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Abstract: Innovation management is the practice of managing new ideas and insights into business environment so as to increase market competitiveness. Therefore, it is vital to address the critical determinants of effective innovation management within an organizational context, hence called organizational innovation. Based on some existing frameworks of managing organizational innovation, several key determinants can be identified. Using industrial survey data, we then proposed and used a neural network-based approach to quantify the connectivity between each of these determinants and organizational innovation. First, the modeling accuracy of our proposed method was examined and benchmarked with multiple linear regression and non-linear least square fitting methods. Next, the relative importance among various key determinants toward organizational innovation can be computed using our method. We then compared the results with the qualitative judgment of field experts and professionals using the analytical hierarchy process. Based on the comparison outcomes, our proposed method is proved to be reasonably useful and practical in capturing the comparative influence among determinants toward organizational innovation. [Copyright &y& Elsevier]

Abstract: Understanding how the various factors in a supply chain contribute to the overall performance of its operation has become an important topic in management science research nowadays. In this paper, we propose and apply a two-stage methodology to an industrial survey data set to investigate relations among the key factors in a supply chain model. Precisely, we use the PC-algorithm to discover the connectivity relation among the factors of interest in the supply chain model. Critical factors in the model are then identified, and we then utilize the neural network to quantify the relative importance of some of the factors in predicting the critical factors. An advantage of our proposed method is that it frees up the researcher from making subjective decisions in his or her analysis, for example, from the needs of specifying plausible initial path models required in a structural equation modeling analysis (which is usually used in business and management research) and of selecting factors for the subsequent predictive modeling. We envision that the analysis results can aid a decision maker in optimizing the system performance by suggesting to the decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on. [Copyright &y& Elsevier]

When developing new products it is important for design teams to understand customer perceptions of consumer products because the success of such products is heavily dependent upon the associated customer satisfaction level. The chance of a new product¿s success in a marketplace is higher if users are satisfied with it. In this study, a new methodology of generating customer satisfaction models using a neuro-fuzzy approach is proposed. In contrast to previous research, non-linear and explicit customer satisfaction models can be developed with the use of the proposed methodology. An example of notebook computer design is used to illustrate the methodology. The proposed methodology was measured against the benchmark of statistical regression to determine its effectiveness. Experimental results suggested that the proposed approach outperformed the statistical regression method in terms of mean absolute errors and variance of errors. [Copyright &y& Elsevier]

Digital technologies, including Virtual Learning Environment (VL) and Social Media (SM), are well adopted in the Higher Education (HE) setting, yet little is known about the role these tools play in supporting students' achievement of HE goals. This research is the first to examine student efficiency by uncovering the direct links between HE inputs and outputs in consideration of technology use. Building on service productivity theory, a two-phase approach is adopted to empirically examine if VL and SM use enhances students' efficiency in HE goals attainment. The first phase identified a range of HE inputs and outputs perceived by HE students via structured interviews. Through questionnaires, the second phase revealed how technology use changed the input-output transformation. Our findings suggest that students are better off without relying on digital technologies. While VL can enhance students' HE goals achievement with additional inputs, students who use SM are the least efficient. This encourages further work to devise more diligent use of VL and SM under the HE setting. • Students are the most efficient in achieving HE goal without digital technologies. • VL supports students' HE goal achievement but requires additional resources. • Students who use SM are the least efficient in achieving HE goals. • HE institutions should not over-privilege digital tools especially post-COVID-19. [ABSTRACT FROM AUTHOR]

Describes a one-step preparation of a chelating resin for ion exchange from sawdust, for use in chemical experiments. Preparation of sawdust; Determination of the ion-exchange capacity of the sawdust; Effect of different storage methods on metal-ion exchange capacity of chelating sawdust; Effect of different exchange conditions; Recovery of heavy metal ions.

The importance of remanufacturing has been increasing since stricter regulations on protecting the environment were enforced. Remanufacturing is considered as the main means of retaining value from used products and components in order to drive a circular economy. However, it is more complex than traditional manufacturing due to the uncertainties associated with the quality, quantities and return timing of used products and components. Over the past few years, various methods of optimising remanufacturing outcomes have been developed to make decisions such as identifying the best End-Of-Life (EOL) options, acquiring the right amounts of cores, deciding the most suitable disassembly level, applying suitable cleaning techniques, and considering product commonality across different product families. A decision being made at one remanufacturing activity will greatly affect the decisions at subsequent activities, which will affect remanufacturing outcomes, i.e. productivity, economic performance effectiveness, and the proportion of core that can be salvaged. Therefore, a holistic way of integrating different decisions over multiple remanufacturing activities is needed to improve remanufacturing outcomes, which is a major knowledge gap. This paper reviews current remanufacturing practice in order to highlight both the challenges and opportunities, and more importantly, offers useful insights on how such a knowledge gap can be bridged. • Integrated decision making (IDM) over multiple remanufacturing activities is a gap. • Models considering economic, engineering and environmental objectives are lacking. • Metaheuristics and non-linear programming are recommended optimising methods. [ABSTRACT FROM AUTHOR]

Product development involves several processes in which product planning is the first one. Several tasks normally are required to be conducted in the product-planning process and one of them is to determine settings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings such that the best customer satisfaction of products could be obtained. To achieve this, models that relate customer satisfaction to design attributes need to be developed first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera design is used to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Emphasizes the finding that high temperature deposition can improve the performance of organic light-emitting devices (OLED), in response to comments on the article 'Organic electroluminescent devices by high-temperature processing and crystalline hole transporting layer,' by Qin et al.