Social Media Influencers and Purchase Intention amongst Social Media Users in developing African economy

Background: Internet disruption has unprecedentedly made social media a global village and global digital market where business and customers interacts boundlessly. People’s interaction has far gone beyond boundary and businesses are seeing and harnessing these opportunities. Objective: This study investigated the effect of social media influencers on purchase intention among social media users. Method: The population of this study consist of active social media users in Anambra state. Sample size of 220 was arrived at using convenience sampling for infinite population. Reliability and validity test were done through construct Reliability and Discriminant validity respectively through smart- pls. Data was computed with SPSS version 24 and analysed with SEM via Smart-Pls. Result: The finding of this investigation showed that trustworthiness, attractiveness and influencer product match up has positive and significant effect on purchase intention respectively. Conclusion: More so, expertise does not influence purchase intentions. Finally, if there is need social media influencer, organizations should painstakingly seek influencers with high trust level among audience, highly attractive and it’s congruity with the intended promoted product.

Role of Governance and Public Trust on Government Debt-Economic Growth Nexus (A Global Empirical Analysis)

High government debts in several countries have the potential to trigger or exacerbate economic instability. These concerns are consistent with the results of this study, where countries that have a high debt ratio tend to have declining economic growth. To provide more understanding about this effect, this study tries to examine the effect of debt on economic growth by utilising the governance and public trust level as a contextual variable and mediator. Empirically, both variables have a prominent role in the debt and economic growth nexus. The debt threshold as a budgetary rule is necessary but might not be sufficient to validate the rationality of rising debt. The capability of government in providing public governance and the effect of additional debt on public trust is another crucial aspect that needs to be seriously scrutinised, or when the addition of debt becomes inevitable (such as in pandemic situation), the government should strengthen governance capability to ensure the productivity of debt and mitigate the decreased public trust. This finding implies that the debt policy should not only be based on budgetary rule but also the capacity of governance and the potential implication of the falling public trust.

The impact of online reviews on renters' perceived trust in property information

Using cognitive consistency theory, we explored the mechanisms of prospective renters' cognitive processing and cognitive outcomes of reviewing rental listing information on the Internet. We conducted an eye-movement experiment with 102 prospective renters to investigate the mechanisms influencing their cognitive processing and outcomes, via a 2 (comment form: subjective, objective) × 2 (risk preference: risk avoider, risk preference) factorial design. The results show that (a) subjective reviews required more cognitive processing effort than did objective reviews, but the perceived trust level of subjective reviews was lower; (b) risk-averse (vs. risk-inclined) participants put more cognitive effort into processing listing information, but their perceived trust in the listing information was lower; and (c) participants' perceived trust in listing information was mainly influenced by the display attributes of the webpage, rather than the review information. Our results will help Internet rental companies understand tenants' information concerns and enhance their online display pages in a targeted manner.

Doctor's Dilemma: Evaluating an Explainable Subtractive Spatial Lightweight Convolutional Neural Network for Brain Tumor Diagnosis

In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis on image data. However, one critical problem is that Deep Learning comes with complicated, black-box models so it is not possible to analyze their trust level directly. So, Explainable Artificial Intelligence (XAI) methods are used to build additional interfaces for explaining how the model has reached the outputs by moving from the input data. Of course, that's again another competitive problem to analyze if such methods are successful according to the human view. So, this paper comes with two important research efforts: (1) to build an explainable deep learning model targeting medical image analysis, and (2) to evaluate the trust level of this model via several evaluation works including human contribution. The target problem was selected as the brain tumor classification, which is a remarkable, competitive medical image-based problem for Deep Learning. In the study, MR-based pre-processed brain images were received by the Subtractive Spatial Lightweight Convolutional Neural Network (SSLW-CNN) model, which includes additional operators to reduce the complexity of classification. In order to ensure the explainable background, the model also included Class Activation Mapping (CAM). It is important to evaluate the trust level of a successful model. So, numerical success rates of the SSLW-CNN were evaluated based on the peak signal-to-noise ratio (PSNR), computational time, computational overhead, and brain tumor classification accuracy. The objective of the proposed SSLW-CNN model is to obtain faster and good tumor classification with lesser time. The results illustrate that the SSLW-CNN model provides better performance of PSNR which is enhanced by 8%, classification accuracy is improved by 33%, computation time is reduced by 19%, computation overhead is decreased by 23%, and classification time is minimized by 13%, as compared to state-of-the-art works. Because the model provided good numerical results, it was then evaluated in terms of XAI perspective by including doctor-model based evaluations such as feedback CAM visualizations, usability, expert surveys, comparisons of CAM with other XAI methods, and manual diagnosis comparison. The results show that the SSLW-CNN provides good performance on brain tumor diagnosis and ensures a trustworthy solution for the doctors.

TINGKAT KEPERCAYAAN MASYARAKAT TERHADAP KULAWU TELLO DALAM MENGATASI KERABANG TELUR LUNAK DI KABUPATEN SIDENRENG RAPPANG

ABSTRACT Kulawu Tello is a stone that is believed by the community that come from inside the egg, usually this stone is used in the breeding of laying hens (layers) by breeders, especially in overcoming the problem of soft shelled egg. In this study aims to determine the effect of Kulawu Tello on the local community. The sample in this study was 78 people consisting of layer farmers who use and know about Kulawu Tello, local historians and the village head of Manisa Village. This research is descriptive, using a questionnaire to determine the level of trust of the Sidenreng Rappang community on the magical ability of Kulawu Tello in improving the performance of layers. The level of trust of each individual varies depending on how they believe in something according to what has been done. In general, people have high level of trust in the efficacy of Kulawu Tello stone because it has been used in their livestock business. It can be concluded that the level of public trust in Kulawu Tello in overcoming soft egg shells is very high, namely 52.56%, the percentage of moderate trust level is 21.38% and those who have a low level of trust are 5.13%. As for the level of public trust in Kulawu Tello in order to improving the performance of layers around 47.44%, the percentage of medium trust level around 26.49% and those who have a low level of trust around 4.27%. Keywords: belief, kulawu tello, magic, soft shelled egg.