Factor analysis and a hierarchy process narrowed eight candidates down to seven and then ranked them.
Work From Anywhere sounds like a perk until a manager asks a harder question. If nobody shares an office, what actually decides whether the work gets done? A research team took that question seriously and replaced opinion with measurement.
The team started with a literature review and pulled out eight factors that could plausibly influence productivity in a distributed setup: gender, marital status, education level, the communication platform in use, work experience, educational background, type of work, and the IT facilities available.
Two methods did the work. Factor analysis groups variables that move together and removes the ones that add nothing. The Analytical Hierarchy Process then builds a structure of criteria and weighs them against each other. The data came from a questionnaire of 100 employees who each had at least a year of Work From Anywhere experience, answered on a Likert scale, with a pilot run of 30 respondents to clean up the questions.
Factor analysis extracted seven factors from the original eight and dropped work experience along the way. That is a useful result on its own, because it suggests years on the job say less about remote output than the working conditions around a person.
The hierarchy process then ranked what remained. The communication platform carried the highest weight at 15.11 percent. The type of work sat at the bottom with 12.89 percent. The spread between the top and the bottom is narrow, so no single factor dominates, but the ordering is still informative.
The authors note that not every job suits the model, and roles that depend on physical presence, such as public services and parts of health care, are poor candidates. For the work that does fit, the message is that productivity is shaped by conditions a company can influence rather than by employee traits alone.
Neyla Nuril Fauziyah et al. (2023). Optimizing Work-From-Anywhere Employee Productivity: A Comprehensive Investigation of the Most Influential Factors Using Factor Analysis and Analytical Hierarchy Process Method. Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology.
