Artificial intelligence (AI) competencies for organizational performance : a B2B marketing capabilities perspective

Mikalef, Patrick (author), Islam, Najmul (author), Parida, Vinit (author), Singh, Harkamaljit (author), Altwaijry, Najwa (author)

The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.

• Language: English
• Keywords: marketing, artificial intelligence, company performance, B2B marketing, AI competencies, core competencies theory
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 11 str.
• Numbering: Vol. 164, article no. ǂ113998
• PID: 20.500.12556/RUL-148235
• UDC: 339.138
• ISSN on article: 0148-2963
• DOI: 10.1016/j.jbusres.2023.113998
• COBISS.SI-ID: 151685891
• Publication date in RUL: 04.08.2023

Record is a part of a journal

• Title: Journal of business research
• Shortened title: J. bus. res.
• Publisher: Elsevier
• ISSN: 0148-2963
• COBISS.SI-ID: 25694208

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: trženje, umetna inteligenca, poslovanje podjetja

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P5–0441
• Name: Regeneracija ekonomije in posla

• Funder: Other - Other funder or multiple funders
• Funding programme: Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Riyadh, Saudi Arabia