Currently, businesses use a variety of artificial intelligence (AI) applications, such as service robots (Wirtz et al., 2018). Aside from the innumerable benefits, their quick and broad deployment has also led to a number of problematic issues (Honig & Oron-Gilad, 2018). For example, several studies focused on how people reacted to failing algorithms (Srinivasan & Sarial-Abi, 2021). Even fewer studies investigated how people react when robots fail (i.e. Choi et al., 2021). Prominent marketing strategies involved depicting resilient and well-engineered robots in states of falling, failing, beating up, and aiming at evoking various feelings (i.e. empathy, warmth, or comfort). Despite it being a significant phenomenon, almost no previous research investigated how consumers react to robots depicted as falling, failing, beaten up, or lost (see Table 1 for a list of popular robot failures and falls).
The most prominently portrayed type of robot failure in popular media is “the fall.” In this research, our goal is to investigate what people think and feel about the phenomenon of “failing robots” in the context of a “fall, as consumers evaluate the same technology (i.e., robots) in somewhat diverse ways (Siino & Hinds, 2005; Gretzel & Murphy, 2019). We present the initial findings of our content analysis to pinpoint the specific concepts consumers focused on when formulating their thoughts and feelings on falling robots.
We started out our in-depth exploratory research by gathering open-ended consumer verbatims from a convenience sample of university students. They responded to a specific robot fall news item, accompanied by a visual, which aided in the discovery of new and relevant issues centered on the “failing robots” theme. Visuals are frequently used in exploratory studies to aid in the probing of meanings and reactions (Christodoulides et al. 2021).
Participants
A convenience sample of eighty-eight (42 female) undergraduates studying business at a major European university took part in the current study in partial fulfillment of their course requirements. We have not prioritized generalizability and scale, which are not key aspects in qualitative sampling (Holloway & Jefferson, 2000). The average age of the participants turned out to be 23.09 (SD = 2.504, ranging between 19-34) and on average they reported medium income.
Procedure
Participants were first shown a piece of news about a robot falling (Appendix 1). The photograph was captioned, “The fall of one of a robotics company’s robots at a trade show.” They were asked to answer a few questions about their evaluation of the robot and the news depiction, as well as some control variables like their involvement in robotics, anthropomorphism level for the robot, and demographics.
Findings
As two researchers, we concurrently open-coded the short-essay reactions to the robot and the news. We used manual coding to gain insight into the characteristics and dimensions of attitudes toward malfunctioning robots. The initial results of consumer verbatim analysis revealed several broad themes of forming an opinion or attitude toward the issue. The following are some of the most notable examples in the three thematic categories: (1) seeing robots as the futuristic technological advancement of humans and getting upset with them falling; (2) seeing robots as yet another simple machine and not minding much about them falling; and (3) in-between: having mixed feelings toward the robots’ falling
Seeing robots as a futuristic technological advancement for humans:
“The sad part is that this shows that we are behind in terms of technology”
“They can develop themselves and improve their technology. Every success comes from after failure.”
“It wasn’t just a falling robot; the ideas and experiences fell, too.”
“I am not interested in how and why it fell off the stage at all, but I am curious about what is planned for the future of this robot in terms of AI improvements.”
Seeing robots as yet another simple machine:
“I feel nothing about the robot’s fall”
“I have no emotion about the robot’s fall”
“Since it’s a machine, a fault could appear at any time, which, on the other hand, shows that we cannot rely on robots 100% and that human interaction is gonna be always needed.”
“Since that was a robot, I did not feel anything.”
Having mixed feelings:
“This news is partly fun and partly sad”
“They’re exciting, but a little scary”
The first group got depressed with the robot’s fall and took it as a sign that the technological advancement and efforts set forth for such were wasted, or at best, they would like to understand what the key learning was to make sure that desired progress can be achieved in future technologies. The second group did not take the robot’s fall as something to be of importance as it was yet another machine that surrounds modern daily life; however, they still felt a bit disappointed that humans were needed to compensate for the robot’s falls. Lastly, the third group had mixed feelings and reported confusion when they saw a robot fall.
Conclusion
Consumers may experience unexpected and mixed emotions after witnessing robotic failures, and these emotions may then influence their attitudes and related behavioral intentions. For example, previous research demonstrated that using service robots attenuated consumers’ embarrassment (Pitardi et al. 2021). It is yet unknown which emotions and mechanisms are in place for evaluating failing robots in a favorable or unfavorable light by the consumers. The apparent polarity reflected in the verbatims points out that using robot fall strategies is a double-edged sword for robotic service providers, producers, and even retailers, and the involved mechanisms need to be analyzed in-depth in order to avoid unanticipated and unintended consequences.
The deployment of AI chatbots is driving significant changes in the way that service is delivered and experienced (Belanche, Casaló and Flavián, 2021; Ostrom et al., 2021). The co-creation literature emphasises actor-to-actor networks that create value for each other (Vargo and Lusch, 2004); however, the introduction of AI chatbots in the frontline adds an uncharted dimension to the study of co-creation, as AI developments promise value co-creation that changes as the AI adapts to other actors (such as customers), and these actors then adapt to the AI. AI chatbots take an active part in the service encounter, and as pseudo frontline employees, become important actors in the value co-creation process (Verhagen et al., 2014).
However, chatbots often fall short of customer expectations, resulting in service failure (Sheehan, Jin and Gottlieb, 2020). In this manner, the failure can also be perceived as co-created, since customers invest considerable time and energy (resources) in order to interact with the chatbot and co-create the interaction (Heidenreich et al., 2015). The negative effects of such service failures may be further compounded if customers are not allowed to choose their preferred customer representative: a human agent or a chatbot; or if customers perceive to be interacting with a human, but would be interacting with a chatbot instead (Robinson et al., 2020). Although previous literature extensively examined the determinants of consumer use and adoption of novel technologies, extant research has largely been conducted in voluntary co-creation contexts, and has neglected the investigation of less harmonious forms of co-creation, such as forced co-creation contexts.
Different co-creation contexts are also likely to be influential in forming customer expectations regarding the forthcoming co-creation experience, the chatbot’s performance, as well as possible chatbot limitations (Crolic et al., 2022). The nature of such expectations can have a significant impact in shaping customer evaluations about their participation in co-creation (Nijssen, Schepers and Belanche, 2016) and eventually in assigning attributions for failure (Belanche et al., 2020). However, the literature only offers contradictory findings regarding the possible impact of different co-creation contexts on customer expectations, and responsibility attributions.
Against this background, the aim of this study is to investigate not only the customer perception of chatbot interactions in different co-creation contexts, but also to understand how such contexts may influence customer expectations and the resulting responsibility attributions when chatbot failure occurs.
Guided by a Pragmatism research philosophy, a mixed methods approach was adopted in order to be able to effectively address the research aim. Specifically, an exploratory sequential design was employed, consisting of the collection and analysis of qualitative data in the first stage, followed by a quantitative phase which built on the findings obtained from the qualitative study.
The qualitative study comprised a total of 39 semi-structured interviews conducted in a face-to-face format. This study demonstrated how customers perceive three distinct co-creation contexts when interacting with chatbots. One context, volitional co-creation, is more aligned with the concept of voluntary participation and collaboration that is prevalent in the co-creation literature. Yet, the other two contexts, coercive and deceptive co-creation, are novel to the co-creation literature and as such, are worthy of further investigation. As a result, the qualitative study advanced a research framework and accompanying hypotheses regarding the impact of distinct co-creation contexts on expectations and attribution of responsibility.
Focusing on the investigation of coercive co-creation contexts, the hypotheses were tested through two experimental research studies conducted in a customer service setting. Data was collected from US participants (N = 315; N = 324), who, as part of the experimental research, were asked to interact with a chatbot that was specifically programmed for this study. Half of the participants were forced to interact, whereas the other half were given the illusion of choice between interacting with a chatbot and an alternative customer service channel. Structural Equation Modelling (SEM) was used to evaluate the experimental data.
The experimental studies found that in situations which result in service failure, customers who were forced to interact with chatbots, attribute more negative responsibility towards the company, than customers who were given a choice among several contact options. In such cases, customers blame the company directly for the failure. When compared to those customers who were offered a choice, customers who were forced to interact with the chatbot showed stronger controllability and stability attributions towards the company; in other words, they perceived the company as having had the ability to prevent the failure, whilst also perceiving the failure as more permanent and likely to happen again. These results were replicated within the two different service settings that were included in the experimental research. These results show that despite all the benefits associated with chatbots, it is important to understand why customers may feel forced into interacting with a chatbot, and the resulting customer evaluations following such interactions. Although the co-creation literature has shown a strong reliance on the notion of co-creation as a harmonious, voluntary collaborative activity, this study challenged this view and contributed to the co-creation literature by demonstrating the possibility of discordant forms of co-creation, which may especially arise in human-to-non-human interactions. Additionally, this study also contributes to the service failure literature by assessing the impact of service failures in an AI context and uncovering the underlying psychological processes following a co-created service failure between a customer and an AI chatbot.
The experimental research also found support for the mediating effect of disconfirmation of expectations. Forcing customers to interact with chatbots results in a more negative disconfirmation of expectations (the experience rated as worse than expected) than those customers who were given a choice. This, in turn, leads to stronger attributions of controllability, stability and causality towards the company. It is likely that forced involvement represents a more comprehensive level of mental involvement for customers (Reinders, Dabholkar and Frambach, 2008), in the process expecting more from the interaction, and ultimately being more disappointed when the service results in failure. This result confirms the role of customer expectations as a reference point when judging the actual service (Oliver, 2015) and contributes to the co-creation literature by establishing disconfirmation of expectations as an important link between the perceived freedom of choice when co-creating and the resulting attributions of responsibility.
The findings also suggest a number of strategic implications for managers who are considering the partial or complete replacement of human staff with chatbots in customer service settings.
The service sector is at an inflection point, with potential industrialisation arising from robotics creating new opportunities for innovation and the potential for a service revolution (Wirtz et al., 2018). A service revolution through technology may address issues including labour shortages (Beesley, 2021). In Ireland, for example, although tourism generates €9.2 billion annually and employs 265,000 people, there is a skills crisis with approximately 40,000 vacant positions (McGowan, 2021). Inflation in wages means businesses struggle to operate below 40% labour cost (McGowan, 2021), and 90% of businesses highlight difficulties in recruiting staff (Finn, 2021). In addition, workers in the gig economy face erratic working hours and uncertainty about pay (Martyn, 2021), with human costs of the gig economy including isolation without supportive colleagues or mentors, affecting perceptions of human dignity at work (Lillington, 2019).
Cognisant of these challenges, we recently conducted research based on a sample of 805 employees in the Irish hospitality sector (Wallace and Coughlan, 2022). Drawing on Conservation of Resources theory (COR), we proposed affective commitment and perceived leader support (specifically LMX) as resources against burnout. We found that the emotional exhaustion component of burnout was associated with counterproductive workplace behaviour, and affective commitment and LMX are effective resources against burnout, and against CWB. We also found that those who perceived they were on zero-hour contracts (or in the ‘gig economy’) were less able to draw on affective commitment or LMX as resources when experiencing burnout, but they were also less likely to ‘act out’ when they experienced burnout. We cautioned that zero-hour workers in the hospitality sector may internalise their stress and cope with the challenges of work in other ways.
One solution to these challenges would be a greater reliance on service robots; to support staffing, reduce uncertainty and work, and allow hospitality to deliver a consistent, efficient offering. Yet extant literature has focused less on the implications of frontline employees working with robotics (De Keyser et al., 2020). For example, if a service encounter is positive experience for the customer, the service robot may receive praise while the human employee remains unacknowledged, negatively impacting employee commitment (Robinson et al., 2020). Yet when technology such as service robots are involved in repetitive work, this may free up time to allow frontline employees to engage in more exciting and varied work (Wirtz et al., 2018). Also, if employees are no longer dealing with trivial requests, this may allow them to deal with higher-level tasks (Robinson et al., 2020).
Building on our earlier research, the first objective of our current study is to examine how service robots could be integrated in services by considering managers’ and employees’ views about these technologies. One could suggest that adding service robots would help to alleviate workload issues leading to burnout, support zero-hour workers in providing more assurance regarding hours worked, and support employees to allow them to engage with customers in a more genuine way. Yet while robots are predicted to have a profound impact on the sector (Lu et al., 2020), they have some weaknesses relative to service employees (Huang and Rust, 2018; Wirtz et al., 2018). For example, although robots offer an advantage of homogeneity in the delivery of repetitive services, customisation may be required to meet specific customer needs, and a heterogeneous delivery may be more appropriate (Palmer, 2011). Additionally, robots’ feigned emotion may be easily distinguished as not genuine, especially over longer- or high-involvement service encounters (Wirtz et al., 2018). Furthermore, customers may be relationship-motivated and expect social relationships with frontline employees, and this form of rapport, rich communication and emotional expression may engender customer trust and satisfaction, which could be lost in inhuman interactions (Robinson et al., 2020).
Rust (2020 p.18) highlighted that we are in a transformation, where artificial intelligence may compete with human intelligence, and this could dramatically change the skillset that humans need to remain relevant in the workplace. Huang et al. (2019) assert that human intelligence must emphasise empathy – a ‘feeling economy’ – where the empathetic dimensions of work are emphasised as mechanical and analytical tasks are replaced by AI and Robotics, especially in the services sector where interpersonal relationships are critical.
A consideration of the ‘feeling economy’ is particularly relevant in researching the implementation of service robots in Ireland’s hospitality sector. In Ireland, frontline service employees are a ‘secret ingredient’ in the sector. The Irish hospitality and tourism industry has built campaigns around ‘Ireland of the welcomes’ (McGrath, 2018). McGrath (2018) cites Niall Tracey of Failte Ireland, who explains “…what visitors constantly come back to is the people they engaged with,” he says. “When we ask visitors what it is about the people, it’s the smile. Irish people will smile at you, and without a word, that smile uniquely says, ‘I’m only here to help, how are you getting on?’”. He adds: “it’s very relaxed. It’s not manufactured friendliness, it’s a really authentic connection…it really makes Ireland quite magical compared to other destinations.” Yet when robots takes over a task, these human skills are displaced (Rust, 2020). How can, and should, the service be best delivered without losing that ‘magic’ that is unique and special within the sector?
The second objective of this study therefore is to explore how to best engage robots to alleviate challenges in the hospitality sector, while retaining the ‘magic’ of its service delivery and build an advantage in the ‘feeling economy’. Drawing on the Irish context provides unique insights into employee response to service robots, where frontline employees are the point of difference. Utilising survey, experimental design and in-depth discussions with managers and employees in hotels in particular, findings elicit their attitudes about service robots. We investigate the relationship between burnout, employee’s’ resources, and their response to service robots working alongside them. We also investigate managers’ views about integrating service robots in a sector famous for its friendly, human face.
Metabolism of pathogens in infectious diseases is important for their survival, virulence and pathogenesis. Mycobacterial pathogens successfully scavenge multiple host nutrient sources in the intracellular niche. It is therefore important to identify the intracellular nutrient sources and their metabolic fates in these pathogens. Metabolic phenotype of an organism is defined by metabolic fluxes. We quantified in vivo fluxes of the pathogens and probed host-bacterial metabolic cross talks in tuberculosis (TB) and leprosy using systems-based strategies and techniques of isotopic labelling, metabolic modelling and metabolic flux analysis (MFA). We show that the TB pathogen metabolizes a number of carbon and nitrogen sources in human macrophages and identified vulnerable nodes such as glutamine and serine biosynthesis as potential drug targets. Mycobacterium leprae, the leprosy causing pathogen, uses host cell glucose in infected schwann cells and the enzyme, phoenolpyruvate carboxylase is a potential drug target. Our research provides an understanding of the intracellular diets and metabolism of these important human pathogens and identified vulnerable metabolic nodes that can be used for developing innovative chemotherapies in TB and leprosy.
Mycobacterium ulcerans is the causative agent of the chronic skin infection Buruli ulcer. In contrast to most mycobacteria, M. ulcerans is predominantly found in the extracellular milieu in patient lesions. This is attributed to the production of a polyketide lactone, mycolactone, which inhibits innate immune responses and is cytotoxic to macrophages. Nevertheless, early in infection, intracellular bacteria are readily detectable and genetic evidence suggests that macrophages may play a role in controlling disease progression. In particular, polymorphisms in components of the autophagy pathway are reported to influence the severity of Buruli ulcer. However, little is known about the interaction between M. ulcerans and macrophages. We are currently investigating the cellular response to M. ulcerans by the autophagy pathway in macrophages. Early findings indicate that autophagy markers are upregulated in infected THP-1 macrophage-like cells, but these rarely colocalise with the bacteria.
The assessment of dietary advice delivered by a personalised mobile application to improve glucose control for adults with type 2 diabetes
Introduction
Finding an optimal diet that suits millions of people has proven to be challenging for nutrition research. Advances in technology, specifically AI, have enabled us to gather and process vast amounts of information as a tool to create a higher level of personalization and subsequently to boost diet adherence. While the number of nutrition apps is infinite, few provide adaptive evidence-based nutrition advice for patients with type 2 diabetes (T2D) residing in Germany. Therefore, as part of the PROTEIN project we aim to trial a dynamic personalized nutrition application to improve dietary compliance in T2D.
Objectives
We aim to customize meal plans according to data from the PROTEIN health expert team, the subjects’ preferences and their continuous glucose monitor (CGM) data. Our primary objective is to increase the time in range (TIR) of the study participants by 5%.
Methods
PROTEIN, a RCT, has a 3-month intervention period. Here, the subjects will use our application, a CGM and an activity tracker to collect essential data to provide personalization. The AI advisor is responsible for providing the meal plans and consists of a food and activity recommender system (FARS) and a reasoning-based decision support system (RDSS). The researchers on site will upload recent CGM data (7-14 days) that will be used to provide customized plans according to postprandial glucose excursions during the intervention period. If the TIR is below 70%, the goal „Decrease carbohydrates“ will be activated for next week’s recommendations. Foods, apart from vegetables and pulses, that caused high glucose levels (≥140 mg/dL over 2-4 hours) will activate a push notification directly to the user. If a food causes high glucose levels on two or more separate occasions, it will be excluded from the meal plans the following week.
Results
The app is being tested in virtual users to assure the appropriateness of the meal plans created by the AI advisor for each user. 300 subjects are planned to be tested.
Conclusions
The PROTEIN system creates adequate meal plans for virtual users with high glucose levels suggesting that an improvement of TIR can be achieved in real patients.
Funding
European Union’s Horizon 2020 research and innovation programme under grant agreement No 817732
The DarT/G toxin-antitoxin system encodes a pair of enzymes that mediate the addition of an ADP-ribose moiety onto thymidine in ssDNA in a reversible, sequence specific manner. Although originally characterised in Thermus aquaticus, the system is present in a number of important pathogens including all members of the mammal adapted M. tuberculosis complex, notably including human and bovine TB. Utilizing CRISPRi technology to silence DarG antitoxin expression, we have shown that DarT performs ADP-ribosylation of gDNA in cellulo in M. bovis BCG, leading to a rapid arrest of DNA replication and cell division, and that is ultimately toxic to the bacterium. In MTBC, darT and darG are transcriptionally linked to the dnaB gene, which encodes the replicative helicase that interacts with ssDNA at the chromosome origin (OriC) to initiate then drive DNA branch migration during replication. We demonstrate in vitro and in cellulo that MTBC DarT heavily ADP-ribosylates TTTW motifs in the AT-rich DnaB-loading region of OriC, suggesting that the DarTG system may work as a reversible regulator of replication. Furthermore, unregulated ADP-riboslyation by DarT induces the DNA damage SOS response, including the ImuA’ImuB/DnaE2 mutasome which has been implicated in DNA damage-induced mutagenesis and acquisition of resistance to antibiotics.
Immunopurification and NGS sequencing of ADP-riboslyated gDNA fragments has given further insight into the role of ADP-riboslyation in M. tuberculosis physiology, confirming ADP-ribosylation of OriC and demonstrating ADP-ribosylation at additional genomic loci, prominently including genes involved in the SOS response, DNA metabolism, and ribosomal proteins. This identifies the potential for ADP-ribosylation to act as a genome-wide epigenetic and cell signalling factor.
We aim to further understand the role of DarTG in bacterial physiology including DNA replication, the DNA damage response, persistence and drug-resistance in Mycobacterium tuberculosis.
Over the millennium, M. tuberculosis complex strains have branched into several lineages and genotypic variations can determine the virulence and transmissibility of clinical M. tuberculosis (Mtb). However, the mechanism behind the variability in transmission of Mtb remains elusive. Therefore, we investigated understanding the pathogenesis and vaccine efficacy between the high, moderate and low transmission Mtb in mice. The study used three Mtb strains based on their transmission within the Kenyan population – high, moderate and low. The project focused on the characterisation of Mtb strains isolated from Kenyan individuals, BCG vaccine efficacy and pathological analysis in mice against Mtb strains.
The causative agent of tuberculosis (TB), Mycobacterium tuberculosis (Mtb), is an intracellular pathogen infecting nearly 10 million people world-wide. Its cousin, Mycobacterium abscessus (MABC), is a major cause of death in immunocompromised individuals. However, despite the availability of multi-drug chemotherapy, the majority of deaths in both are due to drug-sensitive strains. This is often due to the presence of a phenotypically-resistant sub-population of bacteria termed antibiotic persisters in TB, which increase upon intracellular exposure, or antibiotic-tolerant biofilms within the lung airways for MABC. Determining antibiotic penetration and targeting persisters and biofilms can be challenging in highly heterogenous mycobacteria. We aim to use single cell techniques, such as microfluidics, nano-scale secondary ion mass spectrometry and atomic force microscopy, to evaluate antibiotic penetration and survival and to identify novel strategies to curtail these global health threats.
No abstract but I have a poster focused on the BEAT diabetes programme – a consortium between industry, the NHS and the university of surrey to roll-out and evaluate a digital, online, supported self-management programme for people living with type 2 diabetes. We have recruited >600 people to take part in the programme and matched with a control group to evaluate the impact of the programme on glycosylated haemoglobin (HbA1c) and our secondary outcomes, including weight, blood pressure and cholesterol. We have also conducted a qualitative process evaluation to sit alongside this implementation study.