Types Of Customers Research Articles

The Role of Small Rural Grocery Stores in Northern Bavaria: Findings of a Quantitative Customer Survey

Grocery shopping is an integral part of everyday life in every household. Due to the increasing decline in the number of grocery stores, it is difficult to find grocery shops close to home, especially in rural areas. For certain population groups, such as older people and/or people with limited mobility, people living alone and single parents, as well as households without a car, it is difficult to get groceries within walking distance. In addition, the gaps in local supply also mean a decline in the quality of life of the affected population. This study addresses the question of what role small rural grocery stores play in the shopping behavior of residents of rural areas and how they rate them. Using a quantitative consumer survey (n = 238), the shopping behavior and relationship to five sites in the Main-Spessart region of Bavaria, Germany were analyzed. The surveyed customers visit the rural stores several times a week (57.1%), especially for necessities (62.2%) and weekly shopping (13.1%). The product range (including fresh products), proximity to the place of residence, as well as the social function are most valued. Four different customer types were identified: the Uninvolved (35.6%), the Supporters (15.5%), the Motivated (25.8%) and the Socials (23.2%). The study shows that small rural grocery stores can contribute significantly to the food supply in poorly supplied areas. The degree of use varies depending on the individual life circumstances and needs. Using the location as a place for social exchange is a very relevant factor (60.5%). The targeted use of the store as a social meeting place is highly dependent on the additional infrastructure provided (e.g., a café corner). These results can help decision-makers to gain a better understanding of the users and consequently to better assess potentials of small rural grocery stores.

Penerapan Algoritma DBSCAN Untuk Clustering Penjualan di Supermarket

Supermarkets are shopping places that provide various daily necessities. Many customers visit supermarkets to buy necessities. The growth of supermarkets is increasing. Supermarkets have a variety of products with different brands, branches, and types of customers. To create a sales strategy, need to know the products that customers are interested in. In this research, supermarket product clustering was carried out based on sales data. The clustering algorithm used in this research is the DBSCAN algorithm. This algorithm is an algorithm for grouping data objects based on density, which is influenced by input parameters, namely the Eps and MinPts values. The data used in this research is secondary data consisting of 100 supermarket sales data, taking 2 attributes. The clustering results show that using the Eps parameter value = 6 and the MinPts value = 9, the product data is divided into 3 clusters, namely cluster 1 of products that are not in demand, cluster 2 of products that are in demand and cluster 3 of very popular products.

Enhancing logistics through a vehicle routing problem with deliveries, pickups, and backhauls

AbstractToday, logistics activities are driven by the pressing need to simultaneously increase efficiency, reduce costs, and promote sustainability. In our research, we tackle this challenge by adapting a general vehicle routing problem with deliveries and pickups to accommodate different types of customers. Customers requiring both delivery and pickup services are mandatory, while those needing only a pickup service (backhaul customers) are optional and are only visited if profitable. A mixed‐integer linear programming model is formulated to minimize fuel consumption. This model can address various scenarios, such as allowing mandatory customers to be served with combined or separate delivery or pickup visits, and visiting optional customers either during or only after mandatory customer visits. An adaptive large neighborhood search is developed to solve instances adapted from the literature as well as to solve a real‐case study of a beverage distributor. The results show the effectiveness of our approach, demonstrating the potential to utilize the available capacity on vehicles returning to the depot to create profitable and environmentally friendly routes, and so enhancing efficient, cost‐effective, and sustainable logistics activities.

BANK COMPETITION AND FINANCIAL STABILITY IN NIGERIA

Introduction: The study examined the impact of bank competition on the financial stability of selected deposit money banks in Nigeria. The study employed panel data (secondary data) that was collected from 2019 to 2023 (both years inclusive). Methods: The panel regression analysis was employed to determine the relationship between the outcome variables and explanatory variable, taking decisions from the Huasman test. Results The findings of the study from objective one revealed that the Herfindahl-Hirchman loan Index has a positive significant effect on capital adequacy ratio along with diversification ratio and bank size, which are control variables. It also has a negative significant effect on capital adequacy ratio. Objective two revealed that the Herfindahl-Hirchman Deposit Index diversification ratio and bank size have a positive significant effect on non-performing loans in Nigeria. Conclusion and suggestion: Based on the findings, it therefore recommended that deposit money banks should diversify their loan portfolio across sectors and customer types to mitigate a concentration risk in the deposit money banks. Reallocating capital from less diversified or larger loans to smaller, more diversified segments of the portfolio would spur the level of competition accuracy of the banks.

Anonymity in Dealer-to-Customer Markets

We use a laboratory experiment to explore the effect of a change in pre-trade anonymity in a quote-driven dealer-to-customer market, organised as a request for quote (RFQ). We consider two treatments in which dealers interact with two types of customers (informed or uninformed). In the first treatment, there is no anonymity: dealers know whether the customer that sent them the request for quote is informed or uninformed. In the second treatment, there is complete anonymity: dealers do not know the type of customers they are interacting with. We find that anonymity improves price efficiency, whereas it does not adversely impact dealers’ trading profits. Our results contribute to the debate on transparency versus the adoption of anonymity in financial markets.

Effect of Job Involvement on Organizational Commitment: Evidence from Financial Institutions

The primary goal of this study was to assess the levels of job involvement and organizational commitment among employees involved in financial institutions, as well as to investigate the effect of job involvement on organizational commitment. A descriptive research design was adopted in this study. The study included 300 questionnaires delivered to 12 financial institutions (commercial banks, development banks, and finance companies). Out of them, only 179 (59.67%) were viable, and these were used for data analysis. The results presented that employees were judged to be highly involved in their jobs and committed to their institutions. Furthermore, it has been demonstrated that job involvement significantly and favorably affects commitment of employees. It indicates that employees who are more involved in their jobs have a stronger sense of commitment to their organizations. Therefore, Nepalese financial institutions must prioritize increasing job involvement and organizational commitment among their employees at all levels. Clear organizational goals provide greater possibilities for actively involved individuals to meet their needs and establish commitment to the organization. Financial institutions must adapt to these realities in order to foster a culture of involvement and commitment. Furthermore, employees should be encouraged to innovate and embrace creative working methods that are appropriate for different types of customers from all areas of society in order to maintain their growing interest and success in their jobs.

Research on quantitative measurement algorithm for e-commerce customer loyalty based on deep learning algorithm

Traditional algorithms cannot fully explore the potential patterns behind big data, lack personalized customer analysis, and cannot provide personalized services and suggestions for different types of customers. This article employs the Bi LSTM (Bidirectional Long Short-Term Memory) model to accurately capture the complex features and patterns of customer behavior, thereby improving the measurement accuracy of customer loyalty. Collect data on customer behavior, browsing history, and search behavior, and preprocess the collected data. Organize customer behavior data into a time series dataset in chronological order, and divide it into weekly windows to extract feature information from the data. Construct a bidirectional LSTM model while considering the forward and backward information of the sequence data, in order to more comprehensively capture the contextual relationships in the sequence data and quantify customer loyalty. The experimental results show that the average accuracy of Bi LSTM in predicting average customer loyalty is 97.1%. And it can effectively improve the prediction effect of repeat purchase rate. The application of Bi LSTM can accurately quantify customer loyalty in e-commerce, provide reference for enterprise decision-making, formulate corresponding marketing strategies and customer management plans, and improve customer loyalty and competitive advantages.

The Importance of Customer Service in Banking

The existence of a company is unthinkable without good customer service. Customer service should be seen as the core of your business, not as an afterthought. When the market is so saturated with all kinds of services and products, in order to save your business, you will definitely need to offer exceptional service to customers who have endless choices. What is special about you? Why should the customer choose you and not any competing company? What sets you apart from others? Have you noticed how often we hear unpleasant stories about poor service, while we rarely get to share pleasant experiences? why Because bad news travels fast. You know what Warren Buffett once said? "It takes 20 years to build a reputation, and 5 minutes to destroy it. It is specified - what circumstances influence the user's action or purchase. Also, what does even a regular customer pay attention to when he wants to get a product. Difficult customer types and when to say sorry or say no. Recommended - rules, phrases and communication tools that affect the user. Recommendations are presented regarding the correct wording of the proposal, which will allow us to retain the customer and increase the profit of our company. Keywords: Effective service, ripple effect, Service+.

Characterization of Genetic Landscape and Novel Inflammatory Biomarkers in Patients With Adult-Onset Still's Disease.

Adult-onset Still disease (AOSD) is a systemic autoinflammatory disorder (AID) of unknown etiology. Genetic studies have been limited. Here, we conducted detailed genetic and inflammatory biomarker analysis of a large cohort with AOSD to investigate the underlying pathology and identify novel targets for potential treatment. We investigated AOSD cases (n = 60) for rare germline and somatic variants using whole exome sequencing with virtual gene panels. Transcriptome profiles were investigated by bulk RNA sequencing whole blood. Cytokine profiling was performed on an extended patient cohort (n = 106) alongside measurements of NLRP3 inflammasome activation using a custom assay and type I interferon (IFN) score using a novel method. We observed higher than expected frequencies of rare germline variants associated with monogenic AIDs in AOSD cases (AOSD 38.4% vs healthy controls [HCs] 20.4%) and earlier onset of putative somatic variants associated with clonal hematopoiesis of indeterminate potential. Transcriptome profiling revealed a positive correlation between Still Activity Score and gene expression associated with the innate immune system. ASC/NLRP3 specks levels and type I IFN scores were significantly elevated in AOSD cases compared with HCs (P = 0.0001 and 0.0015, respectively), in addition to several cytokines: interleukin (IL)-6 (P < 0.0001), IL-10 (P < 0.0075), IL-12p70 (P = 0.0005), IL-18 (P < 0.0001), IL-23 (P < 0.0001), IFN-α2 (P = 0.0009), and IFNγ (P = 0.0002). Our study shows considerable genetic complexity within AOSD and demonstrates the potential utility of the ASC/NLRP3 specks assay for disease stratification and targeted treatment. The enriched genetic variants identified may not by themselves be sufficient to cause disease, but may contribute to a polygenic model for AOSD.

The importance of an accurate numerical model for the simulations of new generation district heating systems

Abstract The aim of this work is to present a fast and accurate district heating simulation tool conceived to support the implementation of successful integrations of renewable energy sources along district heating networks. New generation district heating systems are facing new challenges related to increasing integrations of distributed renewable energy sources and new type of customers, such as prosumers. These connections introduce a great variety of low temperature highly variables energy profiles in the network that traditional modelling tools cannot properly simulate. Therefore, a specific thermo-hydraulic simulation model has been built to simulate the complex dynamics of entire network and its components, combining accuracy and rapidity. The model uses different numerical approaches to properly taking into account the thermal inertia of the components capacity, namely the water network and the users. The rapidity and reduction of computational effort is reached by the use of a Lagrangian approach based on the method of characteristics applied to the piping elements. Compared with the commonly used finite-volume and the node method, the proposed model shows accurate results at a lower computational expense and reducing numerical diffusion and artificial smoothing. In order to properly simulate the effects of the user behaviour on energy demand, flow rates and return temperatures of the network, a simplified but accurate user substation model is built with a lumped capacity approach. The model accuracy is investigated under real operating conditions with one-year hourly monitoring data of an Italian district heating network.

Revolutionising Skincare Through Airtificial Intelligence

Abstract: Background: Over the last 10years there has been a drastic shift in the development of computer-based systems, artificial intelligence, algorithms and technology in the world of dermatology be it cosmetic or even retail production of products sold to customers with high-tech technology and innovative means to build their client base through customer delight. Artificial Intelligence is particularly helpful in recognizing the type of skin customers have and suggesting solutions which help customers pick from the range of options given to them. Most e-commerce platforms have begun using such AI driven soft wear to help customers while they are in the comfort of their homes to choose from a range of cosmetic options right from lipsticks, concealers, foundation, eyeshadows, shades of tins, serums etc. Given that every person has a different skin type, these applications ensure 100 percent personalized suggestions. This new trend is sending shock waves across the retail sector around the world attracting a massive customer base to adapting to AI Derma logy and retail. The intelligent software used by sever companies not only benefits the customer, but also helps companies with their operational efficiency as they can use AI algorithms to predict and forecast the inventory to help reduce over production and excessive stock production minimizing product wastage and improving sustainably.AI is booming in both clinical diagnosis as well as giant companies such as Loreal and Olay who are dedicated to providing customers with advanced tools like skin analyzers or apps based on predefined rules which help AI-powered tools analyze skin conditions, age, and personal skincare history to provide highly personalized product recommendations. In clinical diagnosis AI offers valuable insight to the patient’s skin and condition be it cancer or other skin diseases making judgments to assist the doctors and dermatologist in making informed decisions regarding the line of treatment for the patient in question. This includes screening, diagnosis, treatments and predictions of the treatment outcomes. These new innovative and robust AI systems leaving customers feeling confident and have not begun relying on AI driven treatments and procedures

Service center in electricity distribution sector with variable peak electricity time: A two-stage queuing model approach with nested simulation

Customer service is essential for any business, significantly impacting customer satisfaction, brand reputation, and reducing associated costs. In the electricity industry, this importance is heightened due to the critical nature of electricity in daily life and growing usage rate of new technologies. Effective customer service in electric distribution companies involves handling a wide range of inquiries, from billing issues to technical support, requiring both remote and on-site assistance. The variability in customer calls, influenced by factors such as peak electricity hours, and different service and customer types, presents unique challenges in designing efficient service centers. This research develops a tailored model for the service center operations of a power distribution company, focusing on optimal staffing levels within budget constraints. A two-stage queuing model is proposed to address the needs of residential and non-residential customers. The study employs nested simulation techniques to account for variability in customer arrival times and peak hours. Key performance indicators (KPIs) are used to evaluate the effectiveness of staffing policies, considering both quantitative and qualitative costs. The study integrates stochastic optimization and ranking and selection methods to identify the best staffing configurations. The findings offer actionable insights for improving customer service efficiency and operational effectiveness in electricity customer service centers.

Social dynamics of luxury customization: The unique impact of name personalization

AbstractAs customized luxury becomes increasingly mainstream, adhering to a uniform treatment of all types of customization falls short of the nuanced insight needed. Our research focuses on the effects of a prevalent form of customization—name personalization—on the perceived “social cost” associated with luxury items from a potential buyer's point of view. Despite most luxury brands offering consumers the option to imprint their names on products for a personalized touch, concerns arise about being perceived to be “showing off.” Our four experimental studies demonstrate that adding one's name to luxury goods heightens apprehensions about the social cost, specifically diminishing metaperceptions of warmth due to impression management (Study 1). This phenomenon applies to both the imprinting of one's full first name, and only their initials (Study 2). However, luxury consumers with a lower social‐adjustive (i.e., less driven by the desire for social approval) show an elevated concern over the social cost of name personalization (Studies 3 and 4). As the trend of customizing luxury items rises, our findings emphasize the significance of luxury researchers and practitioners to discern both the positive and negative outcomes associated with various forms of luxury customization.

Experiential education, customer knowledge and adoption of electric vehicles: an exploratory study

ABSTRACT While previous research has primarily explored functional and informational drivers, this study investigates how experiential education can facilitate electric vehicle adoption. Based on the BMW i3 test drive program case and blogs data, this research identifies two types of customers who participate in such programs: early adopters who have a high level of perceptual knowledge about electric vehicles but a low interest in the brand or the model, and the early majority, i.e. users with a moderate level of perceptual knowledge about electric vehicles and are hesitant to adopt it. This research contributes to the literature on customer education by demonstrating the positive role of experiential education in innovation adoption. Additionally, it contributes to the literature on customer knowledge by showing that customers do not participate in experiential education for the same reason. While early adopters compare similar models in the market, the early majority seek answers to technical questions.

Antecedents of Customer Participation on Sharing Platforms: A Meta‐analysis

AbstractGiven the substantive influence of the digital revolution on the sharing economy, it is timely and relevant to ask why some sharing platforms (e.g. Airbnb and Uber) achieve significant success while others fail. To determine which factors encourage customers to participate in sharing goods and services on sharing platforms, and when they do so, this study conducts a meta‐analysis of empirical findings from 192 independent samples, extracted from 167 studies involving 171,344 customers. As the results clarify, customer‐related factors (customer motives, customer competence, customer satisfaction and subjective norms) are key antecedents. However, platform‐related factors (service quality of the platform, trust in the platform, performance expectancy and effort expectancy) and service‐provider‐related factors (service quality of the provider, trust in the provider and provider gender) also exert meaningful effects. To assess the generalizability of these antecedents, the meta‐analysis includes contextual moderators, namely customer type (previous provider experience), provider type (private/professional supply), platform characteristics (rivalry on the platform, prestige of ownership and services/goods) and exchange type (for‐profit/non‐profit and ownership transfer). The findings advance the literature on the sharing economy and provide specific guidance for platform managers about when to focus on certain antecedents.

Do all complain the same? Examining the role of luxury hotels reviewer attributes using text mining

Purpose Although extant studies found the determinants of customer complaints in luxury hotels, comparison of complaint severity using reviewer characteristics (nationality, reviewer type and expertise) is limited. This study aims to identify the variation in customers’ complaint attributes among different customer categories. Design/methodology/approach This study tried to address this gap by using structural topic modelling and text mining techniques to find the sentiment score, word count, complaints attributes and severity score. The research sample covers luxury hotels in India, and online review text and their associated data has been collected and analysed. Findings The authors found that leisure customers wrote lengthier reviews and complained about the facility-related aspects (valet parking, entertainment and amenities) and food-related aspects (food variety, service and menu) while the business customers’ complaint severity score is more on salient aspects (room size, bathroom and staff related services). The international customers wrote lengthier reviews and complained more about the facility-related aspects (amenities, pick-up and entertainment) and core service offerings (stay experience, bathroom, dirtiness and complaint resolution). Whereas domestic customers complained more about stay-related aspects (food variety, quality, services, staff behaviour and stay experience). Also, this study used correlation analysis to find the dependencies between topics and uncovered underlying patterns, trends and associations, aiding in a deeper understanding and interpretation of the complaints. This study can help service providers tailor their services based on customer attributes to ensure maximum customer satisfaction. Originality/value The findings of this study can enable hotel service providers to customise their services based on customer attributes to reduce dissatisfaction among customers and promote customer loyalty. The findings of this study can also be used to devise differential promotional strategies for different types of customers.

Monetary discounts, metaphoric communication and mass prestige based branding: a comprehensive assessment

PurposeThis study aims to examine the role of price discounts and how to communite such discounts for masstige brands. While a price discount might encourage potential (first time) customers seeking higher status to buy a masstige brand, it might deter existing (repeat) customers from purchasing the brand due to a decline in perceived status. Such paradoxical effect of price discounts on masstige brand’s purchase requires a detail investigation into whether masstige brands should offer price discounts and if so, how to communicate such discounts. Current research investigates this phenomenon.Design/methodology/approachFour experimental studies were executed. Study 1 investigated the impact of monetary discount (absent vs. present) on the purchase intention of masstige brands for different customer types (potential vs. existing). Study 2 investigated the mediating role of perceived status. Study 3 examined the effectiveness of metaphoric communication of monetary discounts (absent vs present) on masstige brand’s purchase. Study 4 tested the moderating effect of customers’ need for cognition.FindingsOverall, monetary discounts positively affect purchase intention of masstige brand; however, the effect is negative for existing customers and positive for potential customers owing to differences in perceived status these customer groups experience (positive for potential and negative for existing customers). Metaphoric communications of monetary discounts restrict the declining purchase intention and status perception of existing customers besides keeping the potential customers’ purchase intention intact.Research limitations/implicationsThis research is confined to a particular country limiting the gneralisability of the study’s findings. Furthermore, this research is cross-sectional in nature.Practical implicationsThe findings of this research provide valuable insights and actionable recommendations for masstige brands to effectively leverage price discounts, especially in the emerging markets.Originality/valueTo date, a question of whether or not masstige brands should offer price discounts and if so, how to communicate such discounts remain opaque. This is the pioneering study exploring this phenomenon.

Technologies for customized housing in modular construction

In the modular construction, the range of customization options is limited compared to traditional construction, which can be seen as a negative aspect. Different customization strategies can be adopted by companies, but the choice of the appropriate strategy is related to the technologies used in the operational process, with emphasis on the design and production stages. This study aims to compare the technologies used in the design, production and exchange of information in the development of customized housing in modular construction companies in Brazil and England. To achieve that, three case studies were conducted in English modular construction companies operating in the residential sector. The results of the case studies were compared with the literature on technologies used by Brazilian companies. It was noted that English companies, regardless of their size and type of customization offered, use Revit software to develop the design, and communication between different designers is done via cloud computing. However, communication between designers and production is often done on paper, which leads to errors. The machinery used on the shop floor varies between companies. The results found are similar to the Brazilian reality. It was noted that companies do not make use of all available potential; however, larger companies that are able to perform mass customization invest more in technologies for control and integration between operations. To ensure greater productivity and efficient use of technologies, companies should adopt mass customization.

Major Customers and the Speed of Leverage Adjustments

Purpose: This study investigates how major customers influence the speed of adjustment (SOA) in a firm's capital structure, specifically examining the effects of governmental versus non-governmental major customers.Study design/methodology/approach: The study employs Blundell and Bond’s (1998) GMM model to estimate SOA. Robustness methods include fixed effects, two-stage least squares (2SLS), propensity score matching (PSM), and the augmented doubly censored Tobit (DPF estimator).Sample and data: The sample consists of 186,240 firm-year observations from Compustat North America annual files and Compustat historical segment files, spanning 1978 to 2013. Firms in the utility and financial sectors are excluded.Results: Findings indicate that major customers significantly influence SOA by reducing market uncertainty through certifications. Firms with major customers have a higher SOA, with governmental customers exhibiting an even higher SOA than non-governmental ones. Results are consistent across various models and settings.Originality/value: This study uniquely identifies major customers as critical determinants of SOA, highlighting the heterogeneities in firms' SOA. It stresses the importance of considering customer type in financial strategies, differentiating between governmental and non-governmental impacts. These findings offer valuable insights for scholars, policymakers, and practitioners.Research limitations/implications: Focusing on publicly listed U.S. firms limits generalization. Future research should extend the analysis to other regions and recent periods. The findings provide insights for supplier firms in strategizing financial decisions and managing relationships with major customers. We emphasize the significant role of major customers in financial strategies, advocating for more detailed disclosure policies in the U.S. and globally to enhance transparency and market efficiency.

Effectiveness of real-world marketing of organic foods and beverages: A systematic review of recent evidence

Increasing consumer demand for organic foods and beverages is critical to boost sustainable agricultural production. Marketing interventions can be an effective way of influencing consumer preferences, but little is known about the effectiveness of these interventions at increasing demand for organic foods and beverages. This review synthesises recent evidence from studies evaluating real-world marketing interventions that aim to increase consumer demand for organic foods and beverages. Five databases were systematically searched and 18,743 titles and abstracts were screened against eligibility criteria. 21 studies covering 29 marketing interventions were included and assessed for risk of bias using the Evidence Project risk of bias tool. We found that price premiums had negative or no effects on overall consumer demand, although a subset of customers were willing to pay higher prices. Price promotions had mixed effects depending on product and customer type. Some place-based interventions–such as arranging organic products in a cluster and ensuring ready availability of these products–had positive and statistically significant impacts on consumer demand. Some promotional interventions–such as including organic messaging near product displays and securing newspaper coverage–were generally effective at increasing consumer demand. Ultimately, the effectiveness of these interventions differed across product categories, message framing, and the type of store and customer, necessitating strategic targeting of organic marketing interventions to promote more sustainable consumption.