The Impact of SEO Specialists on Local Businesses in the USA

Pre-understandings are described as presumptions for the concept of understanding to even exist and affects the way humans interpret reality as well as the direction of scientific research (Gadamer, referred in Gilje et al., 2007, s. 179). An important component of pre-understandings are our personal experiences, which are always present in our consciousness and affects our interpretation of the world (Gilje et al., 2007, s. 183). As interpretive research 

particularly reflects the author’s interpretation (Bryman & Bell, 2005, p. 443), this type of research requires pre-understandings to be described (Geanellos, 1998, p. 238). Consequently, we should not strive towards being completely objective in our research, but instead make use of the understandings we hold to our advantage (Geanellos, 1998, p. 238). In the context of this research, both of us had our own previous experiences of AI, as well as 

our own interpretations of what it is and how it can be utilized. Furthermore, as this knowledge and experiences was very much limited, we had to immerse ourselves in the field of AI and its applications in marketing before initiating this research. We believe these pre-understandings were advantageous to us throughout this research, as it enabled us to find this research gap, find appropriate previous research to build the theoretical framework upon, as well as to write 

An interview guide grounded in previous 

knowledge. Furthermore, our pre-understandings were of much help when collecting the data through interviews, as it allows us to better grasp the answers of the respondents, thus we were able to ask follow-up questions, which we believe contributed to a greater depth of our research. As such, we made our pre-understandings to an advantage, in line with the recommendations of Geanellos (1998, p. 238). Moreover, this aligns with our interpretivist 

view of knowledge, as the social scientist must understand the social meaning of social action (Bryman & Bell, 2011, p. 17).  According to Rahman et al. (2017), chatbots—even in the age of development—will be the next major tool in the era of conversational services as the use of them reflects.the purpose of the research was to generate further insights into the field and develop theory. Consequently, this study is of an exploratory nature, in which an inductive 

approach is the most suitable path. The deductive approach to research on the other hand can be thought of as the way of evidence (Patel & Davison, 2011, p. 23), where theory is the basis for observations and results (Bryman & Bell, 2017, s. 43, 45; Saunders et al., 2012, s. In this approach, existing theory is used to generate hypotheses, which is then tested empirically in the current case (Patel and Davison, 2011, p. 23). The difference between the approaches thus lies in whether the study aims to test or develop theory (Saunders et al., 2012, s. 12). A 

Deductive approach would have

been suitable for this study if there were more research in the focal field, which would allow us to confirm rather than explore possible connections. Although the deductive and inductive approaches are often opposed to each other, they should advantageously be considered as tendencies instead of mutually exclusive (Bryman & Bell, 2017, s. 45). According to Patel and Davidson (2011, p. 24), the abductive approach can be viewed as a mix of deduction and 

induction. Abduction starts with induction, where a hypothetical pattern is formulated that can explain a specific case, in other words a suggestion for a theoretical depth structure (Patel & Davidson, 2011, p. 24). After this comes the deductive process where the generated hypotheses are tested on new cases, which may cause the hypotheses to evolve or expand (Patel & Davidson, 2011, p. 24). This may then continue in a back-and-forth manner until the 

best explanation is found (Bryman & Bell, 2011, p. 27). This approach would likely have been useful in this case, if it was not for the constraints in time and resources, as described in section 7.5. However, the deductive approach was partly used in this research as the theoretical framework was developed simultaneously as the interview guide, and as such before the collection of data. Accordingly, we argue that deductive elements increased our 

Understandings further which

allowed for a more successful and insight-generating data collection. A risk when conducting research with an inductive approach, is that one does not know how generalizable the theory is, as it is based on empiricism typical to a specific situation, time or group of people (Patel & Davidson, 2011, p. 23). However, due to our constructionistic view of social entities as described in section 2.1, we do not strive towards generalizing the findings of this study, but 

rather transfer the findings into contexts with similar characteristics, as described by Bryman and Bell (2017, p. 53). Another risk, according to Patel and Davidson (2011, p. 23), is that our preunderstandings of the problem, as well as the information gathering before initiating the research, will color the produced theories. Pre-understandings and information gathering before initiating the research can be viewed as deductive elements, which according to 

Bryman and Bell (2017, p. 45), are common in inductive research.Of them, the mainstream market has been seeing increasing growth. This paper investigates the application of chatbots in the field of customer support for an e-commerce website based on our conviction that the expansion of a company depends critically on customer service. As shown in figure 1, we can 

Observe that the highest

number of predictions about the use of chatbot is similar to the job desk that a customer service has which is to understand the problem that a customer may face and give the best response that fulfills the customer expectation.The employment of a chatbot readily solves the issues one may run across utilizing traditional customer care run by people including the time efficiency, the long hold time, conventionality and error in information provided. According to 

IBM, a chatbot could readily address up to 80% of common customer support issues for several clients at once, therefore lowering company costs up to 30% with minimal upkeep. Another major factor in using chatbots is the great interest people have in interacting with them instead of conventionally especially with millennials who value convenience and generally avoid other people for service and thus require the least human interaction as 

possible. The greater rating satisfaction on most live chats than any other customer care avenue helps to justify this.We have also conducted a survey to provide us additional information and validation on this matter.n this work, we investigate a chatbot using a keyword search. Unlike other chatbots on the present market, ours emphasizes on the ability of the  to adapt to the speech pattern and vocabulary of the user since Coperich et al. (2017) 

Conclusion

recommended a chatbot with those abilities thought to be more entertaining and have a better positive view by users in their service performance. That background helps the bot later on to detect the term from the texts, so facilitating their processing. Should the bot fail to locate the required term to complete their task, the user will be asked to re-input since chatbot usuae consumer wish and will ask and know what next to process. Should the bot lack the 

necessary knowledge, it will then probe the user to learn more about the more particular demand the user meant to make. The bot will search their database and provide the output as the answer to the user when the required information is gathered by it.Deep learning and concept model of NLP is utilized to enhance the accuracy of responses produced by the  By means of the recognition of phrases or words in the input and subsequently works through 

pattern matching to search for relevant responses, this will enable the bot to be able understand natural language, identify meaning and emotion to provide the most suitable answers that the user searching with much larger accuracy uses. Then, utilizing Natural Language Processing and Deep Learning, the chatbot sector grows as the time passes. Unlike ELIZA, a chatbot named ALICE built in 2009 uses Artificial Intelligence Markup