Customer Preferences Research Articles

THE ROLE OF PERCEIVED ATTRACTIVENESS IN SHAPING CONSUMER PREFERENCES: A STUDY OF SPORTS BRAND T-SHIRT PURCHASING BEHAVIOR AMONG COLLEGE STUDENTS IN ZHENGZHOU, CHINA

The preference of customers is very impactful for the sports brand T-shirt making companies. The growth in the sportswear industry of China and preference of college students towards it, is significant, however, changes like change in trend and personality riots of consumers is considered significant. In this literature review chapter, previous research findings are presented about perceived attractiveness and its influence on purchasing behaviour of T-shirts with sports brands among college students in Zhengzhou, China. The review also investigates general theories like social identity theory as well as consumer buying behaviour theories where determinants like customer’s attitude, brand image and advertisement have been identified as determinants in the buying process. Brand reputation is identified in the literature to play a huge role in increasing perceived product attractiveness as well as how customer attitudes act as the mediating factor between perceived brand image and actual purchase behaviour. The quantitative method has been used by selecting 410 students to conduct surveys for using their purchasing behaviour and preferences. Along with this, SPSS and SmartPLS have also been used here. Some recommendation has been provided and some limitations have also been identified while conducting the study.

Portfolio Evolution Analysis for SPL Scoping: Unveiling the dynamics with dedicated time-series dashboards

Software Product Line Engineering (SPLE) is a recognized methodology for systematically developing reusable software components and tailored software products for specific market segments. A critical activity within domain engineering is SPL Scoping, aimed at profiling the SPL boundaries and fitting this market segment. Yet, this market is a moving target, which requires SPLs to be proactive in anticipating and addressing future challenges and opportunities. In this setting, rather than all the possible products potentially derived from the SPL’s Feature Model (i.e., the potential portfolio), the subset of products actually deployed is a better indicator of market adoption and customer preferences, and ultimately, of the SPL’s fitness to the market. This study characterizes the phenomenon of ‘deployed portfolio evolution’ in terms of the size and frequency of portfolio changes. We then elaborate on the advantages of utilizing this data source and the necessity for dedicated tools for Portfolio Evolution Analysis integrated within variability managers. To achieve this, we introduce the functional requirements, implementation, and architecture of PortfolioScan, a dashboard for Portfolio Evolution Analysis built on top of pure::variants. The dashboard enables the evaluation of feature usage through time-series charts such as temporal heat maps, line charts, and also a scoping-criteria comparison matrix. Evaluation results (n=13) indicate a positive perception. Practitioners also recognize the benefits of Portfolio Evolution Analysis for Testing Resource Optimization and Configuration Assistance. The ultimate goal of the study is to establish a basis for more responsive and market-oriented decision-making in SPL tasks.

The Intelligent Upgrading of Logistics between an Internet Enterprise and a Logistics Enterprise Based on Differential Game Theory

At the background of “Internet + Logistics”, intelligent logistics has high operational efficiency and provides a superior customer experience, meeting the requirements of sustainable development. It also plays a crucial role in promoting the modernization of the industrial chain in China. This paper develops a mathematic model based on differential game theory, which sets the intelligent level of logistics and the goodwill of intelligent logistics as state variables. The research reveals the collaborative strategies between a logistics enterprise and an Internet enterprise for the intelligent upgrading of logistics, and separately calculates the optimal effort levels and optimal revenues of participating enterprises under the non-cooperative mechanism, the cost-sharing mechanism, and the cooperative mechanism. This paper also observes the crucial parameters that affect the optimal revenue for the intelligent upgrading of logistics. The research findings are as follows: (1) Cost-sharing mechanism and cooperative mechanism can motivate the Internet enterprise and the logistics enterprise to improve their effort levels and increase the total revenue, which achieves the Pareto improvement. Under the cooperative mechanism, the intelligent level of logistics and the goodwill of intelligent logistics are the highest. (2) Participating enterprises can promote the intelligent upgrading of logistics by accumulating innovative resources for intelligent logistics, attaining cost-conversion efficiency, and cultivating customer preferences for intelligent logistics services. (3) When the revenue-sharing ratio of the logistics enterprise is relatively low, although the cost-sharing mechanism can continuously motivate a logistics enterprise to make an effort in the intelligent upgrading of logistics, it is not conducive to enhance the goodwill of intelligent logistics. This paper highlights the pivotal role of enterprise collaboration in the intelligent upgrading of logistics, and proposes practical recommendations.

Harnessing the Power of Web Scraping and Machine Learning to Uncover Customer Empathy from Online Reviews

In today's information-driven world, companies need to grasp customer sentiment and pinpoint key product features to make informed choices and improve customer satisfaction. The abundance of online customer feedback provides businesses with a rich source of information about customer perceptions and preferences. Yet manual analysis of large volumes of reviews is a tedious and resource-intensive task. It describes a web scraping application that sorts reviews into positive, negative, or neutral sentiments that businesses can use for insight. By assigning weights to reviews based on perceived impact, the approach offers a nuanced understanding of customer opinions. Experimental results demonstrate its effectiveness in accurately categorizing and analysing reviews, distinguishing between genuine and fake feedback. Additionally, AI-powered examination of product reviews employs NLP methods to extract important information from customer feedback, identifying particular elements that customers like or find troubling. After the successful identification of gain points, they can be used to improvise product development and marketing. So, product-specific focused crawler is developed to extract customer reviews from the most trusted websites. As a side chain, this can also be used as a tool for analysing the competitors’ products to understand their pain as well as gain points. The approach can uncover the most discussed aspects of products, enabling businesses to better understand customer perceptions and preferences to make better decisions thereby saving time and money.

Implementation of K-Nearest Neighbors Algorithm in Analyzing Public Interest in Shoping at Supermarkets

People's shopping patterns and behaviors continue to develop along with technological advances and lifestyle changes, thus requiring retail business actors, especially supermarkets, to better understand their customers' interests and preferences. In this context, accurate analysis of customer shopping interests is very important to improve customer satisfaction and optimize marketing strategies. One solution that can be implemented to analyze people's shopping interests is the application of the K-Nearest Neighbors algorithm, a simple yet effective nearest neighbor-based classification method for recognizing patterns from existing data. This study aims to apply the K-Nearest Neighbors algorithm to classify people's interest in shopping at supermarkets. This study also evaluates the effectiveness and performance of the algorithm in the context of business decision-making in the retail sector. The research methodology includes collecting data on people's shopping interests, data pre-processing, implementing the K-Nearest Neighbors algorithm, and evaluating model performance using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this study indicate that the K-Nearest Neighbors algorithm is able to achieve an accuracy of 88%, with precision, recall, and F1-score all reaching 92.86%. These results indicate that the K-Nearest Neighbors model is very effective in classifying people's shopping interests, with a low error rate. The resulting confusion matrix also shows the model's ability to identify customers who are interested in shopping with little prediction error. This study concludes that we can rely on the K-Nearest Neighbors algorithm to analyze people's shopping interests in supermarkets. This model not only shows good performance in classification but also has great potential to be implemented in recommendation systems and customer segmentation in the real world. This study contributes to the development of consumer behavior analysis methods in the retail sector, as well as providing a basis for further research to explore other algorithms or combinations of techniques to improve the accuracy and effectiveness of classification models.

Informal contracts' influence on shipping efficiency: a customer- perspective of package delivery agent

Understanding the role of informal agreements can help delivery services prioritize customer preferences, leading to improved service quality and customer satisfaction. Unfortunately, where there are gaps either on general understanding of communication stand in-between the different of failure and success. This qualitative research study delves into the dynamics of informal contracts between customers and package delivery agents, and their impact on the efficiency of the shipping process. The research aims to gain a deeper understanding of how these unwritten agreements affect various aspects of the delivery process from both the customer and delivery agent viewpoints. Through in-depth interviews and thematic analysis, this study sheds light on the nuances of these informal arrangements and their role in shaping the overall shipping experience. The study revealed that informal agreements play a significant role in package delivery experiences. Participants described unspoken arrangements formed through trust and effective communication. Adherence to these agreements positively influenced service efficiency, with prioritized deliveries and improved accuracy. However, deviations from agreements led to emotional reactions such as frustration and disappointment, impacting the perceived trustworthiness of the delivery service. Additionally, the study highlighted cultural variations in the adoption of informal agreements and the importance of clear communication in establishing and maintaining these arrangements.

The Potential of Thymus serpyllum Essential Oil as an Antibacterial Agent against Pseudomonas aeruginosa in the Preservation of Sous Vide Red Deer Meat.

Foodborne infections caused by microbes are a serious health risk. Regarding this, customer preferences for "ready-to-eat" or minimally processed (MP) deer meat are one of the main risk factors. Given the health dangers associated with food, essential oil (EO) is a practical substitute used to decrease pathogenic germs and extend the shelf-life of MP meals. Nonetheless, further data regarding EO use in MP meals are required. In order to evaluate new, safer alternatives to chemicals for disease control and food preservation, this research was carried out in the following areas to assess the antibacterial and antibiofilm characteristics of Thymus serpyllum (TSEO) essential oil, which is extracted from dried flowering stalks. Furthermore, this study applied an essential oil of wild thyme and inoculated the sous vide deer meat with Pseudomonas aeruginosa for seven days at 4 °C in an effort to prolong its shelf-life. Against P. aeruginosa, the essential oil exhibited potent antibacterial action. The findings of the minimal biofilm inhibition concentration (MBIC) crystal violet test demonstrated the substantial antibiofilm activity of the TSEO. The TSEO modified the protein profiles of bacteria on glass and plastic surfaces, according to data from MALDI-TOF MS analysis. Moreover, it was discovered that P. aeruginosa was positively affected by the antibacterial properties of TSEO. The anti-Pseudomonas activity of the TSEO was marginally higher in vacuum-packed sous vide red deer meat samples than in control samples. The most frequently isolated species from sous vide deer meat, if we do not consider the applied bacteria Pseudomonas aeruginosa, were P. fragi, P. lundensis, and P. taetrolens. These results highlight the antibacterial and antibiofilm qualities of TSEO, demonstrating its potential for food preservation and extending the shelf-life of deer meat.

ЦИФРОВАЯ ТРАНСФОРМАЦИЯ РЫНКОВ: КОГНИТИВНЫЙ АСПЕКТ

The article is dedicated to a comprehensive analysis of the impact of digital transformation on changes in consumer markets and behavior patterns. The main focus of the study is on the cognitive aspect associated with consumer adaptation to a dynamically developing digital environment, including changes in customer preferences and their decision-making processes. The paper highlights the challenges of the digital economy related to information security, monopolization of markets and differences in digital education and access to technology. The article also shows the interrelationships between technological innovations and consumer behavior, emphasizing the need for companies to develop adaptive strategies. In the context of this work, the authors present a conceptual framework and develop hypotheses for subsequent quantitative studies that contribute to understanding the impact of digital transformation on market participants. The article is published as part of the implementation of the Consortium agreement for conducting joint scientific research projects between IRES Russian Academy of Sciences and Baltic State Technical University «Voenmeh» D.F.Ustinov.

The Mediating Role of Self-Efficacy in the Relationship Between Salespersons’ Personal Factors and Sales Performance

A skilled salesperson can build strong relationships with clients, which fosters loyalty and repeat business. They provide valuable insights into customer preferences and competitive landscapes as well as creating value for their customers, manage customer relationships, and gather information for their firms. Hence, this paper explores how self-efficacy functions as a mediator in the correlation between improvisational behaviour, adaptive selling, and sales performance of salespersons. This study investigates the direct and indirect relationship of self-efficacy with sales performance. This study used a survey research approach using clustered random sampling. Data are collected from 315 salespersons employed by Malaysian commercial banks in Klang Valley via survey questionnaires. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyse the data. The findings indicate that self-efficacy serves as a partial mediator in the relationship between improvisational behaviour and adaptive selling with sales performance. Additionally, the results also showed that improvisational behaviour and adaptive selling are positively related to sales performance. The study concluded that personal factors of salespersons are important in determining their sales performance. Hence, for managers to have a competitive advantage in the banking sector, they need to adopt a more comprehensive understanding of what makes a good customer service provider.

The Evolving Influence of Artificial Intelligence on Customer Engagement Dynamics

This study examines the evolving influence of artificial intelligence (AI) on customer engagement dynamics, aiming to provide insights into how AI technologies can enhance engagement, loyalty, and trust among consumers. A quantitative research approach was employed with a causal research design. Data was collected from 140 individuals in the Kathmandu Valley, primarily undergraduate students aged 20-29 years, through a questionnaire utilizing a 7-point Likert scale. Correlation analyses were conducted using SPSS software version 23 to analyze the data. The study's findings highlight the transformative potential of AI in shaping customer engagement dynamics. By leveraging AI technologies, businesses can significantly enhance customer engagement and foster stronger relationships, ultimately driving competitive advantage and sustainable growth in an increasingly digital marketplace. The implications of this research suggest that understanding AI's capabilities can empower businesses to develop more effective customer engagement strategies. This presents opportunities for further investigation into the factors influencing the adoption and effectiveness of AI in customer engagement. Overall, this study contributes to the understanding of AI's role in customer engagement within the context of Nepal. Future research can provide valuable insights for businesses operating in emerging markets, informing the development of AI solutions tailored to the specific needs and preferences of local customers. By exploring these dynamics, organizations can better navigate the challenges of customer engagement in a rapidly changing technological landscape.

An Impact of CRM at Max Fashion Retail Limited

Effective customer relationship management (CRM) is essential for maintaining competitive advantage in the retail fashion industry. At Max Fashion, CRM strategies have been designed to enhance customer loyalty, streamline communication, and improve overall customer satisfaction. This study examines the various CRM practices employed by Max Fashion, including the use of customer data analytics, personalized marketing campaigns, and loyalty programs. By integrating technology such as CRM software and social media platforms, the company aims to better understand customer preferences and purchasing behaviour. The research highlights how these strategies contribute to increased customer retention and long-term business success. Through a combination of qualitative interviews with Max Fashion's management team and quantitative data analysis from customer feedback, the study identifies the key factors driving CRM effectiveness. This research provides valuable insights for fashion retailers seeking to implement or refine their own CRM strategies in an increasingly digital marketplace

Development of Ergo-Bundling System for Food Retailer based on Data Mining

In retail, a major challenge is quickly selling perishable food items like cakes or pastries within a limited time. If unsold, these products lead to losses, even with discount promotions. Retailers must forecast stock accurately and develop more effective strategies to meet consumer demand within the required timeframe. One suggested strategy is bundling, where high-demand products are combined with less popular ones. However, when done manually, this approach often fails to align with consumer preferences. This study aims to develop an automated bundling system using data mining techniques. Market Basket Analysis is used to understand consumer purchasing patterns, while Association Rules with the Apriori Algorithm help identify relationships between different products. These methods reveal which items are frequently bought together, making bundling strategies more effective. The system will be designed with usability and ergonomic principles, ensuring it is user-friendly. The implications of this system include improved stock management, more accurate bundling, and better alignment with customer preferences, ultimately increasing satisfaction. Additionally, automation reduces errors and inconsistencies that occur with manual bundling. The expected outcome is a more efficient, effective, and comfortable system for food retailers, leading to higher sales, reduced losses, and greater overall customer satisfaction.

Product Design Incorporating Competition Relations: A Network-Based Design Framework Considering Local Dependencies

Abstract System design has been facing the challenges of incorporating complex dependencies between individual entities into design formulations. For example, while the decision-based design framework successfully integrated customer preference modeling into optimal design, the problem was formulated from a single entity’s perspective, and the competition between multiple enterprises was not considered in the formulation. Network science has offered several solutions for studying interdependencies in various system contexts. However, efforts have primarily focused on analysis (i.e., the forward problem). The inverse problem still remains: How can we achieve the desired system-level performance by promoting the formation of targeted relations among local entities? In this study, we answer this question by developing a network-based design framework. This framework uses network representations to characterize and capture dependencies and relations between individual entities in complex systems and integrate these representations into design formulations to find optimal decisions for the desired performance of a system. To demonstrate its utility, we applied this framework to the design for market systems with a case study on vacuum cleaners. The objective is to increase the sales of a vacuum cleaner or its market share by optimizing its design attributes, such as suction power and weight, with the consideration of market competition relations, such as inter-brand triadic competition involving three products from different brands. We solve this problem by integrating an exponential random graph model (ERGM) with a genetic algorithm. The results indicate that the new designs, which consider market competition, can effectively increase the purchase frequency of specific vacuum cleaner models and the proposed network-based design method outperforms traditional design optimization.

Exploring Customer Insight Analytics with Descriptive Methodology: A Case Study of Adventure Hardware Group

In contemporary business, understanding consumer behavior is essential, giving firms the knowledge needed to make educated decisions and improve customer experiences. Hence, the main objective of this research is to conduct an assessment of retail outlets adaptation to changes in customer preferences to uncover profit opportunities and take advantage of customers demography visibility in the face of heightened volatility. This research delves into the dynamic landscape of Customer Insight Analytics through descriptive procedure to offer a comprehensive exploration of Adventure Hardware Group as a pivotal case study. The study employed the Power BI, a Business Intelligence and Data Visualization tool to transform data from various sources into interactive dashboards and analysis reports, facilitating insightful visualizations and data-driven decision-making. The research shows that customer demography has largely remained the same for the years under consideration, therefore, forming the basis for determining customer preferences, pursuing customer satisfaction and improved sales. In the case of Adventure Hardware Group in particular, the key influencer analysis shows that Germany has the highest influence for driving revenue, and single customers have a higher influence on improving revenue growth for the group. Thus, this information provides the background for future market segmentation for the group’s products. The research concludes that adequate knowledge of customers demography and geography will enhance informed decisions that improve customer experiences, thereby increase sales revenue.

Pemodelan Data Mining Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus: Kedai Ngodeng & Smoothies)

The role of UMKM is very large in the growth of the Indonesian economy, Kedai Ngodeng & Smoothies, as one of the UMKM that is active in the food and beverage industry. This shop has not made maximum use of sales transaction data. The transaction data is only used as an archive and is not used to organize sales strategies. In this research, the method used is the a priori algorithm to determine the relationship between one product item and other product items so that it can be used as a promotional package or bundling to increase sales profits. The data used is sales transaction data for the Ngodeng & Smoothies shop from January to May 2024. The research began with initial data collection, followed by data description and evaluation of data selection. After that, attribute selection is carried out. The CRISP-DM method is applied in this research which includes six stages, namely business understanding, data understanding, data preparation, modeling, evaluation and dissemination. Based on the results of the research, there are 95 Association Rules based on previously determined parameters, namely the minimum support value is 50% and the minimum confidence is 90%. One of the rules that was formed is that Smoothies Mangga Large, Twister, Kembang Cumi --> Odeng Spicy has a confidence value of 100%. The information obtained can provide important insights into customer preferences and can be used to design more effective product bundling promotions.

Global Integration vs. Local Responsiveness: Managing Strategic Dilemmas in Multinational Corporations

In the dynamic landscape of multinational corporations (MNCs), achieving a balance between global integration and local responsiveness poses a significant strategic dilemma. This study investigates how MNCs navigate this dual challenge to maintain competitive advantage and operational efficiency in diverse global markets. theoretical frameworks and empirical evidence to understand the strategic choices made by MNCs in integrating global operations while responding to local market demands. the tensions between standardized global strategies aimed at achieving economies of scale and localized strategies tailored to meet unique customer preferences, regulatory requirements, and cultural nuances. Key aspects analyzed include the strategic decision-making processes, organizational structures, and management practices that facilitate or hinder effective global-local integration. The study reviews case studies and comparative analyses of MNCs across different industries to illustrate successful strategies and best practices in managing these strategic dilemmas.

A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation

Advertising is the most crucial part of all social networking sites. The phenomenal rise of social media has resulted in a general increase in the availability of customer tastes and preferences, which is a positive development. This information may be used to improve the service that is offered to users as well as target advertisements for customers who already utilize the service. It is essential while delivering relevant advertisements to consumers, to take into account the geographic location of the consumers. Customers will be ecstatic if the offerings displayed to them are merely available in their immediate vicinity. As the user’s requirements will vary from place to place, location-based services are necessary for gathering this essential data. To get users to stop thinking about where they are and instead focus on an ad, location-based advertising (LBA) uses their mobile device’s GPS to pinpoint nearby businesses and provide useful information. Due to the increased two-way communication between the marketer and the user, mobile consumers’ privacy concerns and personalization issues are becoming more of a barrier. In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural networks. The proposed hybrid model is made using two neural networks, i.e., CNN and LSTM. Geographical information systems (GIS) are used to acquire initial location data to collect precise locational details. The proposed LBA for GIS is built in a Python simulation environment for evaluation. Hybrid CNN-LSTM recommendation performance beats existing location-aware service recommender systems in large simulations based on the WS dream dataset.

Shoprite Holdings LtdCase Study: Challenges for Expansion in Africa

This study investigates the obstacles faced by the African expansion efforts of Shoprite Holdings Ltd., a South African retailer. Shoprite pursued mergers and acquisitions within its native country but established wholly owned subsidiaries as its preferred entry route for expanding into other African states. As a result, Shoprite significantly increased its operations by expanding to 16 nations by 2020. However, Shoprite currently focuses its operation in ten countries, exclusively in southern African countries and its home country. This study seeks to comprehend the reasons behind Shoprite's rapid withdrawal from multiple African countries and shed insight into the challenges of conducting business in Africa's demanding business climate. The analysis revealed a combination of economic variables, such as reduced purchasing power of customers, currency volatility, increased operational costs, and the economic shocks caused by the COVID-19 outbreak. Additionally, high customer preference for informal markets and social factors were also identified. The findings indicate that Shoprite should prioritize strategic location selection, adopt a lower entry mode, and consider the potential for developing e-commerce as feasible measures. The report proposes that African retail businesses should comprehend the intricacies of the African market and employ strategies for international expansion, entrance techniques, and the significance of adjusting operations to local conditions and regulatory environments. These discussions offer insights to organizations considering international expansion, seeking to mitigate risks, and aiming to optimize their market entry strategy in various locations, specifically Africa.

Embracing the future of interactive marketing with contactless technology: evidence from tourism businesses

PurposeThis study explores how predictor variables (value congruence and customer–AI-assisted exchanges) lead tourism businesses to meet customer needs through contactless technology and enhance their willingness to pay more.Design/methodology/approachThe research model was designed from the perspective of consumer-brand relationships and acceptance of AI device use. This study collected and analyzed 647 valid questionnaires using the structural equation modeling (SEM) approach.FindingsThe findings indicate that value congruence and customer-AI-assisted exchanges (CAIX) affect intimacy. Intimacy affects satisfaction and the willingness to pay more for contactless technological services. Moreover, brand trust and sensory brand experience play a role in moderating contactless technological services.Originality/valueEffective interactions between customers and AI-assisted services significantly contribute to overall satisfaction. When AI systems can understand and respond appropriately to customer queries, needs, and preferences, they enhance customer experience and satisfaction levels, increasing overall customer satisfaction with AI services.

Impact of Chatbots on Satisfaction and Loyalty in Lima's Telecom Sector

The migration towards digital environments has transformed the expectations of consumers, who now not only look for quality products and services, but also convenience, personalization and efficiency in each interaction with the brand. This change presents significant challenges for companies, as they need to constantly adapt to meet their needs and thus maintain loyalty in a highly competitive market. The pressure to offer added value and maintain customer preference is intensified by the ease with which consumers switch to competitors. which drives companies to innovate and adapt AI such as chatbots, which could be the answer to this challenge. The objective of this research work is to know if the use of chatbots positively influences the satisfaction and perceived loyalty of consumers of telephone services, since in this case the speed, quality of the service and the customer's need for understanding. They are critical and represent a greater challenge to differentiate. The methodology used for this research maintains a quantitative, correlational, non-experimental approach. The type of sampling was non-probabilistic, and the strategy was snowball. The instrument used was a questionnaire with a sample of around 130 consumers in the city of Metropolitan Lima in Peru. The findings suggest that chatbots are not only innovative tools, but also strategic ones for telephone companies seeking to improve customer satisfaction and loyalty. The implementation of appropriate chatbots can transform the customer experience, providing a more agile, personalized and effective service, thus seeing a close relationship between the dimensions of the chatbot with the satisfaction and loyalty perceived in consumers. In a world where these variables are key to business survival, chatbots represent an opportunity to create exceptional experiences. Uncover how cutting-edge AI chatbots are revolutionising customer satisfaction and loyalty in the competitive telephone service industry, offering transformative, personalised, and efficient interactions.