The post-pandemic has forced it Indonesian cloth sellers in Indonesia are facing changes in consumer behavior who tend to shop online. They need to find innovative ways to market products and reach potential customers. One of the problems faced is how to present traditional fabrics effectively to potential buyers. Therefore, a more creative and interactive marketing approach is needed. Using the needs analysis method to design innovative augmented reality (AR) integration in the sale of Indonesian fabrics could be an interesting solution. With AR, which will later be called PARTIKA, sellers can show potential buyers how the fabric looks in various situations, provide virtual interaction with the product, and provide detailed information. This application can thus provide a more in-depth and convincing experience, helping customers make better purchasing decisions.

In this study, the author proposes to evaluate the effect of sales growth, Receivable Turnover and operating cash flow on the liquidity of PT. Unilever Indonesia Plc. The research method used is descriptive method with a quantitative approach. In this statement, the population used in this study is the financial statement data from PT. Unilever Indonesia Plc. from 2010 to 2018, the technique of determining the sampling uses Purposive Sampling. This research data uses secondary data from PT. Unilever Indonesia Plc financial statements from 2010 to 2018. All data sources were obtained from the website of the Indonesia Stock Exchange at https://www.idx.co.id, the company's website and Google search. Our analysis reveals that sales growth and accounts receivable turnover from PT. Unilever Indonesia Plc. has no influence on the liquidity of PT. Unilever Indonesia Plc, while operating cash flow has an influence on the Liquidity of PT Unilever Indonesia Plc. This means the ups and downs of the value of sales and accounts receivable turnover of a company has no influence on the liquidity of PT. Unilever Indonesia Plc, while operating cash flow has increased or decreased has an influence on the liquidity of PT Unilever Indonesia Plc. The value of sales growth, accounts receivable turnover and operating cash flow can explain the liquidity of PT Unilever Indonesia Plc. by 78%, while 22% is explained by other factors which are not included in this study.

The aim of the study is to characterize the U.S. and Lithuanian defense cooperation from 1990–2020 including arms procurements. Findings suggest close defense cooperation between countries at Presidential, Congressional, and Defense Ministry/Secretary levels. The most developed cooperation areas are joint participation in international operations, improvements of the Lithuanian Armed Forces’ capabilities, and an increase of the U.S. military presence in the Baltic region. An analysis of Lithuanian defense procurements between 1990–2020 indicates that the majority of defense armaments were acquired from the U.S. using the Foreign Military Sales venue while other options to obtain armaments were not used.

With the rapidly increasing of people’s purchasing power, the fast moving consumer goods (FMCG) industry is supposed to grow dramatically. In order to gain more market access and profile, it is important for the FMCG manufacturers and retailers to find the preferences and provincial characteristics of consumers, to develop more suitable goods distribution strategy. Based on retails marketing data with geographic characteristics, this paper proposes a new combination of geography methods to solve the problems in distribution of FMCG. Via multiple K-means clustering and cross validation of KNN half off, the mesoscopic sales features are extracted through the classification of retails, which can indirectly grasp the consumer behavior characteristics. Based on space division and Moran’ I spatial autocorrelation arithmetic, two strategies are developed to satisfy consumer’s needs and promote sales, including conservative and positive strategies. According to our analysis, the total sales volume of the regions will increase by 5.1% and 10.3%. This study can be applied to the provide purchase strategies for FMCG retails according to their locations. The research can explore the consumption potential of different areas, thus improving the profile of retails and the development of economy in more mesoscopic scale.

Point of Sales (POS) systems are an important component of sales and inventory management for many businesses. This article discusses the development of a POS application designed to simplify the process of selling products in stores. The application uses PHP for the server, JavaScript for the client, Bootstrap for responsive design, and MySQL as the database. Its main features include a dashboard with daily sales summary, visual graphs, product category management, product addition and editing, admin and access rights management, ease of sales transaction processing, access to sales reports and analyses by leaders, and provision of sales reports in various formats. The clean and easy-to-use interface is expected to improve the efficiency of sales and inventory management, and assist businesses in making data-driven decisions.

Recently, during the last century, Communication & Information revolution caused an essential dynamic in society, especially in legal issues, This dynamic development of information data through this web, it has an essential effect upon the way of civil treatment, which then called "electronic agreements". The industrial and technological development lad to obvious changes in producing processes either for goods or services and then its distributing methods, through using modern media channels, but this factor has a negative side because there is no legal protection for the contractors. This fact leads to the necessity of publishing certain conditions to protect consumers.

In this paper, we study the usage of machine learning models for sales time series forecasting. The effect of machine learning generalization has been considered. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking technics, we can improve the performance of predictive models for sales time series forecasting.

This article examines the impact of network degree distribution on the performance of evolutionary algorithms, specifically in solving the Traveling Salesperson Problem (TSP). It evaluates the integration of various network types to regulate population crossovers, enhancing candidate solutions and their refinement over generations. The experimental analysis includes complex networks like Erdős–Rényi and Barabási–Albert, along with regular graphs such as Balanced trees and Harary graphs. The study focuses on how different network structures influence exploration, convergence, and computational efficiency, particularly in terms of CPU time. Results show that highly connected networks, like Barabási–Albert, facilitate faster convergence and improved solution quality by guiding the search process more effectively. The Harary graph with k=2 demonstrated superior performance, reducing execution times by up to sevenfold compared to traditional approaches. This improvement highlights the advantages of specific degree distributions in balancing exploration and exploitation. Networks with hubs, such as Barabási–Albert, accelerate information flow and the evolutionary process, while more regular networks, like Harary graphs, provide controlled structures that maintain high solution quality. These findings emphasize the importance of network topology in evolutionary algorithms and suggest further exploration of hybrid models and other network types to optimize NP-hard problems.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

This study provides an evaluation of a web-based platform for game management and sales, developed using PHP, MySQL, and SASS. The development process utilized the SCRUM methodology, concentrating on essential modules such as user management, game catalog, payment processing, and technical support. The platform's performance was assessed using Google's Lighthouse tool, which evaluates four critical areas: performance, accessibility, best practices, and SEO. The results demonstrated exceptional performance with a score of 99/100 and perfect adherence to best practices with a score of 100/100, reflecting high efficiency and compliance with web development standards. Accessibility received a score of 83/100, indicating potential areas for improvement, while SEO achieved a score of 91/100, suggesting effective search engine optimization. The findings reveal that the platform exhibits high levels of optimization and operational efficiency. However, there remains an opportunity to enhance accessibility features. This study highlights the significance of rigorous web development practices and optimization efforts in achieving superior performance and user experience in web-based systems.

This study aims to investigate how the adoption of an online to offline (O2O) transition strategy impacts the sales volume of Kampoeng Merdeka Clothing. The O2O strategy includes various components, such as establishing O2O platforms, delivering personalized services, ensuring secure transactions, and engaging in offline business activities. The research employs a quantitative approach, focusing on consumers who have made clothing purchases from Kampoeng Merdeka Clothing in Macassar. The sample size comprises 83 respondents, deliberately selected using the Slovin approach. The data analysis and methodology involve the application of Partial Least Squares (PLS) structural equation modeling to assess both the external and internal models. The findings of this study emphasize the significant and positive influence of the variables "Build O2O Network Platform," "Provide Personalized Experience," "Safeguard Transactions Security," and "Offline Business" on driving the sales growth of Kampoeng Merdeka Clothing. These results indicate that the O2O transition strategy has a substantial impact on the brand's sales performance, with the establishment of O2O platforms and personalized services playing crucial roles. Additionally, ensuring secure transactions and engaging in offline business activities further contribute to the overall success of Kampoeng Merdeka Clothing in the market. This research sheds light on the effectiveness of the O2O approach in boosting sales for clothing brands and underscores the importance of integrating online and offline elements to maximize business outcomes.

The South African government introduced a nationwide lockdown in March 2020 to mitigate the spread of Covid-19. Among other restrictions, the government banned the sale of tobacco products, which lasted for nearly five months. We performed a Google search using the keywords smok*, puff*, lockdown, tobacco, and cigarette*, for articles published in English from 23 March 2020to 18 December 2020 and analysed 441 online media articles. We identified the main arguments made by proponents and opponents of the tobacco sales ban, which were categorised into themes. Three themes were prominent: medical, legal, and economic/financial. Legal aspects were covered in 48% of articles, followed by economic (34%), and medical aspects (18%). The media was generally ambivalent about the tobacco sales ban during the first five weeks of lockdown. Sentiment then turned against the ban. because the medical rationale was not well communicated by the government, there was limited empirical evidence of a link between smoking and contracting Covid-19, and the sales ban was ineffective, since most smokers still purchased cigarettes. Policy framing in the media plays an important role in how the public receives the policy. Any future tobacco control policy intervention should be well-considered and appropriate cessation support services offered.

This research studies the impact of shopping tourism on retail sales and rents, using the Covid-19 pandemic as a quasi-experiment. Shopping tourism refers to individuals who travel primarily for shopping purposes, and their spending patterns can have significant effects on the retail sector. The Covid-19 pandemic disrupted global travel and resulted in a decline in shopping tourist arrivals, leading to a downturn in sales for retailers dependent on shopping tourism. Additionally, the decline in shopping tourism affected retail rents, as the reduced demand for retail spaces posed challenges for property owners in attracting tenants. The study focuses on Hong Kong, a prominent shopping destination, which experienced a significant decline in tourist arrivals and retail sales during the pandemic. The research analyzes the relationship between tourist arrivals, retail sales, and rents using time series analysis and identifies the impact of shopping tourism on retail spaces and rents. The findings suggest a positive association between tourist arrivals and retail sales, particularly during the period of shopping tourism growth. However, the pandemic severely reduced this effect, revealing the impact of shopping tourists on the retail sector. The study concludes by discussing the implications on retail resilience and highlighting the need for further research on the impacts of shopping tourists on retail sales and rents.

Background: Accurate sales forecasting in the mobile phone industry is critical to informed decision making by both the government and corporations. This study seeks to enhance mobile phone sales predictions by incorporating brand exposure as a predictive factor, hence addressing the gap left by the existing forecasting models that do not consider the impact of brand awareness. Method: This study thereby indicates a quantification method for brand exposure that merges word-of-mouth reviews with online search data. Word2Vec is used to extract initial keywords from a word-of-mouth corpus, whereas time-lag correlation analysis refines the core keywords. Later, PCA synthesizes brand exposure. The attention-LSTM framework hereby develops a forecasting model of mobile phone sales, effectively combining long short-term memory networks with the ability to capture time series dependencies and an attention mechanism to reduce data redundancy. Results: By considering brand exposure, the experiments demonstrate that the attention-LSTM model decreases the RMSE and MAPE indicators by 2.02% and 0.96%, respectively. In addition, compared with the ARIMA, SVR, BP neural network, and LSTM models, the attention-LSTM model decreases the average percentage errors by 6.52%, 3.42%, 2.56%, and 0.81%, respectively. Conclusion: Attention-LSTM captures dynamic sales trends very well and holds great significance over traditional models. Overall, including brand exposure, this model is very strong for the interpretation of mobile phone sales predictions, which has important implications in terms of policy as well as corporate strategies.

Environmental responsibility and economic benefits have promoted the development of closed-loop supply chain (CLSC), and shortages and channels are considered to be two important issues in CLSC. This paper explores ordering and channel decisions in CLSC with new and differentiated remanufactured products, and considers the price and sales effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses, and establishes an integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. Numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. Wherein, the upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, decisions of integrated channel are better than those of decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out of stock costs. According to the research in this article, some management insights are also provided.

The purpose of this paper is to explore which financial performance indicators (FPIs) evaluate the level of supply chain capability (SCC) that explicitly touches all of the business functions and processes within and beyond the company. The authors investigated nine FPIs that were selected from the financial statements of 155 companies within nine industries. The authors find that suitable FPIs to measure SCC for shareholders’ value are return-on-assets (ROA), days-sales-outstanding (DSO), and current ratio (CR). This means that higher ROA, shortened DSO, and an appropriate level of CR could reach a sustainable supply chain. These results will help the industry to avert a major disruption in supply chain processes and activities using suitable financial performance indicators.

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

The purpose of this manuscript is to examine the extent to which Polish SMEs use digital marketing tools in the context of their impact on economic performance. The choice of the objective is due to the research gap related to the lack of studies on the impact of the use of digital marketing tools on economic performance by SME companies in Poland. In order to achieve the objective, a two-stage approach was used. The first stage identified the degree of use of digital marketing tools by Polish SMEs compared to EU countries based on secondary data. The second stage analyzed the impact of the use of selected digital marketing and sales communication tools on the economic performance of Polish SMEs. The analysis was based on primary data, which were the results of surveys conducted on a sample of 574 Polish SMEs in the industrial sector. The analysis used statistical methods such as difference significance tests (Mann-Whitney U test, ANOVA Kruskal-Wallis test and Dunn's post-hoc tests), Kendall's rank correlation coefficient and Multivariate Adaptive Regression Splines (MARSplines). The results obtained have theoretical and practical values. The theoretical implications are the enrichment of the marketing concept of customer value creation taking into account the digital customer, which is also the originality of the research. Practical implications refer to indicating to SME managers the need for, as well as ways of implementing and financing digital technologies within the framework of various EU programs for digital transformation of member states.

The objective of this research is to analyze the effect of determinants of Business ethics (HR management ethics, ethics in corporate governance, and ethics in sales & marketing) on the organizational performance of the high-tech industry. The study further examines the influence of multiple serial mediations of human, structural, structural, and relational capital on the connection between Business ethics and organizational performance. Additionally, this research examines how technological change moderates the relationship between Business ethics and organizational performance. Data was collected from the information technology sector in various cities of South Asian countries through structured questionnaires. PLS-SEM modeling was used via Smart-PLS 4.0 software for analysis. The hypothesized & modified measurement and structural model were validated using the HTMT matrix, Fornell-Larcker criterion, R-square values, f-square values, outer loading, construct reliability and validity, path coefficient analysis, Blindfolding, and predictive relevance. The research results suggest that ethics in corporate governance, HR management ethics, and ethics in sales & marketing have a notable positive effect on business ethics, leading to a substantial and affirmative impact on the organizational performance of the high-tech industry. The findings further suggested that dimensions of intellectual capital (human capital, structural capital & relational capital) significantly moderate between exogenous and endogenous constructs. Moreover, technological change significantly & positively moderates the relationship between Business ethics and organizational performance of the high-tech industry. In conclusion, this research provides insights into how these variables interact and what potential mechanisms act upon them. Organizations can make informed decisions about improving their performance and ethical standards by understanding these relationships. This research offers substantial theoretical and managerial implications to research scholars and industry practitioners.

The integration of Customer Relationship Management (CRM) systems within Small and Medium Enterprises (SMEs) is increasingly recognized for its potential to enhance business performance. While CRM adoption is on the rise, the specific impacts on operational efficiency, customer satisfaction, and financial outcomes in SMEs require further investigation. This systematic review evaluates the impact of CRM systems on SME performance, with a focus on operational improvements, sales growth, and customer retention. The study aims to identify key determinants of successful CRM implementation and assess the long-term effects on SMEs' competitive advantage. The review encompasses studies published between 2014 and 2024, sourced from Google Scholar, Web of Science, and SCOPUS. A total of 46 studies were included after screening 18,306 records. The mixed-methods approach involves quantitative analysis of survey data from SME owners and managers, along with qualitative synthesis of case studies. Metrics evaluated include customer acquisition, retention rates, revenue growth, and organizational efficiency. The analysis indicates that CRM adoption leads to a 25-40% improvement in customer retention and a 15-30% increase in sales across SMEs. Operational efficiency gains range from 20-35%, primarily driven by process automation and enhanced data management capabilities. Successful CRM implementation is strongly linked to managerial support, system customization, and user training. However, barriers such as limited financial resources and technical expertise hinder the full realization of CRM benefits. CRM systems significantly contribute to SME growth by optimizing customer relationships and enabling data-driven decision-making. The review underscores the importance of strategic CRM implementation tailored to the specific needs of SMEs to maximize its impact. Future research should focus on overcoming adoption barriers and refining CRM strategies for sustained performance improvements.

The recent consecutive economic and social crises impose sustainable “form farm to fork” food chain management to feed the global population. In this study we investigate young consumers (Gen Z) price perceptions in purchasing foods in Greece to find out the determinants ensuring sustainable, future food consumption. We used 8 overall price perceptions determinants, 5 with negative roles namely value & price consciousness, coupon & sales proneness, price mavenism and 3 with positive roles namely price-quality, price-value, and prestige-sensitivity for the formation of the study’s questionnaire. A total of 514 students (Gen Z by 85%) answered the questionnaire, promoted through the Google platform during September and October 2023. Data was analyzed with statistical tools, combining cross and chi-square tests. Between the negative determinants the “value consciousness” price perceptions (71.02%) were the most important parameters in purchasing food, followed by “price consciousness” (55.02%) parameters. “Coupon proneness” by 48.4% and “sales proneness” by 49% parameters were equally lower to the participants’ references, while “price mavenism” parameters were minimally preferred by only 26.4% by the participants. Participants exhibited major preference for “value to price” interconnection (66.7%) such as for good value for money, value exceeding its price, overvalued low priced foods, while their preference for “quality to price” interconnection was significantly lower (48.8%) such as for getting what you pay, more money for better quality, priced quality foods. The “prestige- sensitivity” price perception was out of their preference for food purchase (only 7.1%). Our findings indicate that young consumers (Gen Z) pay more attention to the value negative and positive parameters concerning price perceptions for food purchase rather than quality, coupons & sales, low price, and mavenism or even prestige of the foods. This means that value issues such as the perceived environmental impact (green value), the climate crisis, the social signaling potential and others are high in their concerns for their future including their price perceptions for food purchase.

This paper explores the pivotal role of personalized product recommendations in enhancing the e-commerce experience. As online shopping becomes increasingly prevalent, the demand for tailored user experiences has surged, prompting the development of sophisticated recommendation systems. This study presents a comprehensive analysis of various methodologies employed to deliver personalized suggestions, including collaborative filtering, content-based filtering, and hybrid approaches. The implementation of a user-centric recommendation engine demonstrates significant improvements in user engagement, satisfaction, and conversion rates. Furthermore, the paper discusses the importance of real-time adaptation mechanisms and user feedback loops in optimizing recommendations. By providing insights into the challenges and solutions associated with recommendation systems, this research aims to equip e-commerce businesses with the tools necessary to leverage personalization effectively, ultimately leading to enhanced customer experiences and increased sales