Flextime and Telecommuting: Examining Individual Perceptions
Bibliographic record
Abstract
As individuals increasingly experience conflicts between their personal lives and the demands of the workplace, many employers offer alternative work arrangements that are designed to help workers achieve a better balance in their lives (Harris, 2003; Shamir & Salomon, 1985). Two such alternatives, flextime and telecommuting, have proven particularly instrumental in helping employees meet the many demands on their time, and these programs have grown dramatically over the past twenty years (Bailey & Kurlan, 2002; Thornthwait & Sheldon, 2004). Indeed, reports from the Bureau of Labor Statistics (BLS, 2005; 2006) show that the number of workers with flexible schedules increased from about 13.1 million in 1985 to around 38.0 million in 2004, representing an annual growth rate of just under 6 percent. Similarly, the number of telecommuters has grown at an annual rate of just over 5 percent, from about 17.3 million in 1986 to around 45.1 million in 2005 (Kraut, 1989; ITAC, 2005). And, while statistics show that the growth rate of both flextime and telecommuting has leveled off during the past five years, it is estimated that more than a quarter of the workforce is presently involved with one of these work options (BLS, 2005; ITAC, 2005).The advantages of both flextime and telecommuting have been widely reported in the popular press, perhaps leaving some managers to conclude that employees will be highly receptive to these alternative work programs and willingly participate when they are offered. However, given some basic differences between flextime and telecommuting, it is reasonable to assume that not all individuals will view these programs in a similar manner. Therefore, the purpose of this study was to examine individual perceptions of flextime and telecommuting. Further, this research explored the role that personality, demographics, and work experiences play in forming these perceptions.Flextime and TelecommutingWhile both flextime and telecommuting can be useful in helping employees balance the various demands on their time, there is a significant difference between these programs. Flextime involves building flexibility into an employee's work schedule. With flextime programs, employees are often required to be at work during certain core hours when all workers are typically needed to satisfy customer demand. However, employees are then granted some latitude in scheduling their remaining hours. These programs provide individuals with the ability and autonomy to schedule work around the demands of their personal life. In general, these programs have resulted in reduced turnover and absenteeism, higher employee morale and productivity, and improved worker well-being (Gale, 2001; Gill, 1998; Lucas & Heady, 2002).Alternatively, telecommuting programs permit flexibility by allowing employees to work from different locations. In a nutshell, telecommuting is the practice of using electronic communication technology to perform work from remote locations. Some employees telecommute on a full-time basis, while others may only spend one or two days a week outside of the traditional workplace. While some studies have identified potential problems with telecommuting (McCloskey & Igbaria, 2003; Tietze, 2005), overall results have been positive (Greer, Buttross, & Schmelzle, 2002; Kurland & Bailey, 1999) . For instance, as a result of their telecommuting program, Merrill Lynch reported over a 15 percent increase in productivity, 3.5 fewer sick days per year, and about a 6 percent decrease in turnover (Wells, 21).Sample and ResultsRespondent ProfileThe sample for this study was comprised of 242 management students at a southeastern university that has a relatively large number of nontraditional students. Four classes, each of which were taught by one of the authors, were surveyed. Participation was strictly voluntary.Two surveys were administered to the students. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".