Today, prescription drugs are much more cautious in the United States, and there is more information about the spread of illegal drugs across the country. Since 2016, the United States has had a national drug control strategy aimed at reducing illegal drug use (Trafficking in Heroin 12). This includes preventing drug addiction in American communities, increasing access to treatment and supporting recovery, and disrupting domestic drug trafficking (Heroin Trafficking 12). Although the heroin epidemic is far from disappearing, the United States is taking steps to mitigate it.
Today, prescription drugs are much more cautious in the United States, and there is more information about the spread of illegal drugs across the country. Since 2016, the United States has had a national drug control strategy aimed at reducing illegal drug use (Trafficking in Heroin 12). This includes preventing drug addiction in American communities, increasing access to treatment and supporting recovery, and disrupting domestic drug trafficking (Heroin Trafficking 12). Although the heroin epidemic is far from disappearing, the United States is taking steps to mitigate it.
Thanks to this, there is hope that the heroin crisis in the United States will be stopped relatively soon. Weaknesses in arguments can be a problem that affects the reliability and validity of individual studies and the judgments of authors. misinterpretation of existing justifications or subjective arguments. The review article analyzes a study by Prentice and Nguyen (2020) that examines the role of artificial intelligence (AI) in customer retention and improving customer service. As the authors argue, AI today is a tool to retain customers and create convenient methods to maintain interest in certain services (Australian hospitality industry) by influencing emotional intelligence (Prentice and Nguyen, 2020).
According to the argument presented, a significant part of the survey participants, despite the convenience and functionality of this engagement strategy, favors the retention of employee service and agrees with only a few factors in favor of AI (Prentice and Nguyen, 2020). Despite the value of research in evaluating the subject in question, this argument contains several shortcomings that can be classified as base index errors, in particular, the focus on some evaluation variables, the subjectivity of the evaluation of innovative development, and the limited functionality of the technique for drawing comprehensive conclusions. The purpose of this article is to present the aforementioned shortcomings from the point of view of counterarguments and to justify the existing errors based on current academic findings. AI in customer service
can be used to address various aspects of targeted customer service as it has several advantages over employee engagement. In their study, Prentice and Nguyen (2020) emphasize several comparisons of the variables underlying these arguments, especially responsiveness, reliability, empathy and assurance. However, this approach limits the number of variables that must be considered when evaluating the usefulness of AI in customer service. According to Huang and Rust (2021), communication with the target group using artificial intelligence technology can be based on various interactive algorithms that include thinking and mechanical mechanisms in addition to emotional intelligence.
Evaluating the different nature of the evaluated variables, Huang and Rust (2021) analyze additional types of target customer engagement and retention and point out that the application of artificial intelligence can promote individual customer communication methods based on the company’s development strategy. specific organization. Emotional perception is taken as a basis, but in addition to this variable, other nuances should also be taken into account. Therefore, focusing on a small number of variables in the study by Prentice and Nguyen (2020) does not provide an opportunity to evaluate the capability of the entire technology and prevents a comprehensive evaluation of its characteristics and capabilities.
The evaluation of artificial intelligence in customer service should be based not only on individual observation, but also with the help of real performance indicators to obtain a comprehensive picture of the importance of this technology in the field under consideration. Prentice and Nguyen (2020) use a fairly large sample in their paper, but their research is based on subjective analysis of AI functions, which in turn may depend on the personal preferences of the researcher. According to the authors, more than half of the participants are not young, which may be related to insufficient experience in using innovative technologies and digital devices (Prentice and Nguyen, 2020).
Gursoy et al. (2019) provide a detailed assessment of AI in customer service, providing detailed demographic characteristics and measurement characteristics of the included sample, including emotional perception, performance expectations and other relevant parameters. On the other hand, Prentice and Nguyen (2020) measure the experiences of Australian hotel guests, which narrows the possible scope of objective assessments and increases the risk of bias due to the limited target population. The fault lies in insufficient data on how consumers of different demographics use AI and which aspects of activity are most interesting. Thus, the subjectivity of the evaluation is the error that must be taken into account.
In the field of customer service, the functionality of artificial intelligence can be applied and it contains various options and possibilities to be applied to attract and retain the target group. Prentice and Nguyen (2020) examine the emotional perception of this technology as a key criterion when customers evaluate its capabilities. However, Libai et al. (2020), service providers must pay attention to the diversity of AI functions when dealing with customer relationships. For example, the authors recommend the development of retention algorithms based on customer information recall, habit analysis, purchase behavior and other related criteria (Libai et al., 2020).
Additionally, Prentice and Nguyen (2020) ignore the important aspect of using AI in social media as one of the most important shopping platforms today. Thanks to modern engagement and retention techniques based on AI, consumers have access to targeted advertising, easier search for goods and services and many other options. Focusing on emotional perception limits the scope of the analysis and is a flaw that affects the credibility of the argument. Thus, a comprehensive evaluation of the functionality allows avoiding the limitations of the conclusions and extending the results of the study on the perception of artificial intelligence by the target consumers.
The research evaluation of the role of revised artificial intelligence in the field of customer service shows the main shortcomings of the claim: few evaluation variables, subjective evaluation of the capabilities of the technology and limited choice about it. work skills . These are classified as base rate errors because conditional probabilities are considered instead of prior probabilities. Further aspects of the role of artificial intelligence in customer service should be presented to confirm the claim.
In particular, more evaluative variables must be considered, but not only emotional perception. The limitations of the sample in terms of demographic structure can be addressed with a wider coverage of the topic, not just one industry in one country. Analyzing the complete functionality of artificial intelligence can provide a sufficient basis for several opportunities to attract and retain customers.
