[AISWorld] ***SPAM*** CFP SI - The dark side of Artificial Intelligence for Industrial Marketing Management

[Savvas Papagiannidis]

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The dark side of Artificial Intelligence for Industrial Marketing Management: Threats and risks of AI adoption

AI draws upon the idea that machines might think and act like humans through the usage of particular software/algorithms. Accordingly, AI has been developed with the aim of capturing and simulating human cognitive abilities, as a “hybrid-human machine apparatus” (Muhlhoff, 2020). Recent studies focused on the factors enabling AI adoption by managers in different organizations (Cao et al., 2021; Chatterjee et al., 2021; Baabdullah et al., 2021); while others have identified the characteristics of AI to be supportive to human tasks, such as social cognition (Van Doorn et al., 2017; Chong et al., 2021), or instance processing (the ability to process and reduce a huge amount of information like the identification of objects in a large number of pictures) (Cheng et al., 2021; Muhlhoff 2020; Pantano et al., 2021). Such characteristics can make AI an effective support tool for employees when it comes to delivering job-related tasks (Dwivedi et al. 2019). Thus, AI might have particular characteristics able to support decision makers in different industrial marketing contexts. When it comes to marketing there is an increasing body of literature covering the potential applications of AI in the field (Davenport et al., 2020). AI in marketing literature has already stressed the benefits of AI adoption from a B2C marketing point of view (Janarthanan and Dwivedi, 2021; Bertacchini, Bilotta, and Pantano 2017; Huang and Rust 2021a; Xiao and Kumar 2021; Kushwaha et al., 2021). However, the application in B2B context is still under investigated. For instance, only few studies focused on how to understand and predict behaviour in both the B2B contexts (de Caigny et al., 2021; Huang and Rust 2021b; Chatterjee et al., 2021), and how to use AI to manage organisational workflow, including employee practices and business processes. Indeed, AI-based technology can be used to automate the workforce and increase efficiency in offices and industrial settings (Dwivedi et al. 2019, Papagiannidis and Marikyan 2020). The COVID-19 pandemic further accelerated the pace of digital transformation (Papagiannidis et al. 2020, Venkatesh 2020), by boosting the use of AI technologies. Therefore, the question as to how to deploy AI to generate business value in B2B markets is still a challenge for managers in general, and marketing managers in particular (Mikalef et al., 2021). For instance, the predictive models based on AI before COVID-19 failed during/after the pandemic. Similarly, AI requires huge human, financial and technical resources that companies need to recover in a certain amount of time (i.e., what is the return on investments in AI?). Moreover, AI systems require a certain level of trust by the end users, which is not always spontaneous. Also, their increasing usage in marketing and management has led to the emergence of ethical and moral issues. AI, as reflective of human cognitive processes and behaviour, might reproduce human-like stereotypes and bias. When it comes to the deployment of AI-based technologies for automating workflow, there is a dearth of research as to how it facilitates employees'/professionals' effective wellbeing, and what long-term implications such applications have. Thus, despite the merits of AI and its potential positive impact, there is still a need to investigate further the “dark side” and any potentially negative consequences as a result of AI adoption. Given the above, this special issue wants to contribute to the debate, by soliciting papers on how to mitigate the dark side of AI in B2B relationships.
Conceptual, methodological, qualitative, or quantitative contributions that offer insight into this area are equally welcomed by the Guest Editors. The Special Issue would accept papers focusing on topics including, but not limited to, the following:
•       Stereotypes and algorithmic biases of AI when it comes to:
o       optimizing the business customer journey through AI
o       new product development and product innovation
o       segmentation, targeting, position and competitive strategy from a management perspective
•       Human-computer interaction and technology acceptance
o       shifting from human-based decisions to AI-based decisions in marketing automation
o       human (negative) responses to certain AI applications in B2B
o       barriers to relationship building between AI (including robots, chatobts, etc) and consumers from a management perspective
o       AI applications and continuance intention for marketing professionals
o       revisiting relationship management between employees and managers in organisations with integrated AI for automating workflow.
•       AI service failure in B2B context
o       preventing failures
o       recovering from service failure
o       limiting the negative effects on users’ responses (including employees, managers, stakeholders, etc.) and wellbeing
•       Benefits vs. costs and risks of
o       AI enabled digital B2B marketing
o       AI fostered new product development and product innovation
o       Ai introduction in marketing team management (e.g., redundancies or impact on work engagement)
o       AI human, financial, and technological investments in industrial markets
•       Ethical marketing practices
o       concerns about AI-enabled marketing decisions
o       governance, legislation, strategy, management policy and control mechanisms of AI in B2B marketing
o       ethical, moral, and societal challenges that AI might face in B2B marketing

Preparation and submission of paper and review process
For guidelines: https://www.elsevier.com/journals/industrial-marketing-management/0019-8501/guide-for-authors

Deadline for submission: November 1, 2022

Guest editors
•       Eleonora Pantano, University of Bristol, UK (e.pantano at bristol.ac.uk<mailto:e.pantano at bristol.ac.uk>)
•       Davit Marikyan, University of Bristol, UK (davit.marikyan at bristol.ac.uk<mailto:davit.marikyan at bristol.ac.uk> )
•       Savvas Papagiannidis, University of Newcastle, UK (savvas.papagiannidis at ncl.ac.uk<mailto:savvas.papagiannidis at ncl.ac.uk> )