This study aims to explore how and what type of team diversity is related to team creativity in R&D organizations, incorporating conflict as a mediator and…
This study aims to explore how and what type of team diversity is related to team creativity in R&D organizations, incorporating conflict as a mediator and transformational leadership as a moderator.
Survey questionnaires were used to collect data from 24 Korean R&D teams (185 team members, 24 team leaders and 24 managers) in the public and private sectors. The dependent variable, team creativity, was measured by questioning R&D team managers to whom R&D team leaders report directly, and the antecedents, mediator and moderator were answered by team members and their leaders.
The data confirmed that team diversity, particularly informational diversity, was positively associated with team creativity. However, conflict did not show a significant mediating effect between team diversity and creativity. Transformational leadership had a negative moderating effect between informational diversity and task conflict in such a way that when transformational leadership was high, teams with higher informational diversity showed lower task conflict between team members.
A growing number of R&D projects require interdisciplinary efforts resulting in incorporating scientists and engineers from multiple disciplines, and growing numbers of women and minorities now choose careers in science and technology. Naturally, R&D project teams have become more diverse than before. This study provides empirical evidence from multiple sources, showing both mediating and moderating effects on the relationship between R&D team diversity and creativity.
It is important for an IT service company (IS company) to fully take into account the differences in customer satisfaction across different customer groups. In this…
It is important for an IT service company (IS company) to fully take into account the differences in customer satisfaction across different customer groups. In this article, we consider three layers of customers in the client company. There are project directors who interface with and accept the final product (i.e. IT system) from the IS company, users who actually use the IT system for their daily operations, and finally operators who do maintenance works for the IT system. We propose that each customer group (i.e. project director, user, or operator) evaluates the IT systems success with a different set of criteria. Transaction relationship and partnership turn out to be important determinants for the project directors: task‐related and IS‐related output performances seem to be less influential. The reverse conclusion can be made for users and operators. One additional insight is that IS company’s efforts to understand its customer’s tasks and share risks with the customer company might have unexpected effects. Although the project directors seem to like such close involvement, it can be detrimental to the users’ satisfaction with the IS outsourcing projects.
U.S. bank holding companies (BHCs) have experienced dynamic changes over a period of 2000–2010. We find that the size distribution of sample banks becomes highly positively skewed with a small number of big banks becoming super-sized, and these big banks tend to take extra risk by holding derivative positions for trading purposes. The ten largest risk-taking banks hold about 70% of total assets of all the sample banks in 2010. We investigate whether the risk-taking activities of the BHCs translate into higher risk-adjusted return performance. In extensive panel regression analyses, we find that the risk-taking strategies of large banks by holding derivative positions for trading purpose do not show the clear evidence of enhancing risk-adjusted performance. We find that negative impacts of extra risk-taking on the risk-adjusted performance become bigger with the size of banks.
Problemistic search is a central part of behavioral strategy because it is a fundamental step in the decision process leading to strategic change. Despite the significant research efforts so far, there is a gap in our understanding of search. Unlike the theory of myopic search, most research so far has emphasized search initiated by performance relative to aspiration levels on goals that are too broad to justify directing search toward the form of strategic change selected for investigation. In the following, I outline the foundation of an extended theory of problemistic search in response to broad goals through either broad search, use of multiple goals, use of power, reliance on cognitive biases, or responses to environmental stimuli. Each of these processes, alone or in combination, can give more specific predictions of where firms search when encountering performance below aspiration levels on broad goals. Substantial progress in empirical research is needed, however, to distinguish which of these processes occur.
To improve the quality of the additive manufacturing (AM) products, it is necessary to estimate surface roughness distribution in advance. Although surface roughness…
To improve the quality of the additive manufacturing (AM) products, it is necessary to estimate surface roughness distribution in advance. Although surface roughness estimation has been previously studied, factors leading to the creation of a rough surface and a comprehensive test for model validation have not been adequately investigated. Therefore, this paper aims to establish a robust model using empirical data based on optimized artificial neural networks (ANNs) to estimate the surface roughness distribution in fused deposition modelling parts. Accordingly, process parameters such as time, cost and quality should be optimized in the process planning stage.
Process parameters were selected via a literature review of surface roughness estimation modelling by analytical and empirical methods, and then a specific test part was fabricated to provide a complete evaluation of the proposed model. The ANN structure was optimized by trial and error method and evolutionary algorithms. A novel methodology based on the combination of the intelligent algorithms including the ANN, linked to the particle swarm optimization (PSO) and imperialist competitive algorithm (ICA), was developed. The PSOICA algorithm was implemented to increase the capability of the ANN to perform much faster and converge more precisely to favorable results. The performances of the ANN models were compared to the most well-known analytical models at build angle intervals of equal size. The most effective process variable was found by sensitivity analysis. The validity of proposed model was studied comprehensively where different truncheon parts and medical case studies including molar tooth, skull, femur and a custom-made hip stem were built.
This paper presents several improvements in surface roughness distribution modelling including a more suitable method for process parameter selection according to the design criteria and improvements in the overall surface roughness of parts as compared to analytical methods. The optimized ANN based on the proposed advanced algorithm (PSOICA) represents precise estimation and faster convergence. The validity assessment confirms that the proposed methodology performs better in varied conditions and complex shapes.
This research fills an important gap in surface roughness distribution estimation modelling by using a test part designed for that purpose and optimized ANN models which uses purely empirical data. The novel PSOICA combination enhances the ability of the ANN to perform more accurately and quickly. The advantage in using actual surface roughness values is that all factors resulting in the creation of a rough surface are included, which is impossible if other methods are used.