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Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation
Open accessAbstract
Purpose This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation. Design/methodology/approach Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools. Findings Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business’ organizational model. A key consideration is a “creative-possibility perspective,” highlighting the future potential to use AI not only for rational but also for creative thinking purposes. Research limitations/implications This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a “creative-possibility perspective.” Practical implications A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013). Originality/value This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.
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“According to the level of analytics suggested by Maoz (2013; Eriksson et al., 2020), data mining methods alongside artificial intelligence are usually used at the predictive and prescriptive analytics levels.”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Eriksson et al., 2020
In the citing paper
“this figure illustrates that LA services are provided at different levels of analytics according to Gartner Analytics Ascendancy Model (GAAM) (Eriksson et al., 2020; Maoz, 2013).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Eriksson et al., 2020
In the citing paper
“According to the level of analytics suggested by Maoz (2013; Eriksson et al., 2020), text analytics is seemed to be more likely used in the descriptive and diagnostic level of analytics.”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Eriksson et al., 2020
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Metadata
- Authors
- Eriksson, T., Bigi, A., & Bonera, M.
- Affiliations
- Luleå University of Technology (SE)
- University of Verona (IT)
- University of Brescia (IT)
- Corresponding author
- Bigi, Alessandro
- Venue
- The TQM Journal
- Publisher
- Emerald
- Published
- 2020-04-22
- Volume
- 32
- Issue
- 4
- Pages
- 795-814
- ISSN
- 0954-478X (ISSN-L)
- 1754-2731
- OpenAlex
- W3019168338
- Cited by
- 193
- References
- 64
- Citations by year
- 2026: 26 · 2025: 37 · 2024: 59 · 2023: 35 · 2022: 19 · 2021: 15 · 2020: 2
- FWCI
- 6.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The TQM Journal (publishedVersion, cc-by)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, marketing strategy formulation, strategic decision-making, organizational culture, organizational models, creative thinking, managerial cognition, contingency theory
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