The Study of Para-Social Interaction with e-Word-of-Mouth for Influencer Marketing by Complex Computing

The purpose of this study was focused on exploring the relationship among the fans’ preferences, fans’ para-social interaction, and fans’ word-of-mouth. A survey consisted of 21 items based on the literature review and developed by this study. An online survey was distributed to the users of YouTube in Taiwan. A total of 606 valid samples was collected by survey. The instrument passed the reliability and validity test. Further, the data process applied the PLS (partial least squares) regression analysis methodology. The result shows that the ‘attractive’ impacted ‘para-social interaction’, ‘e-word-of-mouth’, and ‘preferences of fans’ positively. In addition, the para-social interaction plays an important role as a mediator between influencer’s attractiveness, w-word-of-mouth, and preferences of fans. Some suggestions were provided for social media influence’ related studies as reference.

Functionality, Emotion, and Acceptance of Artificial Intelligence Virtual Assistants

This paper aims to expand the acceptance of the AI Virtual Assistant model from the perspective of user’s cognition. Based on the 240 samples, we used multi-layer regression analysis to investigate the influencing factors and differential effects of users' acceptance of AI Virtual Assistant. The results show that functional cognition and emotional cognition of users are important influencing factors for an artificial intelligence virtual assistant. This provides a new perspective for user acceptance processes of the AI Virtual Assistant. We also examined the moderating effect of social norms between user cognition and AI Virtual Assistant. At last, a new AI acceptance model of AI Virtual Assistant was established.

Business Continuity During Adversity and Strategies to Revive Certain Sectors

The study investigates the factors influencing business continuity during adversities like COVID-19. It further sheds light on priorities for preparedness measures that need to be taken to ensure continuity during these adversities. Exploratory research was conducted in the form of focussed interviews with 20 senior management industry professionals, and these were analyzed using N-Vivo, and four important determinants of business continuity were identified based on which a research model was conceptualized using business continuity as dependent variable and others as independent variables. The model was further tested using quantitative research. For this purpose, a questionnaire was prepared, and a total of 200 responses were collected representing 26 sectors. These responses were analyzed using factor and variance analysis, and a multiple regression analysis was performed to test the role of these variables on business continuity. It was further concluded that the factors that ensure business continuity differ according to the industry in which the business is operating.

Freight Fleet Management Problem: Evaluation of a Truck Utilization Rate Based on Agent Modeling

The article deals with the problem of estimating the rational number and utilization rate of the vehicles' fleet. According to the analysis results of the state-of-the-art literature it has been revealed that the issue of substantiating the rational fleet size and the rate of its utilization were not fully solved. The purpose of the study was to increase the efficiency of servicing transportation orders by determining the required number of vehicles. The goal of the research was the influence of the transportation process parameters on the truck utilization rate. Originating from the probabilistic nature of the transportation process, it has been proposed to use the AnyLogic software product to develop a simulation model for vehicle orders' servicing. From the processing of the experimental results by the regression analysis methods, it has been found that the dependence of changes in the vehicle utilization rate is of a linear form.

Predicting the death rate around the world due to Covid-19 using regression analysis

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

xamination of Self-Control in Positive Psychotherapy Structures

Every day, individuals can encounter events which cause them to check their wishes and impulses. They need to provide self-control in the face of these events. It is observed that psychotherapies aimed at increasing self-control are limited. Positive psychotherapy, which is a structural and analytical psychotherapeutic method, can expand our viewpoint on this subject. Structures in positive psychotherapy were examined in the context of using the balance model, coupled with the ability of self-control. The dependent variable of the research is self-control, the independent variable is positive psychotherapy structures. Of the 151 (52.6%) of the participants (52.6%) were women, 136 (47.4%) were men. The Personal Information Form, which was created by the researcher as a data collection tool, the self-control scales and Wiesbaden positive psychotherapy and family therapy inventory were used. Multiple regression analysis was performed during the analysis of the data. As a result of multiple regression analysis, primary abilities (r = .51, r2 = .26; f = 11.840; p <.01), secondary abilities (r = .52, r2 = .27; f = 9.209; p <.01) and the balance model (r = .39, R2 = .15; f = 11.964; p <.01) significantly announced the self-control. According to the results of the analysis, patience, relationship, hope, and love are among the primary abilities; the secondary abilities are honesty, achievement, conformity and fairness. From the balance model, it was revealed that success and body were a significant predictor of self-control.