Abstract

We assumed that an employee's performance in an organization was related to his perception of such factors as socio-economic environment, organizational structure and processes, job itself, personal characteristics, and personal life outside the oganization. Under this assumption, we attempted to abstract major determinants in predicting the performance from subfactors of these factors. The detailed procedures and findings of the four parts research are as follows. First, multiple regression analysis by 22 predictors was conducted to 462 bule-collars engaged in electronics industry. Multiple correlation coefficient(R) to performance predicted by all of 22 predictors was .588, therefore 34.6% of the performance variance could be explained by all predictors. Results of forward stepwise regression analysis showed that 6 predictors-motivation(R=.484), morale(R=.517), competence(R=.538), cognitive need satisfaction(R=.548), job communication with leader(.555) and loyalty to organization(R=.563)-had statistically significant multiple regression coefficient to performance. These 6 factors explained 31.6% of the performance variance, which meant only 3% of the variance was explained by 16 surplus predictors. Second, it was assumed that the constructs having significant regression coefficient to performance prediction of electronics industry blue-collars also might be important factors of performance in inudstrial organizations in general, without taking the effects of the industry-specific or the organizational hierarchy-specific factors into consideration. We tested the validity of this assumption by substituting the measures of each constructs for the multiple regression equation. Namely, the predicted performance level of each company were derived from the regression coefficients and means of each constructs, then whether these predictions were really correlated to actual performance ratings of each company or not was tested. The results showed significant Pearson correlation coefficient r=.782(p<.05) between the predicted values from multiple regression equation and the actual performance ratings. Third, another 281 blue-collars in the same electronics industry rated how much the content of each item affected their performance. We found, as the result, that 8 constructs-motivation(2.320), loyalty to organization(2.375), leader's comprehension of subordinates' jobs(2.244), morale(2.273), leader's support for goal setting(2.247) and feedback(2.244)-had effects upon performance. Four of these factors(motivation, morale, loyalty to organization and leader's comprehension of subordinates' jobs) were also found to have significant regression coefficient at multiple regression analysis. Finally, it was also found that age, marriage status, tenure and salary among 7 demographic variables are significantly related to the performance level.