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Model of Intelligent Performance Management in New Ventures

Document Type : Research Paper

Authors

1 PhD Student in Business Policy Management, Department of Business Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran.

2 Associate Professor, Department of Business Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran.

Abstract

Grounded in the pragmatist paradigm and employing an inductive–deductive approach, this study was conducted as an exploratory mixed-method research (qualitative–quantitative). The primary objective was to a model of intelligent performance management in new ventures, placing the study within the category of applied–developmental research. Methodologically, it adopts a descriptive strategy through a cross-sectional survey.
In the qualitative phase, data were collected via semi-structured interviews with 12 experienced managers of new ventures, selected using purposive sampling until theoretical saturation was reached. In the quantitative phase, a sample of 140 managers and experts from these ventures was selected using cluster-random sampling. Data collection tools included semi-structured interviews and a researcher-designed questionnaire. Qualitative data were analyzed through thematic analysis using MAXQDA software, and quantitative data were examined using Partial Least Squares method via SmartPLS software.
Findings indicate that components such as strategic intelligence, organizational culture, and digital technology and transformation significantly influence data analytics, intelligent decision-making, and intelligent human capital management. These, in turn, enhance intelligent performance management. Moreover, intelligent performance management exerts a significant impact on organizational agility, innovation, and product development. Ultimately, outcomes such as marketing performance and branding, customer management, and user experience contribute to improved financial performance in new ventures.

Keywords

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Management Studies in Development and Evolution
Volume 34, Issue 117
September 2025
Pages 153-196
Files
History
  • Receive Date: 29 April 2025
  • Revise Date: 11 June 2025
  • Accept Date: 14 July 2025
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