Application of the Sandelowski and Barroso Technique in Identifying the Components of a Knowledge-Based Business Model in a VUCA Environment with an Artificial Intelligence Approach
Keywords:
Sandelowski and Barroso technique, knowledge-based business model, VUCA environment, Artificial Intelligence, meta-synthesis methodAbstract
In a VUCA environment, unpredictable changes occur, and businesses must respond rapidly and make appropriate decisions. Artificial intelligence, through the analysis of big data and the use of intelligent algorithms, enables businesses to predict future events and trends and assists them in making strategic and operational decisions (Borges et al., 2023). The present article aims to apply the meta-synthesis technique to identify the components of a knowledge-based business model in a VUCA environment using an artificial intelligence approach. In this study, by employing a systematic review and meta-synthesis approach, the results and findings of previous researchers were analyzed. By implementing the seven-step method proposed by Sandelowski and Barroso (2007), the influential factors were identified. Out of 198 articles, 35 were selected based on the Critical Appraisal Skills Programme (CASP) method, and the validity of the analysis was confirmed with a Cohen’s kappa coefficient of 0.810. In this regard, to assess reliability and control quality, the transcript method was used, which showed an excellent level of agreement for the identified indicators. The analysis of the collected data was performed using MAXQDA. Finally, from the indicators extracted from the texts of the related articles, after eliminating synonymous and repetitive indicators, and categorizing the final indicators, eight categories and seventy-one codes were obtained. The codes resulting from the meta-synthesis method include: interaction and customer relationship management, data prediction and analysis, risk management strategies, intelligent leadership and decision-making, agility and flexibility, knowledge management, resource and process optimization, and continuous innovation and improvement. These were identified as key and influential components of the model.
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Copyright (c) 2024 Majid Robat Sarpoosh, Seyed Abbas Heydari, Majid Fattahi (Author)
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