نشریه مدیریت تبلیغات و فروش
سال پنجم شماره 4 (پیاپی 20، زمستان 1403)

The aim of this study is to investigate the underpricing and long-term performance of initial public offerings of shares of state-owned petrochemical and refining companies transferred within the framework of the general policies of Principle 44, and to evaluate their pricing mechanism in initial sales by comparing these two factors with other state-owned companies as well as petrochemical and refining companies.

To evaluate short-term performance, accumulated abnormal returns were used, and for the long-term, abnormal buy-and-hold returns were used, and the quantitative method - one-sample and independent T-test was used.

The findings show that state-owned petrochemical and refining companies transferred have positive abnormal returns in the short term and higher than the abnormal returns of other state-owned companies and private sector petrochemical and refining companies. The long-term abnormal returns for divested state-owned petrochemical and refining companies are not positive and higher than the long-term abnormal returns for other state-owned companies and private sector petrochemical and refining companies. The positive adjusted performance of short-term and long-term IPOs confirms that most of the IPO returns are due to underpricing.

In the Iranian capital market, undervaluation is higher than overvaluation, and the most important reason for this could be the existence of a price fluctuation range that does not allow excessive price increases on the day of the offering; therefore, when deciding to amend the laws of initial public offerings.

customer experience management has become an important strategy in e-banking services , this study seeks to provide a customer experience management model for e-banking services in the banking and e-payment services industry.

In order to achieve the research goal, the mixed research method has been used. In this regard, first, the customer experience management literature in e-banking services was analyzed and the initial conceptual model was proposed. Then, in-depth interviews were conducted with a number of banking industry experts in the field of electronic banking services, and data content analysis was performed using the data analysis method, and a conceptual research model was developed. In order to measure the final model in the quantitative part, the required data were collected using a questionnaire and the customer experience management model in e-banking services was confirmed by field data analysis and structural equation modeling.

The research results were presented in the form of customer experience management model for electronic banking services. This model includes 8 dimensions of customer experience, e-banking service provider tools, e-banking service marketing factors, e-banking technology infrastructure factors, e-banking service organizational factors, customer factors, e-banking industry factors and macro environment factors that 22 categories and 58 subcategories were identified for these dimensions.

In the research model, improving customer experience is the centralized goal of customer experience management, which depends on internal and external dimensions, and in this regard, customer experience management strategies in e-banking services were presented.

This study examines the role of Artificial Intelligence (AI) in optimizing marketing processes. The goal is to analyze the effects of AI on cost reduction, improving decision-making accuracy, enhancing customer experience, and strengthening the competitive capabilities of companies. The research approach is mixed (qualitative and quantitative). In the qualitative section, data were collected through semi-structured interviews with 13 experts in the fields of marketing and AI, and analyzed using thematic analysis, leading to the identification of 9 main themes and 57 sub-themes. In the quantitative section, the Analytical Hierarchy Process (AHP) was used to prioritize the main themes, and data were collected through pairwise comparison questionnaires from 8 experts in marketing and information technology and analyzed using specialized software.The findings revealed that AI has significant impacts on marketing processes. The main effects identified include reducing operational and advertising costs, decreasing human error, enhancing decision-making accuracy, improving marketing operation speed, and enhancing customer experience. Reducing human error was identified as the most important effect, while improving resource management was the least significant. AI also helps personalize customer interactions and makes marketing processes more targeted, thus increasing the competitive advantage of companies, especially in international markets.Effective use of AI requires the development of precise strategies, staff training, and attention to customer privacy. Additionally, in international markets, it is essential to adapt AI-based marketing strategies to cultural differences and local needs. This study emphasizes the importance of smart AI usage to enhance competitiveness and customer satisfaction.

The purpose of this study is to analyze and investigate the effect of psychological factors on the willingness to use artificial intelligence makeup software, electronic word-of-mouth advertising by strengthening real purchases in cosmetics marketing among social media users. This research is based on the structural equation model in terms of its applied purpose and in terms of the method of collecting descriptive and correlational data. To achieve the research goal, a conceptual model based on the psychological characteristics of individuals is presented. The statistical population of the research is all social media users who use artificial intelligence makeup software. Based on the Morgan table, a minimum sample size of 384 people was determined for the aforementioned unlimited population and was selected using the convenience sampling method. The research data was also collected through a questionnaire. The questionnaire was distributed among 400 people from the statistical population and finally, 384 questionnaires were received without defects and their reliability was confirmed with composite reliability, homogenous reliability, and Cronbach's alpha indices. The convergent validity of the questionnaire was also confirmed through variance extraction and divergent validity using the Fornell and Locker criteria. The construct validity was also confirmed by confirmatory factor analysis. The conceptual model of the research was measured using path analysis in the structural part of the structural equation model and SmartPLS software.

This study aims to identify the drivers of digital banking in Refah Kargan Bank using the cross-effects analysis method. The statistical population of this study includes all experts familiar with and active in the banking industry with master's and doctoral degrees who have experience in digital banking at Refah Kargan Bank. In this study, 30 people were selected as a sample based on the snowball sampling method. To create data, experts were surveyed about the importance and impact of macro trends affecting digital banking by designing two separate researcher-made questionnaires. In this study, based on a review of the research literature, the three-stage Delphi method, and the use of the agreement coefficient between experts, 59 macro trends affecting digital banking in Iran were finally identified, which were then evaluated using the Friedman rank means test and cross-effects analysis. The results of this study led to the identification of 6 drivers affecting digital banking at Refah Kargan Bank. These drivers, in order of influence, include “approval of laws and regulations regarding the use of new tools”, “intensity of foreign sanctions”, “generational changes in society towards the acceptance of digital banking”, “the existence of a sustainable, efficient, reliable and supervised public infrastructure”, “the existence of specialized human resources” and “cybersecurity costs”.

The aim of this research is to identify the challenges of electoral candidate advertising for candidates representing the Majlis and Islamic Council in the Kermanshah electoral district based on political marketing. The research is applied in terms of purpose and qualitative in terms of method. The participants in the research include experts (academic experts and candidates and representatives of previous periods of the Kermanshah Majlis), 16 of whom were selected until theoretical saturation of the data using the snowball method. Content analysis was used to analyze the data in this research. The results led to the identification of 51 initial codes in the form of 22 themes, which were ultimately grouped into 6 main concepts of social participation (use of famous figures, misuse of media and advertising tools, voter segmentation, disregard for political commitments and powers of parliamentarians), candidate/party positioning (misuse of intellectual symbols, misuse and misuse of religious symbols, misuse of revolutionary symbols, and mistaken symbolization), candidates' environmental focus (focus on the environment, wrong market segmentation, and segmentation of voting districts), candidates' party and political tools (ignoring party and political tools, lobbying of interest groups), inappropriate financing strategy (lack of a strategy for collecting financial resources and lack of financial capacity), candidates' wrong competitive analysis (pre-election competitions and competitor analysis), and candidates' campaign focus (comparative advertising, fake polls, (e.g., political advertising, subversive advertising, and political advertising).

This study investigates the role of storytelling on social media in enhancing sales within the cultural tourism industry. It aims to illustrate how storytelling can serve as a strategic tool to boost competitiveness and revenue, while also analyzing successful campaigns and identifying key storytelling elements. Additionally, the research offers insights for marketers to design and implement impactful campaigns.A qualitative approach with thematic analysis was employed to uncover the intricate relationship between storytelling and sales performance. Data were gathered through semi-structured interviews with 12 stakeholders, including social media managers, marketing managers, and sales supervisors from tourism organizations and agencies. These interviews explored participants' views on storytelling's impact on audience engagement, sales growth, and associated challenges.Purposeful sampling targeted those actively utilizing storytelling on social media, focusing on cultural tourism, and demonstrating measurable success in engagement or sales. MAX-QDA software was used to analyze the data through open coding, axial coding, and theme extraction. The analysis identified six main themes: emotional trust, feedback and engagement, technology and personalization, content production challenges, the power of storytelling in sales, and strategic storytelling.The findings reveal that storytelling significantly enhances audience engagement and sales growth, with message personalization and continuous feedback as critical success factors. However, challenges like creating culturally relevant content and maintaining consistency were highlighted as key barriers. The study concludes that storytelling on social media is a powerful tool for improving engagement and revenue in cultural tourism. The identified themes provide marketers with a strategic framework to develop and execute effective storytelling programs.

This study aims to identify and analyze common mistakes in marketing research and provide a comprehensive framework to improve research processes. Common mistakes can significantly affect the quality of research outcomes and ultimately influence managerial decision-making. The research is applied in purpose and descriptive in nature, employing a survey method with a questionnaire as the data collection tool. A three-stage mixed approach was used: first, a systematic review of reputable scientific sources was conducted to identify the main components of common mistakes in marketing research. Next, semi-structured interviews with experts in marketing management and research were conducted to validate and refine these components. Finally, the relationships between the components and their levels of influence were analyzed using interpretive structural modeling (ISM). The findings indicate that ambiguity in problem definition is the most significant factor leading to errors in marketing research. Other factors include errors in selecting the research approach, weaknesses in questionnaire design, failure to conduct pilot studies, and human and non-human errors in data analysis. The final model illustrates the interactions between these components and provides a precise ranking of their impact and dependencies. This study offers a useful tool for researchers and marketing managers to enhance research quality by identifying and preventing common mistakes. Using this model in future projects can lead to better solutions and more reliable results in marketing research.

Inspiring customers enables retail industry professionals to enhance customers purchase intentions and loyalty. Given the significance of analyzing, and understanding consumer behavior in retail based on research literature, this paper aims to explore the perceived innovation of different sales channels in inspiring customers and fostering customer loyalty. This is achieved by considering guerrilla marketing strategies as a moderating variable that affects the relationship between perceived innovation in sales channels and customer inspiration across diverse sales and marketing channels in the retail industry. The research is applied in nature and descriptive-survey in methodology. The study population consisted of customers of Ronix brand tools in Tehran. The sample size was calculated to be 222 individuals using G-Power 3.1 software, and non-probability convenience sampling was employed. Structural equation modeling and Smart PLS 3 software were utilized for data analysis. The analysis revealed that the first hypothesis regarding the positive impact of perceived innovation in sales channels on customer inspiration, the second hypothesis regarding the positive effect of customer inspiration on customer motivation, the third hypothesis regarding the positive influence of customer motivation on customer loyalty, and the fourth hypothesis concerning the positive moderating role of guerrilla marketing on the relationship between perceived innovation in sales channels and customer inspiration were all confirmed. Therefore, the results indicated that there are reciprocal effects among states of customer inspiration, emphasizing the roles of perceived innovation in sales channels and customer loyalty as antecedents and consequences of inspiration, respectively.

due to the ever increasing demand for energy and the limitation of natural resources, reducing energy consumption, especially electricity, has become one of the main challenges of societies. Reduction marketing as a new approach, with the aim of changing the behavior of consumers and encouraging them to reduce electricity consumption, can play an effective role in energy management. This research aims to investigate the effect of various reduction marketing strategies on reducing electricity consumption with the moderating role of consumer attitude among Tehran electricity consumers, and seeks to find practical solutions to reduce energy consumption in the domestic and industrial sectors.This study is applied in terms of its objective and descriptive and correlation in terms of its method. The participants includes households and electricity consumers in Tehran, comprising thermal and water electricity consumers,, considering the unlimited nature of the population and based on the Krejcie and Morgan table and the convenience sampling method, a sample size of 384 individuals was determined. For data collection, a questionnaire derived from the article by Salem was used, and data analysis was conducted using SPSS and AMOS software.4 of the 5 main hypotheses were rejected and 4 new hypotheses were designed according to the researcher's evidence, and all 4 new ones were confirmed. it seems that the moderating role of electricity consumers' attitude in reducing marketing strategies for electricity consumption among Tehran consumers does not have much impact, and of course, the details of the marketing mix alone have a high impact.

Artificial intelligence has become one of the key tools for enhancing advertising strategies. By leveraging capabilities such as data analysis, machine learning, and natural language processing, AI enables transformative advancements in advertising and marketing processes. Exploring the role of AI in fostering advertising innovation, increasing audience engagement, and optimizing advertising strategies is of significant importance.The primary aim of this study is to map the knowledge structure of research on the application of AI in advertising using co-word analysis. To achieve this, 3,577 scientific articles published between 1994 and 2024 were retrieved from the Scopus database. The titles and abstracts of these articles were analyzed using VOSviewer software to generate a comprehensive map of the most frequent terms and key topics. The results revealed that the keyword “advertising” had the highest frequency, serving as a focal point for numerous studies in this field.The co-word analysis identified five main clusters: (1) prediction and optimization of advertising performance, (2) AI-driven content generation, (3) precise targeting and audience identification, (4) personalization of advertisements for enhanced engagement, and (5) the development of recommender systems to improve advertising efficiency. These clusters reflect the key trends in AI applications in advertising and marketing.This analysis provides a comprehensive and structured map of the existing knowledge on AI applications in advertising and serves as a reference for future research in this area. It also offers guidance for developing policies and strategies in the advertising industry.

This study aims to investigate sustainable consumption behaviors in the Asaluyeh region to identify the environmental outcomes of companies operating in this area. This research was conducted using a qualitative approach, employing in-depth interviews for data collection. The data were analyzed using thematic analysis, where key and subsidiary themes discussed in the interviews were extracted and examined. The results of this study indicate that organizational environmental behaviors have multiple consequences, including organizational efficiency, corporate branding, cost reduction, and increased social responsibility. In total, 165 open codes were extracted from the collected data, categorized into 47 sub-themes (axial codes) and 70 main themes (selective codes). Among these main themes, 32 key consequences were identified, highlighting the significance of environmental behaviors. The present research reveals that no similar study with this approach has been conducted regarding the environmental behaviors of industrial organizations in Iran. The findings can assist managers in the petrochemical industry in making more effective decisions to protect the environment and improve their organizations' performance. Implementing appropriate policies in this area not only helps reduce environmental pollution but also enhances organizational efficiency, strengthens corporate branding, and reduces operational costs. Furthermore, this research provides valuable insights for scholars and experts to advance studies related to environmental behaviors and develop more effective strategies for environmental sustainability.