A Model of Electronic Customer Relationship Management System Adoption In Telecommunication Companies

Employee satisfaction is key to electronic–customer relationship management (E-CRM) systems in telecommunication companies. The purpose of this study is to investigate the direct and indirect effect of technological factors, individual factors, and organizational factors on employees' level of satisfaction. For this study, data was collected from 300 employees' workings in Malaysian telecommunication companies; and the data was analyzed using PLS-SEM. The findings revealed that technological, organizational, and individual factors are positively and significantly related to satisfaction and perceived usefulness. The results also supported the direct and positive relationship between perceived usefulness between employees' job satisfaction. The study has contributed to the body of literature by exploring the implications of various significant factors in terms of employee satisfaction. Besides, the management of the telecommunication companies may benefit from this study by adopting strategies that not only employee satisfaction but may also enhance the companies' performance. The limitations and the direction for future research are discussed in the end

Data Mining Techniques with Electronic Customer Relationship Management for Telecommunication Company

Organizations must improve decisional quality, and the continuous usage of data mining techniques is a crucial issue for management. This issue mostly involves an individual's motivation to engage in the behavior. This could perhaps be characterized in terms of the working regimen. technology utilization and employee activity are the two main difficulties that this dilemma revolves around. This study aims to address the aspect associated with data mining and E-CRM in the telecom industry. The methods that are used in the current study,  analysis studies of the data mining techniques are applied to E-CRM that has been identified. Moreover, PHP with the update of the DeLone and McLean methods has been used in the current study. The results show the significance in affecting the continuance used intention of data mining techniques. User satisfaction, technology, and data mining are critical predictors of employment intentions

Integrating Delone and Mclean and Task Technology Fit Models to Evaluate the Influence of E-CRM on Individual Performance

Electronic customer relationship management (E-CRM) is a growing technology that has already captured the attention and focus of researchers. The important development in end-user use, users’ satisfaction, and performance outcomes have been one of the most welcome significant developments in E-CRM. The objective of the research is to assess the influence of E-CRM on employees. We propose a framework that integrates the DeLone and McLean Information System (IS) success model with the Task Technology Fit (TTF) framework. The empirical approach is based on 300 open questionnaires. The findings show that utilization and users’ satisfaction are major predictors of individual performance, as well as the significance of TTF's modulating influence on employee performance. User satisfaction is favorably impacted by system quality, information quality, and service quality

Applied Fuzzy and Analytic Hierarchy Process in Hybrid Recommendation Approaches for E-CRM

To create a personalized E-CRM recommendation system, the electronic customer relationship management system needs to investigate low accuracy and lack of personalization through applied hybrid recommendation system techniques such as fuzzy and AHP. The main purpose of this research is to enhance the accuracy and deep understanding of common recommendation techniques in E-CRM. The fuzzy and AHP techniques have been used in the current study to the available information of objects and to extend recommendation areas. The findings indicate that each of these strategies is appropriate for a recommendation system in a technological environment. The present study makes several noteworthy contributions to the fuzzy Analytic Hierarchy Process (AHP) and has the maximum accuracy of any of these approaches, with 66.67% of accuracy. However, AHP outperforms all others in terms of time complexity. We advocate the concept and implementation of an intelligent business recommendation system dependent on a hybrid approval algorithm that serves as a model for E–CRM recommendation systems. This recommendation system's whole design revolves on the hybrid recommendation system. The systems additionally incorporate the recommendation modules and the recommendation measurement updating framework. The recommendation modules include the formulation and development of material recommendation algorithms, element collaborative filtering recommendation algorithms, and demography-based recommendation algorithms

Analysis of Perceived Usefulness and Perceived Ease of Use in Relation to Employee Performance

The adoption of electronic customer relationship management (E-CRM) is an important consideration for communication and collaboration companies. Several research efforts have focused on studying the success variables that contribute to the effectiveness of E-CRM systems. This study investigates whether enhanced Technology Acceptance Model (TAM) and TAM3 can be used to understand employee adoption of E-CRM. The present study evaluates the scale on two key variables: perceived value and ease of use, are considered the main predictors of acceptability. The results show that perceived usefulness significantly affects 3.83 values out of 5.0, with a confidence interval of 839. Additionally, the employee performance rating is 0.886, which is above the acceptable limit of 0.70. The results indicate that the data fits the extended TAM model effectively. Performance measurement is heavily influenced by perceived ease of use and user satisfaction

Food Recommender System: A Review on Techniques, Datasets and Evaluation Metrics

With the rise of digital platforms and the availability of large amounts of data, food recommender systems have become a powerful tool for helping people discover new and delicious meals. Today, these systems use algorithms and machine learning models to analyze ingredients and recommend meals based on factors such as cuisine, dietary restrictions, and ingredient compatibility. Hence, this paper aims to review the various recommendation techniques employed in the food recommender system. We also discussthe various algorithms that are used in meal recommender systems, including collaborative filtering, content-based filtering, and hybrid approaches. Overall, this paper provides a comprehensive overview of the current state-of-the-art meal recommender systems and to identify the opportunities for future enhancement and development in this field

Content-based Recommender System with Descriptive Analytics

A recommendation system (RS) is an information filtering system that provides users with information, which one may be interested in. Ontology modelling has been widely used to conceptualize items and their semantic relationship together. Hence, in this paper, we propose an intelligent CB RS that allows users to not only access the product recommendations, but also the dashboard systems, which contain descriptive analytics, modeled using ontology. The dashboard allows users to have insight into past data. It consists of five main features: (i) Highlight Dashboard, (ii) Customer Dashboard, (iii) Advanced Search, (iv) Pivot Table and Pivot Chart, and (v) Report. Experimental evaluations show that the CB RS can return the accurate recommended product in a real propriety dataset

Improving the Prediction Resolution Time for Customer Support Ticket System

Processing customer queries on time will able to engage customer satisfaction, and thus improve the customer retention of a company. Increasing the labour to process these queries is certainly not an ideal solution. Advancing technology such as artificial intelligence and machine learning has led to the goal of automating this process, by predicting the time needed to resolve certain issues based on past similar cases. In this paper, we present the architecture for the Customer Support Ticket System to improve the accuracy of the predicted resolution time. In this research, we first perform the one hot encoding on the categorical variables, followed by feature selection. Next, a combination of classification and regression models is being utilised in our prediction pipeline. Experimental evaluations demonstrated that the Random Forest (RF) regression model has the best performance as compared to Neural Network and ADA boost. In addition, by adding the extremity feature as the attention, a significant performance boost for RF is observed

How Private Blockchain Technology Secure IoT Data Record

Scalability, security, and communication delays in the Internet of Things can be resolved using blockchain technology, which is the final piece of the puzzle. Blockchain technology may provide the IoT sector with the panacea it needs. Blockchain technology can record massive amounts of access points, allowing for the recording of data and collaboration between equipment, and providing manufacturers in the IoT sector with significant cost reductions. By removing individual points of failure, this decentralized strategy would build a more robust environment for the operation of equipment. Wondering in the era of information technology. Blockchain has attracted a lot of interest in recent decades for its ability to provide decentralized, definitive, and auditable usage in the Internet of Things (IoT). The majority of Internet of Things (IoT) devices are facing significant adaptability and privacy difficulties. This study proposes an understanding of How blockchain works with IoT for security data records

Utilization of Blockchain Technology In Human Resource Management

With the advancement of time and the advancement scientific knowledge, the blockchain technology has expanded in various applications. The security and data privacy of employee recruitment system has been compromised. Therefore, hiring managers can enhance job satisfaction by adopting blockchain technology to secure human resource data management. This paper provides current research status, issues and a deep investigation on the blockchain technology's potential use in the management of human resources