The property is a unique product that cannot be contrasted with other commercial products due to pricing conditions. Property price determination is one of the crucial aspects of property development activities because of the profit margin made by the developer and the purchasing preferences. This study attempts to extend the literature that has largely focused on factors of housing prices in developed markets and provided recent evidence of housing price determinants in two countries (i.e., Indonesia and Malaysia). Thus, this study examines the factors affecting housing prices in Jakarta Metropolitan Region and Greater Kuala Lumpur. A quantitative approach was used involving two countries, namely Indonesia and Malaysia. The data was collected using a survey questionnaire through purposive sampling. A total of 100 respondents (Indonesia) and 134 respondents (Malaysia) participated in this study. The data was analyzed using descriptive (frequency) and inferential statistics (chi-square test and multinomial regression). The results indicated that housing location, property funding, and health have a significant effect on residential property prices in Indonesia. Besides that, the results displayed that housing physical design, home design and construction, developer and real estate products, development concepts, housing location, property funding, social status, health, law provisions, and external factors do not affect residential property price in Malaysia. Despite being neighbors, Indonesia and Malaysia have distinct economic and landscape characteristics. Furthermore, considering Indonesia has a higher number of Covid-19 cases than Malaysia, significant information on how the pandemic has affected the demand, cost, and pricing of residential housing in Jakarta and Kuala Lumpur will be provided. The findings of this study will provide recommendations to investors, buyers, and policy about the residential housing industry's prospects for growth in emerging nations following the pandemic.

Sentiment analysis, a key component of natural language processing and data mining, plays a pivotal role in extracting subjective insights from textual data, particularly on social media platforms. In response to the growing importance of digital engagement, understanding public sentiment has become essential for formulating effective marketing strategies. This study aims to enhance the marketing strategy of energy products in subsidiaries of State-Owned Enterprises (SOEs) by employing a hybrid sentiment analysis model that integrates the Valence Aware Dictionary and Sentiment Reasoner (VADER) with Long Short-Term Memory (LSTM) neural networks. Utilizing a mixed-method approach that combines both quantitative and qualitative analyses, the study collects and processes data from multiple social media sources to identify and classify consumer sentiment. The results demonstrate that the hybrid VADER-LSTM model achieves an accuracy rate of up to 84%, enabling a more nuanced interpretation of consumer opinions. These insights inform the development of data-driven, responsive, and targeted marketing strategies. Furthermore, the study highlights the significance of fostering interactive communication between companies and consumers to enhance the impact of digital marketing efforts. Theoretical implications include a contribution to the academic discourse on information systems and digital marketing, while practical outcomes offer valuable guidance for SOEs in adopting adaptive, sentiment-informed marketing approaches within the energy sector.

One of the most important methods for building good marketing strategy is to learn more about existing competitors, suppliers, etc. This paper presents an empirical investigation to study the relationship between wholesalers, bargaining pricing, new rivals and pricing strategy on preparing appropriate marketing strategy in meat market in city of shiraz, Iran. The proposed study designs a questionnaire in Likert scale and distributes it among 200 experts in this market. Cronbach alpha has been calculated as 97.3%, which validates the overall survey. The results of the implementation of Pearson correlation ratio indicate that there were some positive and meaningful relationships between all components of the survey with marketing strategy.

In the new marketing paradigm that is based on the relationship marketing, corporations and organizations look for retaining and enhancing the long run relationships with their customers. Customer relationship management (CRM) as a heart of the new marketing paradigm includes numbers of mechanisms that endeavor to manage sustainable and profitable long-term relations with valuable customers. Every year, there are many programs and resources dedicated for marketing strategy and planning. Evaluation of these endeavors especially the CRM strategy is much important. Hence, the primary purpose of this research is to analyze CRM using balanced scorecard as a valuable strategic tool. Required data was gathered from one of the biggest commercial banks of Iran and they were analyzed using BSC and statistical software packages. Results indicate that there is a meaningful relationship between 3 main aspects of CRM strategy and 4 main aspects of BSC.

Marketing the network of organizations, including manufacturers, wholesalers, and retailers, that distributes goods or services to consumers is one of the most important decisions for marketing managers and producers. In this study, we identify and prioritize the factors, which affect marketing strategy in selecting distribution channels by using fuzzy multiple criteria decision-making (FMCDM). The proposed study uses Fuzzy Delphi Analytic Hierarchy Process to determine the weights of the criteria by decision makers and marketing strategies is ranked by Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, the case study within a Kaleh Company (Dairy products) is performed and the results indicate that the “diversifying product” is the most important marketing strategies considered by experts.