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The purpose of this paper is to report on the findings and lessons that were learned from the many cases of internet fraud complaints that are gathered by the Federal Trade Commission in the USA. The implications that are contained in the behavior of the complaints and trends identified are critical for addressing all types of online criminal activities in the increasing world of cybercrimes.

Data for this paper are extracted from the Consumer Sentinel Network Data Handbook covering a period of 13 years. Using the raw data, the occurrences were plotted and trends (number of cycles, time between cycles, and leading state and lagging states) were identified. Descriptive statistics covering the 13 years were examined and discussed. Using 20002 as the base year, the rate of growth of each of the states were ranked annually and were tested for stability and predictability using non-parametric approaches.

The key findings are indigenous to the occurrences of online fraud complaints in the USA. However, as the leading nation with the best database on fraud complaints, the findings are mission critical to fraud prevention across the globe. Specifically, this study found that since 2002, there have been four distinct cycles. Each cycle is clearly noticeable because there is a rapid growth in the number of crime complaints in the beginning half of the period followed by a slowing down period. However, the speed of change from one cycle to the next is steeper and the time gap is faster. While it is true that from the perspective of all the 50 states, the ranks appear to change every year and thus are not stable. However, the majority of the changes are relatively small so their relative positions by rank are still predictable.

The paper extends existing understanding of online fraud occurrences in the USA. The findings are timely and based on longitudinal data that span over a decade. The four new cycles identified are critical to the body of knowledge. The observed change in time from one cycle to another and its changes from a mathematic to a geometric one are also new. The findings are invaluable to persons working in law enforcement related occupations (auditors, lawyers, forensic experts, among others) and who must deal with the increasing problem of online fraud and cybercrimes.

The application of mobile augmented reality (MAR) for enhancing user experiences and consumer patronizing intention has been the focus of recent MAR literature. Few studies examine the differences between apps. This study fills the research gap by examining how consumers assess their experiences with different MAR applications and how their decision-making process is performed, particularly in the setting of smartphones.

A web-based online survey was administered to collect data on consumers' perceptions of two different MAR apps: utilitarian and hedonic apps. Reliability and validity of the measurement scales, non-response bias and comment method bias were assessed. With the support of measurement model, partial least square (PLS) was employed to test the research hypotheses.

This study reveals that the technological attributes of augmented reality (AR) apps have significant effects on consumer perceptions of their utilitarian and hedonic benefits, including interactivity, visual quality, service quality, technicality and aesthetics. Moreover, this study shows that consumers of hedonic apps place more importance on their enjoyment with the MAR app; consumers of utilitarian apps focus more on the accrued functional values. The findings provide practical insights for retailers in AR marketing and application development in the MAR environment.

This study provides a comprehensive viewpoint for analyzing ongoing use and purchase intentions simultaneously in a unified theoretical framework. In addition, it compares different types of MAR apps: hedonic and utilitarian. Furthermore, it is one of the first few studies attempting to provide a comprehensive understanding of the predictive role of MAR technologies by incorporating privacy concerns into the research model based on user and gratification framework.

The purpose of this paper is to propose and test a healthcare framework of service quality, perceived value, and satisfaction in Ghana. More specifically, this study investigates the role and effect of service quality on patient satisfaction and perceived value in Ghana’s healthcare delivery.

Data were gathered through surveys administered to 113 healthcare patients in Ghana. partial least square-structural equation modeling analysis was used to empirically test the research model.

Results show healthcare quality significantly influences satisfaction and perceived value of healthcare delivery. Additionally, perceived value’s impact on satisfaction and behavioral intention shows that increasing perceived benefits while reducing perceived costs leads to repeat behavior and paves the way for retention strategy for healthcare management.

This study yields a series of limitations in its results and conclusions. These limitations and future research are discussed in Section 7 of the study.

This study contributes to the literature by examining the effects of healthcare service quality on patient satisfaction and perceived value, determining the effect of healthcare service quality on patients’ behavioral intention, and testing the proposed framework in Ghana, a fast growing and economically liberalized emerging country in Sub-Saharan Africa.

The purpose of this study is to explore the direct and indirect effects of perceived provider professionalism and service recovery in enhancing patient satisfaction in a developing country.

This study used a survey method to investigate satisfaction among health-care consumers. This study used data collected from 210 health-care consumers to empirically test the hypotheses via structural equation modeling

This study found that service recovery has a significant direct effect on patient satisfaction. Though this study did not find perceived provider professionalism to have a direct effect on patient satisfaction, it found an indirect effect in the relationship via service experience. Thus, service experience fully/completely mediates the relationship between perceived provider professionalism and patient satisfaction, while partially mediating the significant relationship between service recovery and patient satisfaction.

The results further underscore the need for health-care organizations in developing countries to focus on mindfully developing operations-oriented strategies that lead to the delivery of memorable service experiences for patients.

Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability.

Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system.

The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively.

The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

The purpose of this paper is to develop, validate, and use a survey instrument to measure and compare the perceived quality of three types of US urgent care (UC) service providers: hospital emergency rooms, urgent care centres (UCC), and primary care physician offices.

This study develops, validates, and uses a survey instrument to measure/compare differences in perceived service quality among three types of UC service providers. Six dimensions measured the components of service quality: tangibles, professionalism, interaction, accessibility, efficiency, and technical quality.

Primary care physicians’ offices scored higher for service quality and perceived value, followed by UCC. Hospital emergency rooms scored lower in both quality and perceived value. No significant difference was identified between UCC and primary care physicians across all the perspectives, except for interactions.

The homogenous nature of the sample population (college students), and the fact that the respondents were recruited from a single university limits the generalizability of the findings.

The patient’s choice of a health care provider influences not only the continuity of the care that he or she receives, but compliance with a medical regime, and the evolution of the health care landscape.

This work contributes to the understanding of how to provide cost effective and efficient UC services.

This study developed and validated a survey instrument to measure/compare six dimensions of service quality for three types of UC service providers. The authors provide valuable data for UC service providers seeking to improve patient perceptions of service quality.

The objective of this study is to modify the SERVPERF scale by incorporating the additional dimension of recoverability, and to empirically test and refine the modified SERVPERF instrument using survey data from China. The study aims to assess the potential antecedents of customer satisfaction in the fast food industry in China. The antecedents include service quality, food quality, and perceived value. Finally, it seeks to examine the relationship between behavioral intentions and customer satisfaction in the fast food industry in mainland China.

A survey was used to evaluate customer perceptions of the service quality in fast‐food restaurants (FFRs). Exploratory factor analysis and confirmatory factor analysis were used to assess the reliability and validity of the modified Chinese FFR SERVPERF instrument. Structural equation modeling was employed to assess the hypothesized relationships.

Reliability, recoverability, tangibles, and responsiveness were all significant dimensions of perceived service quality. Food quality, perceived value and service quality all had a direct and positive relationship with satisfaction, which in turn influenced behavioral intentions.

Western‐style fast food franchises are increasingly crossing national boundaries and looking for growth among customers in China. China is becoming a major player in the global market because of its size and growth potential, particularly after its accession to the World Trade Organization. The transference of a western business model is not appropriate in the context of China without testing because of dietary and cultural differences. The study modified the SERVPERF instrument and empirically validated the instrument using data from China. The results contribute to the understanding of service quality in China's fast food industry and provide insight about service management and improvement opportunities in Chinese service operations.

This study aims to explore the potential dimensions of service quality, and examine the relationship among service quality, food quality, perceived value, customer satisfaction and behavioral intentions in fast‐food restaurants (FFRs).

The construct reliability and validity was assessed using exploratory factor analysis and confirmatory factor analysis. Structural equation modeling was employed to estimate the relationship among service quality, customer satisfaction, and behavioral intentions.

Results indicated that five dimensions were significant: tangibles, reliability/responsiveness, recovery, assurance, and empathy. Service quality and food quality were two main determinants of customer satisfaction. The insignificance of perceived value is potentially due to the homogeneous nature of the construct within the FFR group rather than the importance of the perceived value construct within food service.

The FFR success model, using the original five in the SERVPERF scale and another new dimension “recovery” to measure service quality, was empirically examined in the fast food industry. Several potential antecedents of satisfaction, including service quality, food quality and perceived value were also tested.

This study aims to investigate the impact of geopolitical risk (GPR) on supply chain concentration (SCC) and the roles of operational capabilities and resources in this relationship.

Secondary longitudinal data from multiple sources is collected and combined to test for a direct impact of GPR on SCC. We further examine the moderating effects of firms’ operational capabilities and resources (i.e. firm resilience, operational slack and cash holding). Fixed-effect regression models are applied to test the hypotheses, followed by a series of robustness tests to check the consistency of the results.

Consistent with the tenets of resource dependence theory, our analysis reveals a significant negative impact of GPR on SCC. Moreover, we find that this adverse effect is attenuated for firms with higher levels of resilience, more operational slack and greater cash holdings. Further analysis suggests that maintaining a diversified supply chain base during heightened GPR is associated with a firm’s improved financial performance.

This study contributes to the supply chain management (SCM) literature by integrating GPR into the supply chain risk management framework. Additionally, it demonstrates the roles of diversification and operational resources in addressing GPR-induced challenges.

This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.

In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.