Customer Usage Research Articles

Procedure of developing more energy efficient and electrified hydraulic systems for loader cranes

This work presents a process for the requirement management and conceptual design phases of hydraulic systems. The aim is to provide a foundation for developing more energy efficient systems for the working hydraulics of construction machines. A framework is developed and applied in a case study of a loader crane. An important part of the framework is to evaluate and compare different designs based on customer usage of the machine. Emphasis has also been put on incorporating the knowledge and experience of both industry and academia. A large number of different designs are evaluated during a concept exploration phase. The design space is then narrowed down to the system design analysis phase. Losses from simultaneous operation are significant, systems with two and four pumps respectively are therefore selected for analysis by demonstrators. In order to reach equal energy performance, two-pump systems need to be more complex, i.e., they require a more sophisticated control and more components, than four-pump systems. When comparing demonstrator results, the conclusion is that for two-pump systems, it is more challenging to reach the theoretical performance, both in terms of energy efficiency and of drivability, than for the less complex systems made possible with four-pumps.

InclusiveWeb : A Smart Browser Approach to Enhancing Web Accessibility for Users with Diverse Disabilities

This article presents InclusiveWeb, an innovative accessibility-focused web browser designed to enhance digital inclusion for users with diverse disabilities. The browser addresses the persistent challenge of widespread non-compliance with web accessibility standards by implementing real-time content adaptation mechanisms, disability-specific tools, and a highly customizable user interface. InclusiveWeb's architecture incorporates advanced machine learning algorithms to dynamically modify web content, offering tailored solutions for users with visual impairments, dyslexia, language disorders, cognitive disabilities, and ADHD. The article details the browser's design principles, technical considerations, and user experience features, supported by a comprehensive evaluation methodology involving user studies with various disability groups. Results demonstrate significant improvements in task completion rates, user satisfaction, and overall accessibility compared to standard browsers. While acknowledging limitations and areas for future research, this article highlights InclusiveWeb's potential to revolutionize web accessibility, shifting the paradigm from reliance on content creators' compliance to browser-level adaptations. The implications of this approach for web accessibility standards, digital inclusion, and future technological developments are discussed, emphasizing the browser's role in creating a more equitable digital landscape.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

The role of institutional innovation and artificial intelligence in the corporate eco-innovation - a case study of high-tech enterprises in Liaoning province of China

This research investigates the intricate interplay of institutional factors and technological adoption in the context of corporate ecological innovation. Grounded in the New Institutionalism Theory and the Technology-Organization-Environment (TOE) framework, the study explores how formal and informal institutional innovations shape the behavior and capabilities of companies engaged in ecological innovation in Liaoning Province, China. Furthermore, the research examines the role of Artificial Intelligence (AI) in promoting ecological innovation. The study classifies AI into three categories: data analytics and predictive AI, automation and intelligent production AI, and customer service and user experience AI. These dimensions are investigated in terms of their influence on ecological innovation, revealing how each element contributes to enhancing companies' capabilities in addressing ecological challenges. The research proposes hypotheses regarding the positive influence of these AI dimensions on ecological innovation outcomes. The findings from this research contribute to the education of the intricate dynamics between institutional innovations, technological adoption, and corporate ecological innovation. The insights garnered are crucial for policymakers, businesses, and researchers striving to cultivate sustainable practices and innovation in ’the contemporary economic and environmental milieu.

Development of a digital amplifier system for cut-open oocyte electrophysiology

The cut-open oocyte Vaseline gap (COVG) technique is a powerful electrophysiological method for the characterization of ion channels. However, traditional amplifiers for COVG are labor-intensive and require significant user expertise. We introduce an innovative, open-source digital amplifier system with high-speed digitization and software-controlled electronics for computer-driven automation. This system compares well to existing commercial systems in terms of conventional specifications of step response (current peak at 25°μs and decay of 36°μstime constant), current noise (1.0 nA at 3 kHz bandwidth), and dynamic range (96.9 dB). Additionally, it unlocks new methods through close integration of the amplifier and software, including machine learning techniques for tuning capacitive compensation waveforms, achieving a 100-fold suppression of mean-squared transient current, and impedance measurement methods to identify system components such as membrane capacitance and electrode resistances. For future extensions, the design has unique attributes such as real-time digital signal processing for feedback; multiple-input and multiple-output; and allows for user customization. By providing open-source access to the circuit board designs, control software, and FPGA code on GitHub, this approach aims to foster cross-disciplinary collaboration and facilitate instrument customization enabling previously inaccessible electrophysiology experiments.

Usability of Pseudo-Haptic Feedback for Manual Precise Manipulation of Objects in Augmented Reality

Manual precise manipulation of objects is a critical skill in daily life, and Augmented Reality (AR) is increasingly used to support such tasks. In this study, we propose a system utilizing pseudo-haptic feedback to support precise manipulation for six degrees of freedom (6DOF). Two types of AR instruction interfaces were developed: Visual Deviation Instruction Interface (VDI) and Pseudo-Haptic Instruction Interface (PHI). A user study with 18 participants compared the two instruction interfaces in terms of performance and user experience. The objective measures of performance (task completion time, deviation), and the subjective measures (system usability scale, NASA Task Load Index) were collected. Results show that both instruction interfaces effectively support manual precise manipulation, achieving position deviations under 2 mm and orientation deviations under 1°. PHI outperformed VDI in speed, mental effort, physical demand, performance, perceived workload, custom user experience elements and reduction deviations of manual precise manipulation. Finally, we discuss research limitations and future directions.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

AISM: An Adaptable Image Steganography Model With User Customization

AISM: An Adaptable Image Steganography Model With User Customization

Optimizing extended warranty options with preventive maintenance service under multinomial logit model

Extended warranty (EW) services have become the primary source of profit for manufacturers. How to design the appropriate EW service to balance customer user experience with the manufacturer’s profit issues has received great attention. Implementing preventive maintenance (PM) actions can not only reduce the customer’s lifecycle cost but also enhance the manufacturer’s brand image. Therefore, EW bundled with PM service has emerged in the after-sales market today. To enrich customers’ choice, we investigate the design of EW menus bundled with PM service options, and then we jointly determine EW prices and the frequency of PM actions for the provided menus. In the proposed model, we combine the multinomial logit model with prospect theory to characterize customer choice behavior when presented with different EW menu options. Based on that, with the aim of maximizing the manufacturer’s profit, we can derive sets of offers, the corresponding EW service prices, and the number of included PM actions. We also introduce two additional models that account for different customer purchase scenarios. Numerical results reveal that when the manufacturer chooses to offer a unified EW menu to all customers, it is consistently more profitable to present the menu at the point of product sales. Another interesting finding is that customized EW menus designed for various customer segments do not always outperform unified ones. Overall, this study will provide a foundation for manufacturers to make informed decisions regarding the selection and design of EW menus in various scenarios.

Enhancing customer service and user experience through the use of machine learning powered intelligent chatbots

The implementation of machine learning powered intelligent chatbots have greatly enhanced customer service and user experience in various industries. With the advancements in artificial intelligence and natural language processing, chatbots have become an essential tool for businesses to effectively communicate with their customers. Machine learning algorithms enable chatbots to analyze customer queries and provide accurate responses, mimicking human conversation. This has led to an increase in customer satisfaction as chatbots can handle large volumes of inquiries and resolve them efficiently. Moreover, chatbots are available 24/7, providing round-the-clock service to customers. Implementing chatbots in businesses has also resulted in cost savings as they reduce the need for human customer service representatives. This has allowed companies to allocate their resources to other areas of their business, ultimately leading to increased productivity. In addition to improved customer service, intelligent chatbots also enhance user experience. By continuously learning from user interactions, chatbots can personalize responses and provide relevant information, making the customer experience more seamless and efficient. Furthermore, chatbots can be integrated into various platforms such as websites, social media, and messaging applications, making them easily accessible to users. This has also led to an increase in user engagement and retention rates. The potential challenges and limitations of this technology was also discussed and recommendations provided for future research in this field. Overall, the development and implementation of machine learning powered intelligent chatbots have greatly enhanced customer service and user experience, making it a valuable tool for businesses in today's digital age.

Factors of Self-Checkout Counter towards Customer Usage Intention in Malaysia Drugstore

Factors of Self-Checkout Counter towards Customer Usage Intention in Malaysia Drugstore

Is it wise for manufacturers to implement preventive maintenance in the sharing business model?

In the sharing business model, manufacturers typically increase customer usage through product design but contend with escalating failure costs stemming from heightened customer usage and moral hazard concerns. The preventive maintenance (PM) typically serves as a means for manufacturers to tackle the growing failure cost problem. However, manufacturers face the challenge of how to make PM strategies in intricate operational environments. We formulate a theoretical model to investigate when a manufacturer should implement PM, taking into account customer usage and product design. Our results suggest that when the fixed cost of PM is low and the customer moral hazard is not sufficiently high, or when both the fixed cost of PM and the customer moral hazard are at moderate levels, it is wise for the manufacturer to implement PM. We also find that the PM can lead to higher consumer surplus by increasing customer utility, achieving a win-win situation. In addition, our findings reveal a positive correlation between the PM effort and product quality, with higher PM effort leading to higher product quality and thus increasing customer usage. In extension models, we further explore the issues of maximising social welfare, non-zero maintenance time and endogenous maintenance frequency

Design, manufacture, and control of a low-cost positive airway pressure device

Current positive airway pressure devices cost NZ$800-$2500, posing a financial barrier for the estimated 1 billion individuals worldwide with sleep apnea and those researching respiratory diseases. Increasing diagnoses and research interest in the area necessitate a low-cost, easily accessible alternative. Thus, the mePAP, a high-quality, multipurpose, low-cost (∼NZ$250) positive airway pressure device, was designed and prototyped specifically for respiratory disease research, particularly for sleep apnea. The mePAP allows user customization and provides researchers with an affordable tool for testing positive airway pressure algorithms. Unlike typical commercial devices, the mePAP offers adaptability with open-source data collection and easily modifiable software for implementing and analysing different control and diagnostic algorithms. It features three control modes: constant; bilevel; and automatic; and provides pressures from 4 to 20 cmH2O, controlled via a phone app through Wi-Fi, with a mini-sensor added at the mask for increased accuracy. Validation tests showed the mePAP’s performance is comparable to a gold-standard Fisher & Paykel device, with extremely similar output pressures. The mePAP’s low cost enhances accessibility and equity, allowing researchers to test ventilation algorithms for sleep apnea and other respiratory conditions, with all data openly available for analysis. Its adaptability and multiple applications increase its usability and usefulness across various research and clinical settings.

Analysis of Factors Influencing Adoption to Digital Financial Services in Gen Z: An Empirical Study on Digital Bank Users in Indonesia

Developing digital technology is changing activities in various fields, including finance. New financial services have emerged such as digital banks, peer-to-peer lending, e-wallets, fintech, and so on. At the time this research was conducted, there was a gap between the level of awareness and ownership between one digital financial platform and another. One of them is the number of monthly active digital bank users which is still relatively small in Indonesia, where currently more customer accounts are low usage or even inactive. Digital banks are a new banking business model, without branch offices and all activities are carried out online. This research analyzes the factors that influence the adoption of digital financial services in digital banks as the key to increasing the number of customer usage of digital banks. UTAUT is the theoretical basis used in this research. This research method is quantitative, where data is obtained from an online questionnaire of 220 respondents and processed using the Structural Equation Model (SEM) analysis technique with the help of the SmartPLS 4.0 application. The findings from this research show that digital literacy has a negative effect on perceived risk which then also has a negative effect on attitude towards digital financial services. On the other hand, direct interpersonal communication between bank staff and customers also increases perceived usefulness and perceived trust. Even though digital banks do not have physical offices, communication with bank staff is still necessary. Apart from that, the influence of colleagues and the surrounding environment also influences a person's level of self-efficacy in adopting digital banking.

The Influence of AI and AR Technology in Personalized Recommendations on Customer Usage Intention: A Case Study of Cosmetic Products on Shopee

With the rapid growth of Augmented Reality (AR) in e-commerce, it is necessary to conduct in-depth studies related to the integration of AR with Artificial Intelligence (AI) technology to improve personalization in product recommendations. AI and AR are expected to work together, where AI systems analyze consumer data to provide more suitable product recommendations, while AR helps consumers visualize these recommendations in the real world. This research explores the influence of AI and AR technology integration in personalized recommendations on customer usage intention, with a focus on the cosmetics industry on the e-commerce platform Shopee. Adopting the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), this research uses quantitative methods with the try-on feature of cosmetic products as the main focus. With a total number of 387 respondents, the results of the PLS-SEM analysis revealed factors impacting the intention to use, such as perceived ease of use, perceived usefulness, and users’ perceived trust, on the intention to use personalized recommendations. This research contributes to the understanding of AI–AR technology acceptance in the context of online shopping, particularly in the cosmetics sector.

Structural load estimation of the wheel loader for customer usage profile monitoring

Structural load estimation of the wheel loader for customer usage profile monitoring

Utilizing predictive analytics to boost customer loyalty and drive business expansion

In the contemporary business landscape, leveraging data to enhance customer experiences and drive strategic decisions has become paramount. Predictive analytics, a key component of advanced data analytics, offers a powerful tool for businesses seeking to boost customer loyalty and drive business expansion. This review explores the utilization of predictive analytics to understand customer behavior, anticipate their needs, and create personalized experiences that foster loyalty and promote growth. Predictive analytics involves analyzing historical data to identify patterns and trends that can forecast future behaviors and outcomes. By integrating predictive models into customer relationship management (CRM) systems, businesses can gain valuable insights into customer preferences, purchasing habits, and potential churn risks. These insights enable companies to tailor their marketing strategies, optimize customer interactions, and deliver targeted promotions that resonate with individual customers. One significant advantage of predictive analytics is its ability to enhance customer retention. By identifying at-risk customers early, businesses can implement proactive measures to re-engage them, such as personalized offers, targeted communication, and loyalty programs. This not only improves customer satisfaction but also reduces the costs associated with acquiring new customers. Moreover, predictive analytics facilitates market segmentation and helps businesses identify high-value customer segments. By understanding the characteristics and behaviors of these segments, companies can develop focused marketing campaigns and product offerings that cater specifically to their needs. This targeted approach not only maximizes the return on marketing investment but also drives business expansion by capturing new markets and increasing market share. Additionally, predictive analytics can support product development and innovation. By analyzing customer feedback and usage data, businesses can identify emerging trends and unmet needs, guiding the creation of new products and services that align with customer demands. This fosters a culture of innovation and continuous improvement, further strengthening customer loyalty and driving long-term growth. In conclusion, the integration of predictive analytics into business strategies is a powerful means to boost customer loyalty and drive business expansion. By leveraging data-driven insights to personalize customer experiences, anticipate needs, and optimize marketing efforts, businesses can achieve sustainable growth and maintain a competitive edge in the dynamic market landscape.

TURKISH PARTICIPATION BANKING WITH THE IMPACT OF DIGITAL BANKING: 2020 - 2023 PERIOD ANALYSIS AND EVALUATION

Participation banking is one of the banking types operating in line with Islamic principles. As is known, the banking sector is one of the most technology-intensive sectors today. The healthy and sustainable development and digital transformation of participation banking is gaining momentum in Turkey as in the global financial sector. The prominent factors in digital banking are mobile banking usage, call centre statistics, total number of digital customers, transaction volume and transaction distribution of participation banks. In this study, it is aimed to make a digital analysis and evaluation of Turkish Participation Banking. Thus, capturing future trends in participation banking, ensuring sustainability and strategic planning analyses can be designed much more efficiently. The analyses cover the period 2020-2023. The fact that the analysis period coincides with the beginning of the pandemic is also meaningful in terms of the effects of the digital outlook, which gained momentum after the pandemic, on participation banking. For example, while the number of customers acquired in remote customer acquisition statistics was 23,618 in the last month of 2021, it corresponds to a proportional increase of 148% with 58,594 in the 9th month of 2023. While digital banking active customer usage was 81.36% only in mobile banking in the 2nd period of 2020, it was 88.86% with an increase of 7.5% in the 2nd period of 2023. As a result of this study, it is expected that participation banking will become more widespread in the future and competition with conventional banks will gain a different dimension in the field of digitalisation.

Leveraging Machine Learning and Patient Reviews for Developing a Drug Recommendation System to Reduce Medical Errors

Background In the rapidly evolving pharmaceutical industry, drug efficacy and safety stand as critical concerns. The vast accumulation of data, including customer feedback, drug popularity, and usage details, offers a rich resource for improving healthcare outcomes. Aims The primary aim of this study is to harness machine learning and Natural Language Processing (NLP) techniques to sift through extensive pharmaceutical data, identifying the most effective drugs for various conditions and uncovering patterns that could guide better decision-making in drug efficacy and safety. Objective This research seeks to construct a sophisticated model capable of analyzing diverse data points to pinpoint the most efficacious drugs for specific health conditions, thereby providing pharmaceutical companies with data-driven insights to optimize drug safety and effectiveness. Methods Employing a blend of Natural Language Processing (NLP) and machine learning strategies, the study analyzes a comprehensive dataset featuring customer reviews, drug popularity metrics, usage information, and other relevant data collected over an extended period. This methodological approach aims to reveal latent trends and patterns that are crucial for assessing drug performance. Results The developed model adeptly identifies leading medications for various conditions, elucidating efficacy and safety profiles derived from patient reviews and drug utilization trends. These findings furnish pharmaceutical companies with actionable intelligence for enhancing drug development and patient care strategies. Conclusion The integration of machine learning and NLP for the analysis of vast drug-related datasets presents a powerful method for advancing drug efficacy and safety. The insights yielded by the proposed model significantly empower the decision-making processes of the pharmaceutical industry, ultimately fostering improved health outcomes for patients.

The mediating roles of customer evaluation and user motivation in the effect of mobile marketing on customer engagement

This study mainly aims to investigate the mediation effects of user motivation and customer evaluation regarding the effect of the perception related to mobile marketing applications on customer engagement. The recommended conceptual model is tested by collecting data from 627 mobile application users living in Istanbul via an online survey. This study reveals that perceived advantage and perceived challenge positively affect the four dimensions of customer engagement, which are co-developing, influencing, augmenting and mobilizing behaviours and also with domain-specific interest, need for cognition and cognition satisfaction, perceived enjoyment, and perceived control positively affect customer engagement, and mediate the relationship of the perception related to the mobile marketing applications with customer engagement. It has significantly contributed to understanding customer behaviours against the technological features of mobile applications.