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.
Abstract-We describe a comprehensive methodology for discovering service similarity (substitutability) by testing. Our solutions do not rely on the service descriptions provided by their authors and they avoid common information retrieval techniques. Our work addresses a variety of challenges raised throughout the process. These include: (1) the generation of unbiased test samples based on individual domains and their statistical properties; (2) the use of progressive sampling and Rand index convergence to minimize sample size; (3) the classification of services by their input and output structures (single values, sets of values, sequences of values, and tables), and the development of corresponding similarity measures; (4) the optimal alignment of services that have multiple inputs and outputs of the same type; (5) the management of two types of service exceptions (null values); (6) the selection of clustering methods that are most appropriate to the sets of services being clustered; and (7) the caching of tests, results, similarities, clusters and other statistical information to enable cluster evolution. Initial testing with a prototype implementation validated our methodology, yielding high accuracy at surprisingly small test sizes.
The authors define a formal model for information services that incorporates the concept of service similarity. The model places services in metric spaces, and allows for services that have arbitrarily complex inputs and output domains. The authors then address the challenge of service substitution: finding the services most similar to a given service among a group, possibly large, of candidate services. To solve this nearest neighbor problem efficiently the authors embed the space of services into a vector space and search for the nearest neighbors in the target space. The authors report on an extensive experiment that validates both their formalization of similarity and their methods for finding service substitutions.
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