Customer Feedback System Research Articles

AI in customer feedback integration: A data-driven framework for enhancing business strategy

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources, including surveys, reviews, social media, and customer support interactions. The proposed framework facilitates real-time feedback analysis, enabling businesses to identify trends, customer preferences, and potential pain points more efficiently. By integrating AI with existing customer relationship management (CRM) systems, businesses can automate the categorization and prioritization of feedback, allowing for timely responses and more effective problem-solving. Furthermore, predictive analytics tools within the framework can forecast customer needs, allowing businesses to tailor products and services to meet evolving expectations. This framework also supports continuous improvement by enabling businesses to track the impact of changes implemented based on customer feedback. Additionally, AI’s ability to personalize the customer experience by recognizing patterns and individual preferences plays a crucial role in increasing customer satisfaction and loyalty. The data-driven insights generated through AI integration can guide businesses in refining their marketing, product development, and customer service strategies, leading to improved operational efficiency and better alignment with customer expectations. In conclusion, the integration of AI into customer feedback mechanisms represents a significant advancement for data-driven business strategy development. This framework not only enhances feedback accuracy and speed but also empowers businesses to deliver more personalized and customer-centric solutions.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: 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 study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Advancing Customer Feedback Systems with Blockchain

AbstractOrganizations have to adjust to changes in the ecosystem, and customer feedback systems (CFS) provide important information to adapt products and services to changing customer preferences. However, current systems are limited to single-dimensional rating scales and are subject to self-selection biases. The work contributes design principles for CFS and implements a CFS that advances current systems by means of contextualized feedback according to specific organizational objectives. The authors apply Design Science Research (DSR) methodology and report on a longitudinal DSR journey considering multiple stakeholder values by utilizing value-sensitive design methods. They conducted expert interviews, design workshops, demonstrations, and a four-day experiment in an organizational setup, involving 132 customers of a major Swiss library. In the process, the identified design principles and the implemented software artifact were validated qualitatively and quantitatively, leading to conclusions for their efficient instantiation. The authors found that i) blockchain technology can afford four design principles of effective CFS. Also, ii) combining DSR with value-sensitive design methods explicitly provides rationale for design principles in the form of identified important values. Moreover, iii) combining DSR with value-sensitive design methods makes the construction of software artifacts more efficient it terms of design time by restricting the design space of a software artifact to those options that align with stakeholder values. The findings of this work thus extend the knowledge about the design of CFS and offer both researchers a theoretical contribution to reasoning about design principles and managers and decision makers a guide for the efficient design of software artifacts.

Non-Repeating, Normalized Questionnaire Sets Generation and Rendering Based on A Parsed Input Domain with Cryptographically Enforced Secure Access

This project introduces a novel approach to questionnaire generation and rendering, ensuring non repetition, normalization, and secure access control. Traditional methods often suffer from redundancy and lack of security measures, leading to compromised data integrity and confidentiality. Our solution employs a meticulously parsed input domain, where questions are intelligently selected to avoid repetition and maintain a balanced distribution across different domains of inquiry. Furthermore, normalization techniques are applied to standardize the questionnaire structure, enhancing comparability and analysis.To ensure data security and privacy, the system leverages cryptographic techniques to enforce secure access controls. This includes robust authentication mechanisms, data encryption, and integrity verification, safeguarding sensitive information throughout the questionnaire lifecycle.The implementation of this project not only improves the efficiency and reliability of questionnaire generation but also addresses critical concerns regarding data security and privacy. This approach has significant implications across various domains, including research, healthcare, and customer feedback systems, where accurate data collection and confidentiality are paramount

Compliance with Business Permits and Licensing System Requirements: Effects on Business Performance and Satisfaction Among MSMEs

This study investigated the relationships and differences among compliance levels, business performance, and satisfaction with the Business Permits and Licensing System (BPLS) requirements among 390 Micro, Small, and Medium Enterprises (MSMEs) in Iloilo City, Philippines. Using a descriptive, quantitative, and correlational design, the study sampled MSMEs from various industry sectors using the Input-Process-Output (IPO) model. Results show that MSMEs, particularly in sectors C and D, reported high compliance with BPLS requirements. It implies strong adherence to the requirement, suggesting effective engagement with the regulatory system. They also rated their business performance, including changes in gross sales, sales growth, capitalization, capital turnover, employee numbers, and overall satisfaction, as very good to excellent. It suggests they are experiencing strong financial performance, indicating a positive economic impact. High satisfaction with BPLS was also noted, which implies that the system is easy and efficient, leading to improved compliance and more favorable business conditions. T-tests and ANOVA revealed differences and similarities in compliance, business performance, and satisfaction across different business types and sectors, suggesting that these factors are affected by specific attributes of each business group. Pearson's correlation analysis indicated a negligible relationship between compliance and business performance, a very strong positive relationship between compliance and satisfaction, and a weak positive relationship between business performance and satisfaction. It implies that compliance significantly enhances satisfaction and has little impact on business performance. The study's recommendations, such as fully implementing an electronic BPLS and customer feedback system as part of the EODB law and including additional financial indicators in the unified application form, are of utmost importance. Future research should extend the analysis to other cities and local governments in the Visayas to further explore the linkage between compliance, business performance, and satisfaction.

Improving feedback analysis: Deep learning approach to college customer satisfaction assessments

Establishing consumers' views via text-based feedback in a questionnaire is crucial for organizations, include education, since it gives a summary of significant areas that help administrators plan, regulations, and decision making. Through surveys, academic organizations have gathered huge quantities of textual data all over the years. For the organization, it is still difficult to analyse the vast quantities of unstructured feedback from customers to understand their concerns and opinions generally. In this study, we propose deep learning (DL) based technique called topic modelling that utilizing Naive Bayesian (NB) to automatically summarize text and retrieve ideas from this raw data. Additionally, it discusses the text mining procedure used to extract relevant information from the vast volume of text-based data. The most significant issues obtained through feedback from customers were subsequently identified. The findings showed particular issues for workplaces, including environment, staffing, IT infrastructure, and customer feedback system. The feedbacks also prominently highlight difficulties with the attitude of student assistance and security staff as well as the library's management and operations.

Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation

An overwhelming majority of previous works find longer product reviews to be more helpful than short reviews. In this paper, we build upon information overload theory and propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the complexity of the line of argumentation. To test this idea, we implement state-of-the-art machine learning methods that allow us to study the line of argumentation in reviews at the sentence-level. Our empirical analysis based on a dataset of Amazon customer reviews suggests that line of argumentation and review length are closely intertwined such that longer reviews with frequent changes between positive and negative arguments are perceived as less helpful. Our work has important implications for marketing professionals and retailer platforms that can utilize our results to optimize their customer feedback systems, enhance reviewer guidelines, and include more useful product reviews.

Connecting the Levers of Platform Control

Drawing from a transurban field research conducted in the ride-hailing sector in Paris and Brussels regions, this article investigates platformization as a productive model defined by the articulation of an outsourced labor regime with an algorithmic and data-driven type of management. Beyond the formal sharing of an independent contractor status, nuanced by a variety of positions including salaried work, platform drivers are unified in practice by their common economic dependence on platforms. This situation gives them the role of an adjustment variable in platforms’ commercial strategy, forcing them to raise their work time to adapt to the cost decreases in the mid-2010s, which dramatically reduced their income. To ensure a flexible flow into the workforce, platforms have favored the development of small intermediaries to outsource hiring and thereby skirt labor law and evade taxes. Market pressure is also enforced by its inclusion, with other productive goals such as service fluidity and quality, into a digital model of management inspired by lean production. The application is configured to operate as a device of technical and hegemonic control that incorporates just-in-time and intensification commands. Digital control is completed by the inclusion of a customer feedback system used to implement service standardization and further involvement in work.

Impact of Customer Feedback System using Machine Learning Algorithms for Sentiment Mining

The process of discovering and analyzing the customer feedback using Natural Language Processing (NLP) is said to be sentiment analysis. Based on the surge over the concept of rating level in sentiment analysis, sentiment is utilized as an attribute for certain aspects or features that get expressed and more attention are provided to the problem of detecting the customer reviews. Despite the wide use and popularity of some methods, a better technique for identifying the polarity of a text data is hard to find. Machine learning has recently attracted attention as an approach for sentiment analysis. This work extends the idea of evaluating the performance of various Machine Learning (ML) classifiers namely logistic regression, Naive Bayes, Support Vector Machine (SVM) and Neural Network (NN).To show their effectiveness in sentiment mining of customer product reviews, the customer feedback has been collected from Grocery and Gourmet Food. Nearly 90 thousands customers feedback reviews of various product related categories namely Product ID, rating, review test, review time reviewer ID and reviewer name are used in this analysis. The performance of the classifiers is measured in terms of accuracy, specificity and sensitivity. From the experimental results, the better machine learning classification algorithm is proposed for sentiment mining using online shopping customer review data.

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

Identifying Effectiveness of Supply Chain Management Using Fuzzy Customer Feedback System

Ex-Post Analysis of Land Title Registration in Ghana Since 2008 Merger

Land ownership and security of title have continued to dominate land management discourses in many developing economies, leading to a proliferation of studies that explore, among other things, the nexus between land title registration and land security, women’s access to land, innovation in agriculture, access to finance, and economic development. For many years, Ghana experienced minimal success in formalizing land ownership and title registration. However, public confidence and expectations were raised once more in 2008 with the merger of four disparate land agencies into the New Lands Commission (NLC) under the overarching ambit of the Land Administration Project (LAP). This article contributes to existing studies by evaluating the impact of the 2008 merger. This is accomplished by matching the project’s stated objectives with actual outcomes and situating the findings in the broader theoretical debate about land title registration and economic development. Using data gathered through interviews with officials of the Greater Accra Lands Commission and with prospective land title holders, the study concludes that there have not been any great gains in achieving the complete digitization of the title registration process and follow-up procedures. However, there has been a reduction in the turn-around time for processing land documents, from more than 36 months to about 3 months, as well as increased public awareness about the process of title registration. The study recommends further training of staff members of the NLC in handling the digitization process, institutionalization of anti-corruption and anti-bribery practices, and the introduction of a well-functioning customer feedback system.

The transparency paradox. Building trust, resolving disputes and optimising logistics on conventional and online drugs markets

BackgroundIn recent years, marketplaces in the darknet emerged where vendors and customers can exchange illicit drugs and other goods on digital platforms by using hidden internet services. The main thesis of this paper is that in an online environment, different practices for building trust and reputation emerge that stabilise market processes. MethodsQualitative and quantitative data from a recent German project on conventional (offline) small-scale drug dealing as well as qualitative case studies on four online vendors operating on Agora market are used to explore alternative practices for building trust and reputation. They also explore the use of violence and logistics established on cryptomarkets in comparison to traditional dealing. To analyse the data we applied qualitative content analyses. ResultsFor conventional commercial illicit drug dealing on various kinds of markets, trust between buyer and seller is a crucial issue, often emphasized by restricting deals to well-known persons. While this typically includes face-to-face contact, the opposite is true with online drug trading. It is characteristic of cryptomarkets that the parties involved in a transaction know neither the personal identity nor the physical location of one another. This is realised by using aliases, anonymising software, and cryptocurrencies for payments. Violence typically only plays a role in traditional drug dealing, but mostly, if at all, just as a latent threat for potential rule-breakers. Processing a transaction anonymously includes escrow services for the buyers, which makes trading more reliable, although they cannot completely prevent scamming. Furthermore, online drug marketplaces usually offer a customer feedback system that allows customers to rate vendors and review products. A positive vendor feedback helps building reputation and trust in such an online environment. With regard to logistics, most conventional small-scale dealers restrict their acts of selling to private surroundings to avoid encounters with law enforcement. In cryptomarkets, the purchased drugs are delivered by traditional postal services, sometimes to false addresses or to someone else's name to conceal the identity and address of the buyer. ConclusionOn virtual drug markets practices of building trust, conflict resolution and logistics is constantly evolving. They offer improved security solutions on the one hand while on the other hand scamming and fraud seem to be widely used on both online and conventional drug markets.

Software quality construction in 11 companies: an empirical study using the grounded theory

This paper investigates software quality construction in 11 software companies using the Grounded Theory research method. Our analysis shows that software quality construction is influenced by the development context, which is affected by customer involvement; agility, which enables cooperation among the development teams; outsourcing; and human factors i.e., developers' creativity, innovation, experience and art, which are not limited by development methods but customer demand and feedback systems. We claim that these findings may be applied in software development companies in the areas of prioritization of human and non-human resources.

Development of customer feedback systems in management of quality and reliability of cars during operation

The paper presents the results of developing customer feedback systems aiming at the solving a problem of increasing of effectiveness of the quality management of passenger automobiles during their operation.

Cost of Quality (CoQ) of Water Testing Laboratory

The purpose of the study is to classify and determine different quality cost and the quantity of such errors in accreditated water testing laboratory. The study indicates that the quality of water testing laboratory, test results can be regarded as good based on the accuracy of the result. In this study total cost of quality is 9.33% from revenues which is not more and COC is 6.93% and CONC is 2.4%.This is an ideal situation, prevention costs will be the largest portion of the Total Cost of Quality. All costs resulting from services not conforming to requirements of customer/user needs which occur after delivery of test report. By minimizing delay and Claims (retest) can reduced external failure cost Laboratory given weighted to customer needs because of the good quality of service. Although, laboratory require improvements in providing test results without delay to the customer. The cost of processing and correcting such errors was minimum. The use of a customer feedback system allows systematic monitoring of external failures. The fundamental point is to monitor the effects of the quality measures taken to reduce the number of failures. The method implemented this study is the classical prevention-appraisal-failure (P-A-F) model.

The Effect of Salesforce Behavior on Customer Satisfaction: An Interactive Framework

Salesforce behavior can have a significant effect on customer satisfaction. This paper presents a conceptual framework examining the impact of the salesforce policy on customer satisfaction. Within the context of the framework, it is suggested that salespeople and sales managers can increase customer satisfaction through adaptive selling behavior and by developing customer feed-back systems respectively. Testable propositions are suggested for future research, and potential managerial implications of the research are discussed. The paper emphasizes that the determination and improvement of customer satisfaction should be essential goals of salesforce management.

Continuous learning using dissatisfaction feedback in new product development contexts

PurposeThe purpose of this paper is to explore how various dissatisfaction feedback transferrals contribute to the decision‐making process in product development contexts.Design/methodology/approachThe paper presents the results from a qualitative interview‐based study of 16 product development organizations and their customer dissatisfaction feedback systems; 84 percent of the companies within a machine industry segment in Sweden are covered.FindingsBased on the empirical investigation, a taxonomy of five decisions based upon dissatisfaction feedback is developed: reactive, preventive and developmental decisions are connected with exploitation of products, while future developmental and future preventive decisions are connected with exploration of future products.Originality/valueThe project is essentially managerial, aiming to provide managers and other decision makers with a framework that displays how various dissatisfaction feedback transferrals contribute to various decisions in product development contexts. The paper highlights that despite costly investigations in customer feedback systems, very few customer dissatisfaction feedback transferrals are connected to explorative future developmental actions.

Utilisation of customer feedback in a university hospital laboratory

The article describes the customer feedback system at a university hospital laboratory and the analysis of the feedback data from clinical customers in 2001–2006. The most common subject matters of the feedback were suspicion of the validity of laboratory results, delay in service and lacking test results, covering 82% of the 115 reports. The investigations of the cases revealed errors or defects in laboratory services in 81 cases. The most common errors or defects were erroneous test results (35 cases; 43%), delayed test results (24 cases; 30%) and lacking test results (15 cases; 18%). The most common underlying causes for a laboratory error or defect were unintended errors and non-compliance with operating instructions. Seventy-six percent of the feedback reports led to corrective actions. It is important to react to every instance of customer feedback and to find out possible errors or defects in the laboratory process. Uncovering the underlying causes makes adequate corrective and preventive actions possible.

Merchant's Tire & Auto Centers: Customer Feedback as a Quality Improvement Tool

This case introduces students to issues surrounding the collection, interpretation, and use of customer feedback for improving the quality of service. Various forms of customer feedback are considered, and a system is described that ties the feedback to employee compensation. The final section of the case describes the key figure who is questioning the appropriateness of the company's customer-feedback system. Data analysis is provided in exhibits for students to interpret.

Implementation of service profit chain initiatives in a franchise system

PurposeThe purpose of this paper is to evaluate the implementation of the service profit chain as an entrepreneurial marketing initiative within a defined franchise system. An objective is also to evaluate these initiatives against service quality dimensions. Despite recognition of the merits and advantages of service profit chain initiatives of retention, related sales and referrals, little research has empirically addressed the perceptions of franchisees regarding these initiatives.Design/methodology/approachMethodology includes the empirical analysis of evaluating the effectiveness of service profit chain implementation, primarily based on initiatives of retention, related sales and referrals. Research methodology comprises the survey approach, using electronic media and Surveypro analysis. This is facilitated by descriptive and inferential statistical techniques using SPSS version 11.0 data analysis. Inferential significance tests include the ANOVA Kruskal‐Wallis hypothesis test; and the Cronbach's coefficient alpha.FindingsHypothesis tests highlight the significance of a positive association between service profit chain initiatives and service quality. All service profit chain initiatives were deemed appropriate. Item analysis highlights specific dominant service profit chain initiatives, linking key relationship marketing and service quality themes.Research limitations/implicationsFindings are indicative of implementation opportunities, including measurement of loyalty within the system, developing customer feedback and complaint systems, and communicating the value of service profit chain enhancement within the franchise system.Originality/valueOriginality includes cognizance of entrepreneurial orientation, relationships and service quality when implementing service profit chain initiatives. Concurrent research opportunities include the implementation of other entrepreneurial marketing initiatives, including relationship marketing and benchmarking for best practice.