Mohammed Rashed Mohamed Al Humaid Alneyadi, Md Kassim Normalini
Aim/Purpose: The UAE and other Middle Eastern countries suffer from various cybersecurity vulnerabilities that are widespread and go undetected. Still, many UAE government organizations rely on human-centric approaches to combat the growing cybersecurity threats. These approaches are ineffective due to the rapid increase in the amount of data in cyberspace, hence necessitating the employment of intelligent technologies such as AI cybersecurity systems. In this regard, this study investigates factors influencing users’ intention to adopt AI-based cybersecurity systems in the UAE. Background: Even though UAE is ranked among the top countries in embracing emerging technologies such as digital identity, robotic process automation (RPA), intelligent automation, and blockchain technologies, among others, it has experienced sluggish adoption of AI cybersecurity systems. This selectiveness in adopting technology begs the question of what factors could make the UAE embrace or accept new technologies, including AI-based cybersecurity systems. One of the probable reasons for the slow adoption and use of AI in cybersecurity systems in UAE organizations is the employee’s perception and attitudes towards such intelligent technologies. Methodology: The study utilized a quantitative approach whereby web-based questionnaires were used to collect data from 370 participants working in UAE government organizations considering or intending to adopt AI-based cybersecurity systems. The data was analyzed using the PLS-SEM approach. Contribution: The study is based on the Protection Motivation Theory (PMT) framework, widely used in information security research. However, it extends this model by including two more variables, job insecurity and resistance to change, to enhance its predictive/exploratory power. Thus, this research improves PMT and contributes to the body of knowledge on technology acceptance, especially in intelligent cybersecurity technology. Findings: This paper’s findings provide the basis from which further studies can be conducted while at the same time offering critical insights into the measures that can boost the acceptability and use of cybersecurity systems in the UAE. All the hypotheses were accepted. The relationship between the six constructs (perceived vulnerability (PV), perceived severity (PS), perceived response efficacy (PRE), perceived self-efficacy (PSE), job insecurity (JI), and resistance to change (RC)) and the intention to adopt AI cybersecurity systems in the UAE was found to be statistically significant. This paper’s findings provide the basis from which further studies can be conducted while at the same time offering critical insights into the measures that can boost the acceptability and use of cybersecurity systems in the UAE. Recommendations for Practitioners: All practitioners must be able to take steps and strategies that focus on factors that have a significant impact on increasing usage intentions. PSE and PRE were f
{"title":"Factors Influencing User’s Intention to Adopt AI-Based Cybersecurity Systems in the UAE","authors":"Mohammed Rashed Mohamed Al Humaid Alneyadi, Md Kassim Normalini","doi":"10.28945/5166","DOIUrl":"https://doi.org/10.28945/5166","url":null,"abstract":"Aim/Purpose: The UAE and other Middle Eastern countries suffer from various cybersecurity vulnerabilities that are widespread and go undetected. Still, many UAE government organizations rely on human-centric approaches to combat the growing cybersecurity threats. These approaches are ineffective due to the rapid increase in the amount of data in cyberspace, hence necessitating the employment of intelligent technologies such as AI cybersecurity systems. In this regard, this study investigates factors influencing users’ intention to adopt AI-based cybersecurity systems in the UAE. Background: Even though UAE is ranked among the top countries in embracing emerging technologies such as digital identity, robotic process automation (RPA), intelligent automation, and blockchain technologies, among others, it has experienced sluggish adoption of AI cybersecurity systems. This selectiveness in adopting technology begs the question of what factors could make the UAE embrace or accept new technologies, including AI-based cybersecurity systems. One of the probable reasons for the slow adoption and use of AI in cybersecurity systems in UAE organizations is the employee’s perception and attitudes towards such intelligent technologies. Methodology: The study utilized a quantitative approach whereby web-based questionnaires were used to collect data from 370 participants working in UAE government organizations considering or intending to adopt AI-based cybersecurity systems. The data was analyzed using the PLS-SEM approach. Contribution: The study is based on the Protection Motivation Theory (PMT) framework, widely used in information security research. However, it extends this model by including two more variables, job insecurity and resistance to change, to enhance its predictive/exploratory power. Thus, this research improves PMT and contributes to the body of knowledge on technology acceptance, especially in intelligent cybersecurity technology. Findings: This paper’s findings provide the basis from which further studies can be conducted while at the same time offering critical insights into the measures that can boost the acceptability and use of cybersecurity systems in the UAE. All the hypotheses were accepted. The relationship between the six constructs (perceived vulnerability (PV), perceived severity (PS), perceived response efficacy (PRE), perceived self-efficacy (PSE), job insecurity (JI), and resistance to change (RC)) and the intention to adopt AI cybersecurity systems in the UAE was found to be statistically significant. This paper’s findings provide the basis from which further studies can be conducted while at the same time offering critical insights into the measures that can boost the acceptability and use of cybersecurity systems in the UAE. Recommendations for Practitioners: All practitioners must be able to take steps and strategies that focus on factors that have a significant impact on increasing usage intentions. PSE and PRE were f","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69313669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The
{"title":"Epidemic Intelligence Models in Air Traffic Networks for Understanding the Dynamics in Disease Spread - A Case Study","authors":"Anbalagan Bhuvaneswari","doi":"10.28945/5214","DOIUrl":"https://doi.org/10.28945/5214","url":null,"abstract":"Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135611285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dat Tho Tran, Khoa Dinh Vu, Phong Ba Le, Phuong Thi Lan Tran
Aim/Purpose: Given the increasingly important role of knowledge and human resources for firms in developing and emerging countries to pursue innovation, this paper aims to study and explore the potential intermediating roles of knowledge donation and collection in linking high-involvement human resource management (HRM) practice and innovation capability. The paper also explores possible moderators of market turbulence in fostering the influences of knowledge-sharing (KS) behaviors on innovation competence in terms of incremental and radical innovation. Background: The fitness of HRM practice is critical for organizations to foster knowledge capital and internal resources for improving innovation and sustaining competitive advantage. Methodology: The study sample is 309 respondents and Structural Equation Model (SEM) was used for the analysis of the data obtained through a questionnaire survey with the aid of AMOS version 22. Contribution: This paper increases the understanding of the precursor role of high-involvement HRM practices, intermediating mechanism of KS activities, and the regulating influence of market turbulence in predicting and fostering innovation capability, thereby pushing forward the theory of HRM and innovation management. Findings: The empirical findings support the proposed hypotheses relating to the intermediating role of KS in the HRM practices-innovation relationship. It spotlights the crucial character of market turbulence in driving the domination of knowledge-sharing behaviors on incremental innovation. Recommendations for Practitioners: The proposed research model can be applied by leaders and directors to foster their organizational innovation competence. Recommendation for Researchers: Researchers are recommended to explore the influence of different models of HRM practices on innovation to identify the most effective pathway leading to innovation for firms in developing and emerging nations. Impact on Society: This paper provides valuable initiatives for firms in developing and emerging markets on how to leverage the strategic and internal resources of an organization for enhancing innovation. Future Research: Future studies should investigate the influence of HRM practices and knowledge resources to promote frugal innovation models for dealing with resource scarcity.
目的/目的:鉴于知识和人力资源在发展中国家和新兴国家企业追求创新中的作用越来越重要，本文旨在研究和探索知识捐赠和知识收集在高参与度人力资源管理(HRM)实践与创新能力之间的潜在中介作用。本文还从渐进式创新和突破性创新两方面探讨了市场动荡对知识共享行为对创新能力影响的调节因素。背景:人力资源管理实践的适应性对于组织培养知识资本和内部资源以提高创新和保持竞争优势至关重要。方法:研究样本为309名受访者，采用结构方程模型(SEM)对问卷调查所得数据进行分析，并借助于AMOS version 22。贡献:加深了对高介入人力资源管理实践的先导作用、KS活动的中介机制以及市场动荡对预测和培育创新能力的调节作用的理解，从而推动了人力资源管理与创新管理理论的发展。研究结果:实证研究结果支持了关于KS在人力资源管理实践-创新关系中的中介作用的假设。它突出了市场动荡在驱动知识共享行为对渐进式创新的主导作用中的关键特征。对实践者的建议:提出的研究模型可被领导者和董事应用于培养他们的组织创新能力。对研究人员的建议:建议研究人员探索不同的人力资源管理实践模式对创新的影响，以确定发展中国家和新兴国家企业创新的最有效途径。对社会的影响:本文为发展中国家和新兴市场的企业如何利用组织的战略和内部资源来加强创新提供了有价值的倡议。未来研究:未来研究应探讨人力资源管理实践和知识资源的影响，以促进应对资源稀缺的节俭创新模式。
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Table of Contents for Volume 18, 2023, of the Interdisciplinary Journal of Information, Knowledge, and Management
《信息、知识与管理》跨学科期刊2023年第18卷目录表
{"title":"IJIKM Volume 18, 2023 – Table of Contents","authors":"","doi":"10.28945/5058","DOIUrl":"https://doi.org/10.28945/5058","url":null,"abstract":"Table of Contents for Volume 18, 2023, of the Interdisciplinary Journal of Information, Knowledge, and Management","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69311229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anas Husain, Mohammad Alsharo, saif Addeen AlRababah, Mohammed-Issa Riad Jaradat
Aim/Purpose: The objective of this research is to investigate the effect of review consistency between textual content and rating on review helpfulness. A measure of review consistency is introduced to determine the degree to which the review sentiment of textual content conforms with the review rating score. A theoretical model grounded in signaling theory is adopted to explore how different variables (review sentiment, review rating, review length, and review rating variance) affect review consistency and the relationship between review consistency and review helpfulness. Background: Online reviews vary in their characteristics and hence their different quality features and degrees of helpfulness. High-quality online reviews offer consumers the ability to make informed purchase decisions and improve trust in e-commerce websites. The helpfulness of online reviews continues to be a focal research issue regardless of the independent or joint effects of different factors. This research posits that the consistency between review content and review rating is an important quality indicator affecting the helpfulness of online reviews. The review consistency of online reviews is another important requirement for maintaining the significance and perceived value of online reviews. Incidentally, this parameter is inadequately discussed in the literature. A possible reason is that review consistency is not a review feature that can be readily monitored on e-commerce websites. Methodology: More than 100,000 product reviews were collected from Amazon.com and preprocessed using natural language processing tools. Then, the quality reviews were identified, and relevant features were extracted for model training. Machine learning and sentiment analysis techniques were implemented, and each review was assigned a consistency score between 0 (not consistent) and 1 (fully consistent). Finally, signaling theory was employed, and the derived data were analyzed to determine the effect of review consistency on review helpfulness, the effect of several factors on review consistency, and their relationship with review helpfulness. Contribution: This research contributes to the literature by introducing a mathematical measure to determine the consistency between the textual content of online reviews and their associated ratings. Furthermore, a theoretical model grounded in signaling theory was developed to investigate the effect on review helpfulness. This work can considerably extend the body of knowledge on the helpfulness of online reviews, with notable implications for research and practice. Findings: Empirical results have shown that review consistency significantly affects the perceived helpfulness of online reviews. The study similarly finds that review rating is an important factor affecting review consistency; it also confirms a moderating effect of review sentiment, review rating, review length, and review rating variance on the relationship between re
{"title":"Content-Rating Consistency of Online Product Review and Its Impact on Helpfulness: A Fine-Grained Level Sentiment Analysis","authors":"Anas Husain, Mohammad Alsharo, saif Addeen AlRababah, Mohammed-Issa Riad Jaradat","doi":"10.28945/5187","DOIUrl":"https://doi.org/10.28945/5187","url":null,"abstract":"Aim/Purpose: The objective of this research is to investigate the effect of review consistency between textual content and rating on review helpfulness. A measure of review consistency is introduced to determine the degree to which the review sentiment of textual content conforms with the review rating score. A theoretical model grounded in signaling theory is adopted to explore how different variables (review sentiment, review rating, review length, and review rating variance) affect review consistency and the relationship between review consistency and review helpfulness. Background: Online reviews vary in their characteristics and hence their different quality features and degrees of helpfulness. High-quality online reviews offer consumers the ability to make informed purchase decisions and improve trust in e-commerce websites. The helpfulness of online reviews continues to be a focal research issue regardless of the independent or joint effects of different factors. This research posits that the consistency between review content and review rating is an important quality indicator affecting the helpfulness of online reviews. The review consistency of online reviews is another important requirement for maintaining the significance and perceived value of online reviews. Incidentally, this parameter is inadequately discussed in the literature. A possible reason is that review consistency is not a review feature that can be readily monitored on e-commerce websites. Methodology: More than 100,000 product reviews were collected from Amazon.com and preprocessed using natural language processing tools. Then, the quality reviews were identified, and relevant features were extracted for model training. Machine learning and sentiment analysis techniques were implemented, and each review was assigned a consistency score between 0 (not consistent) and 1 (fully consistent). Finally, signaling theory was employed, and the derived data were analyzed to determine the effect of review consistency on review helpfulness, the effect of several factors on review consistency, and their relationship with review helpfulness. Contribution: This research contributes to the literature by introducing a mathematical measure to determine the consistency between the textual content of online reviews and their associated ratings. Furthermore, a theoretical model grounded in signaling theory was developed to investigate the effect on review helpfulness. This work can considerably extend the body of knowledge on the helpfulness of online reviews, with notable implications for research and practice. Findings: Empirical results have shown that review consistency significantly affects the perceived helpfulness of online reviews. The study similarly finds that review rating is an important factor affecting review consistency; it also confirms a moderating effect of review sentiment, review rating, review length, and review rating variance on the relationship between re","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135649161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niko Ibrahim, Putu Wuri Handayani, Betty Purwandari, Imairi Eitiveni, Fadhil Dzulfikar
Aim/Purpose: This study aims to create customer segmentation who use Online Travel Agent (OTA) mobile applications in Indonesia throughout their hotel booking journey. Background: In the context of mobile hotel booking applications, research analyzing the customer experience at each customer journey stage is scarce. However, literature increasingly acknowledges the significance of this stage in comprehending customer behavior and revenue streams. Methodology: This study employs a mixed-method and exploratory approach by doing in-depth interviews with 20 participants and questionnaires from 207 participants. Interview data are analyzed using thematic analysis, while the questionnaires are analyzed using descriptive statistics. Contribution: This study enriches knowledge in understanding customer behavior that considers the usage of mobile apps as a segmentation criterion in the hotel booking journey. Findings: We developed four user personas (no sweat player, spotless seeker, social squad, and bargain hunter) that show customer segmentation based on the purpose, motivation, and actions in each journey stage (inspiration, consideration, reservation, and experience). Recommendations for Practitioners: The resulting customer segmentation enables hospitality firms to improve their current services by adapting to the needs of various segments and avoiding unanticipated customer pain points, such as incomplete information, price changes, no social proof, and limited payment options. Recommendation for Researchers: The quality and robustness of the customer segment produced in this study can be further tested based on the criteria of homogeneity, size, potential benefits, segment stability, segment accessibility, segment compatibility, and segment actionability. Impact on Society: This study has enriched the existing literature by establishing a correlation between user characteristics and how they use smartphones for tourism planning, focusing on hotel booking in mobile applications. Future Research: For future research, each customer segment’s demographic and behavioral factors can be explored further.
{"title":"The Segmentation of Mobile Application Users in The Hotel Booking Journey","authors":"Niko Ibrahim, Putu Wuri Handayani, Betty Purwandari, Imairi Eitiveni, Fadhil Dzulfikar","doi":"10.28945/5190","DOIUrl":"https://doi.org/10.28945/5190","url":null,"abstract":"Aim/Purpose: This study aims to create customer segmentation who use Online Travel Agent (OTA) mobile applications in Indonesia throughout their hotel booking journey. Background: In the context of mobile hotel booking applications, research analyzing the customer experience at each customer journey stage is scarce. However, literature increasingly acknowledges the significance of this stage in comprehending customer behavior and revenue streams. Methodology: This study employs a mixed-method and exploratory approach by doing in-depth interviews with 20 participants and questionnaires from 207 participants. Interview data are analyzed using thematic analysis, while the questionnaires are analyzed using descriptive statistics. Contribution: This study enriches knowledge in understanding customer behavior that considers the usage of mobile apps as a segmentation criterion in the hotel booking journey. Findings: We developed four user personas (no sweat player, spotless seeker, social squad, and bargain hunter) that show customer segmentation based on the purpose, motivation, and actions in each journey stage (inspiration, consideration, reservation, and experience). Recommendations for Practitioners: The resulting customer segmentation enables hospitality firms to improve their current services by adapting to the needs of various segments and avoiding unanticipated customer pain points, such as incomplete information, price changes, no social proof, and limited payment options. Recommendation for Researchers: The quality and robustness of the customer segment produced in this study can be further tested based on the criteria of homogeneity, size, potential benefits, segment stability, segment accessibility, segment compatibility, and segment actionability. Impact on Society: This study has enriched the existing literature by establishing a correlation between user characteristics and how they use smartphones for tourism planning, focusing on hotel booking in mobile applications. Future Research: For future research, each customer segment’s demographic and behavioral factors can be explored further.","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135753870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tianpei Xu, Ying Ma, Changyu Ao, Min Qu, XiangHong Meng
Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets a
{"title":"A Novel Telecom Customer Churn Analysis System Based on RFM Model and Feature Importance Ranking","authors":"Tianpei Xu, Ying Ma, Changyu Ao, Min Qu, XiangHong Meng","doi":"10.28945/5192","DOIUrl":"https://doi.org/10.28945/5192","url":null,"abstract":"Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets a","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135914571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Omar Mohammed Horani, Ali Khatibi, Anas RATIB ALSoud, Jacquline Tham, Ahmad Samed Al-Adwan, S. M. Ferdous Azam
Aim/Purpose: This study utilized a comprehensive framework to investigate the adoption of Business Analytics (BA) and its effects on performance in commercial banks in Jordan. The framework integrated the Technological-Organizational-Environmental (TOE) model, the Diffusion of Innovation (DOI) theory, and the Resource-Based View (RBV). Background: The recent trend of utilizing data for business operations and decision-making has positively impacted organizations. Business analytics (BA) is a leading technique that generates valuable insights from data. It has gained considerable attention from scholars and practitioners across various industries. However, guidance is lacking for organizations to implement BA effectively specific to their business contexts. This research aims to evaluate factors influencing BA adoption by Jordanian commercial banks and examine how its implementation impacts bank performance. The goal is to provide needed empirical evidence surrounding BA adoption and outcomes in the Jordanian banking sector. Methodology: The study gathered empirical data by conducting an online questionnaire survey with senior and middle managers from 13 commercial banks in Jordan. The participants were purposefully selected, and the questionnaire was designed based on relevant and well-established literature. A total of 307 valid questionnaires were collected and considered for data analysis. Contribution: This study makes a dual contribution to the BA domain. Firstly, it introduces a research model that comprehensively examines the factors that influence the adoption of BA. The proposed model integrates the TOE framework, DOI theory, and RBV theory. Combining these frameworks allows for a comprehensive examination of BA adoption in the banking industry. By analyzing the technological, organizational, and environmental factors through the TOE framework, understanding the diffusion process through the DOI theory, and assessing the role of resources and capabilities through the RBV theory, researchers and practitioners can better understand the complex dynamics involved. This integrated approach enables a more nuanced assessment of the factors that shape BA adoption and its subsequent impact on business performance within the banking industry. Secondly, it uncovers the effects of BA adoption on business performance. These noteworthy findings stem from a rigorous analysis of primary data collected from commercial banks in Jordan. By presenting a holistic model and delving into the implications for business performance, this research offers valuable insights to researchers and practitioners alike in the field of BA. Findings: The findings revealed that various technological (data quality, complexity, compatibility, relative advantage), organizational (top management support, organizational readiness), and environmental (external support) factors are crucial in shaping the decision to adopt BA. Furthermore, the study findings demonstrated a positive r
{"title":"Antecedents of Business Analytics Adoption and Impacts on Banks’ Performance: The Perspective of the TOE Framework and Resource-Based View","authors":"Omar Mohammed Horani, Ali Khatibi, Anas RATIB ALSoud, Jacquline Tham, Ahmad Samed Al-Adwan, S. M. Ferdous Azam","doi":"10.28945/5188","DOIUrl":"https://doi.org/10.28945/5188","url":null,"abstract":"Aim/Purpose: This study utilized a comprehensive framework to investigate the adoption of Business Analytics (BA) and its effects on performance in commercial banks in Jordan. The framework integrated the Technological-Organizational-Environmental (TOE) model, the Diffusion of Innovation (DOI) theory, and the Resource-Based View (RBV). Background: The recent trend of utilizing data for business operations and decision-making has positively impacted organizations. Business analytics (BA) is a leading technique that generates valuable insights from data. It has gained considerable attention from scholars and practitioners across various industries. However, guidance is lacking for organizations to implement BA effectively specific to their business contexts. This research aims to evaluate factors influencing BA adoption by Jordanian commercial banks and examine how its implementation impacts bank performance. The goal is to provide needed empirical evidence surrounding BA adoption and outcomes in the Jordanian banking sector. Methodology: The study gathered empirical data by conducting an online questionnaire survey with senior and middle managers from 13 commercial banks in Jordan. The participants were purposefully selected, and the questionnaire was designed based on relevant and well-established literature. A total of 307 valid questionnaires were collected and considered for data analysis. Contribution: This study makes a dual contribution to the BA domain. Firstly, it introduces a research model that comprehensively examines the factors that influence the adoption of BA. The proposed model integrates the TOE framework, DOI theory, and RBV theory. Combining these frameworks allows for a comprehensive examination of BA adoption in the banking industry. By analyzing the technological, organizational, and environmental factors through the TOE framework, understanding the diffusion process through the DOI theory, and assessing the role of resources and capabilities through the RBV theory, researchers and practitioners can better understand the complex dynamics involved. This integrated approach enables a more nuanced assessment of the factors that shape BA adoption and its subsequent impact on business performance within the banking industry. Secondly, it uncovers the effects of BA adoption on business performance. These noteworthy findings stem from a rigorous analysis of primary data collected from commercial banks in Jordan. By presenting a holistic model and delving into the implications for business performance, this research offers valuable insights to researchers and practitioners alike in the field of BA. Findings: The findings revealed that various technological (data quality, complexity, compatibility, relative advantage), organizational (top management support, organizational readiness), and environmental (external support) factors are crucial in shaping the decision to adopt BA. Furthermore, the study findings demonstrated a positive r","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ling Meiyi, Ree Chan Ho, M. Amin, Nelvin XeChung Leow
Aim/Purpose: Based on the stimulus-organism-response (SOR) model, the current study investigated social commerce functions as an innovative retailing technological support by selecting the three most appropriate features for the Chinese online shopping environment with respective value co-creation intentions. Background: Social commerce is the customers’ online shopping touchpoint in the latest retail era, which serves as a corporate technological tool to extend specific customer services. Although social commerce is a relatively novel platform, limited theoretical attention was provided to determine retailers’ approaches in employing relevant functions to improve consumer experience and value co-creation. Methodology: A questionnaire was distributed to Chinese customers, with 408 valid questionnaires being returned and analyzed through Structural Equation Modeling (SEM). Contribution: The current study investigated the new retail concept and value co-creation from the consumer’s perspective by developing a theoretical model encompassing new retail traits and consumer value, which contributed to an alternative theoretical understanding of value creation, marketing, and consumer behaviour in the new retail business model. Findings: The results demonstrated that value co-creation intention was determined by customer experience, hedonic experience, and trust. Simultaneously, the three factors were significantly influenced by interactivity, personalisation, and sociability features. Specifically, customers’ perceptions of the new retail idea and the consumer co-creation value were examined. Resultantly, this study constructed a model bridging new retail characteristics with consumer value. Recommendations for Practitioners: Nonetheless, past new retail management practice studies mainly focused on superficial happiness in the process of human-computer interaction, which engendered a computer system design solely satisfying consumers’ sensory stimulation and experience while neglecting consumers’ hidden value demands. As such, a shift from the subjective perspective to the realisation perspective is required to express and further understand the actual meaning and depth of consumer happiness. Recommendation for Researchers: New retailers could incorporate social characteristics on social commerce platforms to improve the effectiveness of marketing strategies while increasing user trust to generate higher profitability. Impact on Society: The new retail enterprises should prioritise consumers’ acquisition of happiness meaning and deep experience through self-realisation, cognitive improvement, identity identification, and other aspects of consumer experiences and purchase processes. By accurately revealing and matching consumers’ fundamental perspectives, new retailers could continuously satisfy consumer requirements in optimally obtaining happiness. Future Research: Future comparative studies could be conducted on diverse companies within the
{"title":"Enhancing Consumer Value Co-Creation Through Social Commerce Features in China’s Retail Industry","authors":"Ling Meiyi, Ree Chan Ho, M. Amin, Nelvin XeChung Leow","doi":"10.28945/5167","DOIUrl":"https://doi.org/10.28945/5167","url":null,"abstract":"Aim/Purpose: Based on the stimulus-organism-response (SOR) model, the current study investigated social commerce functions as an innovative retailing technological support by selecting the three most appropriate features for the Chinese online shopping environment with respective value co-creation intentions. Background: Social commerce is the customers’ online shopping touchpoint in the latest retail era, which serves as a corporate technological tool to extend specific customer services. Although social commerce is a relatively novel platform, limited theoretical attention was provided to determine retailers’ approaches in employing relevant functions to improve consumer experience and value co-creation. Methodology: A questionnaire was distributed to Chinese customers, with 408 valid questionnaires being returned and analyzed through Structural Equation Modeling (SEM). Contribution: The current study investigated the new retail concept and value co-creation from the consumer’s perspective by developing a theoretical model encompassing new retail traits and consumer value, which contributed to an alternative theoretical understanding of value creation, marketing, and consumer behaviour in the new retail business model. Findings: The results demonstrated that value co-creation intention was determined by customer experience, hedonic experience, and trust. Simultaneously, the three factors were significantly influenced by interactivity, personalisation, and sociability features. Specifically, customers’ perceptions of the new retail idea and the consumer co-creation value were examined. Resultantly, this study constructed a model bridging new retail characteristics with consumer value. Recommendations for Practitioners: Nonetheless, past new retail management practice studies mainly focused on superficial happiness in the process of human-computer interaction, which engendered a computer system design solely satisfying consumers’ sensory stimulation and experience while neglecting consumers’ hidden value demands. As such, a shift from the subjective perspective to the realisation perspective is required to express and further understand the actual meaning and depth of consumer happiness. Recommendation for Researchers: New retailers could incorporate social characteristics on social commerce platforms to improve the effectiveness of marketing strategies while increasing user trust to generate higher profitability. Impact on Society: The new retail enterprises should prioritise consumers’ acquisition of happiness meaning and deep experience through self-realisation, cognitive improvement, identity identification, and other aspects of consumer experiences and purchase processes. By accurately revealing and matching consumers’ fundamental perspectives, new retailers could continuously satisfy consumer requirements in optimally obtaining happiness. Future Research: Future comparative studies could be conducted on diverse companies within the","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69313691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ammar Abdallah, A. Abran, Malik Qasaimeh, Alaeddin Mohammad Khalaf Ahmad, Abdullah Al-Refai
Aim/Purpose: This study identifies the scale types and measurement units used in the measurement of enterprise architecture (EA) and analyzes the admissibility of the mathematical operations used. Background: The majority of measurement solutions proposed in the EA literature are based on researchers’ opinions and many with limited empirical validation and weak metrological properties. This means that the results generated by these solutions may not be reliable, trustworthy, or comparable, and may even lead to wrong investment decisions. While the literature proposes a number of EA measurement solutions, the designs of the mathematical operations used to measure EA have not yet been independently analyzed. It is imperative that the EA community works towards developing robust, reliable, and widely accepted measurement solutions. Only then can senior management make informed decisions about the allocation of resources for EA initiatives and ensure that their investment yields optimal results. Methodology: In previous research, we identified, through a systematic literature review, the EA measurement solutions proposed in the literature and classified them by EA entity types. In a subsequent study, we evaluated their metrology coverage from both a theoretical and empirical perspective. The metrology coverage was designed using a combination of the evaluation theory, best practices from the software measurement literature including the measurement context model, and representational theory of measurement to evaluate whether EA measurement solutions satisfy the metrology criteria. The research study reported here presents a more in-depth analysis of the mathematical operations within the proposed EA measurement solutions, and for each EA entity type, each mathematical operation used to measure EA was examined in terms of the scale types and measurement units of the inputs, their transformations through mathematical operations, the impact in terms of scale types, and measurement units of the proposed outputs. Contribution: This study adds to the body of knowledge on EA measurement by offering a metrology-based approach to analyze and design better EA measurement solutions that satisfy the validity of scale type transformations in mathematical operations and the use of explicit measurement units to allow measurement consistency for their usage in decision-making models. Findings: The findings from this study reveal that some important metrology and quantification issues have been overlooked in the design of EA measurement solutions proposed in the literature: a number of proposed EA mathematical operations produce numbers with unknown units and scale types, often the result of an aggregation of undetermined assumptions rather than explicit quantitative knowledge. The significance of such aggregation is uncertain, leading to numbers that have suffered information loss and lack clear meaning. It is also unclear if it is appropriate to add or multip
{"title":"Analysis of the Scale Types and Measurement Units in Enterprise Architecture (EA) Measurement","authors":"Ammar Abdallah, A. Abran, Malik Qasaimeh, Alaeddin Mohammad Khalaf Ahmad, Abdullah Al-Refai","doi":"10.28945/5113","DOIUrl":"https://doi.org/10.28945/5113","url":null,"abstract":"Aim/Purpose: This study identifies the scale types and measurement units used in the measurement of enterprise architecture (EA) and analyzes the admissibility of the mathematical operations used. Background: The majority of measurement solutions proposed in the EA literature are based on researchers’ opinions and many with limited empirical validation and weak metrological properties. This means that the results generated by these solutions may not be reliable, trustworthy, or comparable, and may even lead to wrong investment decisions. While the literature proposes a number of EA measurement solutions, the designs of the mathematical operations used to measure EA have not yet been independently analyzed. It is imperative that the EA community works towards developing robust, reliable, and widely accepted measurement solutions. Only then can senior management make informed decisions about the allocation of resources for EA initiatives and ensure that their investment yields optimal results. Methodology: In previous research, we identified, through a systematic literature review, the EA measurement solutions proposed in the literature and classified them by EA entity types. In a subsequent study, we evaluated their metrology coverage from both a theoretical and empirical perspective. The metrology coverage was designed using a combination of the evaluation theory, best practices from the software measurement literature including the measurement context model, and representational theory of measurement to evaluate whether EA measurement solutions satisfy the metrology criteria. The research study reported here presents a more in-depth analysis of the mathematical operations within the proposed EA measurement solutions, and for each EA entity type, each mathematical operation used to measure EA was examined in terms of the scale types and measurement units of the inputs, their transformations through mathematical operations, the impact in terms of scale types, and measurement units of the proposed outputs. Contribution: This study adds to the body of knowledge on EA measurement by offering a metrology-based approach to analyze and design better EA measurement solutions that satisfy the validity of scale type transformations in mathematical operations and the use of explicit measurement units to allow measurement consistency for their usage in decision-making models. Findings: The findings from this study reveal that some important metrology and quantification issues have been overlooked in the design of EA measurement solutions proposed in the literature: a number of proposed EA mathematical operations produce numbers with unknown units and scale types, often the result of an aggregation of undetermined assumptions rather than explicit quantitative knowledge. The significance of such aggregation is uncertain, leading to numbers that have suffered information loss and lack clear meaning. It is also unclear if it is appropriate to add or multip","PeriodicalId":38962,"journal":{"name":"Interdisciplinary Journal of Information, Knowledge, and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69313929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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