High-Performance Tax Consulting for Productivity and Professional Excellence
Year: 2026
Type: article
Abstract: The tax consulting profession is experiencing increasing demands arising from regulatory complexity, digital transformation, tight reporting deadlines, and growing client expectations. These challenges require productivity management approaches that extend beyond task completion and time efficiency to incorporate technical accuracy, compliance reliability, client satisfaction, and sustainable professional performance. This study aims to develop a conceptual framework for high-performance tax consulting through an integrated productivity management approach. Drawing on the literature on knowledge-worker productivity, the Job Demands–Resources model, digital transformation, and professional service management, the study proposes five interrelated dimensions: workload management, digital optimization, quality and compliance, client service excellence, and sustainable professional performance. A qualitative exploratory research design is proposed, using purposive sampling and semi-structured interviews with tax consultants and related professionals. The data will be analyzed through reflexive thematic analysis to identify recurring patterns, contextual challenges, and relationships among the five dimensions. The proposed framework conceptualizes productivity as a multidimensional professional performance system in which operational efficiency must be balanced with technical accuracy, responsible technology utilization, regulatory compliance, and client value creation. Its potential contribution lies in integrating these dimensions into a unified analytical framework tailored to the tax consulting context. The framework is intended to guide future empirical investigation and support the development of practical productivity management strategies for tax consulting professionals and firms. However, its applicability and theoretical contribution require validation through empirical data and comparison with existing research.
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7225617613
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