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The purpose of this paper is to examine the relationship between open innovation (OI) and innovative performance, and to explore the moderating effect of knowledge structure, including component knowledge and architectural knowledge, on this particular relationship.

The proposed model was tested with regression analysis of data collected through a questionnaire survey of 231 employees of Chinese manufacturing firms.

The findings indicate that inbound OI is positively related to innovative performance and that outbound OI has an inverted U-shaped effect on innovative performance. By presenting empirical evidence of the moderating effects of component and architectural knowledge, our analysis of results demonstrates that the strong alignment between knowledge structure and OI results in superior innovative performance.

This study addresses the controversial issues brought up by previous studies with findings of an inverted U-shaped effect of outbound OI on innovative performance. By exploring the moderating effect of knowledge structure, the authors provide insights into how internal contextual factors in relation to organizational knowledge can affect the efficacy of the inbound and outbound OI on innovative performance.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Apart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model.

Certainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment.

Specifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment.

The work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).

The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design.

The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning.

The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems.

Future work will consider the application of the proposed ACACO to the recurrent fuzzy network.

The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.

In light of the global concern about the impact of trade liberalization on environment, this paper intends to measure, theoretically as well as empirically, the impact of freer rice trade on environmental quality in terms of chemical intensity, measured by a decreasing scale indicator.

A dynamic land allocation model is designed that incorporates rational expectations on rice price and soil degradation due to long‐term cultivation on the same piece of land. Both demands for land and pesticides are derived and empirically estimated using time‐series data in Taiwan.

The results reveal that chemical intensity may increase with freer trade. The effect of freer trade on chemical intensity was less significant in earlier days such as the 1980s than in recent years. The basic model explains, to some extent, why some country representative conveyed their concerns in the Committee of Trade and Environment under WTO about the environmental degradation due to freer trade in agriculture. It also implies that more investment on environmental protection is warranted in the course of trade liberalization.

Given the dramatic inflation of energy prices, more elaboration on the price specification with respect to pesticides may be warranted for future researches.

The paper develops a rational expectations model to measure the impact of rice trade liberalization on environmental quality.

With increasing competition in the global marketplace, a prime and basic question for many enterprises is how to construct a global strategy for existing product mixes through standardization or localization, so as to achieve success in regional or local markets. The purpose of this paper is to investigate customer value perceptions of products, to view what variables are important to consumers, and lastly to make comparisons via a cross‐cultural study. Using the same survey method to collect data from Taipei (Taiwan), Seoul (South Korea), Shanghai (China), and Tokyo (Japan), perceived customer values of products were collected together in a List of customer values. The results yielded a wide array of both crosscultural differences and similarities in East Asia.

The aim of this paper is to use a new approach of performance evaluation, grey relation analysis (GRA), which is a concept borrowed from the study of industry and is increasingly applied to commerce. GRA is used to evaluate the relative performance of three of Australia's major banks The result of the study indicates that although the sample size is small and the distribution of data is unknown, GRA can still be successfully used in evaluating bank performance. In addition, this paper compares the GRA results with the financial statement analysis and shows that the same result can be obtained.

This study focuses primarily on the business operating departments (hereinafter, DMUs) of a case telecom company (hereinafter, the Company) in the northern and eastern areas of Taiwan. In 2007, the Company finished the first stage of its reorganization by consolidating 14 DMUs into 12. In 2011, the Company completed the second stage of its reorganization by consolidating the 12 remaining DMUs into 8. This study intends to explore the effects of each stage of the Company’s reorganization on the efficiency and ranking of the various DMUs. The results show that the DMUs became more efficient after each stage of the Company’s reorganization. Moreover, the efficiency and ranking of the new DMUs, A6, A7, and B7 increased post-consolidation. This suggests that both the first and second stages of the reorganization were necessary. The findings of this study could help the Company and other telecom companies to design strategies for the future consolidation of other units, and thereby maintain their competitiveness and continued growth.

The current leadership literature has paid little attention to understanding the intervening mechanism by which leaders influence followers. In order to partially bridge this gap, the article aims to present a value‐fit charismatic leadership theory which focusses on the key intervening mechanism – person‐organization values fit.

The model was tested empirically on 180 participants, including 51 managers and 129 employees from 37 large‐scale companies in Taiwan.

Based on the block regression analysis, the results showed that CEO charismatic leadership has both direct and indirect effects on employees’ extra effort to work, satisfaction with the CEO, as well as organizational commitment, which are mediated by employees’ perceived person‐organization values fit. The findings also provided evidence that the relationship between charismatic leadership and person‐organization values fit is significant. Furthermore, the analysis also showed the significant effects of person‐organization values fit on employee outcomes.

The study shows how CEO charismatic leadership can, through the mediating effect of person‐organization values fit, have profound influence on employee outcomes.