The ability for an organization to innovate has become one of the most important capabilities needed in the new knowledge economy. The research has demonstrated that an organization’s culture of innovation in particular predicts organizational innovativeness across multiple industries. To provide support to these organizations in their abilities to understand the culture of innovation, researchers have developed instruments to measure culture of innovation, and while many of these instruments have been widely used to inform organizational opportunities for improvement, few of these instruments have been validated or replicated beyond their initial use. The current study employs multiple factor analytic methods to validate the factor structure of the Innovation Quotient instrument developed by Rao and Weintraub and assess the extent to which the instrument is reliable for multiple organizational groups. The results of this study, as well as implications for researchers interested in culture of innovation, are presented.

The study's purpose was to identify demographics, educational background, finances, formal and informal education and experiences, reading habits, external environmental factors, psychological factors, and computer efficacy factors that predict a student's ability to successful complete an online (Web-based) distance learning community college course. Major student retention theories and student attrition and persistence research guided the study. Distance learners (N = 926) completed four surveys, which collected data for 26 predictor variables that included age, gender, marital status, ethnicity, support others, course load, first-time student, last semester attended, student type and location, financial stability, tuition payment, prior learning experiences, reading habits, family support, enrollment encouragement, study encouragement, time management, study environment, employment, extrinsic and intrinsic motivation, locus of control, self-efficacy, computer confidence and skills, and number of prior online courses. Successful or unsuccessful course completion was the dependent variable. Statistical analyses included Cronbach's alpha, Pearson chi-square, two-sample t test, Pearson correlation, phi coefficient, and binary logistic regression. Variables in each factor were entered sequentially in a block using separate binary logistic regression models. Statistically significant variables were course load, financial stability, prior learning experiences, time management and study environment, extrinsic motivation, self-efficacy, and computer skills. Selected predictor variables (N = 20) …