Identifying Criteria for Continuous Evaluation of Software Engineers for Reward and Recognition: An Exploratory Research

Identifying Criteria for Continuous Evaluation of Software Engineers for Reward and Recognition: An Exploratory Research

Sreejith S. S. (Department of Management Studies, Indian Institute of Science, Bangalore, India) and Muthu Mathirajan (Department of Management Studies, Indian Institute of Science, Bangalore, India)
DOI: 10.4018/IJHCITP.2016100105
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Reward and Recognition (R&R) should be given to employees in a timely manner, based on continuous evaluation of their performance. Success of an R&R process lies in clear and well defined criteria for continuous evaluation of employee performance. Often such criteria are decided by the organization with no input from the employees. The purpose of this paper is to use qualitative research methods to explore and identify the criteria to be used for continuous employee performance evaluation for R&R in Information Technology organizations, from the perspectives of software engineers (SEs) and project managers (PMs). Exploratory research was conducted in two phases. In Phase I, unstructured interviews are used to elicit information from 7 SEs. Caselets are prepared based on these interviews and 19 criteria are identified. In Phase II, the criteria identified in Phase I are confirmed using content analysis of semi-structured interviews, conducted on relatively larger group of SEs (in stage 1) and PMs (in stage 2). Additionally, 12 criteria are also identified in Phase II. Collectively 31 criteria are identified. The proposed criteria set is expected to comprehensively cover the SE performance on a continuous basis in various dimensions to award R&R.
Article Preview

1. Introduction

Employee performance in organizations can broadly be classified into two – typical and maximum (Sacket et al., 1988). In general, maximum performance is regarded as the level of performance an individual can achieve when highly motivated (“can do”); While typical performance is the average level of performance an individual usually achieves over a period of time (“will do”) (Deadrick & Gardner, 2008). Employees exhibit typical (or average) performance when: (i) they are not aware that they are being evaluated on the job, (ii) they are not instructed to do their very best, and (iii) their job performance is assessed over an extended period of time (Klehe & Latham, 2008). So as to enable employees to exhibit maximum performance, the organizations need to make sure that their performances are evaluated in shorter intervals continuously; that the employees are aware that they are being observed and evaluated; and that the employees know what they should concentrate on.

Employee performances in organizations are formally evaluated often using the traditional performance appraisal system (PAS) (Arvey & Murphey, 1995). Performance appraisal is considered as a formal interview that generates social interactions between managers and employees to formulate action plans through a discussion of the individual’s previous job performance and future developmental needs (Murphy & Cleveland, 1995). The PAS usually runs at pre-determined intervals such as annually or half-yearly (Agunis, et al., 2011) and evaluates the past performance of employees at the end of an appraisal period. However, due to the nature of rate of change of technology (Mendelson, 2012), a dynamic industry like Information Technology (IT) needs to have an agile and continuous performance evaluation process, running at continuously in shorter intervals (Sreejith, 2015). To facilitate a continuous evaluation at shorter intervals, the existing PAS should be made to run more frequently. However, because of the administrative effort associated with the existing PAS, Gray (2002) opines that it is almost impossible to have the PAS altered from its current frequency. Hence it seems logical to develop an alternative process for continuous performance evaluation.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 9: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing