginally positive while those in the required course rated them marginally negative.These outcomes suggest that instructors should be selective in the way they integrate online units into traditional, classroom-delivered courses. This integration should be carefully planned based on learner characteristics, course content, and the learning context. For most participants of the study (83 percent), this was their first experience completing an online learning activity or module. In addition, the largest dissatisfaction factor reported among the participants was the time required to complete the online modules. Future research is encouraged to explore: (1) how previous experience with technology and online learning affects students' attitudes towards and success with e-learning; and (2) the effects of interspersing online units that are considerably shorter in length into the traditional classroom model. This additional research can provide greater insight into which factors promote e-learning success.

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Section: Discussionmentioning

confidence: 99%

Students’ Perceptions of Online Learning: A Comparative Study

Smart¹,

Cappel²

2006

JITE:Research

258

114

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“…Table 5 shows that all the values of χ 2 /df were smaller than the three criteria, indicating a good fitness of the models. Moreover, all the indices, except for the values with asterisk, met the criteria proposed by numerous scholars (Henry & Stone, 1994;Hairs et al, 1998;Hu & Bentler, 1999;McDonald & Ho, 2002). The convergent validity can be ascertained according to the fact that all the SFLs were between 0.67 and 0.86 with their t values being bigger than the 1.96 criteria under α = .05 significant level.…”

Section: Willingness To Try Sample Products (Items 1 and 4) And Willimentioning

confidence: 89%

The Anatomy of Brand Name Translation Under Skopos Theory: A Model of Customer Satisfaction, Awareness of Translation Strategy Use and Purchase Intention

2014

UCFL

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The purpose of the study was to apply structural equation modeling (SEM) to build relations among customer satisfaction (CS), customers' awareness of translation strategy use (CA), and purchase intention (PI) on English-Chinese brand name translation. To ensure construct validity, exploratory factor analysis (EFA) was first applied in a pilot study (N = 150) to extract factors for each latent variable, including semantic ambiguity, faithful reflection, and smoothness under CS; grammatical competence, illocutionary competence, and sociolinguistic competence under CA; willingness to try the sample products and willingness to be updated about the products under PI. In a formal survey (N = 550), three self-report scales were administered on customers chosen through stratified sampling from a database. Results showed that CA exerted a direct effect on both CS and PI. In addition, CS exerted a direct effect on PI. Moreover, CA exerted an indirect effect on PI through CS as an intervening factor.More importantly, the indirect effect of CA on PI through CS was statistically more significant than the direct effect of CA on PI. This is good proof that CS assumed a critical role in the model. The model drew implications for marketing and advertising sectors on the potential value of translation strategy use and CS in brand name translation. Theoretically, the findings substantiated the referential/informative, expressive and operative functions in the realm of Skopos theory and highlighted the dual roles that translators need to play both linguistically and social-culturally when orienting strategies for brand name translation.

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“…Thus, the notion of outcome expectancy appears to enhance the use of self-efficacy as an independent variable. For example, in three different studies, Henry and Stone (1994, 1995a, 1995c found that computer self-efficacy and outcome expectancy mediated the effects of previous experience, management support, IT ease of use on job performance, IT satisfaction, and end user perceptions of the quality of patient care in a hospital.…”

Section: Psychometric Propertiesmentioning

confidence: 99%

“…Additionally, computer selfefficacy and outcome expectancies have been shown to relate in a meaningful fashion with a variety of other constructs in theoretically sound frameworks. These constructs, measured using similar scales, include job performance, computer system satisfaction, andjob satisfaction (Henry & Stone, 1994, 1995a, 1995c. These results and those from previous studies across a variety of settings lend additional evidence supporting the construct validity of the self-efficacy scale and both outcome expectancies scales.…”

Section: Psychometric Propertiesmentioning

confidence: 99%

“…We have used variations of the scales described in past research (e.g., Gist et aI., 1989;Hackett & Betz, 1989;Meier, 1985Meier, , 1988. However, the results were inconsistent and did not strongly predict relevant outcome variables (e.g., satisfaction, performance) in organizational and classroom settings (Henry, 1989;Henry & Martinko, 1997;Henry & Stone, 1993;Henry, Stone, & Pierce, 1995).Thus, we began a series ofresearch studies based on our past findings, taking the best ofeach previous study to develop and refine an instrument that is theoretically and psychometrically sound and predicts relevant outcome variables in IT research-for example, performance, impact of IT, and job satisfaction (Henry & Stone, 1994, 1995a, 1995b, 1995c.…”

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The development and validation of computer self-efficacy and outcome expectancy scales in a nonvolitional context

Henry

Stone

1997

Behavior Research Methods, Instruments, & Computers

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Computer self-efficacy and outcome expectancy scales were developed using 306 responses to a questionnaire distributed by a national mail survey to end users of computer systems in a variety of functional business areas, Confirmatory factor analysis using a structural equations approach was used to develop three scales. The scales were found to demonstrate satisfactory psychometric properties. The reliability coefficients for these scales were as follows: .85 for computer self-efficacy; ,88 for work-related outcome expectancy; and .89 for personal outcome expectancy. The scales provide a strong foundation from which to refine the measurement of computer self-efficacy and outcome expectancy. From these refinements, empirical models that include self-efficacy and outcome expectancy as determinants of information technology acceptance at the individual level of analysis can be improved.The introduction of computers into the workplace has elicited a large number of studies in the past 20 years. The information technology (IT) literature is replete with reports ofcomputer attitude studies and how these attitudes affect computer use. In addition, anxiety (Igbaria & Chakrabarti, 1990;Igbaria & Parasuraman, 1989; Jacobson, Holder, & Deamer, 1989) and vague concepts such as "computerphobia" and "technophobia" have been related to resistance to IT (Jay, 1981). Specifically, IT refers to any product whose underlying technological base is composed of computer or communications hardware and software (Cooper & Zmud, 1990).Attitudes have received the most attention in IT research (LaLomia & Sidowski, 1991). The notion that attitudes affect behavior has been proposed for years (Oskamp, 1977). For example, the work of Fishbein and Ajzen (1975) identified specific conditions under which attitudes should predict behavior. Fishbein and Ajzen developed the theory of reasoned action (TRA), which proved successful in predicting behavior across numerous contexts. The TRA model posits that actual behavior (e.g., computer system use) is influenced by behavioral inten- tions regarding the behavior, while behavioral intentions are affected by attitudes toward the behavior, subjective norms, beliefs and evaluations, and normative beliefs and motivation to comply (Davis, Bagozzi, & Warshaw, 1989). This work was later adapted to IT research in the form of the technology acceptance model (TAM). The TAM theoretically links external variables, perceived usefulness, and ease of use of a computer system to actual computer system use through attitudes and behavioral intentions toward using the computer system (Davis, 1986;Davis et al., 1989). The TAM was relatively successful in predicting computer usage in a number of studies in which IT use was voluntary (Davis et aI., 1989). However, over time, inconsistencies in studies using attitude scales were noticed. Gutek, Winter, and Chudoba (1992) have posited that these inconsistencies may be due to the increase in nonvolitional IT use. Doll and Torkzadeh (1996) also stated that "where use is manda...

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A Structural Equation Model Of End-User Satisfaction With A Computer-Based Medical Information System

Abstract: Numerous studies in the information systems literature have attempted to identify the determinants of end-user reactions to computer-based technology

Cited by 184 publications

References 47 publications

Students’ Perceptions of Online Learning: A Comparative Study

Students’ Perceptions of Online Learning: A Comparative Study

The Anatomy of Brand Name Translation Under Skopos Theory: A Model of Customer Satisfaction, Awareness of Translation Strategy Use and Purchase Intention

The development and validation of computer self-efficacy and outcome expectancy scales in a nonvolitional context

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