The Employee Suggestion System: A New Approach Using Latent Semantic Analysis

Abstract

Employee suggestion systems are often used as a way to improve participation from members of the organization to help solve problems that cannot be solved through traditional organizational practices. In the government sector, employee involvement programs are the most difficult to implement mainly because management regularly changes with new administration and these changes bring about many short‐term management practices and systems. Toyota's approach to employee suggestion programs has been widely benchmarked and studied, yet there is little research to show that these practices can be applied or are successful in the public sector. This work uses a statistical data‐mining technique to compare which types of human resource management practices are prevalent in employee suggestion programs at Toyota and a target government organization. This work shows that Toyota emphasizes organization‐centered factors to stimulate employee participation in solving small problems that relate to an employee's job. On the contrary, government organizations tend to emphasize employee factors that make conditions right for employees to make larger improvements in their jobs that lead to improvements outside their work areas. Findings suggest that Toyota's approach to employee suggestion programs is not a way to weaken management's obligation to perform problem solving, but instead is another medium to highlight problems that do not require management's intervention. These new insights and others provide an increased understanding of employee suggestion programs in the public sector that are unique to manufacturing. © 2012 Wiley Periodicals, Inc.

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