The Effect of E-Marketing with the AISAS Model on Shariah Mutual Fund Investment Decisions at Bibit Mutual Fund Fintech

This study aims to examine the effect of E-marketing with the AISAS model on mutual fund investment decisions. This research using quantitative research. Data was obtained by distributing online questionnaires to 34 respondents who had or were investing in Bibit Mutual Fund Fintechs that had been registered in the OJK. The sampling technique in this study is purposive sampling, while the Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach is using a data analysis technique. Based on the results of data analysis, Attention variable has a significant effect on Interest variable, Interest variable has a significant effect on Search variable, Search variable has a significant effect on Action variable, Action variable has a significant effect on Share variable, and Share variable has a significant effect on Investment Decisions. Keywords: E-Marketing; Investment Decision; AISAS Model

ANALISIS PENGARUH MODEL AISAS (ATTENTION, INTEREST, SEARCH, ACTION & SHARE) TERHADAP TINGKAT LITERASI KEUANGAN SYARIAH (STUDI KASUS FOLLOWERS INSTAGRAM PERBANKAN SYARIAH)

The Financial Services Authority (OJK) states that one of the root causes of the slow growth in the market share of Islamic banking is due to the weak literacy of Indonesian people towards sharia finance. Based on a 2016 National Literacy and Financial Inclusion survey involving 9,680 research respondents from 34 provinces in 64 cities / districts. shows that the national Islamic financial literacy rate is 8.11 percent. While on the other hand, there has been an increase in internet usage among Indonesian people. The research aims to find out how the AISAS model which consists of Attention, Interest, Search, Action and Share affects the level of literacy of Islamic banking in followers of Islamic banking in Indonesia. Research consists of three stages namely literature study. Then a field study was conducted by distributing questionnaires to 183 respondents. The analysis method used is Structural Equation Modeling (SEM) using AMOS software. The results show that (1) Attention variable has a significant and positive effect on the dependent variable, namely interest. (2) Interest variables have a significant and positive effect on the dependent variable, namely search. (3) The search variable has a significant and positive effect on the dependent variable, namely action. (4) The action variable has a significant and positive effect on the dependent variable, which is share. (5) The search variable has a significant and positive effect on the dependent variable, namely Sharia Banking Literacy. (6) The action variable has a significant and positive effect on the dependent variable, namely Sharia Banking Literacy. (7) The share variable does not have a significant effect even though it has a positive value on the dependent variable, namely Sharia Banking Literacy.

The Effect Of E-Marketing With Aisas Model (Attention , Interest, Search, Action , Share) On Investment Decisions In Fintech Syariah

This study aims to examine the effect of E-marketing with the AISAS model on investment decisions. This research using quantitative research. Data was obtained by distributing online questionnaires to 30 respondents, who had or were investing in registered Islamic Fintechs in the Financial Services Authority (OJK). The sampling technique in this study is purposive sampling, while the Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach is using a data analysis technique. Data is processed using PLS 3.2.8. Based on the results of data analysis, the Attention variable has a significant effect on the Interest variable. Also, the Interest variable has a significant effect on the Search variable. Then, the Search variable has a significant effect on the Action variable, while the Action variable has not a significant effect on the Share variable. The Share variable has not a significant effect on investment decisions.

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzing the immune cell components of tumors. To that end, many deconvolution methods have been proposed for inferring cell subpopulations within tissues. Nevertheless, two problems limit the practicality of current approaches. First, all approaches use external purified data to preselect cell type-specific genes that contribute to deconvolution. However, some types of cells cannot be found in purified profiles and the genes specifically over- or under-expressed in them cannot be identified. This is particularly a problem in cancer studies. Hence, a preselection strategy that is independent from deconvolution is inappropriate. The second problem is that existing approaches do not recover the expression profiles of unknown cells present in bulk tissues, which results in biased estimation of unknown cell proportions. Furthermore, it causes the shift-invariant property of deconvolution to fail, which then affects the estimation performance. To address these two problems, we propose a novel deconvolution approach, BayICE, which employs hierarchical Bayesian modeling with stochastic search variable selection. We develop a comprehensive Markov chain Monte Carlo procedure through Gibbs sampling to estimate cell proportions, gene expression profiles, and signature genes. Simulation and validation studies illustrate that BayICE outperforms existing deconvolution approaches in estimating cell proportions. Subsequently, we demonstrate an application of BayICE in the RNA sequencing of patients with non-small cell lung cancer. The model is implemented in the R package “BayICE” and the algorithm is available for download.

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.