Marketing Decision Makers Research Articles

ТОВАРНА ПОЛІТИКА ПІДПРИЄМСТВА ЯК ОБ’ЄКТ МАРКЕТИНГОВОГО АНАЛІЗУ І ПЛАНУВАННЯ

Introduction. For the effective organization of marketing activities of the enterprise, it is important to rationally build a product policy, which is the basis for making marketing decisions related to the features of the product purchase and the mechanism of its promotion from the manufacturer to the end consumer. The purpose of the article is an in-depth study of the theoretical and methodological aspects of the analysis and planning of marketing product policy (PMP) by developing the structure of the enterprise's PMP strategic plan and building a product management model. Methods. Using the method of analysis and synthesis made it possible to propose an own approach to marketing planning of product policy. Results. The article provides a theoretical analysis of literary sources regarding the interpretation of the concept of PMP by various authors, the expediency of marketing analysis and product policy planning is substantiated. Based on the study of foreign experience, the structure of the enterprise's PMP strategic plan was developed. The relationship between the formation of the PMP «roadmap» and the need to check the maturity of the marketing product policy team, which is represented by such entities and processes as management, employees, PMP implementation tools, business processes and production organization, is presented. Conclusions. Improving MTP planning is one of the most important ways to increase the efficiency of the enterprise. It is important to carry out such marketing planning, which helps to substantiate the impact of the enterprise's general corporate strategy, business strategies on the product strategy.

Behavioral Economics and Stock Market Decision-Making: Investor Biases and Investment Strategies

This study examines the application of behavioral economics to stock market decision-making, focusing on common cognitive biases of investors and their impact on investment decisions. Unlike the complete rationality assumed in traditional economics, behavioral economics reveals the irrational behaviors that investors exhibit when facing risks and uncertainties, such as overconfidence, loss aversion, and herd effect. By examining classical theories such as prospect theory, loss aversion, and the anchoring effect, as well as specific case studies such as the 2008 global financial crisis, this study aims to reveal how these psychological biases affect investors’ decision-making process. Further, this study examines how behavioral economics theories can be used to improve investment strategies by recommending methods such as low volatility investment strategies to help investors make more rational and effective investment choices in a complex market environment. Through in-depth analysis of the core theories and cases of behavioral economics, investors are better able to identify biases in the decision-making process, thereby optimizing their investment decisions and improving long-term investment returns.

Bibliometric Assessment of Data-Driven Marketing Research Trends in the Last Two Decades

This study presents a bibliometric analysis of data-driven marketing research trends over the last two decades, with a focus on its intersection with innovation, decision-making, and related topics. Using VOSviewer for network visualization, we analyze co-authorship, keyword co-occurrence, publication frequency, and country collaboration to uncover key themes and research developments. The findings indicate a significant rise in data-driven marketing research, particularly from 2015 onwards, driven by advancements in big data, machine learning, and artificial intelligence. Co-authorship networks reveal strong interdisciplinary collaboration, while keyword co-occurrence maps highlight the growing role of innovation, decision-making, and machine learning in data-driven marketing. Additionally, country collaboration networks show the United States, China, the United Kingdom, and India as central contributors to global research. The keyword density heatmap emphasizes the increasing focus on data-driven innovation and product development. These insights offer valuable implications for academics and practitioners seeking to understand and apply data-driven marketing in a rapidly evolving digital landscape.

A Stock Market Decision-Making Framework Based on CMR-DQN

In the dynamic and uncertain stock market, precise forecasting and decision-making are crucial for profitability. Traditional deep neural networks (DNN) often struggle with capturing long-term dependencies and multi-scale features in complex financial time series data. To address these challenges, we introduce CMR-DQN, an innovative framework that integrates discrete wavelet transform (DWT) for multi-scale data analysis, temporal convolutional network (TCN) for extracting deep temporal features, and a GRU–LSTM–Attention mechanism to enhance the model’s focus and memory. Additionally, CMR-DQN employs the Rainbow DQN reinforcement learning strategy to learn optimal trading strategies in a simulated environment. CMR-DQN significantly improved the total return rate on six selected stocks, with increases ranging from 20.37% to 55.32%. It also demonstrated substantial improvements over the baseline model in terms of Sharpe ratio and maximum drawdown, indicating increased excess returns per unit of total risk and reduced investment risk. These results underscore the efficiency and effectiveness of CMR-DQN in handling multi-scale time series data and optimizing stock market decisions.

Natural Language Processing (NLP) for Sentiment Analysis in Social Media

Purpose: This study sought to analyze Natural Language Processing (NLP) for sentiment analysis in social media. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Natural Language Processing (NLP) for sentiment analysis in social media. Preliminary empirical review revealed that advanced computational techniques significantly advanced the understanding of sentiments across diverse social media platforms. Methodologies such as machine learning algorithms and deep learning models like CNNs and RNNs demonstrated robust capabilities in categorizing sentiments accurately and capturing contextual nuances such as sarcasm and slang. The research highlighted the interdisciplinary nature of NLP applications, integrating linguistics with computer science and social sciences to develop effective frameworks for analyzing large-scale social media data. These findings contributed to enhancing decision-making in marketing, politics, and public opinion research, pointing towards future directions in hybrid NLP models for improved sentiment analysis across different languages and cultural contexts. Unique Contribution to Theory, Practice and Policy: The Social Constructionism, Cognitive Linguistics and Discourse Analysis Theory may be used to anchor future studies on Natural Language Processing (NLP). The recommendations aimed to advance theoretical foundations by exploring deep learning models and nuanced sentiment lexicons. Practical applications were enhanced through the development of scalable NLP tools for real-time data processing and integration into social media platforms. Policy implications focused on establishing ethical guidelines for data privacy and bias mitigation in sentiment analysis algorithms. Cross-disciplinary collaboration fostered innovation by integrating insights from computer science, linguistics, psychology, and social sciences. Education initiatives and international collaborations were prioritized to build capacity and standardize methodologies globally, ensuring advancements in both research and practical deployment of sentiment analysis technologies.

Shaping Social Proof: A Fission Marketing Model Based on the Herd Mentality Take Pinduoduo, for an Example

This study takes Pinduoduo as an example to explore the influence of herd psychology on consumer decision-making in fission marketing. Fission marketing uses consumer social networks to realize the rapid dissemination of information, in which the herd psychology plays an important role. Through the questionnaire survey, this study analyzes the motivation and effect of Pinduoduo users participating in the fission marketing activities such as "Kanyidao". In that context, "Kanyidao" means helping others bargain. The results show that the majority of respondents participated in activities motivated to save money or make money, and tend to learn about activity information through friends or software channels. User-sharing activity links promote social fission and achieve a rapid growth in the number of users. The research conclusion points out that the herd psychology plays a significant role in the fission marketing, and the fission marketing can effectively expand the market share by stimulating the consumers' enthusiasm for participation.

Navigating start-ups: a qualitative exploration of causal and effectual decision-making in entrepreneurial marketing

Purpose This study aims to enrich the field of entrepreneurial marketing (EM) by examining decision-making processes in the unique context of start-up ventures. To do so, it extends research on the distinct EM dimensions to the behavioral context by revealing how causation and effectuation principles shape entrepreneurs’ actions. Design/methodology/approach The study investigates EM behavior through 12 semi-structured interviews with 10 start-up founders and two founder associates in Germany. Use of established frameworks of the EM dimensions and causation/effectuation principles paves the way for an in-depth analysis. This methodology uncovers a distinct pattern of decision-making behaviors characterizing various activities within start-ups. Findings The findings show that causal logic prevails in start-ups’ EM, and effectual reasoning serves a complementary role. On the dimensional level, the findings reveal a predominant goal-driven focus on customer intensity and value-creation processes. Predictive logic guides opportunity focus, proactiveness and risk management, with nonpredictive behaviors providing adaptability. The principle of affordable loss is also evident in risk management. Finally, start-ups exhibit a blend of causal and effectual logic in innovativeness and resource-leveraging. Originality/value To the best of the authors’ knowledge, this study is the first to illuminate the interplay of behavioral logics in start-up firms’ EM by exploring the nuanced principles underpinning the decision-making processes of entrepreneurs. In doing so, it advances understanding of the marketing–entrepreneurship interface and enriches decision-making literature.

The Strategic Decision Making Analysis for a Semiconductor Company Based on SWOT Model

The focus of this work is to use SWOT analysis to assess the current situation of a company and determine its strategic direction. By gaining insight into a company's internal and external environment, we can better understand its strengths and weaknesses and use this information to make effective strategies and decisions. In addition, our research also shows that while companies perform well in new product development, customer-oriented approaches, and efficient market decision-making, there are still some limitations in financial management, production control, and product diversification that need to be addressed. The innovation of this work is the application of SWOT analysis to the strategic planning of fabless semiconductor companies. While SWOT analysis has been used in various industries and fields, applying it to fabless semiconductor companies is a novel approach. The significance of our study is that it provides a practical method to evaluate a company's competitive position and guidance for its future development. The paper concludes by emphasizing the importance of leveraging strengths, addressing weaknesses, and capitalizing on opportunities to ensure competitiveness and sustainable growth.

Marketing Metrics in private healthcare institution

Public health care institutions same as private health care institutions are present in almost every city in Croatia. Public health care institutions provide stability, security, and most of all they are traditionally among the people in Croatia. Private health care institutions are basically young and modern institutions with different and new management system. Main analyses and metrics which Policlinic Intermed uses in its business are SWOT analyses and BCG metrics. Through those methods and analyses this polyclinic can make right decisions in order to bring required marketing activities for this kind of business. A purpose of this research is to bring out strategies that are necessary for making marketing decisions and activities and clarify that private institutions are different then public ones and their need to be advertise and promote in order to survive on the market.

Stock market decision-making in the light of prospect theory

Stock market decision-making in the light of prospect theory

Quantum 3.0: Quantum Learning, Quantum Heuristics and Beyond

Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information processing, and is the next frontier for data sciences, machine learning and artificial intelligence. Progress in quantum learning paradigms is driven by multiple factors: need for more efficient data storage and computational speed, development of novel algorithms as well as structural resonances between specific physical systems and learning architectures. Given the demand for better computation methods for data-intensive processes in areas such as advanced scientific analysis and commerce as well as for facilitating more data-driven decision-making in education, energy, marketing, pharmaceuticals and healthcare, finance and industry.

Quantum 3.0: Quantum Learning, Quantum Heuristics and Beyond

Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information processing, and is the next frontier for data sciences, machine learning and artificial intelligence. Progress in quantum learning paradigms is driven by multiple factors: need for more efficient data storage and computational speed, development of novel algorithms as well as structural resonances between specific physical systems and learning architectures. Given the demand for better computation methods for data-intensive processes in areas such as advanced scientific analysis and commerce as well as for facilitating more data-driven decision-making in education, energy, marketing, pharmaceuticals and healthcare, finance and industry.

Investigation of user-product interaction by determining the focal points of visual interest in different types of kitchen furniture: An eye-tracking study

The furniture industry faces the challenge of designing furniture that attracts greater visual attention from customers, leading to increased sales and positive impacts on the industry, businesses, economy, and the environment. In pursuit of this objective, the imperative lies in the development of user-centric designs that amplify the interaction between users and products within the furniture sector. This research aims to identify the focal points of interest in kitchen furniture through the utilization of eye-tracking technology. By analyzing the gaze points of 60 participants observing 10 different kitchen furniture models introduced in the showroom, valuable insights can be provided to designers. These insights, in turn, empower designers to fabricate products that effectively capture heightened user attention. The investigation involves the assessment of average gaze counts and the generation of visual interest heatmaps. The discernments derived from the research underscore that areas at eye level garner a more pronounced level of attention. Furthermore, the study observes that factors such as material diversity, accessory utilization, and the integration of lighting elements exert a positive influence on visual interest. The results, within the context of neurodesign, offer valuable guidance to the furniture sector. These results can support decision-making in new product design, manufacturing, and marketing, enabling the industry to produce attention-grabbing furniture.

Transformative Impact of Deep Learning in Stock Market Decision-Making: A Comparative Study of Convolutional Neural Networks

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence. The study follows a comprehensive methodology, emphasizing the importance of high-quality datasets in leveraging deep learning for enhanced decision-making. Results demonstrate the superior performance of CNN models over traditional algorithms, with CNN (VGG19) achieving an accuracy rate of 89.45%. The findings underscore the potential of deep learning in extracting meaningful insights from complex data, offering a paradigm shift in optimizing various organizational processes. The article concludes by emphasizing the significance of investing in infrastructure and expertise for successful CNN integration, ensuring ethical considerations, and addressing data privacy concerns. This research contributes to the growing discourse on the application of deep learning in organizational management, providing a valuable resource for businesses navigating the dynamic landscape of the global market.

Determinants of Marketing Channels Choices: Evidence from Vegetable Farmers in the Eastern Cape Province of South Africa

Vegetable farmers are faced with computational and informational restrictions when making marketing decisions. Fresh produce markets in South Africa lack an efficient approach to supply chain management, from the farmers to the consumers. Improving market access can incentivise smallholder farmers to scale up their production. The present paper investigates factors influencing smallholder vegetable farmers' choice of market channels in the Eastern Cape Province of South Africa. A structured questionnaire and a multi-stage sampling technique were used to select 149 vegetable farmers. Multivariate probit regression and descriptive statistics were used for analysis. The results show that most sampled vegetable farmers in the study area sell their vegetable produce to traditional, wholesale, and retail outlets compared to other outlets. The study found that vegetable farmers can substitute one market with the other. Various socio-economic characteristics, transaction costs, asset ownership, membership of farm organisations, and access to extension services influence the selection choice. The study recommends intensive education and training to capacitate smallholder farmers through extension officers and farm organisations to enable farmers to understand and meet the requirements of high-value markets.

Optimizing Digital Marketing Efforts Through Neuromarketing: Systematic Review

Neuromarketing, as a neuroscientific approach to understanding human behavior and decision-making in marketing, holds significant promise for optimizing digital marketing strategies. This systematic review addresses the gap in research regarding the impact of neuromarketing on digital platforms like social media, websites, and e-commerce. By synthesizing existing literature, the review aims to bridge consumer behavior analysis with neuromarketing applications in digital marketing. Key themes identified include a deeper understanding of consumer behavior, improved targeting and personalization, optimized user experience (UX), enhanced ad effectiveness, and data-driven decision-making. These themes highlight the practical benefits of integrating neuromarketing insights into digital marketing strategies, allowing businesses to connect with their target audience more effectively. Compared to traditional methodologies, neuromarketing techniques offer a more accurate understanding of consumer responses and preferences, facilitated by advanced neuroscience tools like EEG, fMRI, eye tracking, and facial coding. Leveraging these tools enables marketers to align digital efforts with consumer preferences, leading to a more efficient and cost-effective marketing approach.

The peer effects of resident stock market participation: Evidence from 2019 CHFS.

Limited resident's participation in the stock market has become a key constraint to the capital market development. Utilizing the 2019 China Household Financial Survey (CHFS) data, our paper designs probit models to examine the peer effects of residents' stock market participation and explore the intermediary mechanisms with a multiple intermediary model. We find that: (1) Resident involvement in stock market decision-making exhibits significant peer effects. (2) Heterogeneity analysis reveals that males and rural residents display more pronounced peer effects than females and urban residents. Additionally, middle-aged residents demonstrate more potent peer effects than their younger and older counterparts, with the intensity of peer effects correlating with education levels. (3)We observe that the peer effects of market participation operate by altering economic expectations and enhancing residents' financial literacy. (4) Further investigation establishes that individuals engaging in stock market investments manifest peer effects when deciding whether to diversify their stock portfolio. This study holds reference value for analyzing the impact of social interaction on financial behaviors and regulating individuals' financial conduct.

Financial Behavior in Personal Investment: Influence of Psychological Factors on Investment Decision

In the ever-evolving realm of financial markets, understanding the underlying psychological dynamics shaping investor behavior is paramount. Financial behavior refers to the choices and actions people make when managing their own cash across a range of investment vehicles, including stocks, bonds, mutual funds, and real estate. It includes components that are vital in determining the results of investments, including risk tolerance, investment objectives, decision biases, emotions, and financial literacy. Achieving long-term investment success and financial security requires an understanding of and ability to control one's own financial behavior. An extensive research model has been painstakingly built to clarify the complex connections between these psychological variables and the process of making investing decisions. This study examines the impact of three psychological aspects on investment decisions made by 220 investors who trade on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE): information asymmetry, problem framing, and risk propensity. Effective investing strategies necessitate an awareness of the interplay between psychological biases and cognitive processes, as these aspects influence personal investment decisions. By using a quantitative research methodology, information was gathered by sending surveys to investors who were actively trading stocks on the BSE and NSE platforms. Regression analysis and correlation studies are two statistical analytic approaches that were used to investigate the connections between investing decisions and psychological aspects. The study's conclusions add to the corpus of knowledge by illuminating the complex dynamics of financial decision-making and the part played by psychological variables. Furthermore, the knowledge gained from this study has important ramifications for financial advisors, investors, and legislators that aim to improve investor welfare and encourage well-informed stock market decision-making.

Computer security technology in E-commerce platform business model construction

The global e-commerce market is expanding rapidly as the big data era advances and the e-commerce industry thrives. This paper aims to discuss the application of computer security technology in constructing an e-commerce platform business model. The research aims to find effective security technology solutions to strengthen the security of e-commerce platforms, protect user information and rights, and enhance the sustainable development of business models. Big Data-assisted E-Commerce Business Model (BD-ECBM) construction is discussed to overcome the e-commerce platform issues and positively impact marketing strategy decision-makers by raising their level of awareness. The business decision-making process is a series of steps that help businesses overcome obstacles by collecting relevant data, analyzing all relevant alternatives, and settling on a course of action. Big data analytics can improve business management with data fusion technology in many ways. The system's framework of the information service layer, the application-level layer, and the client session layer was developed using the Business model paradigm for accounting for e-past, commerce's capabilities of the platform, and an analysis of supply and demand. It can monitor its rivals in near real-time, adjusting prices, offering more attractive deals, and analyzing negative customer comments to see if it can outperform its rivals. The trial results demonstrate the effectiveness of this method of making marketing decisions and formulating a strategy. Hence, the BD-ECBM technique improves customers' overall satisfaction and experience with the brand while raising the latter's profile.

Using big data for optimizing advertising campaigns in social networks

In the digital era, advertising on social networks has become a crucial element of marketing strategies. With the increasing number of users on platforms such as Facebook, Twitter, Instagram, and others, companies have unprecedented opportunities to target and engage consumers. This paper aims to examine the role and importance of Big Data usage in optimizing marketing campaigns on social networks. In an era where data is limitless, Big Data has become a valuable resource for businesses looking to understand consumer behavior and improve the efficiency of advertising campaigns. In this paper, we will shed light on the history and development of Big Data, including their volumes and sources. We will explore the potential of Big Data and categorize their sources to better understand how they can be leveraged for social media marketing. Furthermore, we will analyze the benefits and challenges of using Big Data in marketing. Additionally, we will examine cases where the use of Big Data has failed to optimize advertising campaigns on social networks and focus on the influence of Big Data in digital marketing, including personalization and sales promotion. We will also explore the technologies and methodologies used for making marketing decisions using Big Data. This study concludes that Big Data offers exceptional potential for innovation in the field of advertising on social networks, helping companies cope with rapid changes in consumer preferences and market dynamics. The results indicate that the strategic use of Big Data can lead to a deeper understanding of consumer behavior and offer a competitive advantage in a crowded and fast-paced market.