Factors Effecting the Performance Management System: A Comparative Analysis Among Men and Women With Reference to Information Technology Sector  

Abstract

This positivist research outcome reports the factors effecting the performance management system (PMS) in information technology sector using a comparative with reference men and women employees. A comparative analysis of an empirical survey involving men and women employees using the factors that effect the PMS in information technology sector carried out. The primary data generated carrying out a survey with Nine hundred and twenty-four employees consisting of 379 women, and 545 men working in information technology sector in and around the Metro of Hyderabad. A structured and undisguised questionnaire, was employed on the respondents for this research study. The questionnaire prepared and published on Google form and link for the questionnaire was provided to the respondents. The six independent factors that are effecting the PMSemployee performance, working environment, personal competencies, knowledge-level, job-knowledge, interpersonal and communication competencies and a dependent factor PMS measured. The reliability and in the internal consistency of the research instrument, the survey questionnaire assessed using reliability statistic Cronbach Alpha. The C-alpha values ranged between 0.67 to 0.86 for men, and 0.63 to 0.84 for women employees for the factors assessed indicating, a strong internal consistency and reliability of the survey instrument. The factors that effect the PMS reported in the manuscript.Factors Effecting the Performance Management System: A Comparative Analysis among Men and Women with Reference to Information Technology Sector

Ambivalent Bias at Work: Managers’ Perceptions of Older Workers across Organizational Contexts

“…ANOVA is used to determine whether there are statistically significant differences between organizational contexts in managers' negative, mixed or positive perceptions about older workers (Kim, 2015). Multinomial regression is an extension of logistic regression that is generalizable to problems with more than two possible discrete outcomes (Prasad and Vaidya, 2016), as in the case of managers' perceptions of older workers. By using this model, we can estimate the odds that managers hold negative or mixed perceptions of older workers relative to the odds of holding fully positive perceptions, as a function of their organization's features, and controlling for managers' age, gender, position, organization size and industrial sector (for similar uses, see Mulders, 2020).…”