This has been a productive year on the writing front, with several long running projects finally materialising in publications. Here is an overview of what I have published, since my previous update.
- Ball, K., Canhoto, A. I., Daniel, E., Dibb, S., Meadows, M. & Spiller, K. (2020). Organizational tensions arising from mandatory data exchange between the private and public sector: The case of financial services. Technological Forecasting & Social Change, 155 (June) DOI: https://doi.org/10.1016/j.techfore.2020.119996
Abstract: This paper examines the organizational tensions arising from mandatory data exchange initiatives between private and public organizations. The focus is the UK financial services sector, which is required to monitor and report on customer identities and transactions under the country’s Anti-Money Laundering/Counter-Terrorist Finance (AML/CTF) regulations. The transferred data are generated from existing organizational activities, systems, processes and working patterns; we examine how government demands for such data affect commercial priorities, customer relationships and working patterns in the sector. We adopt an exploratory approach to investigate this phenomenon, consisting of 16 in-depth interviews, analysis of documents and two case studies. Three contributions are made. First, we use remediation theory to show that existing organizational arrangements are reconfigured at multiple analytical levels, creating tensions between the organizations’ commercial and compliance roles. Second, we establish the information flow as an appropriate unit of analysis in the study of data exchange mechanisms and reveal the flows that characterise AML/CTF compliance for financial services organizations. Finally, we adopt a ‘set theoretic’ perspective on multi-level organizational research, to argue that the multi-level effects of this regulation can be examined in parallel.
Keywords: Data exchange mechanisms, information flows; financial services; anti-money laundering; counter-terrorist finance; remediation; multi-level analysis.
You can find this paper, here. And a related blog post, here.
The paper investigates the consequences for Financial Services organisations, of having to share customer data with law enforcement agencies, under Anti-Money Laundering/Counter-Terrorist Finance (AML/CTF) regulations. We identified the following implications for organisations:
|Level||Organisational element reconfigured|
|Individual and Task:||Changes in tacit knowledge of front-line employees about suspicious behaviour. Changes in customer handling skills of front-line employees.Changes in the content of sales work to include compliance.Changes in the performance management of sales work.|
|Intra organisational:||Emergence of chains of secrecy in organisational communication patterns.Changes to internal organizational boundaries as financial crime departments reinforce their expertise.|
|Organisational and Inter-organisational:||Recognised pressure on organisational resource distribution towards regulatory compliance.Changes to ability of organisations to compete on a level playing field because of investments required in AML/CTF.Perceived centrality of AML/CTF to corporate reputation.|
- Castillo, D., Canhoto, A. I & Said, E., (in press). The Dark Side of AI-powered Service Interactions: exploring the process of co-destruction from the customer perspective. The Service Industries JournalDOI: https://doi.org/10.1080/02642069.2020.1787993 online
Abstract: Artificial intelligence (AI)-powered chatbots are changing the nature of service interfaces from being human-driven to technology-dominant. As a result, customers are expected to resolve issues themselves before reaching out to customer service representatives, ultimately becoming a central element of service production as co-creators of value. However, AI-powered interactions can also fail, potentially leading to anger, confusion, and customer dissatisfaction. We draw on the value co-creation literature to investigate the process of co-destruction in AI-powered service interactions. We adopt an exploratory approach based on in-depth interviews with 27 customers who have interacted with AI-powered chatbots in customer service settings. We find five antecedents of failed interactions between customers and chatbots: authenticity issues, cognition challenges, affective issues, functionality issues, and integration conflicts. We observe that although customers do accept part of the responsibility for co-destruction, they largely attribute the problems they experience to resource misintegration by service providers. Our findings contribute a better understanding of value co-destruction in AI-powered service settings and provide a richer conceptualization of the link between customer resource loss, attributions of resource loss, and subsequent customer coping strategies. Our findings also offer service managers insights into how to avoid and mitigate value co-destruction in AI service settings.
Keywords: Value co-destruction, customer resource loss, Artificial intelligence, automated service interactions, chatbots, service robots, value co-creation
You can find this paper here. And related blog posts here and here.
This project identified five types of factors that can destroy value for customer, when interacting with chatbots. Furthermore, this research examined how blame attribution (i.e., who customers blame, when things go wrong in interactions with chatbots) impacts on customer dissatisfaction and subsequent behaviour.
- Chen, W., Braganza, A., Canhoto, A. I. & Sap, S. (in press). Productive Employment and Decent Work: The Impact of AI Adoption on Psychological Contracts, Job Engagement and Employee Trust.Journal of Business Research. DOI: https://doi.org/10.1016/j.jbusres.2020.08.018
Abstract: This research examines the tension between the aims of the United Nations’ Sustainable Development Goal 8 (SDG 8), to promote productive employment and decent work, and the adoption of Artificial Intelligence (AI). Our findings are based on the analysis of 232 survey results, where we tested the effects of AI adoption on workers’ psychological contract, engagement and trust. We find that psychological contracts had a significant, positive effect on job engagement and on trust. Yet, with AI adoption, the positive effect of psychological contracts fell significantly. A further re-examination of the extant literature leads us to posit that AI adoption fosters the creation of a third type of psychological contract, which we term “Alienational”. Whereas SDG 8 is premised on strengthening relational contracts between an organization and its employees, the adoption of AI has the opposite effect, detracting from the very nature of decent work.
Keywords: Artificial intelligence, Psychological contract, Employee engagement, Job trust, Sustainable development goals, Decent work
You can find this paper here. And a related blog post here.
This research finds that while AI has the potential to promote inclusion of workers in at risk groups, such as disabled workers, employee engagement falls as a consequence of adopting AI technologies.
In addition to journal articles, I also wrote a piece for The Conversation UK about the rationale of Apple’s bet on privacy, for iOS 14:
- Canhoto, A.I. (2020) ‘Apple is starting a war over privacy with iOS 14 – publishers are naive if they think it will back down’, The Conversation, published 15th September 2020.
You can find it here.
Reach out if you want to learn more about these articles, discuss how my research can help your organisation, or explore opportunities to work together.