Special Issues

A number of special issue editors will be attending the conference, making it possible to identify relevant work that could meet the objectives of recent calls. Presenting at the conference is not compulsory for submitting to the special issues below and presenting a paper at the conference is not guarantee of acceptance.

The dark side of Artificial Intelligence: Threats and risks of AI adoption

Journal: Industrial Marketing Management

Deadline for submission: 1st November, 2022

Editors: Eleonora Pantano (University of Bristol, UK), Davit Marikyan (University of Bristol, UK) & Savvas Papagiannidis (University of Newcastle, UK)


Overview and purpose of the special issue

In 1942, Isaac Asimov imagined a futuristic world where AI systems were largely diffused in the society, by considering the role of robots in society. To this end, Asimov (1942) proposed Three Laws of Robotics as a set of rules to limit robot behaviour: (1) a robot may not injure a human being or, through inaction, allow a human being to come to harm (2) a robot must obey the orders given to it by human beings except where such orders would conflict with the First Law (3) a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

To date, AI and robots are not as diffused as imagined by Asimov. 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:
    • optimizing the business customer journey through AI
    • new product development and product innovation
    • segmentation, targeting, position and competitive strategy from a management perspective
  • Human-computer interaction and technology acceptance
    • shifting from human-based decisions to AI-based decisions in marketing automation
    • human (negative) responses to certain AI applications in B2B
    • barriers to relationship building between AI (including robots, chatobts, etc) and consumers from a management perspective
    • AI applications and continuance intention for marketing professionals
    • revisiting relationship management between employees and managers in organisations with integrated AI for automating workflow.
  • AI service failure in B2B context
    • preventing failures
    • recovering from service failure
    • limiting the negative effects on users’ responses (including employees, managers, stakeholders, etc.) and wellbeing
  • Benefits vs. costs and risks of
    • AI enabled digital B2B marketing
    • AI fostered new product development and product innovation
    • Ai introduction in marketing team management (e.g., redundancies or impact on work engagement)
    • AI human, financial, and technological investments in industrial markets
  • Ethical marketing practices
    • concerns about AI-enabled marketing decisions
    • governance, legislation, strategy, management policy and control mechanisms of AI in B2B marketing
    • ethical, moral, and societal challenges that AI might face in B2B marketing

Sustainability: New insights into the role of information systems and digitisation

Journal: International Journal of Information Management

Deadline for submission: 15th of December 2022

Editors: Savvas Papagiannidis (University of Newcastle, UK) & Davit Marikyan (University of Bristol, UK)


Climate change has been one of the critical challenges of the 21st century. With the need for a global response to the ongoing ecological deterioration, the recent UN Climate Change Conference UK 2021 (COP26) brought together politicians, business leaders and concerned citizens to discuss the opportunities and possible ways to rebuild or transform carbon-intensive sectors and economies (Dwivedi et al., 2022). The outcome of the conference was the Glasgow Climate Pact, suggesting accelerated action in relation to carbon emission mitigation, adaptation to the consequences of the climate impact, investment, and cross-country and cross-industry collaboration. The achievement of sustainability goals can hardly be imagined without the role of information systems. Digitalisation and the transition to zero-emission technology are assumed to be one of the key solutions to slow the pace at which global warming is happening (Dwivedi et al., 2022).

IS can be the catalyst for sustainability through its indirect and direct implications for the environment (Melville, 2010; Papagiannidis & Marikyan, 2021). The direct impact is realised when current technology is replaced by its sustainable alternatives (Jenkin et al., 2011; Simmonds & Bhattacherjee, 2012), such as energy-efficient hardware and data servers. For example, the replacement of old phones and laptops by sustainable ones decreases energy consumption and the time required to operate technology, thus positively contributing to environmental sustainability (Watson et al., 2010). Multiple IS solutions, such as smart cities, smart manufacturing, smart offices, smart home and other smart technologies, are indirectly beneficial for the planet. They deliver various services in a sustainable way, as they require significantly less energy to function, they can sense the changes in the environment and promote pro-environmental behaviour by feeding back the data about resource consumption (Marikyan et al., 2019; Neirotti et al., 2014; Papagiannidis et al., 2020; Papagiannidis & Marikyan, 2020, 2021; Pee & Pan, 2021). The development and production of such technology have been increasingly fuelling scholarship on the role of IS in environmental sustainability.

The current scientific evidence on IS and sustainability draws on design and behavioural research. From the design perspective, researchers work on the development and utility of sustainable technology (Aarras et al., 2014; Vezzoli, 2018). As technology has been the driving force of productivity, the dominant body of literature has been about the exploration of the capability of industrial and enterprise ICT to solve environmental problems and drive organisational performance (de Camargo Fiorini & Jabbour, 2017). From the behavioural perspective, there are plentiful works exploring the drivers of the utilisation and the interaction with technologies which are beneficial for the environment (e.g. smart homes, smart manufacturing). Those works explored the predictors of adoption, measured mostly by hedonic/utilitarian values, beliefs about operating technology and potential use outcomes (Mulcahy et al., 2019; Wunderlich et al., 2019). However, given the diversity of technologies and their capabilities, the current research still falls short when it comes to providing sufficient understanding about their applications/implications for the achievement of sustainability goals and the alignment of users’ behaviour with them.

Specifically, there are a few under-explored areas that require more attention. For example, in the research on the adoption of environmentally sustainable technology, the dominant interest is placed on technology adoption factors (Canziani & MacSween, 2021; Marikyan et al., 2021). Hence, the role of sustainability aspects of utilisation has been peripheral for researchers. Identifying the factors that facilitate the adoption of sustainable technologies, beyond technology adoption theories, can be an important aspect for understanding the individuals’ behaviour when it comes to the use of such technologies (Wunderlich et al., 2019). In the post-adoption stage, it is not clear whether the promised sustainable benefits meet individuals’ expectations. Also, although it was confirmed that ICT can help lower the consumption of resources (electricity, water, gas, and waste generation) (Marikyan et al., 2019), whether individuals follow the best practices to enable those benefits is an under-researched topic. Therefore, the assessments should not just be about the sustainability potential of technology but also user behaviour and habits. The impact of innovative systems and their applications needs further investigation too. For example, one emerging technological solution, the blockchain, has been growing in popularity in the supply chain, finance, and organisation management due to the capability of this technology to disintermediate transactions, enabling the efficiency and security of data (Abbas et al., 2020; Ali et al., 2020; Notheisen et al., 2017; Rimba et al., 2020; Schmidt & Wagner, 2019). However, the use of blockchain-based systems requires computational power and consumes a lot of energy, which increases carbon emissions (Dwivedi et al., 2022; Schinckus, 2020). This example, however, should not devalue the potential role of innovative technologies powered by IoT and AI, in developing solutions that could help in fighting climate change. Such solutions could be used in dynamic climate change assessment in areas such as environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining (Salam, 2020). Given this, future research should consider the duality of innovative technology capabilities and their impacts on the environment. In addition, the growing adoption of technology contributes to the increase in energy consumption levels, greenhouse gases and electronic waste (Dwivedi et al., 2022; Rene et al., 2021), which needs to be examined empirically in the long term. The above demonstrates a need to provide a deeper insight into the role of ICT in addressing climate change.

This call invites studies that would provide evidence on the relationship between information systems and environmental sustainability. Qualitative and quantitative empirical studies that offer insights into the selected areas are equally welcomed by the Guest Editors.

The Special Issue would accept papers focusing on topics including, but not limited to, the following:
  • Technology adoption and post-adoption
  • The implications of technologies
  • User behaviour and decision-making
  • Infrastructural and institutional factors