Deadline for submission: 31 January 2021
Overview and purpose of the special issue
Artificial Intelligence (AI) is revolutionizing many aspects of B2B marketing activities. According to the MIT Sloan Management Review’s report, 90% of surveyed marketers from the B2B professional services sector recognize that AI could be a significant source of competitive advantage (Ransbotham et al., 2019). It is predicted by Gartner (2018) that one-third of all B2B marketers will adopt AI-enabled technologies, such as virtual customer assistant (VCA) and chatbot, to drive marketing automation by 2020. As a current example, Siemens AG, a large industrial manufacturing company in Europe, applies machine learning to analyse vendor proposals to support decision making. This has resulted in a 20-30% acceleration of the tendering process (Ransbotham et al., 2019).
Academics and practitioners have recognized a number of benefits of using AI techniques in B2B settings. These include driving sales (Syam & Sharma, 2018), reshaping the buyer and supplier relationship (Gordini & Veglio, 2017), creating targeted B2B marketing campaigns (Liu, 2019), and delivering decision-support for managers (Jabbar, Akhtar, & Dani, 2019). Yet despite the potential transformative benefits, so far AI adoption is confined to a finite group of leading B2B marketers. Furthermore, while the majority of the extant literature has focused on the technological understanding of AI (Dwivedi et al., 2019), our knowledge of the implications of AI for B2B is far from conclusive.
Recently, academic attention has turned to a number of research topics driven by the challenges of AI implementation. First, generating economic value with AI is heavily reliant on having access to high quality and quantity data, and managing those data in a proper way. However, data for B2B marketing are relatively rare and their value is difficult to extract (Lilien, 2016). Given this lack of precious data, and the fact that what data are collected are often irrelevant and poorly managed, B2B marketers often miss out on actionable insight, which in turn leads to ineffective marketing and sales strategies. Addressing the lack of data orchestration is therefore a priority for AI use in B2B marketing.
Second, using AI to power B2B sales is still in its infancy and has not yet achieved a significant impact. Recent studies have sought to explore the potential impact of AI on B2B sales management (e.g., Syam & Sharma, 2018; Paschen, Kietzmann, & Kietzmann, 2019), but to date there has been no research that examines how AI can optimize customer communication and engagement or how AI-fostered new product development can be achieved with governance of AI-related resources and capabilities. These issues are worthy of deeper investigation, as many firms have been found to continually invest in AI in order to maintain competitiveness (Ransbotham et al., 2019).
Third, harvesting and processing customer data using biased AI algorithms gives rise to serious issues regarding privacy concerns, lack of transparency and ineffective marketing communication (Davenport et al., 2019). For instance, irrelevant and invasive advertising circulated by automatic email and social media marketing with AI often exasperate B2B customers. Such ineffective marketing initiatives created by AI may result in significant brand and reputation harm. It is therefore imperative that B2B marketers rethink how to minimize ethical concerns when designing AI solutions for B2B marketing.
Finally, managing AI is not merely a simple technical issue. Rather, it requires seamless synergy between human beings’ capabilities and knowledge and AI technologies, enabling them to work cooperatively (Duan et al., 2019; Dwivedi et al., 2019). Some B2B firms aim to achieve AI explainability for making sound marketing decisions by optimizing collaboration between human and AI (Wang, Xiong, & Olya, 2020). However, while these firms have a clear goal, they may find it difficult to integrate smoothly the work of marketing professionals and that of AI algorithms/robots.
In view of these challenges, in this special issue we invite the submission of original manuscripts that advance our knowledge of how AI can be leveraged to increase the economic impact of B2B marketing, and explore ways to tackle the technological, ethical and governance challenges that AI might face in the B2B marketing context. We welcome submissions from multiple perspectives, including marketing, operation and supply chain management, information systems, psychology, and sociology. All approaches (empirical, analytical, or conceptual) that create novel B2B marketing insights by AI are welcome.
Relevant topics for the special issue include, but are not limited to:
Preparation and submission of paper and review process
To submit a paper please visit the IMM editorial site at https://www.evise.com/profile/#/IMM/register. Please login, register as an author, and submit the paper following the instructions on the website. Submissions are welcome up to and including 31 January 2021. Papers submitted must not have been published, accepted for publication, or presently be under consideration for publication elsewhere. Submissions should be about 6,000-8,000 words in length. Copies should be uploaded on Industrial Marketing Management’s homepage through the EVISE system. You need to upload your paper using the dropdown box for the special issue on AI for B2B Marketing. All papers will be reviewed through the standard double-blind peer review process of IMM. Authors are advised to adhere closely to the Author Guidelines when preparing their manuscripts. A guide for authors is available http://www.elsevier.com/wps/find/journaldescription.cws_home/505720/authorinstructions. Papers not complying with the notes for contributors (cf. homepage) or poorly written will be desk rejected. Suitable papers will be subjected to a double-blind review; hence, authors must not identify themselves in the body of their paper. (Please do not submit a Word file with “track changes” active or a PDF file.)
Guest editors:
Yogesh K. Dwivedi, Professor of Digital Marketing and Innovation, Swansea University. Email: y.k.dwivedi@swansea.ac.uk.
Yichuan Wang, Senior Lecturer/Associate Professor of Digital Marketing, University of Sheffield, UK. Email: yichuan.wang@sheffield.ac.uk.
A selection of related references
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-019-00696-0
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Galanos, V. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. DoI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Gartner (2018). Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020. Available at: https://www.gartner.com/en/newsroom/press-releases/2018-02-19-gartner-says-25-percent-of-customer-service-operations-will-use-virtual-customer-assistants-by-2020
Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.
Jabbar, A., Akhtar, P., & Dani, S. (2019). Real-time big data processing for instantaneous marketing decisions: A problematization approach. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2019.09.001
Lilien, G. L. (2016). The B2B knowledge gap. International Journal of Research in Marketing, 33(3), 543-556.
Liu, X. (2019). Analyzing the impact of user-generated content on B2B firms' stock performance: Big data analysis with machine learning methods. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2019.02.021
Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing, 34(7), 1410-1419.
Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B., & Kiron, D. (2019). Winning With AI. MIT Sloan Management Review. Available at: https://sloanreview.mit.edu/projects/winning-with-ai/
Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.
Wang, Y., Xiong, M., & Olya, H. G. (2020). Toward an Understanding of Responsible Artificial Intelligence Practices. In Proceedings of the 53rd Hawaii International Conference on System Sciences, Maui, Hawaii, USA.