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The purpose of this study was to examine the determinants of sport consumers' mobile ticketing adoption by the technology readiness constructs and quality–satisfaction–behavioral intentions framework. A total of 295 participants were included in the analysis. Data analysis was performed using structural equation modeling and PROCESS macro. A content analysis was conducted to provide further insight into the proposed model using open-ended responses. The findings indicate that consumers' technology readiness alone was not a positive driver of mobile ticketing but suggest a role for technology readiness in promoting mobile ticketing, combined with service quality, satisfaction, and online ticket purchasing. The importance and originality of this study are that it confirms the sport context as a unique and effective vehicle in advancing existing knowledge of consumers' ticket consumption behavior via self-service technology across various disciplines. Also, the findings can be used to set out recommendations for policy or practice aimed at facilitating and sustaining mobile ticketing consumption. [ABSTRACT FROM AUTHOR]

Prior research shows that using mimetic isomorphism to select price/quality product strategies results in superior performance in the United States, the European Union, and Japan for both developed economy and emerging market manufacturers. However, do these results generalize in the case of emerging market service providers? Due to the nature of services, customers cannot gauge service quality prior to consumption, and they must rely on other cues; commonly, consumers rely on the brand name as a proxy. We theorize that for emerging market service providers, choosing a strategy of "distinctiveness" (pursuing a nondominant price/quality strategy) offers a way to differentiate their offering from the mimetic choice, resulting in superior performance. Implications for theory and practice are discussed. [ABSTRACT FROM AUTHOR]

One question that needs to be asked in understanding the consequences of humour in service encounters is whether the prevalent bivariate approaches capture the full range of humour consequences. Therefore, this study examines the influence of frontline employees' (FLEs) combined use of affiliative and self-defeating humour on customers' perceptions of service quality in 268 service encounters. Bivariate results show that the positive effect of affiliative humour is much stronger than the negative effect of self-defeating humour. On the contrary, response surface analyses show that bad may be stronger than good. FLEs' humour use is particularly advantageous with customers who appreciate humour, while for those who do not, there is an optimal margin effect when affiliative humour exceeds self-defeating humour. [ABSTRACT FROM AUTHOR]

This article presents a 2-year study on how quickly Texas library staff members greeted a library patron after eye contact. Results from 140 library visits found a mean of nearly 4 min, but a mean of 9 s after removing outliers. The study also calculated the perceived customer value by measuring the service, quality, image, and price of the customer service experience. Findings suggest that each patron/staff interaction may alter a patron's perceived Return on Investment (ROI). Given libraries are primarily supported by tax dollars, poor patron service experiences may impact the perceived values of library programs and services, jeopardizing future funding. The article highlights the importance for libraries to retain satisfied employees and build trust within their communities. [ABSTRACT FROM AUTHOR]

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), vehicle data exchange for cooperative assessment can enhance the experience of driving along with service quality. Nevertheless, concerns with security, bandwidth, and privacy prevent service providers from actively participating in the knowledge exchange procedure. This paper proposes energy-efficient routing and secured data transmission through a deep learning model. In the initial stage, this work adopts the Bidirectional Long Short Term Memory- Modified Attention Layer (Bi-LSTM-MAL) approach for energy prediction of each node. The optimal cluster head selection is the crucial step, for which this work proposes the Combined Beluga with Black Widow Optimization (CBBWO) algorithm by considering distinct constraints like energy, risk, distance and delay. This ensures precise routing, which is optimally done by taking account of constraints including trust, mobility and link quality. Also, this work concerns secure data transmission by protecting the data with a security standard, Fusion Key induced in Elliptic Curve Cryptography (FK-ECC) that aids the model to transmit the data securely between source and destination. Finally, the work evaluates its performance in optimal routing and secured data transmission over conventional models. The CBBWO recorded the minimized cost value of 1.4850 at the final (25th) iteration while existing methods such as BWO, BWOA, BMO, MFO, SBO, ROAONC and SDO-BM obtained maximum cost rates respectively. [ABSTRACT FROM AUTHOR]

While software-defined networks (SDNs) were introduced to enhance the performance of traditional networks, they brought forth significant challenges, such as lack of scalability, low reliability, and the presence of a centralized controller as a single point of failure. This led to the emergence of distributed software-defined networks (DSDNs). Despite the numerous advantages of DSDNs, there are challenges, with improper traffic distribution identified as the primary issue, significantly affecting its performance and efficiency, resulting in increased latency, reduced throughput, and migration costs. This article introduces a novel approach called MBL-DSDN (micro-clustering bidirectional LSTM-DSDN) to balance server traffic distribution. The improvement of quality of service (QoS) parameters and optimal load distribution in DSDNs is accomplished by utilizing two modules: micro-clustering (MC) and a prediction module based on the B-LSTM method. The Davies–Bouldin (DB) index from the MC module is utilized to enhance the accuracy of clustering results. Since the B-LSTM prediction module possesses suitable memory to store all relevant past and future features with high precision, it is suitable for selecting the best controller with the lowest migration cost and response time. Moreover, the proposed method reduces the synchronization cost between data and controller layers, leading to reduced response time and, ultimately, lower processing costs and increased load balancing. In comparison with similar methods, as a result of multiple simulations, migration count, migration cost, and response time parameters have improved by 31%, 43%, and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices generate and transfer a vast volume of data in interaction with various objects. These data rely on cloud computing (CC) or fog computing (FC) for processing and calculations. Unlike CC, FC delivers task processing closer to IoT devices. Consequently, FC leads to reduced latency, increased efficiency, ease of deployment, and flexibility. The primary concern between the service requester and provider when agreeing to task offloading is trust. While several types of research are based on intelligent models for trust management, trust management in FC requires direct and indirect trust computation due to the presence of malicious nodes. To ensure security and maintain the quality of service when interacting with fog servers, trust is essential. In this paper, a new trust model for FC is proposed using a combination of the Genetic Algorithm (GA) and Learning Automaton (LA). The crossover operator in GA by LA is improved. The changes in the search space and finding optimal solutions are dependent on the crossover rate. Thus, determining an optimal value for the crossover operator impacts the problem's solutions. The proposed methods evaluation was done in a MATLAB 2020 simulation environment. The results show that the performance of the proposed method with 200 iterations and 1000 tasks is superior in terms of reliability rate, energy consumption, and delay compared to GA and particle swarm optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

With increasing competition in the food franchise industry, the success of a franchise operation depends on its ability to instigate franchisee patronage. This study focuses on franchisor characteristics that help in creating positive behavioural outcomes. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS software is used to analyze 266 U.S. food franchisees. Findings show the relative impact of franchisee evaluation of information quality, service quality, rewards and recognition, and customization on perceived value. Also, positive perceived value in franchisees influences their willingness to pay a premium and their referrer intentions towards franchise brands. Multigroup analysis using demographic characteristics shows that the impacts of information quality and service quality on perceived value are higher in male and older franchisees than in female and younger franchisees. In contrast, the effect of customization on perceived value is greater in female and younger franchisees. This study provides insights to develop business strategies. [ABSTRACT FROM AUTHOR]

The fog computing environment expands the capabilities of cloud computing by moving computing, storage, and networking services closer to IoT devices. These resource‐constrained IoT devices often face challenges like high task failure rates and extended execution latency due to data traffic congestion. Distributing IoT services through task offloading across different layers of computing paradigms enhances QoS (Quality of Service) parameters. This endeavor aims to allocate custom workflow‐based real‐time tasks or jobs for processing across various cloud/fog/edge layers, optimizing QoS factors like makespan, energy consumption, and cost. In the fog computing environment, challenges arise due to uncertainties related to job execution locations and the ability to predict future user requirements. Fuzzy logic offers low‐complexity solutions for handling unpredictable and rapidly changing conditions. This paper proposes a hybrid fog‐cloud‐based computing architecture and an intelligent fuzzy logic‐based computation offloading approach. This approach effectively allocates workloads among edge, fog, and cloud layers, resulting in improvements in makespan time (7.51%), energy consumption (4.63%), and cost (13.60%). The proposed method selects suitable processing units or compute nodes for job execution, utilizing heterogeneous resources. Simulation results demonstrate that the proposed methodology outperforms current state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

ABSTRACT Alumni associations are established by higher education institutions to formalise relationships with alumni, and to nurture alumni loyalty over time. This study investigates drivers of perceived value for an alumni association, and the effects that perceived value and satisfaction have on alumni loyalty. Data were collected from 1711 respondents to an online survey of alumni at a university college in Norway. Responses were analysed using partial least squares structural equation modelling. Findings reveal that service quality of alumni activities and attractive quality of the association drive perceived value. Satisfaction among those that have participated in alumni activities has a significant moderating effect on the relationship between perceived value and alumni loyalty. However, the direct effect of perceived value on alumni loyalty is not significant, meaning that perceived value alone is not enough to drive alumni loyalty. It is necessary to also achieve satisfaction among participants. [ABSTRACT FROM AUTHOR]

Pure self-management model, pure aggregation business model and Self-support + aggregation model are three commonly used business modes on ride-hailing platforms. We use an analytical model to study these three business models and give the optimal business model decision of the platform. The research shows that the heterogeneity ratio of drivers, the cost of the platform under the Self-support model, the franchise fee received by the platform under the aggregation model and the dissatisfaction of the original users on the platform play a key role in the selection of the platform's business model. When the difference between the franchise fee under the aggregation mode and the platform cost under the Self-support mode fails to generate positive feedback on the platform profit, the platform should choose the pure Self-support mode. When riders are more sensitive to the heterogeneity of service quality of the platform and user stickiness can be ensured, the platform should choose the pure aggregation business model. When user stickiness can be guaranteed and the cost of the platform under the self-run model is controllable, the platform should choose the Self-support + aggregation business model. [ABSTRACT FROM AUTHOR]

We conducted two studies to analyze the relations between dissatisfaction with experiences with the national healthcare system and trust in political (political parties and parliament), super partes (judiciary and police), and international (European Union [EU] and United Nations [UN]) institutions via the mediation of trust in the national healthcare system. Study 1 (longitudinal study on a quota sample of the Italian adult population, N = 689, surveyed in April 2021, T1, and in April 2022, T2) showed that dissatisfaction with experiences with the national healthcare system was negatively associated with trust in the national healthcare system, which, in turn, was positively associated with an increase in trust in political, super partes, and international institutions. Study 2 (between‐participant experimental design, N = 285) showed that priming a negative versus a positive experience with the national healthcare system decreased trust in this system, which, in turn, was positively associated with trust in political, super partes, and international institutions. The strengths, limitations, and possible development of this research are discussed. [ABSTRACT FROM AUTHOR]

With the increasing advancements in the Internet of Things (IoT) and the growing production of tasks by IoT devices, the demand for cloud computing centers has become more critical than ever. The energy consumption in cloud computing servers has a significant impact on the overall costs and environmental pollution. This article addresses the task allocation problem to cloud computing servers with the aim of reducing energy consumption in those servers while maintaining Quality of Service (QoS). Evolutionary algorithms have been employed to solve this NP-hard problem. In this paper, a novel version of Equilibrium Optimization algorithm is defined and used for finding good solutions for this problem. In the proposed algorithm, a tournament operator is introduced to control selection pressure and enhance the algorithm's exploration capability during local optima convergence, added to the EO algorithm. The utilization of this operator in the proposed algorithm eliminates the need for sorting all search agents at each iteration, resulting in reduced execution time. The simulation results indicate that the proposed algorithm has demonstrated a 24% improvement in performance compared to existing algorithms in solving the task allocation problem to servers in cloud computing environments. [ABSTRACT FROM AUTHOR]

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network's physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem's assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics. [ABSTRACT FROM AUTHOR]

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Research indicates that racially and ethnically minoritized populations are more likely to receive low‐value services in comparison to White populations. It is crucial to de‐implement (i.e., replace or discontinue use of) these practices to make room for more effective alternatives and reduce disparities in outcomes. However, there is limited understanding of how, when, and why practices are de‐implemented. De‐implementation has been shown to improve client outcomes through a variety of mechanisms, such as improved service quality. This scoping review had two aims. First, we examined empirical articles related to de‐implementation to determine whether culture (the practitioner's or client's values and beliefs) and race/ethnicity were assessed or reported. Second, we examined how these factors were considered throughout the de‐implementation process. A total of 20 empirical studies were identified. Results suggested there is a gap in the literature regarding how culture and race/ethnicity may impact de‐implementation and ensuing outcomes. We translated research across settings to highlight the importance of considering culture and race/ethnicity when engaging in de‐implementation. Lastly, we provide suggestions for future research and discuss implications for practitioners. Practitioner Points: This article reviews the literature on the de‐implementation of low‐value, ineffective, or harmful practices in schools and other contexts that serve children and adolescents.Results indicated that the de‐implementation literature has thus far failed to fully include culture, race, or ethnicity into the process.Incorporating culture, race, and ethnicity, into the de‐implementation process, might promote positive and equitable outcomes for populations of color. [ABSTRACT FROM AUTHOR]

Serious leisure enthusiasts, including hobbyists, volunteers and amateurs, continuously need information to deepen their knowledge and improve their skills in their chosen leisure pursuits. The Serious Leisure Perspective (SLP) is a conceptual model that categorises leisure activities and outlines the main components of each category. This paper aims to demonstrate how the SLP can inform policy and practice for public programming and information services across the GLAM sector. To achieve this goal, the author used the SLP as the theoretical framework, reviewed the literature and re-analysed the data pool in his ongoing research trajectory on the informational aspects of serious leisure to address the emergent research questions. The findings suggest that the SLP can inform and transform the policies and practices within the sector in various ways. Firstly, information professionals can benefit from the SLP as a conceptual model to review and revise their current policies and practices. Moreover, they can seek partnerships with local clubs and communities of serious leisure enthusiasts to foster collaboration and use their knowledge and expertise. Finally, adopting the new approach can extend their domain of action from conventional information services to vibrant cultural hubs, which can empower their communities. [ABSTRACT FROM AUTHOR]

Introduction/aim: Turkey has experienced a heavy migration burden in recent years due to its location and benevolent policies. This study aimed to retrospectively examine and discuss the reports prepared for foreign nationals who requested assistance at the Forensic Medicine Department of Tokat Gaziosmanpaşa University Hospital in Tokat, which is located in the Middle Black Sea Region of Turkey, between 2014 and 2022. Materials and methods: This study retrospectively evaluated reports prepared between 2014 and 2022 in the outpatient clinics of Forensic Medicine at Tokat Gaziosmanpaşa University Hospital. Results: Based on the files reviewed, 219 cases were included in the study, of which 70.8% (n = 155) were male and 29.2% (n = 64) were female. Among the 75 cases referred by judicial authorities, 34.6% (n = 26) involved assault, 28% (n = 21) involved determination of the ability to understand the legal significance and consequences of the act, 16% (n = 12) involved traffic accidents, 8% (n = 6) involved poisoning, 9.3% (n = 7) involved abuse, and 4% (n = 3) involved age determinations. Discussion: Problems with immigrants, which have always been a reality due to Turkey's location on migration routes, have increased significantly in recent years. For this reason, we believe that studies with multicenter and larger series should be conducted to determine the current situation that foreigners create for themselves and Turkey to facilitate necessary arrangements, determine proposed solutions, increase the quality of services offered, and develop plans for the future. [ABSTRACT FROM AUTHOR]

This article presents marketing educators with an approach to teaching an undergraduate services marketing class with a unique emphasis on service design. As part of the class, the content on services marketing fundamentals is blended with a detailed discussion of service design. Additionally, hands-on experience with the service design process is provided as part of a semester-long project in which students design a service delivered through a mobile application. The findings suggest that students develop a strong understanding of service design fundamentals and find the class to be a valuable learning experience. [ABSTRACT FROM AUTHOR]

In China, healthcare has lagged relative to its economic boom during the past 40 years. While the top tier hospitals offer pediatric perioperative care like high‐income countries, lower‐tier hospitals deliver lesser services of variable quality and safety related to equipment, supplies, clinician education, and availability. The national residency training program and the pediatric anesthesia fellowship program was established in 2013 and 2018 respectively. Increasing clinician workload from patient demand and a lack of consistency in quality and capability between rural and urban areas remain challenging. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore perceptions of mental health services from the perspectives of adolescents with no prior service experience. Thirty students in the Transition Year (fourth year) of secondary school participated in this study: 22 females and 8 males, aged 15 to 16. There was equal distribution across rural and urban settings. Participants had no prior experience of accessing mental health services. Data were collected in secondary school settings in the Republic of Ireland. An interpretive description approach guided this study. Data were collected through individual interviews. To complement interviews, participants drew images of mental health care environments and mental health staff members; drawings were not subjected to analysis. Data (participant interviews) were analyzed using qualitative content analysis. Three themes were identified. Participants suggested that "mental health services" differed from "psychiatric services," with the latter term perceived more negatively. Uncertainty about how to access these services was voiced. Considering the vulnerability of adolescents to the experience of mental distress, education around mental health services is warranted. It is expected that provision of education in this area would enhance service delivery, improve adolescents' help-seeking behaviors for mental distress, and reduce stigma. [ABSTRACT FROM AUTHOR]

In most Internet of Things (IoT) systems, Quality of service (QoS) must be confirmed with respect to the requirement of implementation domain. The dynamic nature of the IoT surroundings shapes it to complicate the fulfilment of these commitments. A wide range of unpredictable events endanger the quality of service. While execution the self-adaptive schemes handle with system's unpredictable. In IoT-based Wireless Sensor Networks (WSNs), the significant self-management objectives are self-configuration (SC) and self-healing (SH). In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed. In this framework, the IoT traffic classes are categorized as several types under XGBoost classifier. In SC phase, the IoT devices are self-configured by allocating various transmission slots, contention access period (CAPs) on the basis of its categories with priorities. In SH phase, the source node cardinally establishes a confined route retrieval method if the residual power in-between node is truncated or the node has displaced far away. The proposed framework is executed in NS-2 and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost. Therefore, the proposed approach has attained 24.7%, 28.9%, 12.75% higher PDR, and 16.8%, 19.87%, and 13.7% higher residual energy than the existing methods like Self-Healing and Seamless Connectivity using Kalman Filter among IoT Networks (SH-SC-KF-IoT), Provenance aware run-time verification mechanism for self-healing IoT (PA-RVM-SH-IoT), and Fully Anonymous Routing Protocol and Self-healing Capacity in Unbalanced Sensor Networks (FARP-SC-USN) methods, respectively. [ABSTRACT FROM AUTHOR]

• Uses the staggered DID method to investigate the impact of culture on tourism. • The cultural system reform promotes tourism mainly by increasing domestic tourism revenues. • Analyzes the complex factors influencing cultural tourism. • Explores potential mechanisms by which the cultural system reform affect tourism. Culture is the cornerstone of regional tourism development, yet the impact of culture is difficult to quantify. This paper utilized a quasi-natural experiment to characterize the impact of culture on tourism. Based on the 295 cities data from 2000 to 2020, we adopted the Staggered Differences-in-Differences strategy to examine whether the Cultural System Reform (CSR) policy could stimulate tourism development. As a crucial component of the institutional reform framework in China, CSR is a pivotal policy aimed at exploring cultural productivity and fostering the development of culture in accordance with societal demands. The results show that the policy significantly increased the total tourism arrivals and the total tourism revenue. Mechanism analysis shows that the CSR policy boosted tourism development by improving service quality, public cultural infrastructure construction, and media integration development. Geographical conditions, tourism resource endowment, and transportation infrastructure also have an impact on the effectiveness of policy implementation. [ABSTRACT FROM AUTHOR]

In online shopping for consumer electronics, information and physical flows are crucial determinants of consumer purchase intentions. This study examines these factors by integrating the Technology Acceptance Model with logistics service quality, analyzing the relationship between retailers and consumers in e-commerce. The focus is on how information and physical flows, as critical supply chain elements, affect consumers' decisions to purchase online. A structural model and machine learning algorithm with SHapley Additive exPlanations are employed to analyze the data, providing a comprehensive analysis of the Technology Acceptance Model in conjunction with logistics service quality. The findings reveal that attitude, perceived usefulness, and informativeness are the most influential factors affecting consumers' purchase intention. This study contributes to the understanding of consumer behavior in the context of e-commerce platforms for consumer electronic products by integrating the Technology Acceptance Model and logistics service quality theoretical perspectives and analyzing the data using innovative techniques, specifically, Shapley Additive Explanations. This research offers valuable insights into the significant role of various features in shaping consumers' purchase intention in the context of online e-commerce platforms for consumer electrical products. [ABSTRACT FROM AUTHOR]

The prevalence of eCCI (electronic customer-to-customer interaction) is rapidly growing as customers increasingly employ online tools to reach fellow customers and voice their opinions, especially after service failures. Adopting a quasi-experimental design, this research examines the impact of eCCI on restaurant customers, considering their need for approval. A total of 201 responses were obtained for the main experiment (Study 1). Results indicated that people with a lower need for approval reported greater social media engagement, customer-customer interaction justice, and empathy quality in the condition of positive eCCI. People with a higher need for approval exhibited similar responses on all dependent variables, regardless of the eCCI condition. The results remained stable across different restaurants' response strategies (Study 2). This paper examines the novel eCCI phenomenon and adds a new twist to the literature on CCI and customer reviews. It further offers valuable guidelines for managerial involvement in digital customer service encounters. [ABSTRACT FROM AUTHOR]

Currently, web service-based edge computing networks are across-the-board, and their users are increasing dramatically. The network users request various services with specific Quality-of-Service (QoS) values. The QoS-aware Web Service Composition (WSC) methods assign available services to users’ tasks and significantly affect their satisfaction. Various methods have been provided to solve the QoS-aware WSC problem; However, this field is still one of the popular research fields since the dimensions of these networks, the number of their users, and the variety of provided services are growing outstandingly. Consequently, this study presents an enhanced Fox Optimizer (FOX)-based framework named EQOLFOX to solve QoS-aware web service composition problems in edge computing environments. In this regard, the Quasi-Oppositional Learning is utilized in the EQOLFOX to diminish the zero-orientation nature of the FOX algorithm. Likewise, a reinitialization strategy is included to enhance EQOLFOX's exploration capability. Besides, a new phase with two new movement strategies is introduced to improve searching abilities. Also, a multi-best strategy is recruited to depart local optimums and lead the population more optimally. Eventually, the greedy selection approach is employed to augment the convergence rate and exploitation capability. The EQOLFOX is applied to ten real-life and artificial web-service-based edge computing environments, each with four different task counts to evaluate its proficiency. The obtained results are compared with the DO, FOX, JS, MVO, RSA, SCA, SMA, and TSA algorithms numerically and visually. The experimental results indicated the contributions' effectiveness and the EQOLFOX's competency. [ABSTRACT FROM AUTHOR]

With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances. [ABSTRACT FROM AUTHOR]

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Network traffic classification has become more important with the rapid growth of the Internet and online applications. The rapid development of the Internet has enabled explosive growth of various network traffic. The challenge lies in how to classify and identify different categories of network traffic among these huge network traffic. The classification with the massive data network traffic suffers from noise and imbalanced data. Traditional classification algorithms are becoming less effective in handling these issues of the large number of traffic generated by these technologies. This paper proposes an advanced clustering model to enhance network traffic classification and improve the quality of services based on Advanced Density-Based Spatial Clustering of Applications with Noise (A-DBSCAN) with similarity and probability distance. A-DBSCAN with adaptive parameters are applied to identify clusters. The similarity distance is utilized to distinguish between clusters to identify the quality of clusters, where the value of similarity between (-1,1). Moreover, the cluster with a value similarity of more than 0 is identified as a highquality cluster. The probability distance is used to re-evolve the instances of negative clusters to suitable positive clusters. This stage results in consolidated optimal clusters to overcome the problem of imbalances data in the dynamic network efficiently. Additionally, the standard classifiers, such as the Random Forest (RF), K Nearest Neighbours (KNN), Decision Trees (DT), and Naïve Bayes (NB) classifier are utilized to classify data network traffic. Finally, the ISCX VPN-nonVPN dataset remarks as a benchmark to evaluate the proposed solution. The experiment results show that the performance evaluation achieves higher accuracy 81.9% compared to the standard classifiers and related works. [ABSTRACT FROM AUTHOR]

Due to the inherent mobility of each node in a Vehicular Network, routing is the primary concern. The mobility enhances the dynamic character of the vehicular networks, which is reflected in controlling of traffic overhead. Consequently, attaining steady end-to-end communication connections is crucial to the success of routing strategy and a kind of mobility of node data. A RCCR (Reliable Cluster based Multi Hop Cooperative Routing) technique based on velocity, distance, and connection quality was offered as a solution to these issues. This method determines the optimum network settings that balance Quality of Service (QoS) requirements with mobility restrictions. By choosing cluster heads and Multi-Point Relays (MPRs) while taking mobility restrictions and quality-of-service requirements into account, it increases the scalability of routing. Each pair of nodes' connection quality is calculated using the suggested method's combination of strength of signal and distance characteristics. To guarantee steadiness, dependability, and longevity of the route, we choose the relay cars using the greatest possible QoS value. To improve Packet Delivery Ratio (PDR) of a multi-hop network with acceptable End-to-End transmission latency, heuristic limits of multi-point relay selection approach by taking into cluster-head coverage area, account link quality, and distance from source vehicle are addressed. Additionally, we optimized and acquired the ideal number of cooperating cars in each hop in an effort to reduce total energy usage. Finally, simulation results show that suggested approach outperforms in terms of throughput, network life time, packet delivery ratio and energy consumption compared to the Cluster-based Adaptive Cooperative Algorithm (CACA), Adaptive Optimal Relay Selection (AORS) and cooperative multi-hop vehicular to vehicular (Coop V2V). Proposed approach improves the network throughput by 225% network lifetime by 40%, energy efficiency by 48.8% compared to the AORS approach. [ABSTRACT FROM AUTHOR]

The modern academic world can hardly boast of substantial research on how people become loyal to businesses thanks to TikTok. The current research seeks to add some knowledge to the field by exploring the impact that TikTok services' quality and satisfaction from using it have on Russian and Chinese users' loyalty to opinion leaders in this social network. The analysis of research data collected from the survey of 400 TikTok users was done by means of the partial least squares structural equation modeling method. This allowed obtaining results that confirmed that e-service quality significantly and positively affects users' e-satisfaction and loyalty to opinion leaders. E-satisfaction was found to affect loyalty and mediate the relationship between loyalty and e-service quality significantly and positively. This study provides a number of practical recommendations for brands and businesses interested in promoting their products or services through TikTok. [ABSTRACT FROM AUTHOR]

The laser powder bed fusion additive manufacturing (LPBF-AM) technique enables the production of near-net-shaped metal components, but the concentrated heat input employed during manufacturing leads to the development of significant internal residual stresses. These residual stresses may cause considerable issues such as distortion, crack initiation during fabrication, and premature failure during service. In this study, neutron diffraction experiments were performed on a nickel-based superalloy, Hastelloy-X, to evaluate the state of residual stresses below and near the surface of as-built specimens as a function of LPBF-AM core process parameters. In addition, a crystal plasticity method was used to determine the diffraction elastic constants to provide a better estimation of residual stresses for textured specimens. The results indicate large tensile residual stresses of about 660 MPa along the scanning direction, counterbalanced by compressive ones below the surface. It is shown that internal and surface residual stresses increase with the laser specific energy. The use of various diffraction peaks for determining residual stresses is discussed and it is shown that while trends do not change, the magnitudes of measured stresses vary. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the mediating effect of a firm's customer centricity and market orientation on the relationship between the knowledge management and business performance. Design/methodology/approach: The hypotheses proposed for this study were tested on the data collected from 274 sample firms using partial least square-based structural equation modeling. Findings: According to the findings of this study, a firm's customer-centricity fully mediates the relationship between explicit and tacit knowledge management and a firm's business performance, whereas a firm's market orientation partially mediates the relationship between tacit and implicit knowledge management systems and a firm's business performance. Originality/value: This study can be considered as a pioneer work that investigates how explicit and tacit knowledge can be transformed into business performance with the mediating effect of a firm's customer centricity and market orientation on this relationship. [ABSTRACT FROM AUTHOR]

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With an emphasis on the mediating role of relational psychological contracts, this study sets out to investigate the structural connection between perceived organizational support (POS), service quality (SERVQ), and customer satisfaction (CS). Using descriptive and inferential statistics, we tested the heuristic model for the relationship using data from 394 clients at selected banks in Nigeria. It was found that both perceived organizational support and service quality significantly affected customer satisfaction, with the former serving as a predictor of the latter. In addition, a positive and statistically significant influence of the relational psychological contract was found in the connection between "POS" and CS, and perceived organizational support and SERVQ. In addition, it was determined that banks must maintain all required standards in gaining customers by continually providing a higher degree of service to keep clients satisfied. The structural nexus between the identified variables stood out as a novel idea with a heuristic model depicted for business practitioners and society's opportunities. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to analyze the supply and demand of marketing and communication consulting services and evaluate actual and perceived gaps. Design/methodology/approach: The paper uses two different datasets to assess the gap. The supply database comes from desk research carried out in the province of Bergamo (n. 159 consulting agencies). The demand dates are the results of 100 structured interviews with local companies that requested marketing and communication consulting services both inside and outside the province. Findings: Findings show that there is no significant shortage in local service supply. Nonetheless, a limited gap exists between the provision of specific services and their overall quality. Conversely, the perceived gap is wider, leading to an impression of scarce availability – a notion disproven by the analysis of the actual supply. Practical implications: The study suggests that local agencies may overcome their "myopic" attitude and need to increase their visibility, competencies and expertise by investing in these areas and improving networking. Originality/value: There are no previous studies that compare the supply and demand for marketing and communication consulting services. The paper also provides insights into actual and perceived gaps in a hypercompetitive environment. [ABSTRACT FROM AUTHOR]

Commercial social media (social commerce in emerging market) have increasingly been used in many emerging markets as an evolutionary form of ecommerce. While this trend has recently been examined from an e-vender's perspective, little is known about how customers, specifically female millennials, perceive of their shopping experiences with commercial social media in relation to ecommerce. Thus, this study explores the underlying motivations for using one e-retailing type over the other. We consider the social constructs of trust, customer service quality and web design in the study. Drawing data from 20 in-depth interviews with young Omani women, the study revealed that ecommerce is most preferred to commercial social media. It highlights the role of unethical marketing practices; unsystematic delivery costs; unprofessional customer service quality; underdeveloped local market and uneasiness of commercial social media in influencing the decision-making of the customers to shift to ecommerce. The study further points to the intersection between product's price, quality and information in building trust; the role of professionalism, responsiveness and empathy in enhancing customer service quality; the importance of product quality and variety in the local market; and the easy navigation and online payment of e-retailing platforms. The study bears implications on enhancing the features and services of commercial social media. It also calls for transforming the local market to meet the customers' needs. [ABSTRACT FROM AUTHOR]

Microservice architecture is a new technology for deploying large-scale applications and services in the cloud. But multivariate time series data with anomalies are increasingly generated in the cloud. Effectively diagnosing the runtime system anomalies is necessary to ensure the quality of service of microservice systems. Typical anomaly detection methods are effective in data quality and computing reliability of cloud computing. However, they all focus on one-class anomaly detection, which may not perform on practical microservice frameworks with diverse types of anomalies. Furthermore, locating the root cause of anomalies to eliminate after detection is essential. To address these issues, we propose an effective parallel convolutional anomaly multi-classification model (PCAC) based on an attention mechanism for fault diagnosis in microservice system. We first construct a parallel convolutional structure that allows subnetworks to extract features independently. Then, channel and spatial attention mechanisms are applied in the parallel convolutional layers to mitigate the loss of feature representation. Finally, causal inference based on the anomalous graph is used to locate the fault in the microservice system. The experimental results clearly show that the proposed model achieves the highest F1 scores on six public microservice datasets, improved by 37.9% in average macro-F1 and 4.4% in average micro-F1 scores respectively, outperforming eight state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hadoop MapReduce (HMR) provides the most common MapReduce (MR) framework, and it is available as open source. MR is a famous computational framework for evaluating unstructured, and semistructured big data and executing applications in the past ten years. Memory and input/output (I/O) overhead are just two of the many problems affecting the current HMR scheduler system. This study aims to improve systems resource use including the processing of data in realtime by creating a memory I/O optimized scheduler (MIOOS) for HMR. The disk I/O seek can be reduced by using MIOOS, which analyzes the entire memory management. Additionally, the MIOOS makespan approach is used to reduce the occurrence of problems in intermediary tasks. Both the MIOOS approach and the current approach are assessed by using complex scientific workflow applications with extreme task inter-dependencies. Further, the comparison study demonstrates that the MIOOS framework outdoes the current approach regarding makespan and overall memory usage. [ABSTRACT FROM AUTHOR]

Fifth generation new radio (5G NR) is bringing revolutions in almost every field such as healthcare, industrial automation, smart farming, and defense. These fields utilize three major use case scenarios of 5G NR viz. eMBB, URLLC, and mMTC. 5G NR deployment relies on two major frequency bands such as sub-6 GHz and high-frequency bands (millimeter waves). Among all sub-6 GHz bands, the n78 band (3300–3800 MHz) is gaining popularity due to its unique characteristics viz. higher capacity and coverage, wide bandwidth, and higher data rate. Despite these unique characteristics, in the 5G standalone architecture, users expect high-speed video/voice transmission without delay and good quality of services (QoS) requirements. QoS is one of the important paradigms that indicates the quality of services experienced by end users instead of services claimed by service providers. In this work, we have designed a QoS scenario that utilizes the n78 band with an "urban macro mixed office scenario with low loss" path loss model. As per types of services are concerned, the scenario utilizes six different traffic templates (i.e., FTP, Voice, Video, COAP, CBR, and Email). The path loss model operates on non-line-of-sight scenarios with Rayleigh fading and eigen beamforming using 64 × 32 antenna elements. The performance of the proposed scenario is examined through different metrics i.e., throughput, packet drop ratio, and delay. Here, priorities to different traffic templates are assigned based on GBR and non-GBR resource types. In addition, the proportional fair scheduling algorithm is employed to schedule the resources. It was observed that video services achieved a higher throughput of 25.93 Mbps due to their prioritization in the QoS model. Conversely, FTP services experienced higher delay, measured at 64 ms, and a packet delivery ratio of 5.94%. [ABSTRACT FROM AUTHOR]

This paper examines the service mode selection strategy of a ride‐hailing platform and analyzes the impact of the strategy on consumers and drivers. The platform has three service modes to select from, including Mode L (only low‐quality services), Mode H (only high‐quality services), and Mode HL (high‐ and low‐quality services). We find that each service mode does not affect the price and the wage of the low‐quality service but affects those of the high‐quality service. Additionally, the platform cannot optimally select Mode L. The platform is better off if the car quality difference between high‐ and low‐quality services (CQD) is small and is worse off otherwise, under Mode HL. Counter‐intuitively, it is conditional for Mode HL to bring the highest surpluses to both consumers and drivers, and the conditions are related to CQD, social status utility of consumers and cost ratio between the drivers providing the two types of services. [ABSTRACT FROM AUTHOR]

The flourishing platform business model has been rapidly integrated into manufacturing industries, and value‐added service (VAS) provided by the platforms has become a critical part of enhancing competitiveness. This study investigates the optimal two‐period pricing decisions and VAS strategies of the two‐sided platform for manufacturing. For both the supplier and manufacturer side, the platform decides the entry fees in each period and provides differentiated quality of basic services, as well as the VAS (if any). Moreover, the manufacturer's utility of accessing is influenced by the cross‐network externality, which is related to the number of suppliers in each period. In the presence of the supplier's varying entry timing, three VAS strategies are available for the platform including: (1) VAS for suppliers in Period 1 (Model S1), (2) VAS for suppliers in Period 2 (Model S2), and (3) VAS for manufacturers in Period 1 or 2 (Model M). We establish a two‐period game model under each VAS strategy. Then, the optimal platform's pricing decisions are derived, and the optimal performances in three VAS strategies are compared. Findings demonstrate that when the cross‐network externality strength is large, the platform always prefers Model S1; only when both the cross‐network externality strength and quality of the platform's basic service are comparatively low, the platform selects Model S2; otherwise, the platform chooses Model M. This study also extends to the scenario that the platform provides bilateral VAS in Period 1 (Model B1) or Period 2 (Model B2). Notably, the platform is not always willing to offer bilateral VAS, especially when the VAS cost coefficient is relatively high. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has increased worldwide mental health conditions, substantially affecting the demand and provision of mental health services. To continue services and safeguard the health and well-being of mental health service users, service providers have responded to the pandemic with the adoption of remote services. The objective of our study was to identify and compare the promoters and challenges of the adoption of remote mental health services experienced by service users in Finland and the Netherlands during the COVID-19 pandemic with help of the FITTE framework. Methods: The study adopted a qualitative descriptive approach, consisting of in-depth semi-structured interviews with mental health service users in Finland (n = 12) and the Netherlands (n = 13) during the COVID-19 pandemic. Qualitative content analysis with both inductive and deductive approach was utilized, and the extended FITTE framework was applied to identify the factors related to the fit between individual, task, technology, and environment that influenced the adoption of remote services. Results: Overall, the adoption of remote mental health services during the COVID-19 pandemic involved several promoters related to easy access to mental health services, support for mental health and well-being, and benefits of the home environment such as not having to leave outside for services and having less risk of getting the corona virus. Most of the challenges were related to lack of non-verbal communication, difficulties with expressing oneself and interpreting others, technological issues, the organization of remote services, usability and suitability of services, affinity of the health professional with the provision of remote services, and the safety and security of remote services. Conclusions: While remote services are accessible, support our well-being and mental health and protect us against the coronavirus, our findings also show that face-to-face contact was often indispensable when adopting mental health services. Therefore, the design of future mental health services should include both remote and face-to-face delivery. Further research should be done on blended approaches and on ways in which the challenges and development needs which emerged from this study can be tackled to improve remote mental health services. [ABSTRACT FROM AUTHOR]