Improve Customer Satisfaction Research Articles

The impact of e-commerce platforms' quality on customer satisfaction and repurchase intention in post COVID-19 settings

PurposeE-commerce expands product and service reach, emphasizing the need for strategic market approaches to enhance e-service quality and drive sales growth. This paper aims to assess the relationship between the perceived quality of e-commerce platforms (characterized by measures of order and return convenience), customer satisfaction with online shopping and repurchase intention from online stores as well as examine whether demographic variables such as age, gender and area of residency (urban/rural) influence the ratings of each of these variables.Design/methodology/approachAn online, self-administered survey gathered 108 valid responses from e-commerce customers. Data were analyzed in Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) through principal component analysis, confirmatory factor analysis and structural equation modeling (SEM) as well as correlation, descriptive statistics, difference of means tests and nonlinear regression.FindingsOnline shopping on e-commerce platforms is seen as convenient for both placing orders and managing returns. Additionally, consumers express satisfaction with their online shopping experiences and exhibit a strong intention to repurchase. The analysis revealed linear relationships between order convenience and customer satisfaction, between order convenience and repurchase intention and nonlinear relationships between return convenience, customer satisfaction and repurchase intention. No significant difference was found between the way the demographic variables rated the convenience, satisfaction and repurchase intention constructs.Originality/valueThis study contributes to the empirical literature on service quality in e-commerce by providing a streamlined model of the interactions among the factors as well as by isolating the nonlinear relationships and comparing results across three demographic variables. From a managerial standpoint, the findings suggest that strategies aimed at providing complete qualitative information and enhancing order and return convenience improve customer satisfaction and foster repurchase intention.

Returns Management of Maize and Paddy Seeds

This study focuses on examining the returns management processes in the selected seed firm, particularly for maize and paddy seeds in the Telangana districts of Karimnagar and Nizamabad. The research identifies significant challenges in managing seed returns, such as inefficiencies that affect seed quality and availability, as well as the financial costs involved in handling returns. The study underscores the importance of a well-organized returns management system, especially since approximately 15 per cent of seed returns occur every year. Viable seeds are reprocessed and stored for future use, while non-viable seeds are safely disposed of or repurposed for other uses. The financial transactions associated with the returns process are carefully managed, ensuring efficient compensation for distribution partners. The findings highlight that an effective returns management process is crucial for preserving seed quality, minimizing waste and improving customer satisfaction. The study concludes with suggestions for refining the returns management process to bolster the sustainability and competitiveness of the selected firm. This manuscript offers valuable insights for researchers, policymakers and industry professionals involved in the agricultural supply chain.

The Impact of Artificial Intelligence and Social Media on Relationship Marketing for Customer Satisfaction

Nowadays, artificial intelligence and social media play essential roles in relationship marketing. AI improves customer engagement with tailored suggestions or automated reactions, while social media channels are essential for SMEs to engage and interact with customers more personally. Furthermore, digital technologies have fundamentally transformed the marketing ecosystem, evolving customer experiences. This shift has challenged SMEs to use modern methods and develop new initiatives to serve customers better. This research explored the impact of artificial intelligence and social media to strengthen and revolutionize relationship marketing, improving customer satisfaction. By integrating AI-driven insights with social media, SMEs can produce personalized and tailored customer experiences, improve satisfaction, and foster long-term relationships that are the key to SMEs’ growth. The author used a mixed methodology, a combination of a survey study and interviews. Respondents were selected using snowball and convenience sampling. Overall, n = 158 and n = 8 for interviews that voluntarily participated in the research process, providing valuable insights exploring the research problem. Findings emphasized that AI and social media provide a superior opportunity for SMEs to alter their relationship marketing approaches and become competitive.

QR-based Canteen Management System

In recent years, the digitization of administrative and operational processes has transformed various sectors in the world, including food service management. This research presents the development and implementation of a QR-based canteen management system, designed to enhance the efficiency of canteen operations by leveraging contemporary technological advancements. The system employs a desktop application built with C# and the .NET framework, enabling users to order and pay for food items using a QR code-based login and cashless payment system. This solution addresses the inefficiencies of traditional pen-and-paper-based canteen management by automating order processing, payment, and sales record maintenance. The primary objective is to streamline canteen operations, reduce service time, and improve customer satisfaction through a user-friendly and reliable system.

Modernising Supply Chain Analytics with Demand Forecasting Engine

In todays’ dynamic market landscape accurate demand forecasting plays a role, in optimizing supply chain operations reducing inventory expenses and improving customer satisfaction. The article is a case study of one of the leading automotive spare parts manufacturers with thousands of products. The main aim of the study is to forecast the demand of the factory on bi-weekly basis and monthly basis, compare the performance metrics with different forecast approaches and suggest suitable model.

A new product development method to incorporating customer sensory preferences in food product design

As the way people consume has changed, the focus of customer satisfaction has moved away from engineering and towards sensory preferences (SPs), necessitating a new approach to product development. This paper introduces a novel product development model that incorporates SP factors. First, utilizing literature and expert input, a domain-specific food sensory attribute knowledge graph is built. Then, SPs/non-sensory preferences (NSPs) are extracted from extensive online product evaluations using Latent Dirichlet allocation (LDA) and sentiment analysis. Customer preferences (CPs) are formed by combining quantifiable SPs/NSPs. Following that, CPs are used to design and distribute product components. To improve customer satisfaction, a new product development approach has been developed. Finally, a genetic algorithm is applied to the optimization model in order to determine the best product development approach. This model has been empirically and comparatively demonstrated to be practical and effective.

New approach for quality function development based on cooperative game-based consensus mechanism and three-way decision method

Quality function deployment (QFD) is a team-based product development approach for identifying critical engineering characteristics according to customer requirements to improve customer satisfaction. Nonetheless, there are some limitations in the current QFD methods when used in real-world situations. For example, most of them cannot handle experts’ conflict opinions regarding the correlation evaluations between customer requirements and engineering characteristics, and only focus on how to acquire a complete importance ranking of engineering characteristics which is unnecessary in many realistic cases. In this study, we aim to develop a new QFD approach by using the cooperative game consensus reaching process and three-way decision theory. First, the cooperative game-based consensus mechanism is applied to help experts reach a consensus on the correlation assessments between customer requirements and engineering characteristics. Then, an extended three-way decision method is adopted to obtain the importance classifications of engineering characteristics and identify the critical ones for product improvement. Finally, the proposed QFD approach is illustrated with a case study regarding renewable energy storage technology improvement, and its efficiency and advantages are verified via comparative analysis and simulation analysis.

The Impact of Facility Management Practice on Business Performance in Selected RETAIL Markets in Port Harcourt, Rivers State

The need to contribute to a deeper understanding of the multifaceted impact of facility management practices on retail business performance in Port Harcourt, Rivers State Nigeria necessitated this study. The aim is to ascertain the impact of facility management on the overall performance of the retail markets. The research endeavours to evaluate the prevailing facility management practices within selected retail markets using a mixed methods research methodology to ensure a thorough exploration of the subject matter. The study population comprised various stakeholders within the retail market sector, including retail market owners, housekeepers, porters, vendors, and facility managers. A sample size of 161 respondents was selected to represent the diverse perspectives and experiences within the retail sector. Data collection involved a combination of qualitative and quantitative techniques. Qualitative data were gathered through interviews, allowing for in-depth exploration of participants' perceptions and experiences. Meanwhile, quantitative data were collected via structured surveys. The study finds that facility management practices has not improved customer satisfaction or employee productivity in selected retail market as respondents were very dissatisfied or dissatisfied with the current level of facility support provided for various operational aspects of the supermarket. Findings indicate that facility management practices such as provision of safe environment, standard of cleaning in facility management, relevance of provided equipment, high performance/ productivity risk mitigation, responsiveness of the facility management team in addressing customer issues, effective utilization of space can impact the overall performance of the retail market. The study concludes that effective facility management practices is a determinant of success in the competitive landscape of the retail market and recommends the setting up of clear standards for facilities management practice in the supermarkets, effective communication with the users/owners, implementation of preventive maintenance, adoption of smart technologies in retail market facility management plan, and the establishment of a facility management department to coordinate uses and services of facilities in the retail market buildings.

THE EFFECT OF PRODUCTS, LOCATION, PRICE AND PROMOTION ON CUSTOMER SATISFACTION (STUDY OF LOTTE WHOLESALE PEKANBARU)

With the rapid growth of modern retail in Pekanbaru, where Lotte Grosir is one of the many existing retailers. The company must understand the importance of analyzing the marketing mix in relation to customer satisfaction to innovate and face competition. In this context, the marketing mix includes product, location, price and promotion, known as 4Ps. The purpose of this study is to analyze whether the components of the marketing mix have an impact on customer satisfaction, providing a descriptive overview of the current conditions and serving as a consideration for the company in improving customer satisfaction. This research uses a descriptive quantitative method. The sample selected in this study consist of Lotte Grosir Pekanbaru customers using the Simple Random Sampling method, resulting in 105 respondents who filled out the questionnaire. Data were analyzed using instrument test, validity test, reliability test, classical assumption test, multiple linear regression, and hypothesis testing with SPSS software. The result show that partially, the product variable (X1) does not significantly affect customer satisfaction (Y), while the location (X2), price (X3) and promotion (X4) variables have an impact on customer satisfaction (Y) variable. Other results indicate that simultaneously, product location, price and promotion have an influence on customer satisfaction with a coefficient of determination of 81.8% and the remaining 18.2% is influenced by others factors not examined in this study.

A Dynamic Scheduling Method for Logistics Supply Chain Based on Adaptive Ant Colony Algorithm

To reduce the dynamic scheduling cost of logistics supply chain and improve customer satisfaction, this paper proposes a dynamic scheduling method for logistics supply chain based on adaptive ant colony algorithm. First, determine the goal of dynamic scheduling in the logistics supply chain. Second, considering supplier satisfaction, transportation costs, and maximum delivery distance constraints, a dynamic scheduling model for logistics supply chain is constructed. Then by smoothing the pheromones and designing a transition function, adjusting factors are introduced to update the pheromone rules. Finally, based on the adaptive ant colony algorithm, the solution of the dynamic scheduling function of the logistics supply chain is solved to achieve the dynamic scheduling of the current logistics supply chain. The experimental results show that after 19 iterations, the method can search for the optimal route A1 group with a length of 33.85 km, with fewer iterations and shorter paths. The total cost is 114,290 yuan, and the degree of cargo loss is low, with a maximum of only 0.14%. The task completion time is short, customer satisfaction is above 0.85, and the scheduling accuracy is 99.9%. It can effectively control costs, improve customer satisfaction, and accurately arrange logistics supply chains.

Review on the Use of Data Analysis for Customer Segmentation and Personalization in Marketing Strategies

This paper studies the application of data analysis in the field of customer segmentation and personalization. With the advent of the era of big data, enterprises and organizations have rich data resources that contain important information about customer behavior, preferences, and purchasing habits. Through data analytics, companies can better understand customer needs and provide personalized products and services, thereby improving customer satisfaction and loyalty. This study aims to explore the application of data analytics in the field of customer segmentation and personalization, and provides some common data analysis methods. Data analysis methods include probabilistic methods and Bayesian methods, among others. Through case studies, the application of data analysis in the field of e-commerce is explained. The research results show that data analytics has important value and potential applications in customer segmentation and personalized marketing. However, data analysis still faces some challenges and limitations in practice, including data security and quality issues. Future research can continue to explore methods and techniques for data analysis, solve problems such as data privacy and security, and apply data analysis in more fields.

Role of Augmented Reality in Improving Remote Technical Support in Nigeria

Purpose: The aim of the study was to evaluate the role of augmented reality in improving remote technical support in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The integration of Augmented Reality (AR) in remote technical support in Nigeria has significantly enhanced troubleshooting efficiency by allowing technicians to visualize issues in real-time, providing immediate and interactive guidance. This technology has improved customer satisfaction by reducing downtime and accelerating resolution processes, making support more accessible, especially in remote areas. Additionally, AR has contributed to cost reduction by minimizing the need for physical technician visits and extensive in-person training sessions. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), media richness theory (MRT) & cognitive load theory (CLT) may be used to anchor future studies on role of augmented reality in improving remote technical support in Nigeria. Implement AR solutions to provide remote technicians with detailed, real-time visual overlays that guide diagnostic and repair processes. Formulate policies that establish standards for AR technology in remote technical support. This includes guidelines for data security, privacy, and interoperability to ensure that AR solutions are safe, reliable, and can work seamlessly with other technologies.

Implementation of Decision Tree Method to Predict Customer Interest in Internet Data Packages

The use of smartphones is on the rise, making internet packages vital in everyday life. In Indonesia, mobile operators offer a variety of data packages to meet customer needs. Understanding preferences is important to making the right decisions when providing products that meet customer needs. However, not all packages are suitable for all customers. Telkomsel is one of the providers that can deliver consistent signals and a wide range of data packages, but it still has a relatively high price. The study uses decision tree methodology to analyze citizens' preferences about Telkomsel services, comparing the relative cost of data packages with other services. This research will use surveys with samples from different communities to determine the representativeness of the results and provide strategic recommendations for Telkomsel to improve customer satisfaction. The research methods employed in this study included data collection, preprocessing, data division, model design, prediction results, and result evaluation. The results showed accuracy levels of 98.7%, precision of 100%, recall of 98.7%, specificity of 100%, and an F1-score of 99.3%. This study demonstrates the effectiveness of the decision tree model in predicting customer interest in Telkomsel services. Despite some limitations, the findings provide valuable insights that Telkomsel can use to develop more effective marketing strategies.

Leveraging digital data to facilitate circular control in the aftermarket – experiences from an international manufacturing firm

PurposeGrowing research attention has been given to both the circular economy and digitalisation in accounting research in recent years, but there are few studies exploring how digital tools are used to develop, analyse and respond to information for circular decision-making in industrial organisations. Therefore, this paper addresses how the data from digital technologies are leveraged in the aftermarket of an industrial firm for circular control.Design/methodology/approachThe paper develops an analytical framework that is then used to frame the findings through a single case study of an international heavy equipment manufacturer for circular control.FindingsThe case provides examples of how digital technologies are used for circular control, framed within the analytical model as the key contribution. The study illustrates the different ways through which the accounting information from such technologies supports the service marketing function through circular control and the types of controls needed for this.Practical implicationsManagers in large industrial organisations should ensure customer-facing staff have adequate digital competences and knowledge of circular products and services for marketing, product design improvements and material recovery that can help decrease costs and improve customer satisfaction. The digital systems need to be integrated with upstream and downstream partners.Social implicationsUnderstanding the transition towards increasingly circular product-service systems in industrial firms is important for current and future generations.Originality/valueThe originality lies in providing an empirical example of how digital technologies can be used to facilitate circular control and support the service marketing function in the aftermarket of an industrial firm.

CUSTOMER SEGMENTATION USING MACHINE LEARNING

Businesses need to identify and segment their consumer base in order to effectively customize their strategies and improve customer satisfaction in the highly competitive market landscape of today. The goal of this study is to employ machine learning techniques to create a strong consumer segmentation model that will classify customers according to their demographics, behaviors, and purchase histories. Through the use of multiple clustering methods, including K-means, DBSCAN, and Hierarchical Clustering, the model seeks to find unique customer segments with shared attributes. To accomplish optimal segmentation, the segmentation process entails three steps: feature selection to identify the most significant features, model training, and data preprocessing to manage missing values and outliers. In-depth segment analysis is also included in the report to offer practical insights for better client retention tactics, tailored recommendations, and focused marketing efforts. The results of the study illustrate how machine learning may be used to find hidden patterns in consumer data, giving organizations the ability to make data- driven decisions. Organizations may improve their marketing efforts, allocate resources more efficiently, and eventually increase customer engagement and profitability by putting this customer segmentation strategy into practice. Key Words: K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Cluster Plotting, Heatmaps, customer relationship management (CRM) system.

Shaping closed-loop supply chain dynamics: Mitigating the bullwhip effect and improving customer satisfaction in production systems with material reuse

Modern societies are currently transitioning towards more circular economic models to reduce environmental impacts and leverage economic opportunities. Managing the emerging supply chains, more complex and uncertain, poses a considerable challenge for many organisations. This work examines the dynamic behaviour of these closed-loop systems under different replenishment policies and varying levels of product returns and lead times. We model a generalised multi-level, closed-loop supply chain of a production system with material reuse, drawing inspiration from the real-world operations of cider makers in Spain. We investigate its ability to effectively and efficiently satisfy the consumer through four performance metrics: fill rate, bullwhip ratio, inventory cover, and reuse demand share. Through experimentation techniques, we observe that the highest performance is generally obtained when the retailer applies a pure pull policy while the manufacturer adopts a level scheduling policy. This strategy, built on lean principles, can achieve much lower operational costs while providing similar, or even better, service levels compared to more popular and complex alternatives. Based on our findings, we provide a set of actionable managerial implications that would help supply chain professionals successfully implement and manage these supply chains.

Customer churn data analysis using data mining

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Impact of Customer Satisfaction on Organizational Performance in Nigeria's Brewery Industry

This study investigated how customer satisfaction influences the profitability of Nigeria's brewery industry. The objective is to establish the connection between customer satisfaction and organizational performance if any. Primary and secondary data were used for this study. The primary data was collected with the aid of questionnaires while the secondary data was compiled and gathered from the selected organization periodicals and journals. The population of the study included 84 staff of three (3) brewery companies in Lagos, Nigeria. The data collected was analyzed using descriptive and inferential statistical tools. The descriptive tools included tables and percentages while the inferential tools included regression analysis and analysis of variance (ANOVA). The findings from the study show the R2 values of 0.630 which revealed that customer satisfaction independently explains 63% of the variation in organizational performance in Nigeria’s brewery industry. The f-statistics of 3.838 of the organization revealed that the model is statistically significant at 0.05 significance level. The study findings also revealed that the satisfaction of customers leads to better organizational performance. It was recommended that brewery companies should invest more in product quality and prioritize customer service in order to improve customer satisfaction levels.

Conceptual foundations of Tech-Driven logistics and supply chain management for economic competitiveness in the United Kingdom

The concept paper explores the transformative potential of technology in enhancing the logistics and supply chain sector to bolster the UK's economic competitiveness. This executive summary highlights the paper's primary objectives, theoretical frameworks, and anticipated outcomes, focusing on the integration of advanced technologies to streamline supply chain operations and drive economic growth. This paper aims to provide a comprehensive framework for understanding how technology can be leveraged to optimize logistics and supply chain management in the UK. It underscores the importance of a robust and efficient supply chain as a cornerstone of economic competitiveness, enabling businesses to reduce costs, improve delivery times, and enhance overall operational efficiency. Central to this paper is the exploration of various technological innovations that can revolutionize supply chain management. It discusses the impact of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data analytics on the logistics sector. These technologies enable real-time tracking, predictive analytics, enhanced transparency, and improved decision-making processes, which are crucial for maintaining a competitive edge in the global market. The paper delves into theoretical models and frameworks that support the integration of technology into supply chain management. It examines concepts such as the Digital Supply Chain (DSC) model, which emphasizes the interconnectedness of digital technologies in creating a seamless supply chain network. Additionally, the paper explores the Supply Chain Operations Reference (SCOR) model, which provides a structured approach to measuring and improving supply chain performance through technology-driven metrics and best practices. Addressing the practical challenges of implementing tech-driven logistics, the paper highlights issues such as cybersecurity risks, high initial investment costs, and the need for skilled labor. It proposes solutions like fostering public-private partnerships, investing in cybersecurity measures, and developing training programs to build a tech-savvy workforce capable of managing advanced supply chain systems. The expected outcomes of tech-driven logistics and supply chain management include increased operational efficiency, reduced operational costs, improved customer satisfaction, and enhanced agility in responding to market changes. These improvements can significantly contribute to the UK's economic competitiveness, positioning it as a leader in the global supply chain landscape. The paper provides a strategic roadmap for integrating advanced technologies into the logistics sector. By leveraging these technologies, the UK can enhance its supply chain efficiency, drive economic growth, and maintain a competitive advantage in the global economy. The paper calls for concerted efforts from policymakers, industry leaders, and academia to foster an environment conducive to technological innovation and supply chain excellence. Keywords: Logistic, Supply Chain, Technology, Cyber Security, UK.

Active learning based hyper-heuristic for the integration of production and Transportation: A third-party logistics perspective

The integration problem of production and transportation (IPPT) is one of the most important decision issues in real-life manufacturing and flow industries. To illustrate its potential for improving difficult production and transportation processes, we focus on a challenging case of IPPT, namely the distributed no-wait flow-shop and transportation integrated scheduling problem (DNWFSTISP) considering third party logistics (3PL), which occurs in many practical industries. DNWFSTISP consists of integrating the well-known distributed no-wait flow-shop scheduling and dedicated vehicle routing in the sense of 3PL while minimizing the total cost. First, we investigate the integration approach of the production and transportation of DNWFSTISP and present three problem-specific loading heuristics, which account for both a make-to-order strategy and 3PL providers to improve customer satisfaction. Second, we propose an active learning based hyper-heuristic algorithm (ALHHA) to solve the problem. ALHHA is unique in that it relies on an active learning based high-level exploration to discover promising search regions and a neighborhood search based low-level exploitation to intensively examine given search regions. Third, we assess the composing ingredients of the proposed ALHHA on 40 instances with up to 200 jobs and 7 factories to shed light on their impacts on the performance of the algorithm. Moreover, we make the 40 instances and solved upper bounds publicly available, which would be valuable for future research on DNWFSTISP.