Factors Affecting in Developing AI-driven Analytics Culture for Achieving Equilibrated Sustainability in B2B Firms: A Morphological Analysis
- AIOR Admin
- 5 days ago
- 2 min read
Shahin Akther
Bangladesh University of Professionals
Purpose: This research aims to explore how organizational culture, integrated with artificial intelligence, is being leveraged to equally emphasize social and environmental sustainability practices in B2B firms. It examines how AI-driven insights influence real-time decision-making and contribute to fostering sustainable business environments through effective ecosystem management. Methodology: This study adopts a systematic literature review (SLR) combined with morphological analysis to investigate how AI-driven analytics culture influence B2B sustainability practices. Relevant secondary data were gathered from peer-reviewed journals, industry reports, and case studies accessed through databases such as Google Scholar, JSTOR. Articles (n=83) were selected based on predefined inclusion criteria emphasizing credibility, relevance, and contribution to the fields of AI and sustainability in SLR process. Morphological analysis was then applied to systematically classify and map the dimensions found in the key literatures such as B2B industry wise sustainability practices, barriers to adoption of AI, and their contributions to assess the environmental and social sustainability. This structured approach enabled a comprehensive synthesis of current knowledge and identified emerging patterns and research gaps in AI-driven B2B sustainability initiatives. Findings: Based on 183 articles from 2015-2025, our study demonstrates that the formation of AI culture fosters benefits in resource management, efficient forecasting, and supply chain transparency, thereby reducing negative environmental impacts and also ensuring social responsibility. This equal monitoring scale promotes balanced sustainability practices in B2B firms by enabling strict alignment of environmental and social goals through both real-time and predictive analytics. However, there are issues like data privacy, high costs of implementation, and organizational inertia for AI readiness act as hurdles to calls for effective AI adoption. Originality: This research demonstrates that AI plays a crucial role in numerous areas, including resource efficiency, predictive maintenance, and green supply chain sustainability, which ultimately minimizes environmentally unfriendly practices and fosters increased corporate sustainability. However, issues like data privacy, high costs of implementation, and organizational culture barriers act as hurdles to calls for more adoption. Research Limitations: The study relies on secondary data only. This can potentially limit the depth of findings in specific industry applications. Future research could conduct in depth interview and then adopt a quantitative approach to elaborate these findings. Practical and Social Implications: The findings of the research are useful to practitioners and policymakers, especially in creating a culture of analytics to support sustainability goals. AI-based analytic solutions can contribute to the fight against climate change and improve resource utilization, which is a pressing need in today's society.
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