Challenges and Barriers in Convergence of IND_AS with IFRS: A Perspective Investigation on Indian Professionals and Officials
Bibliographic record
Abstract
Purpose: The paper examines the perception of officials and professionals in implementing IFRS at pre-initial stage. India accepted to implement the IFRS from 1st April 2016 despite reluctance from practitioners. Paper explores the responses towards challenges in implementation of IFRS rather than its effects after implementation. Design / Methodology / Approach: Quantitative research is used with use of structured questionnaire. The survey provided 192 responses from across India.Findings: Study questioned the readiness in adoption of IFRS and handling the challenges. Both officials and professionals have similar concern in implementing IFRS. Both groups agreed on the importance and usefulness of globally harmonizing AS but do not find it easy to adapt IND-AS to IFRS and have many concerns over it. Moreover, IASB is not properly emphasising in smoothening the process of implementation other than just organizing training sessions. Young and skilled professionals appreciated more of IFRS implementation than elderly and less skilled. Female participants are more receptive than men. Indian software companies were already using US GAAP norms for more than a century.Implications / Contributions: Analysis suggested IASB to focus on implementing issues and must work in coordination with national boards of every country to address those issues locally.Suggestions: Involvement of corporate houses, local authorities, boards, national agencies, professionals and officials are required for success of IFRS adoption. The Author is preparing to submit a report to IASB to facilitate them in bringing effectiveness for the same purpose. Originality of Study / Importance: The study focuses on issues specifically raised by officials and professionals of India. No study conducted with this perspective especially on India.Scope of Study: Based on the literature review, corporate governance is identified as an important area as the scope of this study in order to bring the effectiveness of implementation phase of IFRS within Indian companies, and to identify different factors that increases risk of failure. Further, it is important to study the role of monitoring institutions, regulators, and government in encouraging professionals and officials to smoothen the implementation phase in India. Nevertheless, effects after implementation need to be studied further.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".