Improving Workforce Planning in Engineering Services Firms Using Sentiment Analysis
Open AccessTraditional workforce planning in engineering services firms, where the unit of work is usually defined in terms of projects, is frequently divided into three decision levels: policy, lifecycle (attracting, hiring, training, and retaining skilled employees), and scheduling (Abernathy et al., 1973) skilled employees to billable projects. Scheduling is a recurrent source of job dissatisfaction since it tends to be carried out reactively by the firm’s managers, who are focused on minimizing the loss of billings when employees are between projects rather than using all available information to make optimal scheduling decisions for each employee.The body of research into resource scheduling has mainly centered on the best approach to achieve an optimal decision given a set of project attributes and employee constraints. These techniques almost always involve a mixed-integer linear programming model as the instrument to help firm managers make decisions. One conspicuous limitation is that no models consider employee desires and emotions. Overlooking skilled employees’ ambitions when scheduling work is a regular source of demotivation and threatens efforts by the employer to retain skilled workers—a critical objective for engineering services firms where the firm’s competitive advantage rests entirely with its knowledge workers.Organizations improve retention in various ways, but the common thread is to improve job satisfaction. Herzberg’s motivation-hygiene theory (also known as the two-factor theory) proposed in 1959 that job satisfaction is more closely tied to doing exciting work with ever-increasing levels of responsibility over time than to hygiene factors such as compensation, work environment, and administrative policies. Herzberg’s motivationhygiene theory is not without criticism in academic circles. However, in practical circles, managers still use Herzberg’s research as a framework to improve job satisfaction.Using this construct with the maturing field of natural language processing, in particular sentiment analysis, this research improved upon existing workforce planning practices by introducing the novel idea that employee desires, emotions, and sentiment should be included in decision models. Moreover, managers who use sentiment analysis in workforce planning are more likely to predict job satisfaction close to real time and take action to minimize it and, very often, the voluntary turnover that accompanies it.
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