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The purpose of this study is to primarily examine the trend of hidden values and use of intellectual capital (IC) information narratives of leading Malaysian companies in the context of the recent financial crisis. The study then assesses the implications of IC information on a firm’s market value.

It examines the trend of hidden values and the corresponding role of IC information releases through analyses of archival data retrieved from Bloomberg and corporate annual reports of 153 firm-year observations across a three-year period (2008-2010). Various statistical and econometric data analyses were performed to examine the aforementioned exercises.

This study documents four main findings. First, the results show a significantly widening gap between market and book values during the financial crisis, with the market values consistently higher than the book values in all three years. Second, the hidden values significantly increased over time, with the increase becoming substantially higher in the years corresponding to the tail-end of the crisis. Third, the findings indicate that an increase in the hidden values results in a simultaneous increase in IC disclosures, with firms using IC information to inform and reflect their hidden values. Finally, it is shown that the overall amount of IC disclosures, and in particular human capital information, has a significant positive association with hidden values and, consequently, the market value of the companies.

For IC researchers, the study shows the existence of a substantial amount of hidden values in the corporate landscape; thus, there is a need to actually uncover the pattern and creation of hidden values within firms through action research. For businesses, the study reveals the importance of releasing IC information narratives to a firm’s value creation process. The results are also important for policymakers in promoting integrated corporate reporting framework to report IC resources of a firm, perhaps a policy extending the recent mandatory requirement of corporate social responsibility (CSR) reporting in Malaysia.

This study presents a rare empirical assessment of the trend of hidden values and use of IC information narratives in the context of a recession. The findings can benefit companies and regulators in getting to know a growing level of hidden values – as well as the usefulness of IC information.

The purpose of this paper is primarily to explore the extent of intangible assets and liabilities of large Malaysian companies. The authors also examine whether intangible assets and liabilities of a firm have similar or contrasting roles in firm performance.

Using a direct and straightforward measure of intangible assets and liabilities, the authors examine a large pool of data from large Malaysian companies over a six-year period spanning from 2008 to 2013.

The longitudinal analyses show a significant number of the sample companies, between 34 and 59.33 percent, have a consistent pattern of intangible liabilities. The authors also find firms with intangible liabilities have significantly underperformed financially than a control group of firms. In addition, the authors find that intangible liabilities have significant negative impact on firm performance whereas intangible assets have a contrasting positive impact on firm performance.

One limitation of this study is that the authors have only used a single measure of intangible assets and liabilities. Albeit the measures used are straightforward and more objective, there could be other measures to capture intangibles.

The research findings have several theoretical as well as policy implications. Theoretically, the authors extend the resource-based view to the intangible asset-liability mix, affirming the crucial role of intangible resources in financial performance whilst introducing the unfavorable role of intangible liabilities in corporate financial performance. In terms of policy implications, the research findings provide initial empirical input to emerging calls for broader perspectives of intangibles, beyond intangible assets to include intangible liabilities, and therefore belong to an emerging paradigm toward the nature of intangibles.

This study documents a rare empirical account of the contrasting roles of intangible assets and liabilities in corporate financial performance.

The paper's purpose is to describe the extent and type of voluntary disclosure of intellectual capital (IC) in New Zealand, and to test for a relationship between “hidden value” (difference between firm's market and book value), and its relationship to voluntary IC disclosure in the annual reports of New Zealand companies. The study aims to incorporate the effect of revaluations and growth expectations.

Content analysis of 70 publicly listed New Zealand firms, and database retrieval of independent variable data. Correlation and regression analysis is undertaken.

Only revaluing firms show a significant positive relationship between their levels of hidden value and their voluntary disclosure of IC and its components of external and internal structure. Explanatory power is increased when an interaction term involving hidden value and growth expectations is introduced.

Further developments in the growth expectation and market value measures are suggested. A need for qualitative interviews is identified in order to further develop theoretical explanation of the observed relationship.

This paper may help external users assess levels of IC in revaluing firms.

The study extends the work of Brennan by increasing the sample size, quantitatively recognising the impact of revaluations and growth expectations, providing a discussion of the theoretical underpinnings for the proposed relationships, and by utilising reliability testing in the content‐analysis process, several measures of hidden value and IC disclosure, and statistical testing.

In this chapter we develop sustainability implications of the Savall, Zardet, Bonett, and colleagues’ approach, known worldwide as socioeconomic approach to management (SEAM). SEAM can be used as a way of doing management and organizational inquiry into the ecological sustainability of practices with planetary boundaries. We conclude that a socially responsible approach to management needs to consider the hidden costs to an enterprise if it is not being sustainable to planetary resource limits.

This paper discusses the concept of hidden assets in the context of Disney’s 2009 acquisition of the Marvel Entertainment Group (Marvel), and its value realization activities post-acquisition.

The paper presents a hidden assets-based value realization analysis of the 2009 acquisition of Marvel by Disney. It draws on a previously published case study of that acquisition as well as further research conducted by the author.

The Disney-Marvel acquisition supports the view that hidden assets-based analysis can be a powerful M&A tool and an equally powerful value realization tool when managed strategically over time.

The Disney acquisition of Marvel is a dramatic example of how knowledge of hidden assets can be used to do a deal in a competitive marketplace and how the disciplined management of those assets over time can realize a “blue ocean” of value post-acquisition.

This is the first paper we are aware that evaluates the hidden assets of the Disney-Marvel acquisition. It follows another paper that evaluated the acquisition (Joseph Calandro, Jr., “Disney’s Marvel Acquisition: A Strategic Financial Analysis,” Strategy & Leadership, Vol. 38, No. 2 (2010), pp. 42-51), which followed a paper that evaluated Marvel’s 1996 bankruptcy filing (Joseph Calandro, Jr., “Distressed M&A and Corporate Strategy: Lessons from Marvel Entertainment Group’s Bankruptcy,” Strategy & Leadership, Vol. 37, No. 4 (2009), pp. 23-32).

Until recently, occupiers have ignored the financial benefits real estate can add to the value of their firms. Corporate real estate (CRE) is now on the corporate agenda and CRE executives are being challenged by shareholders and senior management to employ best practice techniques to unlock the hidden value in the firm’s real estate portfolio. This paper offers a practical decision framework to allow this to happen and explores the potential for them and for real estate investors to capture this value. The paper is based on the author’s experience of advising occupiers and investors and his increasing knowledge of corporate finance principles.

The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control (SMC). Through this integration, a novel structure of GRNN is designed to enable online and. This structure is then combined with SMC to develop a stable adaptive controller for a class of nonlinear multivariable uncertain dynamic systems.

In this study, a new hybrid (SMC-GRNN) control method is innovatively developed.

A novel structure of GRNN is designed that can be learned online and then be integrated with the SMC to develop a stable adaptive controller for a class of nonlinear uncertain systems. Furthermore, Lyapunov stability theory is utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system. Eventually, two different numerical benchmark tests are employed to demonstrate the performance of the proposed controller.

A novel structure of GRNN is originally designed that can be learned online and then be integrated with the sliding mode SMC control to develop a stable adaptive controller for a class of nonlinear uncertain systems. Moreover, Lyapunov stability theory is innovatively utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Strategies often go awry because organizational leaders are not fully aware of the values held by its members. No matter how brilliant the analysis that underlies a strategy, it is the people ‐‐ from the board room to the factory floor ‐‐ who must understand and implement the strategy. That only happens when the strategy fits their values. Values matter. Ethical decisions and actions are based on values. The authors present a psychological theory that describes three levels of values: surface values, hidden values, and deep values, and explain how these values affect an organization’s leaders and followers as they pursue their mission.

This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations.

The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept.

Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique.

Authors confirm that this manuscript is original.