Social Loafing Construct Validity in Higher Education: How Well Do Three Measures of Social Loafing Stand Up to Scrutiny?

The purpose of this study was to examine the construct validity of social loafing using convergent and discriminant validity principles. Three instruments that purport to measure social loafing were factor analyzed: A ten-item instrument by George (1992), a 13-item instrument by Mulvey and Klein (1998), and a 22-item instrument by Jassawalla, Sashittal, and Malshe (2009) for a total of 45 items that were compiled into a single instrument with which data were collected, correlated, and factor analyzed. One hundred and sixty graduate and undergraduates enrolled in management courses at a small private Northern California university were surveyed. Thirteen classes were surveyed and data was collected over three semesters. Data collected were factor analyzed using Principle Axis Factoring and rotated using Promax with Kappa = 4 for each instrument. Correlations, Keyser-Meyer-Olkin, and Bartlett’s test of sphericity were inspected for reasonable factorability, sampling adequacy, and appropriateness of running a factor analysis. Eigenvalues \u3e 1 and Scree plots supported the number of factors extracted with primary factor loadings of .4 or higher. Pattern, structure, and factor correlation matrices were inspected for content, loadings, and correlations among the derived factors. Derived factors were compared to each author’s theoretical framework. Additionally, the eight derived factors were factor analyzed using the same procedures. The result was three final derived factors. Findings showed correlations among the author’s scales indicated that the three instruments do not measure the same thing. George’s and Jassawalla et al.’s instruments share 55% of the variance. Mulvey and Klein’s instrument shares little in common with Jassawalla et al. and virtually nothing with George. Further, George, Mulvey and Klein, and Jassawalla et al. had hypothesized10 scales whereas my factoring had eight factors. Findings showed that the 8-factor solution supported George, partially supported Mulvey and Klein, and did not support Jassawalla et al. The final 3-factor solution does help to define the social loafing construct. The findings suggest using the instruments with caution. Further research to ensure accurate conceptualizations of the social loafing construct should be continued

Visual Co-occurence Learning using Denoising Autoencoders

Modern recommendation systems are leveraging the recent advances in deep neural networks to provide better recommendations. In addition to making accurate recommendations to users, we are interested in the recommendation of items that are complementary to a set of other items. More specifically, given a user query containing items from different categories, we seek to recommend one or more items from our inventory based on latent representations of their visual appearance. For this purpose, a denoising autoencoder (DAE) is used. The capacity of DAEs to remove the noise from corrupted inputs by predicting their corresponding uncorrupted counterparts is investigated. Used with the right corruption process, we show that they can be used as regular prediction models. Furthermore, we measure experimentally two of their specificities. The first is their capacity to predict any potentially missing variable from their inputs. The second is their ability to predict multiple missing variables at the same time given a limited amount of information at their disposal. Finally, we experiment with the use of DAEs to recommend fashion items that are jointly fashionable with a user query. Latent representations of items contained in the user query are being fed into a DAE to predict the latent representation of the ideal item to recommend. This ideal item is then matched to a real item from our inventory that we end up recommending to the user