I Am Distinctive When I Belong: Meeting the Need for Optimal Distinctiveness through Team Identification

The development of stronger team identity has previously been explained through the social identity aspect of belonging. Although this has contributed much to our understanding of sport fans, it has neglected an alternative explanation for team identity, specifically the search for distinctiveness. How then do fans develop stronger team identity by \u27standing out\u27 as opposed to \u27fitting in\u27? This paper provides evidence of seven identity management strategies used by fans with a strong psychological connection to their chosen team. Saturation sampling was employed to interview 29 South African rugby union fans via semi-structured interviews, followed by a directed approach to content analysis. The results contribute a stronger explanation of how the psychological need for optimal distinctiveness functions within the attachment process towards stronger fan loyalty, and provides a more complete explanation for the way in which fans can \u27stand out\u27 while still belonging

Probability of Automation of Occupations 2036

We used a similar CBTC methodology to Frey and Osborne (2017), Autor and Dorn (2013), and Caines et al. (2017). The US Department of Labor’s Dictionary of Occupations Titles (DOT) classification system and the 2010 Standard Occupation System (SOC), which allowed for the respective data sets to be cross-referenced. The 2010 SOC system classified 840 detailed occupations with similar job duties, skills, education and training into 461 broad occupations, 9 minor occupations and 23 major groups (U.S. Bureau of Labor Statistics, 2010). We also used a secondary data set, namely US Occupational Employment Statistics (OES) data as it contained detailed occupational descriptions and employment statistics per occupation for the US economy, broken down per industry according to the NAICS classification system. The NAICS is an industry classification system that grouped organisations into industries based on similarities in production processes. It used a six-digit coding system to classify all economic activities in the US across 20 industry sectors and 1 057 detailed industries (U.S. Office of Management and Budget, 2017). Employment data was classified according to the 2010 SOC system that contained 820 occupations. With the emergence of new roles and reclassification of the SOC system over the same duration, certain classifications needed to be cross-referenced (“crosswalked”) to provide comparable figures across different years. In their studies, Frey and Osborne (2017), Caines et al. (2017) and Autor and Dorn (2013) aggregated the occupations slightly differently. For example, Frey and Osborne (2017) aggregated specific “postsecondary teaching” occupations into a single category and omitted occupations containing “all other”, whilst Caines et al. (2017) omitted selected “farm and agricultural” occupations. In total, Frey and Osborne (2017) calculated the probability of job automation for 702 occupations. Autor and Dorn (2013) calculated job routineness for 330 aggregated occupations. Caines et al. (2017) calculated job complexity for 315 aggregated occupations. Our approach combined and cross-referenced all of the above-mentioned data sets to produce a total of 291 occupations for the analysis. In projecting the change in workforce structure from 2016 to 2036, the 2016 OES employment numbers were adjusted for the “probability of job automation” at an occupational level, and aggregated to illustrate the relative change in workforce structure for the entire US economy. Occupations which did not have a corresponding measure of “probability of job automation” were omitted from the analysis. After omissions, this analysis represented 96.3% of the US workforce in 2016