مشخصات مقاله:

عنوان فارسی مقاله:

یک چارچوب یکپارچه هوش مصنوعی برای ایجاد دانش و تصمیم گیری منطقی بازاریابی B2B برای بهبود عملکرد شرکت

عنوان انگلیسی مقاله:

An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance

کلمات کلیدی مقاله:

هوش مصنوعی، اطلاعات بزرگ، مدیریت دانش، بازاریابی B2B، عملکرد ثابت

مناسب برای رشته های دانشگاهی زیر:

مدیریت

مناسب برای گرایش های دانشگاهی زیر:

بازاریابی، مدیریت بازرگانی، مدیریت کسب و کار، مدیریت فناوری اطلاعات، مدیریت دانش

وضعیت مقاله انگلیسی و ترجمه:

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فهرست مطالب:

Abstract
Keywords
1. Introduction
2. Theoretical foundation and hypothesis development
2.1. Big data powered artificial intelligence
2.2. Knowledge management theory (KMT)
2.3. Hypothesis development
2.3.1. Big data powered artificial intelligence and customer knowledge creation
2.3.2. Big data powered artificial intelligence and user knowledge creation
2.3.3. Big data powered artificial intelligence and external market knowledge creation
2.3.4. Customer knowledge creation and B2B marketing rational decision making
2.3.5. User knowledge creation and B2B marketing rational decision making
2.3.6. External market knowledge creation and B2B marketing rational decision making
2.3.7. B2B marketing rational decision making and firm performance
3. Research methods
3.1. Sample selection
3.2. Survey instrument
3.3. Data collection
3.4. Common method variance
3.5. Non-response bias
4. Data analysis and findings
4.1. Partial least square-based structural equation modelling
4.2. Measurement model
4.3. Results
5. Discussion
5.1. Theoretical contributions and implications
5.2. Practical implications
5.3. Limitations and future research direction
6. Conclusion
Appendix A. Operationalization of constructs
Appendix B. Combined loadings and cross loadings
References

قسمتی از مقاله انگلیسی:

1. Introduction
Business-to-business (B2B) marketing has offered new prospects and challenges in this digital age (Bhandari et al., 2017; Rust, 2020). Digital B2B marketing models have been found to surpass traditional models (Bhandari et al., 2017). Furthermore, Davenport, Guha, Grewal, and Bressgott (2019) indicated that artificial intelligence is on course to disrupt marketing management. In a complex business environment, B2B marketers need smart solutions to automate the process of structuring, standardising, aligning and customising data (Fensel et al., 2001; Jabbar, Akhtar, & Dani, 2019). Leveraging big data to activate artificial intelligence (AI) technologies provides marketers with a competitive edge and is reflected in marketing strategies and customer behaviours that generate further positive outcomes (Jabbar et al., 2019). Big data coming from social media and search engines can offer important insights for B2B marketers and help build programmes for online advertisements and customer assistance in a pioneering manner (Jabbar et al., 2019). Lee, Dabirian, McCarthy, and Kietzmann (2020) provided a roadmap for performing AI-enabled content analysis in the field of marketing. The role of the sales force is changing, and reliance on technology and analytics in order to achieve success is increasing (Sleep, Dixon, DeCarlo, & Lam, 2020). To avoid problems when decoding big data sets, Chen et al. (2020) recently provided an overview approach using cognitive computing techniques to evaluate unstructured data sets generated from users. Advances in technology have created a deep impact on marketing. It may be difficult to fully comprehend, but big data has enabled AI to such an extent that it has disrupted marketing management and made the traditional “4P” model increasingly outdated (Rust, 2020). The more available big data sets are, the stronger AI applications will be, since machines will be able to reason and act more effectively. AI technologies can help in the selection of international marketing strategies and effective marketing programmes (Katsikeas, Leonidou, & Zeriti, 2019). The literature indicates that although B2B companies use digital marketing tools, most of them fail to exploit them fully. There is a dearth of academic literature available in this area (Pandey, Nayal, & Rathore, 2020).

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