Voluntary disclosures of intellectual capital : an empirical analysis

Kansal, M ORCiD: 0000-0003-4392-1072Purpose: This paper aims to investigate inter firm intellectual capital (IC) disclosures and its variations in top 20 listed pharmaceutical companies in India, study the category wise and element wise IC disclosures (ICD), find out the impact of ICD on the creation of IC in monetary terms, find out correlation between IC valuation and its disclosure, and test significance of correlation. Design/methodology/approach: This is an exploratory and empirical study of ICD by sample companies in 2009 using content analysis. IC is valued as market value minus book value. Five-point scale (0-4), mean disclosure score, range, Chi- squares, Karl Pearson's correlation and Student's t-test are used for analysis and interpretation. Findings: Although top 20 companies of knowledge-led industry, ICD are low, narrative and varying significantly among companies. ICD score varies in range of 4 to 36 against expected score of 96. External capital with mean score of 18.78 is the most disclosed category. Brands and business collaborations is most disclosed element of IC, followed by employee competence and internal organizational capital respectively. ICD leads to creation of IC in some companies. Markets reflected true valuations of ICD in seven companies, and high degree of inconsistency in 13 companies. Overall correlation between IC valuation and disclosure is negative, weak and insignificant. Practical implications: Sector-specific intangible asset monitors should be formulated to capture ICD. Originality/value: The paper measures ICD using five-point scaling technique, it uses Chi- square test (non-parametric test) to calculate inter-firm variations. The paper also correlates ICD and valuation of respective companies with Spearman's correlation for the first time in pharmaceutical companies in India. It proposes inclusion of fourth category i.e. sector-specific items in existing models of ICD. © Emerald Group Publishing Limited

Accounting for information: Information and knowledge in the annual reports of FTSE 100 companies

The purpose of this study was to assess the ways in which a sample group of companies discuss information and knowledge. Quantitative and qualitative content analyses were used to survey the way that companies present and value information and knowledge, based on the annual reports of the FTSE 100, the United Kingdom's largest publicly-listed companies. A novel content analysis approach is used, based on a set of categories proposed by Oppenheim, Stenson and Wilson. Many of the companies analysed made explicit the importance of information and knowledge, through either discussion in the text of the annual report or through an attempt to assign a monetary value to information assets. Where the importance of information and knowledge was not made explicit, the study revealed links between successful performance and effective use of information assets. Different categories of information assets were identified within the annual reports. Conclusions drawn from the analysis include that information and knowledge are demonstrably important to FTSE 100 companies, although the specific term “knowledge” does not appear to have a special significance in the companies’ lexicon; and that certain sectors, such as General Financial, General Retail, Travel & Leisure, Mining, Aerospace & Defence and Software & Computer Services, mention information and knowledge more than others

Preferences and Values for the Gulf Coast Ocean Observing System

Integrated Ocean Observing Systems (IOOS) provide real time oceanic data and sea state forecasting information that is utilized by numerous public and private sectors engaging in maritime activities. The U.S. Gulf Coast constituent of this system (GCOOS) consists of 321 platforms, buoys, and sensors that provide measurements of wind speed, wave height, water quality, and other parameters. Government entities have proposed an expansion of this infrastructure by 40% at an estimated cost of $35 million for installation and$33 million annually for maintenance. As part of a larger project commissioned to estimate monetized benefits of this expansion, this study applied contingent valuation (CVM) methodology in a survey of avid IOOS users located in the Gulf and Atlantic regions of the United States (N=18,000; n=484). The objective was to estimate general preferences for IOOS data and specific values for the proposed GCOOS expansion. A probit model was used to examine factors associated with a respondent’s likelihood to support the expansion under a public referendum. Responses were solicited via six randomized treatments containing varying tax levels. A majority of respondents (74%) indicated support for the measure, with imputed willingness-to-pay (WTP) estimates ranging from $14.11 and$36.47 annually. Consistent with economic theory, the dollar amount of the tax was significant and negatively associated with referendum support. Proxies for avidity; however, proved either irrelevant or contrary to hypothesized effects. Vessel ownership, vessel size, distance traveled, and hours per trip were non-factors while the number of trips taken proved to be a significant, but negative predictor of referendum outcome. Alternatively, Gulf respondents engaged in fishing and fee-based services were more likely to support the measure indicating that proximity could be a more influential driver than avidity. Interpretation of these results is limited by the relatively small population queried. A broader depiction will emerge parallel versions of this survey are completed with larger populations. Taken together, these studies should prove valuable in characterizing preferences for IOOS data, assessing the economic merit of GCOOS expansion, and demonstrating the potential for non-market approaches in the valuation of publically-funded information systems

Value of Information in Design of Groundwater Quality Monitoring Network under Uncertainty

The increasing need for groundwater as a source for fresh water and the continuous deterioration in many places around the world of that precious source as a result of anthropogenic sources of pollution highlights the need for efficient groundwater resources management. To be efficient, groundwater resources management requires efficient access to reliable information that can be acquired through monitoring. Due to the limited resources to implement a monitoring program, a groundwater quality monitoring network design should identify what is an optimal network from the point of view of cost, the value of information collected, and the amount of uncertainty that will exist about the quality of groundwater. When considering the potential social impact of monitoring, the design of a network should involve all stakeholders including people who are consuming the groundwater. This research introduces a methodology for groundwater quality monitoring network design that utilizes state-of-the-art learning machines that have been developed from the general area of statistical learning theory. The methodology takes into account uncertainties in aquifer properties, pollution transport processes, and climate. To check the feasibility of the network design, the research introduces a methodology to estimate the value of information (VOI) provided by the network using a decision tree model. Finally, the research presents the results of a survey administered in the study area to determine whether the implementation of the monitoring network design could be supported. Applying these methodologies on the Eocene Aquifer, Palestine indicates that statistical learning machines can be most effectively used to design a groundwater quality monitoring network in real-life aquifers. On the other hand, VOI analysis indicates that for the value of monitoring to exceed the cost of monitoring, more work is needed to improve the accuracy of the network and to increase people’s awareness of the pollution problem and the available alternatives