Service Platform Research Articles

Comparing three contract types to optimize profits in service firm – digital service platform relationships

PurposeThe importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.Design/methodology/approachThe authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.FindingsThis study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.Practical implicationsThe paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.Originality/valueThis study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

Using Intelligent Screening Service Platform (ISSP) to improve the screening process of clinical trial subjects during COVID-19 pandemic: an experimental study

Using Intelligent Screening Service Platform (ISSP) to improve the screening process of clinical trial subjects during COVID-19 pandemic: an experimental study

A Triple Bottom Line optimization model for assignment and routing of on-demand home services

‘On-demand home services’ is a fast-growing industry where online platforms match independent service professionals with customers seeking aid for household tasks. In this paper, we study the assignment and routing of service professionals for serving customers of an on-demand home services platform considering the Triple Bottom Line (TBL) criteria for ensuring sustainability in operations. We characterize this as the Home Services Assignment and Routing Problem with the Triple Bottom Line (HSARP-TBL) and implement a Mixed Integer Linear Programming (MILP) model for solving it. We assign service professionals to customers based on their desired time slots and also transport modes for each customer visit by a professional, considering either combinations of public transport or a personal vehicle for each professional’s tour. The objective is to minimize costs due to time window violations and uncovered customers, catering to the economic pillar of the TBL. We incorporate additional constraints related to the TBL by improving customer satisfaction based on the ratings of assigned professionals to customers, with and without subscription (economic), controlling emissions due to transportation of professionals (environmental) and ensuring equity in service allocation and net earnings between professionals (social). For tackling large instances we implement a Hybrid Genetic Search (HGS) algorithm adapting it to our problem setting. We demonstrate that the HGS outperforms the MILP model systematically for large instances in terms of solution value and computational time. Finally, we observe that for some instances, without worsening the primary economic objective, all the TBL indicators can be improved.

Liability of the Operator of an NFT Digital Artwork Trading Service Platform for Contributory Copyright Infringement

Liability of the Operator of an NFT Digital Artwork Trading Service Platform for Contributory Copyright Infringement

Driving Mechanism of Customer Participation in Service-Oriented Enterprises: A Perspective of Value Co-Creation

Based on service-dominant logic and service quality theory, this study explores the influence mechanism of customer participation on value co-creation of service-oriented enterprises. Employing a quantitative approach, the study utilized a questionnaire survey administered to 400 participants, examining the mediating and moderating effects of platform service quality and digital technology. The research results indicate that all three dimensions of customer participation have a significant positive impact on the value co-creation of service-oriented enterprises. Customer participation can also have a significant positive impact on the value co-creation of service-oriented enterprises through the mediating variable of platform service quality. Digital technology plays a significant positive moderating role between customer participation and platform service quality, as well as between platform service quality and value co-creation of service-oriented enterprises.

Determinants of user acceptance of digital insurance platform service on InsurTech: an empirical study in South Korea

ABSTRACT In recent days, InsurTech services and users are growing rapidly worldwide. InsurTech, a combination of ‘insurance’ and ‘technology’, as an area of Fintech has become an essential innovation strategy of insurance companies. Even if InsurTech services and users are prevalent, there is a lack of empirical research on determinants of user acceptance of InsurTech. The objective of this study is to explore factors determining user acceptance of digital insurance platform services most popular within InsurTech in South Korea. The research model was derived from mainly three theories: the unified theory of acceptance and use of technology (UTAUT), information systems success model (ISSM) and innovation diffusion theory (IDT) with service characteristic factors (system quality, information quality, and service quality) and user characteristic factors (personal innovativeness, self-efficacy, and perceived risk). 249 survey responses from South Korea were analysed. According to the results, information quality exhibited an effect on performance expectancy and effort expectancy whereas service quality was shown to affect only effort expectancy. Furthermore, personal innovativeness affected performance expectancy, effort expectancy, and social influence, self-efficacy exhibited an effect on performance expectancy, and perceived risk affected performance expectancy and effort expectancy.

Crossing the principle–practice gap in AI ethics with ethical problem-solving

AbstractThe past years have presented a surge in (AI) development, fueled by breakthroughs in deep learning, increased computational power, and substantial investments in the field. Given the generative capabilities of more recent AI systems, the era of large-scale AI models has transformed various domains that intersect our daily lives. However, this progress raises concerns about the balance between technological advancement, ethical considerations, safety measures, and financial interests. Moreover, using such systems in sensitive areas amplifies our general ethical awareness, prompting a re-emergence of debates on governance, regulation, and human values. However, amidst this landscape, how to bridge the principle–practice gap separating ethical discourse from the technical side of AI development remains an open problem. In response to this challenge, the present work proposes a framework to help shorten this gap: ethical problem-solving (EPS). EPS is a methodology promoting responsible, human-centric, and value-oriented AI development. The framework’s core resides in translating principles into practical implementations using impact assessment surveys and a differential recommendation methodology. We utilize EPS as a blueprint to propose the implementation of an Ethics as a Service Platform, currently available as a simple demonstration. We released all framework components openly and with a permissive license, hoping the community would adopt and extend our efforts into other contexts. Available in the following URL https://nkluge-correa.github.io/ethical-problem-solving/.

Slope stability prediction based on GSOEM-SV: A mobile application practicably deploy in engineering verification

Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering.

Design of Intelligent Pediatric Nursing Management Platform from the Perspective of Big Data

With the popularization of network technology, the management mode of pediatric nurses has also changed. Because the traditional pediatric nursing work is restricted by the traditional mode, it not only affects the service quality of children’s outpatient departments and wards, but also has a negative impact on the psychological and physical health of patients. Therefore, the development of an intelligent pediatric nursing management platform is a prerequisite for the realization of the whole process management of children. Pediatric nursing management is a comprehensive service platform, which is fundamental and public welfare, and is of great significance to achieve high-quality nursing services. Based on relevant experience, this paper designed and implemented an intelligent pediatric nursing management platform, which has certain practical value and can provide decision-making basis for clinical work.

Forecasting healthcare service volumes with machine learning algorithms

AbstractAs an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinical arrangements, and generating precise projections of medical platform service volume to enhance patient satisfaction and alleviate physicians' workload. To this end, grounded in the data‐driven method, this paper assembles an exhaustive feature set encompassing hospital features, physician features, and patient features. Through feature selection, appropriate features are screened, and machine learning algorithms are leveraged to accurately forecast doctors' online consultation volume. Subsequently, to glean the influence relationship between online medical services and offline medical services, this paper introduces features of offline medical services such as hospital registration volume and regional gross domestic product (GDP) to solve the prediction of offline medical service volume using online medical information. The findings signify that online data feature prediction can pinpoint superior machine learning models for online medical platform service volume (with the optimal accuracy up to 96.89%). Online features exert a positive effect on predicting offline medical service volume, but the accuracy declines to some degree (the optimal accuracy is 73%). Physicians with favorable reputations on the online platform are more susceptible to attain higher offline appointment volumes when online consultation volume is a vital feature impacting offline appointment volume.

Perceived Risks of Social Media in the University Libraries of Pakistan: An Empirical Investigation

Purpose The research on social media adoption and use is on the rise in Pakistan. However, researchers did not focus on investigating the risks associated with social media use for library services. So, this study examines perceived risks of library services through social media in the university libraries of Pakistan. Also, suggest possible solution to overcome the observed risks. Methodology Using a structured questionnaire, a national-level survey was conducted. Respondents of the study were professional librarians designated as Head or Chief or University librarian representing their organization. The study was conducted in an organizational perspective and required a single response from each organization. The questionnaire was distributed among 181 university librarians working both in public and private sector university libraries in Pakistan. However, valid responses were 124 (69%). Findings The Pakistani university libraries are using social media platforms for library services. However, the research findings reveal that there are several risks to offering library services through social media. Among the responded libraries, the libraries that do not use social media platforms for service delivery feel riskier compared to the libraries that offer library services through social media. It is also found that there is no formal policy on social media usage in the libraries. Practical implications The research findings provide valuable insight to university administration, library social media users, and developers on how to consider the identified risks while designing state-of-the-art library services through social media. Also, the study findings may be easily generalized at national level due to the coverage of the whole population. Originality This is a novel study that explores the perceived social media risks associated with using social media to offer library services. Also, the study is first attempt that covers whole population through a census survey in an organizational perspective.

QBDD: Quantum-resistant blockchain-assisted deep data deduplication protocol for vehicular crowdsensing system

Vehicular Crowdsensing System (VCS) has emerged as a promising paradigm for alleviating traffic congestion and improving driving safety due to its convenient collection and aggregation of various driving and traffic-related reports. However, the rapidly growing number of vehicles means that VCS faces the severe challenge of large-capacity data storage. It has been observed that various data deduplication techniques have been devised for VCS. Unfortunately, in most solutions, the deduplication operations are centralized, unable to simultaneously reduce storage costs and bandwidth consumption. Moreover, these schemes are mainly constructed based on number-theoretic assumptions, which makes them susceptible to quantum attacks. Inspired by this fact, we introduce QBDD, a quantum-resistant blockchain-assisted second-level data deduplication protocol for privacy-preserving VCS, enabling efficient deduplication on the Road Side Unit (RSU) and Cloud Service Platform (CSP). We construct this protocol based on the Ring Learning with Errors (RLWE) problem signcryption and proxy re-encryption schemes to ensure data security. The improved Message Locking Encryption (MLE) mechanism hides the label of ciphertext and allows for quick duplicate crowdsensing report checks without compromising data privacy. Additionally, a blockchain-based distributed system is used to store crowdsensing data and maintain greedy lists, enhancing the system’s efficiency and fairness. We provide theoretical proof of QBDD’s security and conduct extensive experiments to demonstrate its practicality in post-quantum secure VCS.

Browsing target extraction and spatiotemporal preference mining from the complex virtual trajectories

Public Map Service Platforms (PMSPs) aggregate and disseminate the earth observation data. Leveraging spatiotemporal preference patterns derived from browsing targets within complex virtual trajectories on PMSPs aids in constructing user-profiles and comprehending their intentions. However, complex virtual trajectories, characterized by numerous trajectory points and overlapping pyramidal spatial structures, introduce inefficiencies and inaccuracies during browsing target extraction. To mitigate this, we propose an Optimized Spatial Structure Segmentation (OSSS) method that divides complex virtual trajectories into sub-trajectories with simplified spatial structures, enhancing the efficiency of browsing target extraction. Spatiotemporal reconstruction of these sub-trajectories establishes sequences of browsing targets, revealing patterns of interest transitions. Moreover, recognizing the spatial uncertainty inherent in complex virtual trajectories, which results in imprecise matching between browsing targets and spatial features, we introduce a spatial co-occurrence semantic modeling approach. This involves constructing a POI semantic space and introducing a semantic similarity matching method to reduce spatial uncertainty and refine the accuracy of mining spatiotemporal preference patterns. We evaluate these methods using real-world data from Tianditu. Results demonstrate that the OSSS improves extraction efficiency by 3.45 times and accuracy by 18.84%. Additionally, the semantic similarity approach combined with spatial co-occurrence effectively mitigates spatial uncertainty. This research contributes to advancing the intelligence of PMSPs.

Messenger Marketing Platforms and Performance of Tour Operating Companies in Kenya

Messenger marketing is part of global digital trend that acts as a new avenue for tour firms to adopt based on convenience in interaction, ability to reach more audiences and collaboration with customers. The study aimed to examine the effect of messenger marketing on performance of tour operating companies in Kenya. The study was anchored on diffusion innovation theory. Cross-sectional and correlational research design were utilized. The study applied census approach to a target population of 212 general managers of tour operating companies. Online and physical questionnaires were used to collect primary data. The supervisors and experts were used to examine validity of the instrument while Cronbach Alpha was adopted to test reliability was 0.783 which was above a threshold of 0.7, hence reliable. Descriptive statistics consisting of mean and standard deviations were used to summarize data while simple linear regression was used to test significance at 5%. The results revealed that messenger marketing platform was practiced by the tour operating companies through using mobile services platform for sending messages, chatting and soliciting sales. It also assisted in obtaining customer feedback, blogging and chatting with customers. However, the Chatbots were hardly used by firms which adopted artificial intelligence in communicating, sending random messages and could be integrated to existing online platforms. The results indicated that messenger marketing had positive significant effect on the performance of tour operating companies (β1=0.428, P = 0.000<0.05). The study concludes that messenger marketing platform marketing significantly influenced performance of tour operating companies. The study recommends tour operating companies should apply Chatbots, mobile service, mobile commerce and conversation commerce to enhance customer relationship and improve performance

DTA: distribution transform-based attack for query-limited scenario

In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack. This may be unacceptable in real applications since Machine Learning as a Service Platform (MLaaS) usually only returns the final result (i.e., hard-label) to the client and a system equipped with certain defense mechanisms could easily detect malicious queries. By contrast, a feasible way is a hard-label attack that simulates an attacked action being permitted to conduct a limited number of queries. To implement this idea, in this paper, we bypass the dependency on the to-be-attacked model and benefit from the characteristics of the distributions of adversarial examples to reformulate the attack problem in a distribution transform manner and propose a distribution transform-based attack (DTA). DTA builds a statistical mapping from the benign example to its adversarial counterparts by tackling the conditional likelihood under the hard-label black-box settings. In this way, it is no longer necessary to query the target model frequently. A well-trained DTA model can directly and efficiently generate a batch of adversarial examples for a certain input, which can be used to attack un-seen models based on the assumed transferability. Furthermore, we surprisingly find that the well-trained DTA model is not sensitive to the semantic spaces of the training dataset, meaning that the model yields acceptable attack performance on other datasets. Extensive experiments validate the effectiveness of the proposed idea and the superiority of DTA over the state-of-the-art.

Effects of high-intensity interval training on depressive symptoms: A systematic review and meta-analysis

The objective of this systematic review and meta-analysis is to investigate the effect of high-intensity interval training (HIIT) on depressive symptoms, including an examination of its impact across different populations, intervention durations, and control groups through subgroup analysis. MethodsA systematic literature search was conducted using the following databases: Cochrane, Embase, PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Data Knowledge Service Platform. The search covered the period from January 1, 2000, to December 31, 2022. ResultsThe meta-analysis included 34 randomized controlled trials, involving a total of N = 1607 participants. HIIT had a small but significant effect on depressive symptoms compared to the control group [SMD = −0.40, 95%CI (−0.60, −0.20), I2 = 73%]. However, subgroup analyses revealed no moderating effect of health status or exercise duration on depressive symptoms. HIIT did not have a statistically significant effect on depressive symptoms when compared with other types of exercise [SMD = −0.15, 95% CI (−0.30, 0.01), I2 = 10%]. HIIT demonstrated a medium effect size in reducing depressive symptoms compared to a non-active control group [SMD = −0.53, 95% CI (−0.84, −0.21), I2 = 80%]. ConclusionsHIIT confers benefits in mitigating depressive symptoms. Compared to non-active control group, HIIT yields moderate improvements in depressive symptoms. We look forward to exploring more moderating effects in the future, such as HIIT modalities, frequency, and so on. In summation, these findings substantiate the use of HIIT as a means to alleviate symptoms of depression.

Research on the assignment of elderly care service personnel with time window constraints

As the population continues to age, there is a growing need for elderly care services. In China, home care is widely embraced as a preferred method of caring for the elderly, and it has a promising commercial outlook. The strategic allocation of Elderly Care Service Personnel (ECSP) is a crucial component of the operational procedures for home care services. By strategically assigning elderly service personnel, it is possible to enhance the satisfaction and simultaneously minimize expenses. The issue of rationalizing the assignment of ECSP in the face of restricted resources is a genuine challenge that requires a solution, given the many types of elderly service demands and the skill requirements and time limits of certain projects. This study presents a strategy for assigning personnel on an hourly basis, taking into account time frame limits. This method involves several steps. Firstly, it involves assessing the specific service needs of the elderly, including the required door-to-door service time, service level, and gender preferences. Secondly, it calculates the service satisfaction and service operation cost of the Home Care Service Platform (HCSP) separately. Finally, it constructs a multi-objective elderly service personnel assignment method. This method aims to minimize the ECSP 's travel time and wasted time, maximize the satisfaction of the elderly by considering priority levels and ECSP scores, and minimize the operation cost of the HCSP. A model is developed to assign ECSP for elderly individuals, with the goal of minimizing travel time and wasted time, maximizing elderly satisfaction by considering priority and service personnel rating, and minimizing operating costs for the HCSP. Additionally, if there are unserved elderly individuals, an optimized path model is constructed using a cross-modified genetic algorithm to obtain the optimal matching result. Ultimately, an arithmetic example is employed to demonstrate the practicality and efficiency of the strategy put forth in this research. The findings demonstrate that the model presented in this research possesses distinct advantages in comparison to other conventional models.

Analysis of Acupoint Selection Rules for Guasha Treatment of Primary Headaches Based on Data Mining.

We aimed to understand the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches using data mining technology, providing a reference for the clinical application of Guasha therapy for primary headaches. Literature related to Guasha therapy for primary headaches in PubMed, Web of Science, Chinese National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Chinese Biomedical Literature Database were searched, up until May 12, 2023. A database of acupoints for Guasha therapy for primary headaches was established in Excel. The frequency of the acupoints used for Guasha in therapy of primary headaches were calculated by SPSS 25.0. The association rules between the acupoints were further described using SPSS Modeler 18.0. A total of 67 papers were included, involving 51 acupoints for Guasha against primary headaches. The most commonly used acupoints were Fengchi, Baihui, Taiyang, Shuaigu, Tianzhu, and Hegu. The common acupoint combinations for Guasha therapy for primary headaches were Fengchi-Taiyang, Fengchi-Baihui, Fengchi-Taiyang-Baihui, Fengchi-Tianzhu-Baihui, and Fengchi-Shuaigu-Taiyang-Baihui. Data mining can effectively analyze the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches, providing a reliable basis for clinical acupoint selection.

Article: Legislation Comment: Considerations on the Digital Markets Act, the Way to a Fair and Open Digital Environment

This paper aims to present how Regulation (EU) 2022/1925 (the Digital Markets Act) will enter into force, as well as a few points of interest related to the essential provisions of this Regulation which establishes clear rules for large online platforms. The article also tackles important challenges and practical determinations that will have to be taken into consideration in the process of implementing this piece of legislation together with significant legislative developments that will have to be enacted worldwide in order to comply or cooperate with the European legislation in the matter of digital markets. Digital Markets Act, Regulation (EU) 2022/1925, gatekeeper, Big Tech, core platform services, business users, end users

WANTED: SAKLOLO The Discourses of “Wanted sa Radyo’s” VAW-related Episodes

Today, the media holds an indisputable influence over public opinion, serving as a formidable tool for information dissemination, persuasion, and the molding of collective perspectives (Allen, 2017; Impe, 2019). This study conducted an in-depth examination of the discourse within the Philippines’ Radyo 5 program, 'Wanted sa Radyo,' hosted by broadcaster and senator, Raffy Tulfo (Afinidad-Bernardo, 2019). Recognized as a public service program aiming to assist those in need, this research specifically scrutinized episodes that shed light on the distressing issue of violence against women (UN Women, 2023). Through a comprehensive utilization of textual analysis as the methodology (McKee, 2003; Luo, 2019), this study aimed to ascertain how the program presented and addressed these sensitive issues during live broadcasts. The findings of this research showed that the program exhibited sensitivity when discussing the delicate topics that surrounded violence against women (GMA Integrated News, 2023). With careful consideration and tactful handling of such issues, Raffy Tulfo, the program's host, showcased a remarkable empathetic approach toward complainants while impartially interrogating the accused (Raffy Tulfo in Action, 2018). As a result, it became evident that 'Wanted sa Radyo' remained true to its core mission as a public service platform (ABS-CBN News, 2022). Shedding light on the effectiveness of public service program in amplifying marginalized voices, addressing pressing social concerns, and ultimately contributing to the betterment of society (Impe, 2019; Atkin, 2013). This aligns with the broader goals of media studies in understanding and evaluating the impact of media on society (Abowitz et al., 2010; Spitulnik, 1993). Keywords: Media, violence against women, abuse, public service, feminism.