i
LET ME TALK TO YOUR HUMAN
Mariana Girão Carrilho
The impact of uncertainty avoidance on chatbot acceptance
Dissertation presented as partial requirement for obtaining the Master’s degree in Statistics and Information
Management
BOOK SPINE
Title: Let me talk to your human
Subtitle: The impact of uncertainty avoidance on chatbot acceptance Mariana Girão Carrilho
MEGI
2022
MGI
i
ii NOVA Information Management School
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa
‘Let me talk to your human’: the impact of uncertainty avoidance on chatbot acceptance
by
Mariana Carrilho
Dissertation presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Marketing Research and CRM
Advisor: Professor Diego Costa Pinto
November 2022
iii ABSTRACT
Chatbots are increasingly becoming part of the service context. While companies around the world choose to integrate chatbots as part of the customer experience as a way to achieve higher efficiency and lower costs, little is known about utterly voice-enabled chatbots substituting a front-line employee in customer service. Since people tend to prefer humans over artificial intelligence, we propose to diminish the gap between customers’ and companies’ desires, by studying why people disfavor chatbots in service encounters, exploring the underlying process via the superior capacity of humans (vs. chatbots) to provide higher perceptions of customer orientation and empathy. Across two experimental studies (N = 563), we show that chatbots have a negative effect on customer satisfaction and loyalty, and this effect is mediated by customer orientation and empathy (study 1). Nevertheless, resistance to non-human agents is mitigated in contexts with low (vs. high) uncertainty avoidance (study 2). Hence, for uncertainty avoidance contexts, i.e., Portugal, human agents show better results than chatbots, but the effects are not relevant in low uncertainty avoidance, i.e., USA. These findings suggest that it is critical for managers to both carefully consider the financial benefits of chatbots and customers’ cultural-related values to guarantee a tangible customer experience.
Keywords: customer service, service recovery, satisfaction, loyalty, artificial intelligence, voice- based chatbots.
iv 1. INTRODUCTION
Artificial intelligence (AI) is on the rise in the marketing industry. AI-driven tools allow companies to perform big data analysis of written language, automate workflows and assist in decision-making, convert spoken language into machine-readable format, identify objects or persons based on images (Longoni & Cian, 2022; Eurostat, 2021).
Chatbots, also known as conversational AI, are part of this progress, with the chatbot market forecasted to grow more than 22 percent over the next few years, rising from USD 6.8 billion in 2021 to USD 18.4 billion by 2026 (Research and Markets, 2021). Recent studies suggest that chatbots can improve customer service and firms’ profitability, by being able to quickly and accurately identify customers’ needs, and reducing firms’ costs when replacing human workers (Marks, 2021). All these benefits attracted interest of companies, more than ever during the COVID-19 pandemic, when customers’ response waiting time expanded from a few hours to days or even weeks and call center costs skyrocketed (Sudhakar, 2021).
While companies' interest in AI tools is increasingly growing, prior studies present mixed results about customers’ responses to AI applications in services. Some findings highlight that consumers tend to favor humans over machines (Castelo et al., 2019; Dietvorst et al., 2015; Longoni et al., 2019; Luo et al., 2019; Yalcin et al., 2022), but opposite results had also been discovered by researchers (Dietvorst et al., 2015; Garvey et al., 2021; Longoni &
Cian, 2022; Luo et al., 2021; Mende et al., 2019; Srinivasan & Sarial-Abi, 2021). These results suggest that whether consumers are averse (or not) to AI significantly differs across fields, contexts, and types of AI-applications. Moreover, despite the extensive literature on AI, few studies focus on consumers’ responses to utterly voice-activated chatbots. This reality could be explained by the ambiguity around chatbot definition. Despite the growing interest of academics around chatbots, there is a lack of consensus regarding its meaning and researchers
v have been using multiple terms interchangeably to refer to chatbots such as (virtual) bots (Crolic et al., 2021; Huang & Rust, 2018; Luo et al., 2019), avatars (Miao et al., 2022), virtual service agents or e-service agents (Chung et al., 2020), conversational agents (Crolic et al., 2021; Graesser et al., 2014; Roy & Naidoo, 2021) and virtual/digital assistants (Puntoni et al., 2021). The ambiguity makes the tasks of comparing empirical results or drawn conclusions across studies challenging for researchers (Miao et al., 2022).
To the best of our knowledge, this is among the first studies to focus on utterly voice- activated chatbots, applied to service recovery. Furthermore, we help to bridge the gap between chatbots and customer satisfaction and loyalty by exploring the underlying role of empathy (e.g., Wieseke et al., 2012) and customer orientation (e.g., Deshpandé et al., 2012;
Homburg et al., 2009; Kennedy et al., 2003), and the moderating role of uncertainty avoidance (e.g., Kubler et al., 2018).
This study is structured as follows. Section two provides the theoretical framework of the chatbots and their interaction with service recovery context, including the literature review as fundamental of our hypothesis development. Section three describes the methodology used across each of the two experimental studies. Section four provides the obtained results and findings. Section five concludes our work, providing the significant contributions and insights of the research, identifying critical questions to be explored by future researchers, and acknowledges encountered limitations.
vi 2. LITERATURE REVIEW AND HYPOTHESES
Chatbots are a form of artificial intelligence capable of mimicking human conversations and acting as virtual assistants to users (Luo et al., 2019), that may deliver socially and emotionally interactive services (Choi et al., 2021). Chatbots can provide several unique benefits to companies since they are equipped with sophisticated speech recognition and natural language-processing tools, understanding complex and subtle dialogs and addressing consumer requests (Luo et al., 2019).
While academic attention to artificial intelligence is growing, highlighting the benefits of incorporating it as part of the customer experience, consumers tend to favor humans over machines. Findings of a recent study, exploiting a field experiment data on more than 6,200 customers who received outbound sales calls, suggest that if consumers are aware that a call will be conducted by a machine (vs. a human worker), the purchase rates drop by over than 79,7% (Luo et al., 2019). In healthcare, consumers are reluctant to utilize services provided by AI. Results show people were less likely to schedule a diagnostic stress assessment and were willing to pay less when the provider was automated (Longoni et al., 2019). Consumers are also less confident and less likely to choose an algorithm over human forecaster, even when AI outperforms the human (Dietvorst et al., 2015; Srinivasan & Sarial-Abi, 2021), a phenomenon known as algorithm aversion (Castelo et al., 2019; Dietvorst et al., 2015;
Srinivasan & Sarial-Abi, 2021).
Since chatbots offer a variety of benefits to companies, they deliver greater efficiency, provide unrestricted availability and instant support to customers, decrease response time and companies’ costs, chatbots have become common in customer service contexts across many industries, increasingly replacing human service agents (Crolic et al., 2021).
vii 2.1. Service recovery context
Service providers always try to deliver customer expectations. However, mistakes are inevitable. Service recovery combines a set of opportunities a company should be quickly adopt after a failure, which translates as a combination of tangible and psychological actions that play as a key role to positively impact customers’ satisfaction and loyalty, two of the most mainstream measures of a company success, distinguished by one being a short-term milestone and the other on a long-term, respectively (Miller et al., 2000). Regarding psychological actions, the company should acknowledge and provide an explanation for the error, empathizing and apologizing to the customer (Van Vaerenbergh et al., 2019). However, these approaches may increase the customer's negative experience if used inappropriately. A non-empathetic apology can be worse than no apology at all (Miller et al., 2000). Since nonhuman agents are incapable to feel, i.e., empathize, as a result we expect that customers perceive higher levels of sincerity when receiving an apology by a human (vs. chatbot), ranking the post-failure efforts as more effective, and consequently, leading to higher levels of satisfaction and loyalty. Formally:
Hypothesis 1: Service recovery efforts by a human (vs. chatbot) agent have a higher positive impact on customer H1a) satisfaction and H1b) loyalty.
2.2. The mediation role of empathy
Empathy can be described as a form of showing concern for others (Fehr et al., 2010;
Levenson & Ruef, 1992). This emotional ability is key to understanding and relating to another’s thoughts, feelings, and experiences and is an important mechanism in consumer–
employee interactions (Wieseke et al., 2012). In a post-failure encounter, empathy has the
viii power to increase the likelihood of forgiveness and mitigate the negative effects of dissatisfaction on customer loyalty (Wieseke et al., 2012). Formally:
Hypothesis 2: The relationship between agent type and customer H2a) satisfaction and H2b) loyalty is mediated by empathy.
2.3. The mediation role of customer orientation
Customer orientation can be defined as a set of beliefs in an organization culture that puts the customer’s interest first, identifying his needs and adapting the solutions, to develop a long-term profitable enterprise, creating satisfied customers (Deshpandé et al., 2012;
Homburg et al., 2009; Kennedy et al., 2003).
Kennedy et al., (2003) states customer orientation should be seen as part of corporate culture, not limited to customers but also to stakeholders such as owners, managers, and employees, and should be evaluated by its customers rather than merely from the company itself. Customer-oriented behaviors have become key elements in building relationships (Palmatier et al., 2007) and past studies linked it to customer satisfaction (Homburg &
Klarmann, 2011) and loyalty (Brady & Cronin Jr., 2001).
Many organizations collect customer-focused data. Previous research suggests that the use of both internal and external customer data, i.e., market intelligence, is collected and becomes a shared organization-wide platform from which decisions are made, a customer orientation prospers and becomes self-reinforcing (Kennedy et al., 2003). Since AI as an ecosystem that connects three fundamental elements – data collection and storage, statistical and computational techniques, and output systems (Puntoni et al., 2021) – in ways and speed much superior to human capabilities, we would expect consumers to see AI as a powerful solution to meet and predict their needs. Nevertheless, considering that satisfaction with a
ix service is massively dependent upon the role of the front-line employee (Pugh et al., 2018) and people tend to prefer humans over machines, even when AI outperforms humans (Dietvorst et al., 2015), due to human superior soft interpersonal communication skills (Luo et al., 2021) and ability to adapt to changes using emotional intelligence (Puntoni et al., 2021), we argue that human agent (vs. chatbot) will lead to higher levels of satisfaction and loyalty and this effect is mediated by customer orientation. Formally:
Hypothesis 3: The relationship between agent type and customer H3a) satisfaction and H3b) loyalty is mediated by customer orientation.
2.4. The moderating effect of uncertainty avoidance
Uncertainty avoidance can be described as the level of a society to tolerate uncertainty and ambiguity (Kubler et al., 2018). It is a dimension linked to the way that a society deals with the fact that the future is unknown and ambiguous, bringing feelings to individuals of anxiety and threat, and creates beliefs and institutions that try to avoid these. While countries with low uncertainty avoidance display a fair degree of acceptance for new ideas and products, and a willingness to try something different, such as technology and business practices, countries with high uncertainty avoidance maintain rigid codes of belief and behavior and innovation tends to be resisted (Hofstede Insights, 2021).
According to Hofstede Insights (2021), on a scale from 0 to 100, Portugal scores 99 on Uncertainty Avoidance, while the USA scores 46, a value below average.
Recent findings on the context of avatars, defined as digital entities with anthropomorphic appearance, able to interact, suggest that AI effectiveness may be highly dependent on users' uncertainty levels (Miao et al., 2022). Individuals in high (vs. low)
x uncertainty avoidance contexts are more (vs. less) concerned with security in life and believe that loyalty to employers is a virtue (Blodgett et al., 2001).
In summary, we argue that in high uncertainty avoidance contexts, i.e., Portuguese consumers, a chatbot agent will lead to lower levels of customer satisfaction and loyalty when compared to a human agent, and in low uncertainty avoidance contexts, i.e., U.S. consumers, the difference between a chatbot and a human, regarding customer satisfaction and loyalty, will be insignificant. Formally:
Hypothesis 4: High uncertainty avoidance context results in lower levels of customer H4a) satisfaction and H4b) loyalty with a chatbot (vs. human) agent, but H4c) the effects are insignificant in low uncertainty avoidance contexts.
Figure 1. Conceptual model.
xi 3. OVERVIEW OF THE EXPERIMENTAL STUDIES
In two studies, we aim to obtain evidence pertaining to our prediction that after a service failure consumers prefer human (vs. chatbot) agents which in turn enhance satisfaction and loyalty with service providers. Study 1 further shows that this effect is mediated via empathy and customer orientation. Study 2 extends our findings by investigating whether the resistance to AI is mitigated in contexts with low uncertainty avoidance, i.e., USA, compared to high uncertainty avoidance, i.e., Portugal.
3.1. Study 1. Human vs. chatbot agents
Study 1 uses a mediation approach to test the hypotheses that consumers’ satisfaction and loyalty towards a human (vs. chatbot) agent is driven by empathy and customer orientation. We posit that human (vs. chatbot) agent will result in higher values of satisfaction and loyalty.
3.1.1. Procedures
A total of 130 Portuguese consumers, recruited via an online platform, participated in the between-subjects experiment on a voluntary basis (94.6% females, Mage = 42.10, SD = 10.34). Using a single factor design, participants were randomly assigned and read a scenario, describing a call with a human (vs. chatbot) agent after receiving a home appliance with physical damages ordered online (nhuman = 64 vs. nchatbot = 66). Full scenario is provided in Appendix A.
xii 3.1.2. Measures
Participants responded to our measures on a scale from 1 (strongly disagree) to 7 (strongly agree). We adapted Wieseke et al. (2012) measures of satisfaction (i.e., "Overall, I'm very satisfied with the service encounter with Mario” (α = 0.81)), and (DeWitt et al., 2008) measures of loyalty (i.e., “I will continue to buy in this company” (α = 0.78)), four items on empathy (i.e., “It's easy for Mario to see from customers' perspective” (α = 0.93)) adapted from Wilder et al. (2014), and five items on customer orientation (i.e., “Mario will provide his services in the time he promised” (α = 0.89)) adapted from (Homburg et al., 2009). For realism checks, we asked participants "The situation described in the scenario is...." on a seven-point Likert scale from 1 (unrealistic) to 7 (realistic).
3.1.3. Results
Participants reported significantly higher levels of satisfaction and loyalty in the human condition (MSatisfaction = 6.39, SD = 0.90; MLoyalty = 6.16, SD = 0.94) as opposed to the chatbot condition (MSatisfaction = 5.87, SD = 1.34; t(113.577) = -2.612, p < 0.001; MLoyalty = 5.42, SD = 1.40;
t(114.136) = -3.517, p < 0.01).
Participants perceived the human agent to be higher in empathy and customer orientation (MEmpathy = 5.30, SD = 1.28; MCustomer Orientation = 5.68, SD = 1.24) compared to the chatbot (MEmpathy = 3.67, SD = 2.05; t(109.35) = -5.461, p = 0.000; MCustomer Orientation = 5.09, SD
= 1.67; t(119.871) = -2.268, p < 0.05).
Additionally, participants perceived the scenario as realistic (Mhuman = 4.50 vs. Mchatbot = 4.59; t(128) = 0.224, p = 0.462).
xiii Figure 2. Customer satisfaction and loyalty on agent type.
3.2. Mediation analysis
Next, we examined whether the human agent vs. chatbot distinction can be explained by empathy and customer orientation mediation, using PROCESS model 4 with 5000 samples (Hayes, 2017). We conducted agent type (human vs. chatbot) as independent variable (IV) → Empathy as mediator (M1) → Satisfaction as dependent variable (DV), agent type (IV) → Empathy (M1) → Loyalty (DV), agent type (IV) → Customer Orientation (M2) → Satisfaction (DV) and agent type (IV) → Customer Orientation (M2) → Loyalty (DV). The results show that agent type (human vs. chatbot) significantly influenced empathy (β = 1.63, SE = 0.30; p < .01), and empathy had a positive effect on satisfaction (β = 0.27, SE = 0.54; p < .01). The indirect effect was significant (β = 0.43, BootSE = 0.1253, BootLCCI = 0.2102, BootULCI = 0.7041, p <
.05). Results show that agent type (human vs. chatbot) significantly influenced empathy (β = 1.63, SE = 0.30; p < .01), and empathy had a positive effect on loyalty (β = 0.24, SE = 0.58; p <
6,39 6,16
5,87
5,42
1 2 3 4 5 6 7 8 9
Satisfaction Loyalty
Study 1
Human Chatbot
xiv .001). The indirect effect was significant (β = 0.39, BootSE = 0.1317, BootLCCI = 0.1495, BootULCI = 0.6672, p < .05). Results show that agent type (human vs. chatbot) significantly influenced customer orientation (β = 0.58, SE = 0.26; p < .05) and customer orientation had a positive effect on satisfaction (β = 0.39, SE = 0.06; p < .01). The indirect effect was significant (β = 0.23, BootSE = 0.1084, BootLCCI = 0.0329, BootULCI = 0.4608, p < .05). Results show that agent type (human vs. chatbot) significantly influenced customer satisfaction (β = 0.52, SE = 0.20; p < .01), and customer satisfaction had a positive effect on loyalty (β = 0.63, SE = 0.73; p
< .01). The direct effect (β = 0.41, SE = 0.17; p < .05) and the indirect effect were both significant (β = 0.33, BootSE = 0.1390, BootLCCI = 0.0745, BootULCI = 0.6221, p < .05).
3.2.1. Discussion
The results from Study 1 support our predictions that consumers react differently to human (vs. chatbot) agents, i.e., after a service failure, customers who interacted with a human (vs. chatbot) agent reported significantly higher levels of satisfaction and loyalty. Our mediation analysis shows that empathy and customer orientation are the underlying mechanism explaining these differences. To broaden our conclusions, Study 2 tests the moderating effect of uncertainty avoidance.
3.3. Study 2: the moderating role of uncertainty avoidance
Study 2 extends our findings by testing the moderating role of uncertainty avoidance.
We posit that in high contexts of uncertainty avoidance human (vs. chatbot) agent will result in higher values of satisfaction and loyalty, whereas in low contexts of uncertainty avoidance the effects are not relevant.
xv 3.3.1. Procedures
A total of 433 Portuguese (n = 232) and U.S. (n = 201) consumers were recruited via an online platform (73.2% females, Mage = 40.13, SD = 11.29). This study employs a 2 (human vs.
chatbot agent) X 2 uncertainty avoidance (high vs. low context) between-subjects design. One hundred-and-thirty-two participants were dropped from the analysis as they failed the attention checks, and 20 participants were excluded considering the answer duration much briefer than expected (below 100 seconds). The final sample size was (N = 281). Using a single factor design, participants were exposed to the same scenario as in Study 1. We randomly assigned participants to the human (vs. chatbot) agent condition (nhuman = 138 vs. nchatbot = 143). Full scenario is provided in Appendix B.
3.3.2. Measures
Participants responded to our measures on a scale from 1 (strongly disagree) to 7 (strongly agree). We adapted Wieseke et al. (2012) measures of satisfaction (i.e., "Overall, I'm very satisfied with the service encounter with Mario” (α = 0.81)), and DeWitt et al. (2008) measures of loyalty (i.e., “I will continue to buy in this company” (α = 0.78)), four items on empathy (i.e., “It's easy for Mario to see from customers' perspective” (α = 0.93)) adapted from Wilder et al. (2014), and five items on customer orientation (i.e., “Mario will provide his services in the time he promised” (α = 0.89)) adapted from Homburg et al. (2009). For realism checks, we asked participants "The situation described in the scenario is...." on a seven-point Likert scale from 1 (unrealistic) to 7 (realistic).
xvi 3.3.3. Manipulation checks
On a seven-point Likert scale from 1 (strongly disagree) to 7 (strongly agree), manipulation checks worked as intend as respondents indicated that the call was answered by a human (Mhuman = 5.81 vs. Mchatbot = 3.41; t(259.889)=10.662, p < .001) versus a chatbot (Mhuman = 3.92 vs. Mchatbot = 6.03; t(236.248)=10.284, p < .001). Further, they perceived the scenario as realistic (Mhuman = 4.51 vs. Mchatbot = 4.50; t(279) = -0.083, p = 0.839).
3.3.4. Results
A 2 x 2 ANOVA on satisfaction revealed a significant main effect of context (Mhigh = 6.44, SD = 1.02 vs. Mlow = 5.79, SD = 1.06, F(1, 277) = 27.10, p < .001). The main effect of agent type was insignificant (Mhuman = 6.20, SD = 1.06 vs. Mchatbot = 6.04, SD = 1.11, F(1, 277) = 1.691, p = .195). More importantly, there was a significant two-way interaction between agent type and uncertainty avoidance context on satisfaction (F(1, 277) = 4.154, p < .05).
Pairwise comparisons revealed that satisfaction in the high uncertainty context, i.e., Portugal, was higher in the human agent condition (M = 6.65, SD = 0.95) than the chatbot condition (M = 6.23, SD = 1.06; F(1,277) = 6.405, p < 0.05). Conversely, there is no difference in satisfaction in the low uncertainty context, i.e., USA, (Mhuman = 5.75, SD = 0.98 vs. Mchatbot = 5.84, SD = 1.13; F(1, 277) = 0.241, p = 0.624).
We conducted a 2 x 2 ANOVA on loyalty revealing a significant main effect of context (Mhigh = 6.08, SD = 1.22 vs. Mlow = 4.94, SD = 0.96, F(1, 277) = 73.602, p < .001). The main effect of agent type was insignificant (Mhuman = 5.73, SD = 1.18 vs. Mchatbot = 5.44, SD = 1.31, F(1, 277)
= 1.088, p = .298). More importantly, there was a significant two-way interaction between agent type and uncertainty avoidance context on loyalty (F(1, 277) = 7.082, p < .01).
xvii Pairwise comparisons revealed that loyalty in the high uncertainty context, i.e., Portugal, was higher in the human agent condition (M = 6.31, SD = 0.95) than the chatbot condition (M = 5.82, SD = 1.43; F(1,277) = 7.886, p < 0.01). Conversely, there is no difference in loyalty in the low uncertainty context, i.e., USA, (Mhuman = 4.82, SD = 0.88 vs. Mchatbot = 5.03, SD = 1.02; F(1, 277) = 1.158, p = 0.283).
3.3.5. Discussion
The findings from Study 2 indicate that high uncertainty avoidance context has a detrimental effect on satisfaction and loyalty in the context of chatbot agents. Conversely, consumers of low uncertainty avoidance contexts tend to be indifferent to agent type.
6,31
4,82 5,82
5,03
1 2 3 4 5 6 7 8 9
High Uncertainty Avoidance
Low Uncertainty Avoidance
Study 2 - Loyalty
Human Chatbot 6,65
6,23 5,75
5,84
1 2 3 4 5 6 7 8 9
High Uncertainty Avoidance
Low Uncertainty Avoidance
Study 2 - Satisfaction
Human Chatbot
Figure 3. Customer satisfaction on agent type and uncertainty avoidance.
Figure 4. Customer loyalty on agent type and uncertainty avoidance.
xviii 4. GENERAL DISCUSSION
Although artificial intelligence has been widely studied, the marketing literature falls short in understanding consumers’ response to utterly voice-enabled chatbot (vs. human) agents.
Most previous research is limited to the U.S. context, not extending how agent type impact consumers’ satisfaction and loyalty across from different cultural contexts.
As our findings indicate, the acceptance of chatbot agents vary across the two uncertainty avoidance contexts (high vs. low). Study 1 shows that consumers exhibit higher levels of satisfaction and loyalty with human (vs. chatbot) agents, and this effect is driven by customer orientation and empathy. Study 2 demonstrates that uncertainty avoidance has a harmful effect on chatbot agents’ acceptance. Taken together, the findings contribute to extend the current literature of artificial intelligence (vs. human) preference (vs. aversion).
4.1. Theoretical implications
From a theoretical perspective, this research provides several implications. First, we contribute to the service-failure literature and delve deeper into consumers’ responses to AI- driven tools. Despite the extensive research regarding AI, few studies focus on utterly voice- enabled chatbots. Moreover, empirical investigations of chatbots fully substituting a front-line employee in the customer service context are limited. Secondly, cross-cultural research of this topic is scarce. This study enhances findings’ generalizability by examining consumers’
responses from two countries, i.e., Portugal and USA. Thirdly, our research is among the first to extend the concepts of customer orientation and uncertainty avoidance to the field of artificial intelligence acceptance. Our mediation analysis shows that not only empathy – extensively studied in the service recovery literature – but also customer orientation are the underlying mechanism explaining consumers’ differences between human and chatbot
xix preference. Finally, although scholars characteristically assess consumers favor humans over machines, especially in scenarios with high connection needs (Huang et al., 2019), by exploring customers’ cultural context, this paper establishes that resistance to non-human agents is mitigated in contexts with low (vs. high) uncertainty avoidance.
4.2. Practical implications
These findings also offer actionable guidance to managers on the operation of chatbots in service context. First, our results highlight empathy and customer orientation as mediators among post-failure satisfaction and loyalty. Second, acceptance of chatbot agents vary across uncertainty avoidance dimensions, showing that consumers with high uncertainty avoidance exhibit higher levels of satisfaction and loyalty with human agents, and this effect is not significant on consumers with low uncertainty avoidance.
Taken together, these findings suggest that chatbots can primarily work with human frontline employees, a process known as augmentation, a transition stage where AI replaces some of a service job’s tasks, and subsequently advance to a fully replacement human job, i.e., substitution (Huang & Rust, 2018). This could be a solution for decision-makers that seek to improve processes efficiency and costs reduction while reflecting consumers’ preference and need for human connection, especially in a following failure scenario. Furthermore, managers should consider integrating anthropomorphized chatbots, i.e., agents who exhibit human characteristics (Waytz et al., 2010). Since they mimic human service agents and are thus perceived as easier to use, anthropomorphized chatbots may be more readily adopted (Sheehan et al., 2020). According to Araujo (2018), the usage of human-like language or name is sufficient to increase customers’ perception of chatbots as being human-like. Moreover, our study assesses consumer responses to feeling AI, the most advanced form of intelligence,
xx designed to perform social, emotional, communicative, and interactive tasks (Huang & Rust, 2021). Nevertheless, companies have available multiple possibilities for machine integration, likewise able to deliver unique benefits to service providers. Considering the current level of AI development, managers can apply our findings to prioritize mechanical AI for simple and repetitive tasks and thinking AI for complex and rule-based tasks before implementing feeling AI as part of the customer journey.
Lastly, summarizing the existing literature and the results on our study, a further proposal to companies who consider assessing AI-driven tools in their service frontline is as intervention based on a pilot case study, since several factors are particularly relevant for customers’
responses to AI, and we don’t believe that a “one size fits all” could be crosswise applied.
4.3. Limitations and future research
Our studies leave some open questions. First, we did not explore all the boundaries of our effect. Although Portugal and the USA are identified as high and low, respectively, uncertainty avoidance countries (Hofstede Insights, 2021), this construct was not measured in our experiment.
Second, data from both studies was collected during the Covid-19 pandemic. Since this unique context drastically influenced social interactions (Vorobeva et al., 2022), which enhanced the social bonding among members of the same community (Calbi et al., 2021), and the need of human connection (Hagerty & Williams, 2020), future research should investigate the post-pandemic framework.
Third, the scenarios used in our studies exclusively illustrated AI full replacement of human tasks. While AI can both replace and augment human tasks (Huang et al., 2019; Larivière et
xxi al., 2017), future research could investigate the potential effects on satisfaction and loyalty while a chatbot assists a human agent rather than replacing it entirely.
Finally, it is worth mentioning that Mario, the chatbot used in our experiments, was described as an error-free agent. Since anthropomorphism was found to have a positive impact on emotional trust (Glikson & Woolley, 2020), and some errors could lead to diminish users’ anthropomorphism perceptions and adoption intents (Sheehan et al., 2020), we suggest future researchers to explore the effects between chatbots vs. human agents if an error is caused by the agent itself.
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xxvi 6. APPENDIX
Appendix A – Scenario Study 1
Imagine you recently made an online order of a home appliance, from brand ABC. You selected home delivery as a shipping method. Your order arrived at the expected date. When you open the box, you see that the item shows clear physical damages with heavy scratches. You decide to call Customer Support. Your call is answered by Mario, a front-line employee [an AI-based agent].
– "Hello, I'm Mario. How can I help you today?"
[you describe your delivery and the damaged item]
– "Sure, I can help you. Can you confirm your order number, please?"
[you provide the information]
Mario, the front-line employee [AI-based agent], kindly asks you a moment to confirm. After a few seconds, Mario says:
– "I’m really sorry for holding you up and I want to apologize. Since the item is factory- packed we had no way of knowing that the item you ordered was scratched. We take full responsibility for what happened. I can schedule a delivery for a new item in the next two hours. Do you have availability for 3PM?"
[you accept]
– "I would like to send together with your order a 15€ gift card to provide a fair restitution for all the inconvenience. Please pardon me for the trouble."
[you accept]
Your call with Mario, the front-line employee [AI-based agent], lasted approximately 5 minutes. In two hours, you received a new item without any physical damages.
xxvii Appendix B – Scenario Study 2
Mario is a customer support assistant [AI-based assistant] from TouchTech, a company specialized in selling tablets online.
TouchTech is a company that recommends solutions that are best suitable to each customer individually.
If you ever need to contact TouchTech, you can talk to Mario, a customer support assistant [AI-based assistant], that is very committed to identifying your needs and finding out what kind of solutions are more useful to you, having your best interest in mind.
Customer orientation guarantees
TouchTech company promises their clients that they focus on customers' needs and provide guaranteed customer-oriented service.
Imagine that you recently made an online purchase of a tablet, with home delivery at TouchTech.
On the day of delivery, when you open the box, you verify that the item is damaged with deep scratches. You decide to contact Customer Support. Your call is answered by Mario, a customer support assistant [AI-based assistant].
– "Hello, my name is Mario. How can I help you?"
[you describe the damaged item and confirm the order data]
Mario, the customer support assistant [AI-based assistant], kindly requests a moment to confirm.
After a few seconds it says:
xxviii – "I am very sorry for the delay, and I would like to apologize. Since the items are packed at the factory, we had no way of knowing that the item was damaged. We take full responsibility for what happened. I can schedule delivery of a new item in the next two years hours. Do you have availability for 3:00 pm?"
[you accept]
– "I would like to send a €15 gift card with the new item to offer a fair refund for all inconveniences. I apologize for the inconvenience."
[you accept]
Your call with Mario, the customer support assistant [AI-based assistant], lasted approximately 5 minutes. Within two hours, you received a new item without any physical damage.
xxix Appendix C – Scales and measurement items
Constructs Measurement items References
Satisfaction
• Overall, I'm very satisfied with the service encounter with Mario.
• The call for this customer support responded to my expectations of an ideal service encounter.
• This interaction fulfilled my expectations.
Wieseke et al. (2012)
Loyalty
• I consider this company as my first choice.
• I will continue to buy in this company.
• I will not acquire products from this company in the future. (R)
DeWitt et al. (2008)
Empathy
• Mario tries to empathize with customers' feelings.
• It's easy for Mario to see from customers' perspective.
• Normally Mario tries to put himself in "customer shoes".
• Mario tries to understand customers' point of view.
Wilder et al. (2014)
Customer Orientation
• Mario tried to figure out what my needs were.
• Mario had my best interests in mind.
• Mario adopted a problem-solving attitude with me.
• Mario recommended me the solutions that were best suitable to resolve my problems.
• Mario tried to find out what kind of solutions were more useful.
Homburg et al. (2009)
