Market Variables Research Articles

Pengaruh Experiential Marketing, Service Quality, Brand Image, Price, dan Customer Expectations Terhadap Customer Satisfaction pada Bioskop XXI Gresik

This study aims to analyze the influence of experiential marketing, service quality, brand image, price, and customer expectations on customer satisfaction at XXI Cinema Gresik. The research method used is a quantitative approach by distributing questionnaires to 240 respondents. The results show that the variables of experiential marketing, service quality, price, and customer expectations have a significant influence on customer satisfaction while the brand image variable does not have a significant effect on customer satisfaction. In conclusion, companies are advised to improve service quality and experience-based marketing strategies.

Electricity Load & Price Forecasting Using Deep Learning

Forecasting electricity prices and loads is essential for grid stability and efficient energy management. This study presents a novel technique to increase short-term load accuracy. and pricing projections by utilizing deep learning techniques. We examine past consumption data, weather trends, and market variables using sophisticated neural network architectures, such as CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory). The suggested models outperform conventional forecasting techniques after being trained on a variety of datasets. The findings show that deep learning greatly lowers forecasting mistakes, allowing utilities and stakeholders to make better decisions. The results highlight how artificial intelligence may be used to optimize pricing and resource allocation for energy, which will ultimately lead to a more dependable and effective power market.

The disparities in health risks of multiple pollutants through soil and dietary exposure in a rural-urban area based on accessibility method.

Rapid urbanization has resulted in disparities in environmental conditions for different communities in suburban area. This study presents a comprehensive investigation into the occurrence of pollutants in the soil and dietary food, and associated health risks in an urban-rural transitional area. The levels of potential toxic pollutants, notably metals, organophosphate esters (OPEs), and agrochemicals in surface soil and dietary food were evaluated. Higher levels of metals and OPEs were found in soils of industrial area, and agricultural soils had an elevated level of agrochemicals. The highest health risk was found for Chromium (Cr) which exceeded 1, indicating a high probability of adverse non-cancer effect to local residents. The levels of contaminants in food showed higher variability in community market and farmers' market than in supermarket, while higher levels of OPEs were found in food from supermarkets. The accessibility to fresh food mainly determined the differences in health risks of different communities. For dietary exposure, residents of industrial areas have higher levels of risk than other neighborhoods, mainly due to the possibility of exposure of foodstuffs with higher metal contents. In terms of market type, community markets mainly contributed to the comprehensive health risk through dietary exposure, especially for industrial and agricultural communities. The findings of this study provided further understanding of the spatial distribution of various contaminants as well as their health risks for different communities, which could guide the monitoring and management of potential toxic pollutants to safeguard public health in rural-urban transitional regions.

Pengaruh Content Marketing dan Celebrity Endorser Terhadap Keputusan Pembelian Dengan Brand Trust Sebagai Variabel Intervening: Studi pada Pembeli Hafez Restorer di Media Sosial Tiktok

The rapid development of digital technology has made social media one of the most influential platforms in the purchasing decision process. TikTok is one of the social media platforms currently popular in Indonesia. The presence of TikTok has attracted many companies to start utilizing it as a marketing tool. The purpose of this research is to examine the influence of Content Marketing and Celebrity Endorsement on purchasing decisions for Hafez Restorer products on TikTok, with Brand Trust as an intervening variable. The research method used is quantitative, with data analysis techniques involving path analysis. The sampling method employed purposive sampling with 106 respondents. Data processing was carried out using SPSS software version 22. The results of the study indicate that the variables of Content Marketing and Celebrity Endorser have a positive and significant effect on Brand Trust. Additionally, Content Marketing and Celebrity Endorser have a positive and significant effect on Purchasing Decisions, and Brand Trust positively and significantly affects Purchasing Decisions. Furthermore, Content Marketing and Celebrity Endorser indirectly influence purchasing decisions through Brand Trust as an intervening variable.

Pursuing the impossible (?) dream: Incorporating attitudes into practice-ready travel demand forecasting models

Despite the fact that our existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help us explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models. Two main objections have been raised to their inclusion: they are too cumbersome to measure, and difficult-if-not-impossible to forecast. This paper reports on the considerable progress that has been made toward overcoming the first objection, through the use of machine learning methods to train a prediction function on smaller-scale research-oriented survey datasets, and then applying that function to impute attitudes into large-scale household travel survey datasets. Internal evaluations show that we can estimate attitudinal factor scores with moderate fidelity when using socioeconomic/demographic, land use, and targeted marketing variables, and with high fidelity when using just a few attitudinal marker variables. External evaluations demonstrate that the imputed attitudes lead to improved behavioral insight and predictive ability for forecasting-oriented models. With respect to the second objection I have only sketched some ideas for moving forward, but there are clearly some practical steps that could be taken at very little marginal cost, such as including as few as 10 attitudinal marker statements in future household travel surveys.

Pengaruh Artificial Intelligence Marketing dan Content Marketing Terhadap Minat Beli dan Keputusan Pembelian

This research aims to determine the influence of Artificial Intelligence Marketing and Content Marketing on Purchase Interest and Purchase Decisions of Digital MSME Consumers in Pasuruan Regency. The type of research used in this research is explanatory research with a quantitative approach. The sampling technique was carried out using Non Probability Sampling with a purposive sampling technique. The total sample in this research was 114 respondents. Data collection was carried out by distributing questionnaires directly to Digital MSME customers in Pasuruan Regency who were the research objects. The data obtained in this research were analyzed using Structural Equation Modeling (SEM) analysis techniques based on the PLS (Partial Least Square) variant with the help of SmartPLS 4.0 software. Testing of this research model was carried out using outer model (measurement model) and inner model (structural model) tests. The results of this research show that the Artificial Intelligence Marketing variable has a positive and significant influence on Purchase Interest, Content Marketing has a positive and significant influence on Purchase Interest, Artificial Intelligence Marketing has a positive and significant influence on Purchase Decisions, Content Marketing has a positive and significant influence on Purchase Decisions Purchase Interest influences positive and significant on Purchasing Decisions.

Pengaruh Content Marketing dan SEO (Search Engine Optimization) Terhadap Keputusan Pembelian Melalui Minat Beli: Studi pada Konsumen Produk UKM F&B di Kabupaten Pasuruan

This study aims to analyze the relationship between Content Marketing and Search Engine Optimization (SEO) on Purchase Decisions through Buying Interest in consumers of F&B SME products in Pasuruan Regency. This study uses an explanatory research approach with data analysis techniques using path analysis. The type of data used is quantitative data, using questionnaires as a data source. The sampling method uses purposive sampling with certain criteria. The results of this study show that: 1) The Content Marketing variable has a significant effect on the Buying Interest variable with a t-calculated value of 3.811 > 1980. 2) The Search Engine Optimization (SEO) variable has a significant effect on the Buying Interest variable with a t-count value of 7.672 > 1980. 3) The Content Marketing variable has a significant effect on the Purchase Decision variable with a t-calculated value of 3.126 > 1980. 4) The Search Engine Optimization (SEO) variable has a significant effect on the Purchase Decision variable with a t-count value of 3.841 > 1980. 5) the Buying Interest variable has a significant effect on the Purchase Decision variable with a t-calculated value of 5.439 > 1980. 6) The Content Marketing variable has a significant effect on the Purchase Decision variable through the Buying Interest variable with a t-calculated value of 3.807 > 1980. and 7) The Search Engine Optimization (SEO) variable has a significant effect on the Purchase Decision variable through the Buying Interest variable with a t-count value of 4,900 > 1980.

Antecedents of Purchase Decisions: A Study on Social Commerce TikTok Azarine Cosmetics

The development of digital marketing today is driven by important factors. Factors included in this study such as rapidly spreading viral marketing that reaches a broad audience, positive reviews from existing customers that can attract new ones, the ease of finding comprehensive product information through interactive features, entertaining and relevant content that aligns with consumer perceptions, and consumer trust in the product. The purpose of this study is to analyze the antecedents of purchasing decisions represented by the variables of viral marketing, online customer reviews, interactivity, entertainment, perceived relevance, and trust. The sampling technique used was purposive sampling. This research utilizes a quantitative approach, collecting data through a questionnaire distributed via Google Forms to Azarine consumers in Indonesia, with a sample size of 358 respondents. The analytical tool used in this study is SmartPLS 3.0. Based on the research findings, viral marketing, online customer reviews, and interactivity influence consumer trust. However, the variables of entertainment and perceived relevance do not affect consumer trust. The results also show that viral marketing, online customer reviews, entertainment, and trust do not significantly influence purchasing decisions, while interactivity and perceived relevance do.

Hospital Customer Competition Issues are being addressed through Confirmation Factor Analysis on Experiential Marketing, Brand Trust, and Loyalty

Introduction The post-COVID-19 pandemic has caused an epidemiological transition that has pushed the pace of development of the health service system even further. Issues and revolutions in the health service industry have made all health service industry providers compete with each other to get service users with competitive advantages in the potential global health market. The hospital has improved revenue sustainability while promoting quality assurance practices. These healthcare industry managers have become adept at introducing business practices into business operations to increase the market for hospital customers. Exploratory factor analysis on important marketing dimensions such as experiential marketing, brand trust, and patient loyalty is very much needed in the issue of competition in the health service industry in hospitals. Materials and Methods This research was quantitative research with a survey approach to outpatients with a sample size of n=255 patients according to Lemeshow (1997). Identification of loading factor analysis includes experiential marketing variables, brand trust, and loyalty. Data analysis used IBM SPSS Statistics 26 Results The research results showed 1) an important loading factor on the experiential marketing variable is the Think dimension indicator (0.845), 2) an important loading factor on the brand trust variable is the brand characteristic dimension (0.945) and 3) an important loading factor on variable loyalty is Repeat Purchase (0.925) Conclusion Dimension of brand trust is the most important dimension in the issue of competition in the health service industry in hospitals. Strengthening and expanding hospital branding as a promotion strategy to add positive value to the reputation of patient and community services at the hospital.

The Influence of The 7P Marketing Mix Strategy and Digital Marketing on Car Purchasing Decisions at PT. Hadji Kalla Toyota Palopo Branch

Marketing mix helps companies design marketing strategies by paying attention to product, price, distribution, and promotion, while digital marketing uses digital technology to reach consumers directly, both work together to create brand awareness, influence consumer perceptions, and facilitate the purchasing process, influencing purchasing decisions consumers positively. This research aims to determine the influence of 7p marketing mix and digital marketing on car purchasing decisions at Pt. Hadji Kalla Toyota Palopo Branch, either partially or simultaneously. The research approach used in this research is a quantitative approach, namely a direct survey using data collection through questionnaires and observation. The research results show that (1) Marketing mix has no effect on purchasing decisions, with a calculated t value of the marketing mix variable of 1.201 < t table value (1.678) and a sig. 0.312 > 0.05. (2) Digital marketing has a significant positive effect on purchasing decisions, with a calculated t value for the digital marketing variable of 2.553 > t table value of 1.678 and a sig value. 0.014 < 0.05. And (3) Marketing mix and digital marketing simultaneously (together) have a significant effect on purchasing decisions with a calculated F value of 20.233 and a significance of 0.000 < 0.05.

The Effect of Product Innovation and Entrepreneurial Marketing on SME Competitive Advantage

This study aims to determine the effect of product innovation and entrepreneurial marketing on the competitive advantage of SME in Medan Tuntungan District, North Sumatra. This type of research uses quantitative methods. The population of this study were all SME in Medan Tuntungan District, totaling 375 SME, and the number of samples obtained from calculations using the Slovin formula, namely 79 samples. The results obtained from this study are that partially product innovation has a positive and significant effect on competitive advantage with a t-calculated of 2.895 and a t-estimated worth 1.99 so that the t-calculated> t-estimated (2.895> 1.99) and the significance value is smaller than 0.05 (0.025 <0.05). Likewise, the entrepreneurial marketing variable has a positive and significant effect on competitive advantage with a t-calculated of 2.014 and a t-estimated worth 1, 99 so that the t-calculated> t-estimated (2.014> 1, 99) and a significance value smaller than 0.05 (0.019 < 0.05). While simultaneously, product innovation (X1) and entrepreneurial marketing (X2) together have a positive and significant effect on the competitive advantage of SME in Medan Tuntungan District with an F-calculated value of 4.027 and an F-estimated value of 3.12, so 4.027> 3.12 with a significance value of 0.022 < 0.05.

Revolutionizing Procurement: How AI & Data Analytics Driving Cost Effective Solutions in EPC

This paper, therefore, discusses the revolution brought by AI and Data Analytics in the procurement processes of EPC (Engineering, Procurement and Construction) and how each of the challenges, including cost control, enhanced efficiency and procurement downtime, could be solved using the two technologies. When the use of the procurement models is applied, the following benefits will be realized: supplier selection is facilitated using artificial intelligence, thereby automating the entire process; there is increased accuracy of costs compared to manual estimation, therefore reducing bias. The use of AI will allow better historical and real-time data analysis for procurement needs forecast, supplier evaluation and material cost prediction. Furthermore, machine learning algorithms can automate adjustments depending on real-time market variability, such for instance, a change in the price of the materials or a disruption in the supply chain, as well as execution flexibility regarding the project needs. Data Analytics also adds more weightage to procurement efficiency because it gives detailed analysis of the past and present procurement data. By utilizing state-of-the-art analytical frameworks, top managements are allowed to make powerful supplier analyses in relation to reliability, lead time, and cost of contracts prior to negotiations on contract supplier contracts and before contracting risks. What this study aims to do is to highlight real-life implementations of these technologies and how EPC firms can capitalize on these technologies to drive procurement efficiency, cost reduction, project delivery, and supplier relationship all for efficient project delivery. This paper thus provides a detailed discussion on the MI and TI of implementing AI and DA within procurement in the EPC sector via case studies and examples.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

Pengaruh Karakteristik Syariah Marketing terhadap Kepuasan Nasabah Bank Syariah Indonesia KFO Mikro Merlung

This research is motivated by the current rapid development of Islamic Banking, causing the banking industry to face a very competitive business environment. Therefore, approaches and breakthroughs are needed to develop the Islamic market in indonesia. Therefore, one of the efforts of Islamic Banks is to implement Islamic Marketing which consists of fou characteristics, namely Theistic, Ethical, Relistic and Humanistic, which is a business process whose entire process applies islamic values.This study aims to determine whether the syariah marketing variables consisting of Theistic, Ethical, Realistic and Humanistic have a simultaneous or partial effect on Customer Satisfacion at Bank Syariah Indonesia KFO Mikro Merlung. This test uses a quantitative research method with an associative research type and uses a questionnaire and observation method. The questionnaire is used to obtain data on the characteristics of respondents, namely customers and to obtain data on the influence of syariah marketing on customer satisfaction at Bank Syariah Indonesia KFO Mikro Merlung through statements in the questionnaire. While observation is used for direct observation of the research location and to dig up data on organizational structure, population an samples.The results of this study indicte that the independent variables of Syariah Marketing consisting of Theistic, Ethicl, Realistic and Humanistic have a simultaneous effect on satisfaction of Bank Syariah Indonesia KFO Mikro Merlung with an effect of 40%, while the remaining 60% is explained by other variables not examined in this study, partially, it is concluded tht of the four characteristics there are there are three variables that have a significant effect on customer satisfaction, namely Theistic, Ethical, and Humanistic while the Realistic variable has an effect but is not significant on customer satisfaction of Bank Syariah Indonesia KFO Mikro Merlung.

The Gender Inequality in The Peruvian Labor Market, 2004-2021

The objective of the research is to describe gender inequality in the Peruvian labor market, 2004-2021, identifying the main factors that trigger this problem. The metho-dology applied is documentary type because different articles, works and research reports are collected. For this purpose, a descriptive and comparative analysis was carried out by gender of the following aspects: economically active population, activity rate employ-ment, economically active employed population, labor income, gender wage gap, emplo-yed population in the secondary sector, underemployment, salaried employment and une-mployment. Despite the advances in gender equality, a constant and wide gap remains between men and women in the labor market. Likewise, the results found show that most of the labor market variables by gender have a positive impact and are statistically signifi-cant on the human development index, the estimates would be affected by other gender inequalities that exist in the labor market such as discrimination, stereotypes, among ot-hers.

The Effect Of Price And Content Marketing On Skintific Product Purchase Decisions In Medan Sunggal District, North Sumatra

The aim of this research is to determine the influence of price and content marketing on purchasing decisions for Skintific products. This research uses quantitative methods. The population in this study were in Medan Sunggal Sub-district, North Sumatra. The number of samples in this research was 100 samples. Data was collected by distributing questionnaires to respondents using Google Form. The results of data processing show that the price variable has a positive and significant influence on purchasing decisions for Skintific products. The content marketing variable has a positive and significant influence on purchasing decisions for Skintific products. Simultaneous research also shows that price and content marketing also have a positive and significant influence on purchasing decisions for Skintific products

Sentiment and Stock Characteristics: Comprehensive Study of Individual Investor Influence on Returns, Volatility, and Trading Volumes

Abstract Background Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. Objectives To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Methods/Approach Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. Results We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Conclusions Our study contributes to understanding the intricate role of individual investor sentiment in financial markets.

Renewable energy technology diffusion model: Evaluation of financial incentives in the electricity market of ecuador

This paper develops a simulation model using the System Dynamics paradigm to evaluate the effect of applying financial incentives on the diffusion of non-conventional renewable energy technologies in the Ecuadorian electricity market. The barriers to the diffusion of renewable energies are studied, the diffusion models are characterised, and the model is built with market variables and financial indicators integrated into its base structure. The Ecuadorian electricity market is described as a case study focusing on the incentive scheme. Based on investment, generation, and indirect incentives, this scheme plays a crucial role in promoting renewable energy technologies. Different long-term diffusion scenarios are simulated and compared with each other and with the baseline scenario to obtain diffusion rates and financial information for five renewable generation technologies: non-conventional hydro, wind, solar photovoltaic, biomass, and geothermal. The results of the simulations show that the combination of incentives leads to high diffusion rates, reassuring the audience about the potential impact of the proposed model. However, the feed-in tariff and tradable green certificate incentives are the most effective in promoting the use of renewable energy. For the Ecuadorian electricity market, the results show that wind and biomass technologies would be the most profitable, as opposed to geothermal energy, whose diffusion will not be feasible within the simulated period.

Impact of Digital Marketing, Influencer, Live Features, and Reviews Shopee Purchase With Brand Image

The advancement of information and communication technology in the digital era has revolutionized various aspects of human life, including in the realm of business and marketing. Marketing activities that once relied on direct interaction between sellers and buyers have now shifted towards digital marketing that utilizes online platforms. Indonesia, as the largest economy in Southeast Asia, has made an e-commerce as the main driver of socioeconomic growth. Supported by a population of over 270 million people and ever-increasing internet penetration, the development of e-commerce in Indonesia has experienced rapid progress in recent years. This study aims to analyze the impact of digital marketing, influencer marketing, live features, and Online Customer Reviews on online purchase decisions on the Shopee platform with brand Image as an intervening variable. This research used a quantitative method with a data collection technique through an online questionnaire to 210 Shopee users in Indonesia. The measurement of construct validity and reliability was carried out using the measurement model (outer model). Then, the structural model (inner model) was used to test the model's strength, predictive ability, model significance, and the direct and indirect influence of independent variables on the dependent variable. The test tool employed SmartPLS 3.0. The results of this study indicate that the variables of Digital Marketing, Live Features, and Brand Image have a significant influence on Purchase Decisions on Shopee e-commerce. Furthermore, the variables of influencer marketing, live features, and Online Customer Review have a significant influence on Brand Image on Shopee e-commerce. Meanwhile, for the indirect influence, the variable live features have a significant influence on purchase decisions on Shopee e-commerce through Brand Image as an intervening variable.

Can migraine be triggered by unemployment in France? A Bi-Probit/Multinomial probit approach

ABSTRACT Migraine is a major public health issue with massive human, économic and social costs, particularly for women. However, the effects of the labour market on this disease are to date little studied. Because unemployment may trigger migraine due to induced stress, we present in this empirical study estimates of the impact of unemployment on migraine using French household data. The causal impact of unemployment is established using instrumental variable methods. We use a rich database (Health and Professional Itinerary Survey) that allows to measure the presence of migraine, mental health and labour market variables, including working conditions. We show that unemployment increases the probability of migraine among women, with an important marginal effect of 14.4% points. We also show that although the issue is unemployment, inactivity must also be included and instrumented, which leads to estimate a generalization of the bi-probit model in which the first step is a multinomial probit. From a public policy perspective, the results show that prevention and care policies must be put in place and target unemployed women.