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This paper aims to explore the relationship of various financial and non‐financial factors to corporate value and how these factors can be used for the purpose of firm valuation. The focus is placed on a developing high‐tech industry.

The authors collect and compare data from companies within the time window of 1997 through 2010. The techniques of stepwise regression and back‐propagation neural network (BPNN) are applied to analyze this data, where the variables of operating profit margin, ROE, ROA, net income ratio, Tobin's Q and stock price are chosen to indicate firm value.

Each firm value variable appears to have a different set of estimator variables consisting of financial and non‐financial factors. The estimator variable in the set that has a high influence relative to the others tends to be financial factor. However, certain non‐financial factors appear to be considered as an estimator variable for different firm value variables more often than financial factors such as employee productivity, wealth created per employee, revenue growth rate, management expense per employee, R&D expense to management expense ratio, and R&D expenditure to total assets ratio. Further, the incorporation of BPNN shows an improvement of the result of the regression method in terms of overall estimation error, especially for operating profit margin.

The authors' investigation highlights the importance of the use of non‐financial factors for firm valuation in developing biotech industries. The result can be helpful for investors who seek to examine information variables and indicators for the opportunity presented by the above industries. In addition, the significant estimation improvement by incorporating the BNPP method into the commonly used regression method suggests the beneficial use of BPNN in refining the traditional methods in the field.

Describes how a statistical Taguchi approach and a backpropagation neural network model were devised to evaluate the effect of various parameters and identify the optimal parameter setup values in a gas‐assisted injection moulding process. In applying the Taguchi approach, an L18orthogonal array was employed to collect the observations, and the same collected data sets, with two additional inputs, were utilized to construct a neural network model to ascertain whether utilizing such a neural network would provide an improved generalization capability over a statistical method. The effect of the learning rate and the number of hidden nodes on the efficiency of the neural network learning algorithm was extensively studied to identify what provides the best forecasting of performance measure. In addition, to verify the generalization capability of the neural model, eight different parameter setups, which had not been included in the full factorial design, were constructed for network testing. The results revealed that the network is more efficient in identifying the real optimal parameter setup.

The purpose of this paper is to illuminate the hidden process of collusion among power holders in state-owned enterprises (SOEs) in an emerging economy, which endures despite comprehensive reforms towards democracy and good governance. Why are mechanisms of checks and balances not functioning in the way they should?

The analysis is based on in-depth interviews with board members, executives, politicians, bureaucrats and representatives from auditing boards involved in the management of SOEs in Indonesia.

The findings reveal practices of collective conservatism, reciprocal opportunism and normalisation of corruption. The costs of getting into powerful positions are so high that conglomerate business owners gain control over the management of SOEs. The authors use the terms “wall-building and gatekeeping” to explain such cases.

There is a continuous process of wall building and gatekeeping occurring among business oligarchs, bureaucrats and elected politicians in Indonesia. New entrants into the system are co-opted by the established elite.

This study shows collusion, rent-seeking and corruption among political and business elites as well as top officials in the government hinder good governance reforms in state-owned Indonesian enterprises.

Collusion and illicit business practices in SOEs are clearly grounded on wall building and gatekeeping. Tackling this problem is a precondition for good governance and an improved legal and regulatory business environment in Indonesia. The ideal separation of powers and the checks and balances for good governance apparently need more than a democracy to break through. A further strengthening of the free press and critical academics will be one crucial contribution.

There is generally a lack of understanding of the context of corruption, such as the influence of institutional and organisational structures. The topic of corruption is also under-researched due to the difficulty of finding empire evidence. This paper contributes to explaining why new political and organisational structures, such as a democratically elected parliament and a particularly designed corruption eradication commission, are not able to hinder rent-seeking practices and illicit political business in state agencies.

Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones).

The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling.

By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective.

This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification.

Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.