Name
Abstract | ||
|---|---|---|
Capturing data from various data repositories and integrating them for productivity improvements is common in modern business organisations. With the well-accepted concept of achieving positive gains through investment in employee health and wellness, organisations have now started to capture both employee health and non-health data as Employer Sponsored electronic Personal Health Records (ESPHRs). However, non-health related data in ESPHRs has hardly been taken into consideration, with outcomes such as employee productivity potentially being suited for further validation and stimulation of ESPHR usage. Here we analyse selected employee demographic information (age, gender, marital status, and job grade) and employee health-related outcomes (absenteeism and presenteeism) to support evidence-based decision making.Our study considered demographic and health-related outcomes of 700 employees. Surprisingly, the analysis shows that employees with high sick leave rates are also high performers. A factor analysis shows 92% of the variance in the data can be explained by three factors, with the job grade capable of explaining 62% of the variance. Work responsibilities may drive employees to maintain high work performance despite signs of sickness, so ESPHRs should focus attention on high performers. This finding suggests new ways of extracting value from ESPHRs to support organisational health and wellness management to help assure sustainability in organisational productivity. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICC.2016.7510830 | 2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) |
Field | DocType | ISSN |
Employee resource groups,Marital status,Computer science,Knowledge management,Computer network,Absenteeism,Employee engagement,Presenteeism,Sick leave,Sustainability,Marketing,Employee research | Conference | 1550-3607 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahesh Fernando | 1 | 0 | 0.34 |
| Tony Sahama | 2 | 36 | 13.63 |
| Colin J. Fidge | 3 | 675 | 65.37 |
| Kamalanath Priyantha Hewagamage | 4 | 4 | 2.82 |