In today's fiercely competitive retail landscape, implementing effective pricing strategies is critical not only for boosting sales but also for securing a larger market share and ensuring long-term business sustainability. The ability to capture a greater share of the market directly influences a retailer's positioning and competitive edge, making pricing decisions pivotal. This paper introduces a hybrid optimization model that strategically combines Everyday Low Pricing (EDLP) and High-Low Pricing (HL) strategies, designed to address the intricacies of dynamic retail markets. The model is initially formulated as a nonlinear optimization problem aimed at maximizing sales to increase market share, all while maintaining profitability within a predefined threshold to ensure the retailer does not incur losses. To enhance the model's practical applicability, particularly in small-scale scenarios, the nonlinear problem is transformed into a Mixed-Integer Programming (MIP) model, facilitating its solvability. However, as retail applications scale up, the computational complexity becomes more challenging, necessitating the use of the Grey Wolf Optimization (GWO) algorithm. The GWO algorithm effectively balances computational efficiency with solution quality, making it a robust approach for large-scale problems. A significant contribution of this research is the linearization of the model under conditions where the products designated for High-Low pricing (referred to as 'Golden' products) are predetermined by the retailer. This linearization simplifies the computational process, enabling the model to scale and be applied in large retail settings. Developed in collaboration with a major Iranian supermarket chain, the model leverages real-world data to optimize discount levels and timing across various product categories. Extensive numerical experiments demonstrate the model's effectiveness in increasing sales, thereby contributing to a larger market share while ensuring that profitability remains within acceptable bounds. By providing actionable insights and strategic recommendations, this research offers a practical, scalable solution for optimizing retail pricing strategies in a data-driven and competitive environment, ultimately supporting retailers in their quest to dominate the market.

An examination of two areas of promotion and assortment planning in an environment is attempted in this paper. Sales promotion is a marketing strategy used by retailers to increase sales and profits by retaining customers and preventing them from switching to their competitors. Various products are available on the market that can substitute each other, so the best product assortment must be determined as well. In order to model the above subject, a nonlinear integer programming problem is proposed. Model solution involves rephrasing the problem as mixed integer linear programming. Small- and medium-sized problems can therefore be solved using MIP solver software. Firefly algorithms are designed to solve large-scale problems. According to the numerical results, determining the best product assortment for stores must also be done simultaneously with finding the optimal promotion. As a matter of fact, the promotion of the products significantly affects the assortment scenarios for the stores. Consequently, the selection of the promotional discount may result in large profit losses if the assortment planning is not taken into consideration. In order to assess the importance and sensitivity of the model parameters, a sensitivity analysis is conducted. The sensitivity analysis demonstrates that the model is able to respond to changes in market demand and competition, and provides an effective tool for chain stores to optimize their promotion and assortment strategies. To further validate the effectiveness of the model, a case study is conducted in Tehran, Iran. The results of the case study demonstrate the ability of the model to effectively optimize promotion and assortment strategies in real-world settings. Overall, the proposed model provides a valuable tool for chain stores to optimize their promotion and assortment strategies, and improve their market competitiveness.

This study examines the impact of Social Media Advertising Content (SMAC) and Social Media Sales Promotion Content (SMSPC) on Brand Image (BI) and customer behavior (CB) within the emergent context of container food courts in Indonesia. Focusing on Jakarta and Surabaya, which are cities at the forefront of culinary innovation, this study aims to uncover how digital marketing practices shape consumer perceptions and behaviors in this novel sector. The study employed rigorous methodology, including G*Power for sample size determination and SmartPLS for data analysis, and engaged 292 participants through a carefully designed survey. The findings indicated significant relationships between SMAC and SMSPC on BI, and subsequently on CB, underscoring the critical role of social media content in enhancing BI and shaping CB. Additionally, this study examines the generational differences between Generation Y and Generation Z, offering insights into tailored marketing strategies that cater to their distinct preferences. This study enriches academic discourse on the impact of digital marketing in the food industry and provides practical recommendations for practitioners aiming to leverage social media to enhance BI and foster positive CB in container food courts. The insights gained from this study not only illuminate the dynamics of social media marketing in an Indonesian context but also suggest avenues for future research in an ever-evolving digital landscape.

E-commerce security threats can undermine public confidence in electronic payments. In addition, most users are relatively unfamiliar with the technical details of electronic payment security. This study aims to determine the effect of price value, hedonic motivation, promotion and technology security on the interests of small and medium businesses using mobile payment applications. The questionnaire was used to collect data from 157 small and medium scale entrepreneurs in Bali Province with an even distribution of the sample and tested using multiple linear regression analysis. The results show that the value of price, hedonic motivation, promotion and technology security had positive and significant effects on the interest of entrepreneurs with small and medium-scale businesses using mobile payment applications. This study uses hedonic motivation variables used in researching financial technology by linking the latest theory, namely UTAUT2, which can effectively explain and analyze the behavior of acceptance of new product information technology.

This paper investigates the factors influencing on customers’ purchase decision of residential properties in Selangor, Malaysia. The study measures the effects of quality, price, location, promotion and corporate image on purchase decision by distributing some questionnaire among 312 randomly selected residents from some selected destinations at Selangor, Malaysia. Descriptive analysis and multiple regression analysis are used for analyzing the data. The findings reveal that quality, price, location, promotion, and corporate image positively and significantly influence on customers’ purchase decision. The results also indicate that location is the most important factor followed by price, quality, corporate image and promotion plays the smallest role on customers’ purchase intention. The study discusses the results and give constructive suggestions for promoting sales figures.

Technology use to buy products online as a new innovation on marketing is significantly influencing the buyer’s behavior in marketing and important to understand. The way suppliers present their product is interesting, especially to encourage people repurchase from their shops. The purpose of the study is to explore the relationship between advertisement, promotion, and lifestyle towards repurchase intention of the university students doing online shopping at e-commerce Shopee platform during the pandemic of COVID-19. The study uses a quantitative method for research. The data were collected using an electronic questionnaire on Microsoft Forms from 212 university students who used the e-commerce Shopee platform during the COVID-19 pandemic to shop. The purposive sampling method was used to collect the data from all the students. SEM-AMOS was used to analyze the data. The results indicated as follows: the advertisement variable has no significant effect on repurchase intention. Promotion and Lifestyle variables have a significant effect on the repurchase intention of university students at Jabodetabek area in shopping online at Shopee during the COVID-19 pandemic. From this study we can conclude that technology to advertise products has no relationship with repurchase intention of students while promotion and lifestyle has a significant relationship for students to repurchase products in the transformation of new normal activities in Indonesia.

During the past few years, there has been tremendous increase in advertisement expenses all over the world. Therefore, it is important to examine, to investigate and to evaluate the performance of advertisement to reach organizations’ objectives. This paper presents an empirical investigation using highly reliable and efficient method called AIDA, which consists of four factors: attention, interest, desire and action. The study is implemented for one of Iranian food suppliers called TABAROK located in city of Mashad, Iran. The survey selects a sample of 300 regular customers and distributes a standard questionnaire among them. Cronbach alpha has been calculated as 0.93, which is well above the minimum desirable level. Using Spearman correlation test, the study confirms that all four components of the survey influences consumer behavior, positively. The study also indicates that there were some positive and meaningful correlations among various components of the survey. In addition, applying Freedman test indicates that advertisement has the highest impact on convincing consumer to purchase product.

Brands are considered as valuable assets of a company both economically and strategically. Thus, having customers who are loyal to a brand is one of the main goals of businesses companies. Identifying and anticipating the customers’ needs are vital to the enterprises gaining competitive advantage and market segmentation. Maintaining and enforcing customers’ loyalty is a strategic challenge for companies seeking to keep and promote their competitive status in the market. High brand loyalty of the customers generates competitive advantage for the company and it increases the income and decreases the costs of marketing. In addition to increasing the market share, loyalty allows to ask for higher prices compared with its competitors. This study seeks to investigate the factors influencing customer loyalty to sport brands. The statistical population of this research was Tehran city. Using simple cluster sampling, 502 customers of four known brand sports, i.e. Nike, Puma, Adidas, Reebok, and Fila were chosen. Furthermore, a questionnaire, which assessed seven variables including reputations, brand name, brand image, brand loyalty, customer satisfaction, promotion and the price was used and the hypotheses were analyzed using statistical tests such as Kolmogorov-Smirnov test (K-S test), Spearman correlation, simultaneous linear regression and binomial test. The findings suggested that the brand name has a strong correlation with brand loyalty. Moreover, variables such as reputation, brand image, customer satisfaction, price and promotions also have positive and significant effect on the brand loyalty.

Social media has grown very quickly and has affected all dimensions of the world’s community. The purpose of this article is to review valid articles on the relationship between social media and promotion using manual and bibliometrics analysis methods and identify top themes in these articles. We review the papers published between 2007 and the first month of 2019 in Scopus. 1,840 articles were published in the mentioned period. In this article, we review various charts including word dynamics, the contributions of different countries, country scientific production, corresponding author's country, the frequency distribution of sources, collaboration network and country collaboration map. The study indicates that Canada, Australia and France were the most productive countries in this area.