Determinants of Intellectual Capital Disclosure: Evidence from Indian Banking Sector
Bibliographic record
Abstract
(ProQuest: ... denotes formulae omitted.)INTRODUCTIONIn a continuously changing environment, the competitiveness of each firm has become the key to its survival. A firm creates competitive advantage in this knowledge based world through its employees, customers, processes, infrastructure, information systems, innovativeness and such other assets called intellectual assets. As a result, research interest in the area of Intellectual Capital (IC) is growing. As observed by Abeysekara (2006), management has shifted its focus from tangible to intangible capital while deliberating over the processes that create value in the firm. This displays the growing importance of IC within. Value creation is considered to be a process which transforms or improves the routine practices of the corporate (Mouritsen, Larsen and Bukh, 2001; and Abeysekara, 2006).The traditional model of financial statements which is based on the historical cost concept, concentrates primarily on the materiality concept and the effects of financial transactions, ignoring certain important factors which determine the value of an enterprise. These factors may include intellectual capital, capacity of the enterprise to create future value. This results in a gap between the balance sheet value of the enterprise and the value estimated by the capital market (Helin, 2001). As claimed by Abeysekera (2008) of IC in annual reports helps to make capital markets more efficient by reducing information asymmetry between 'insiders' and investors. Shareholder value and market value of an organization is enhanced by more IC disclosure in the annual reports of companies, to the capital markets (Abdolmohammadi, 2005).Banking industry being truly representative of knowledge based industry where value creation is mainly through intangible assets and resources have therefore been taken for study. This paper focuses on the extent of Intellectual Capital Disclosure (ICD) and the factors influencing ICD in the Indian banking industry. As far as awareness is, this research study is the first attempt of research in the field of intellectual capital disclosures in the Indian Banking sector. The objectives of this study are:* To study the extent of ICD in Indian banking sector.* To study if there any relationship exists between the ICD and bank size, bank risks, efficiency, bank age, human capital pressure, ownership pattern, leverage level, structural complexity and board composition.LITERATURE REVIEWIn recent times, there's been growth in research on ICD across developed and developing nations. These studies have frequently investigated the status of ICD in a particular country (usually cross-sectional). Examples comprise of Guthrie and Petty (2000) on Australia, Bontis (2003) on Canada, Abeysekera and Guthrie (2005) on Sri Lanka, Li, Pike and Haniffe (2008) on UK; and Yi and Davey (2010) on China. Studies with regard to a specific industrial sector have also been conducted. Such studies include White, Lee and Tower (2007) on bio-technology firms, Campbell and Rahman (2010) on a single company (Marks & Spencer), Cohen, Naoum and Vlismas, 2014) on the SME sector. It was observed by most researchers that disclosure level of firms across different countries was low and generally in qualitative form (Goh and Lim, 2004; Guthrie, Petty and Ricceri, 2006; and Whiting and Woodcock, 2011).Literature also shows that some studies were undertaken to compare ICD practices across different countries. These studies include studies undertaken by Vergauwen and Alem (2005) on France, Netherlands and Germany; Vandemaele, Vergauwen and Smits (2005) on Sweden, Netherlands and UK; Guthrie, Petty and Ricceri (2006) on Australia and Hong Kong; and Abeysekera (2008) on Singapore and SriLanka. This type of research resulted in a better understanding of ICD practices in an international context.Some researchers attempted to study the trend of ICD in a particular country or industry by undertaking a longitudinal research. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".