MIS professionals: Education and performance

Khan, M. B.; Kukalis, Sal

doi:10.1016/0378-7206(90)90034-f

Cited by 34 publications

(24 citation statements)

References 0 publications

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“…Interestingly, the CFA based on the second-order model of CreaSE (i.e., the bottom image in Figure 3) indicated a moderate to poormoderate model fit with a significant chi-square statistic Page 129 (p < 0.001), GFI = 0.829, CFI = 0.925 and RMSEA = 0.088 [33]. These fit statistics were slightly better than those of the third-order model; therefore, we retained the second-order model.…”

Section: Assess the Validity Of Pretest Datamentioning

confidence: 99%

“…To improve the content of the items and the discriminant validity, we implemented what MacKenzie et al [33] termed as the "content adequacy test" which was created by Hinkin and Tracey [23] but has rarely been used in IS research until recently [49]. This test is very useful for identifying items that do not truly discriminate between unique subconstructs.…”

Section: Assess the Validity Of Pretest Datamentioning

confidence: 99%

“…Next, MacKenzie et al [33] recommend performing an EFA on the pretest data and evaluating reliability, convergent validity, and discriminant validity. The EFA revealed only one large factor regardless of the method of extraction or rotation.…”

Section: Assess the Validity Of Pretest Datamentioning

confidence: 99%

“…Content adequacy tests (CAT) [33,23] are administered in the form of a matrix survey where each survey item is listed down the rows with descriptions of each construct listed across the columns. Participants must indicate how "closely" each survey items seems to "fit" within each category (e.g.…”

Section: Content Adequacy Testsmentioning

confidence: 99%

“…Following detailed procedures outlined for construct development [33], we specify CreaSE as a second order formative construct with five reflective sub-constructs based on an individual's belief in their (1) cognitive ability, (2) domain knowledge, (3) training (including business, technology, and data skills), (4) "people" skills, and (5) affect toward creative problem solving. Our validation process includes six distinct data collections across a variety of populations-including business students, IS students, business professionals, IS professionals, executives, and university professors-which allowed us to repeatedly refine the scale.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of the Information Systems Creative-Self-Efficacy Scale

Payne¹,

Keith²,

Babb³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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High-performing information systems (IS) professionals harness creativity as they build systems to solve new and unstructured business problems. Psychology has developed useful scales and techniques for measuring creativity. However, "being creative" is not sufficient. IS professionals must also have confidence in their creative ability to succeed. The belief in one's ability to be creative is termed creative self-efficacy (CreaSE). CreaSE is defined in the general business context, but scales are not thoroughly developed or refined. CreaSE has also never been studied in the IS context. We detail steps to develop and validate a theoretically-based measure of CreaSE as related to IS. Our process includes six datasets collected during refinement. Participants include business and IS students, online respondents, university professors, IS executives, and IS professionals. The validated instrument is a secondorder formative measure with reflective first-order sub-constructs based on belief in cognitive ability, affect, domain knowledge, skills, and understanding of people.

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Section: Assess the Validity Of Pretest Datamentioning

confidence: 99%