تأثیر محرک‌های فناوری هوش‌مصنوعی و چت‌بات‌ها بر تجربۀ هوش‌مند مشتریان و بازاریابی

Introduction: Today, the use of artificial intelligence technology capabilities is very popular. The rapid evolution in AI technology has redefined the customer experience and also created many opportunities for companies to interact with customers using chatbots. During a customer interaction, a chatbot must understand the customer's request, maintain and update the customer's status, and ask clarifying questions while keeping the customer engaged. Although there are diverse and fruitful studies in the field of artificial intelligence technology drivers and customer experience, the study on the impact mechanism between these aspects has three major and significant gaps. The first research gap is how intelligent customer experience acts as a mediator between AI technology drivers and chatbots and recommendation marketing. The second research gap is the moderating role of technology readiness which has two dimensions of optimism and a sense of lack of control. Finally, the third gap is the research field of platform businesses, which is selected as the target statistical population in this research. To address the mentioned research gaps, the model of this research was created in the framework of the stimulus-organism-response model. Finally, in this research, a model will be proposed that will examine the relationships between artificial intelligence technology drivers and chatbots, intelligent customer experience, and recommendation marketing, and will test the moderating role of technology readiness in these links. In short, this research aims to answer the following questions: To what extent can the adoption of artificial intelligence technology and chatbots improve the intelligent experience of customers and the result of technology readiness in platform businesses? How are AI technology drivers and chatbots impacting referral marketing?

Methodology: The research method of the present research is in terms of applied purpose and terms of nature; it is in the category of descriptive research of the survey branch. To collect data in this research from domestic and foreign articles and theses that are related to the research topic, written reviews, and studies that are written in the field of artificial intelligence, customer experience, recommendation marketing, platform businesses, and stimulus-organism-response framework, related sites and statistically valid in the field of the research subject and finally, the use of standard questionnaire as the most important tool of data collection has been used in the quantitative method. The questionnaire, as one of the most common tools for collecting information in survey research, evaluates the opinions, views, and insights of respondents with a set of targeted questions using various scales. The statistical population of this research includes customers of Snap platform businesses and since the size of the statistical population is unlimited, G*Power software was used to determine the number of sample members. According to this software, with an effect size of 0.05, error of 0.05, test power of 0.9, and the number of predictive models equal to 9, the sample size was 406 people; But in the end, 453 electronic questionnaires were collected and for more caution, checks were done on the same number. Finally, to analyze the data, the structural equation technique was used using Smart PLS version 4 and SPSS version 27 software. Also, to measure convergent validity, content validity, convergence and divergence, and Cronbach's alpha and composite reliability were used to measure reliability.

Results and Discussion: The values of the coefficient of skewness corresponding to all the observed variables are in the range of [-3,3]; On the other hand, except for the second variable, the elongation coefficient values are in the [-5,5] range for all variables. On the other hand, the standard deviation for the observational variables shows the dispersion of the responses of the participants in the research, which should not be less than 0.5. As it is evident in the research, the answers corresponding to the questions of the questionnaire have a good dispersion and this means that the answers are not concentrated on one side of the spectrum. The skewness coefficient corresponding to all existing structures is in the [-3,3] range; On the other hand, except for the optimism structure, the elongation coefficient values for all structures are in the [-5,5] range. In this way, the condition of normality of the data distribution, which is the placement of skewness and kurtosis coefficients in the ranges [-3,3] and [-5,5] 2, is not fulfilled, and therefore the data distribution is abnormal. Finally, the results of this research showed that all the assumed variables of artificial intelligence technology drivers and chatbots affect the variables of intelligent customer experience. Relative advantage and perceived interaction, which are both variables of intelligent customer experience, affect recommendation marketing; But in technology readiness variables, only the moderating effect of optimism on the relationship between enthusiasm and perceived interaction was confirmed.

Conclusion: The changing role of information and communication technologies in marketing has faced both marketing researchers and managers of this industry with a fundamental challenge to identify trends in this field. Thanks to technological advances, marketing has become more innovative, interactive, and personal, and this leads to more efficient marketing activities for businesses. It is clear that the impact of artificial intelligence on the lives of people and businesses is undeniable, and the pattern and behavior of people's purchases have changed a lot with the advent of this technology. Therefore, businesses should examine all the required knowledge of e-marketing and all aspects related to the business environment so that they can benefit from the benefits of such platforms to promote their brand.