User Ratings Research Articles

Development and user evaluation of an online caregiver education resource for Malaysian parents of children with leukemia or lymphoma

ObjectiveMalaysian parents of children diagnosed with leukemia or lymphoma stand to benefit from a comprehensive Malay-language online resource, complementing existing caregiver education practices. This study aimed to develop and assess the efficacy of e-Hematological Oncology Parents Education (e-HOPE), an online caregiver education resource in Malay, designed to enhance the knowledge of parents with children diagnosed with leukemia or lymphoma in Malaysia. MethodsA user profile and topic list were established based on previous needs analysis studies. Content was developed for each identified topic. An expert panel assessed the content validity of both informational content and activity sections. Subsequently, the contents were presented via a learning management system to parents of children newly diagnosed with leukemia or lymphoma. Parents evaluated the quality of e-HOPE using the Website Evaluation Questionnaire (WEQ) after an 8-week period. ResultsThe scale content validity index (S-CVI/Ave) achieved 0.996 for informational content and 0.991 for the activity section. Sixteen parents provided evaluations of e-HOPE after an 8-week usage period. Mean WEQ scores for various dimensions ranged from 4.23 for completeness to 4.88 for relevance. ConclusionsE-HOPE was meticulously designed and developed to offer Malaysian parents a Malay-language resource complementing current caregiver education practices. It exhibited strong content validity and received positive user ratings for quality. Further assessment is warranted to evaluate its effectiveness in supporting parents of children with leukemia or lymphoma. The resource is anticipated to enhance information accessibility and support for Malaysian parents facing hematological cancers in their children. Trial registrationClinicaltrials.govNCT05455268.

Crowd‐sourced and incentive driven UAV system to assist with network slices

AbstractOwing to their flexibility, fast deployment, increasing affordability, and numerous application opportunities, unmanned aerial vehicles (UAVs) became an undisputed target of studies and research to discover and exploit the immense opportunities they provide. Moreover, network slicing is a critical enabler for 5G and beyond. Network slices guarantee certain network user expectations such as data rate and latency thresh‐holds. There is no doubt that crowd‐sourced and incentive driven solutions are on the rise. Crowd‐sourcing allowed companies and organizations both small and large to gather data and perform other large‐scale tasks with minimum expenses and maintenance. Indeed, the major concerns for 5G and network slicing are guaranteeing high throughput, reliability, and latency. With data rates being the most important attribute for common regular users, that is, mobile devices, video streaming, navigation, and content consumption. As a result, the focus of this work is to utilize crowd‐sourced UAVs in an incentive driven network slice assistance system to extend coverage and provide mobile users with the high throughput they require. This work presents some basic 5G cellular radio access technologies and network slicing background and proposes a novel system, namely UAV slice assistance (UAVSA), and its hybrid alternative version: UAV hybrid slice assistance (UAVHSA). The hybrid mode combines benefits from the proposed UAVSA system and the traditional base station (BS) system. The proposed algorithms address the issue of coverage and guaranteeing high data rate slices for users. The proposed systems maximize satisfaction rate of network slice users. Moreover, all systems are extensively tested and compared in various scenarios to determine their performance and provide a coherent study. Finally, future works and opportunities are highlighted in conclusion.

Numerical Evaluation of the MU-MIMO Beamforming Performance with Channel Estimation

This paper presents the numerical evaluation of the ZF beamforming algorithm and DFT precoding using the LS channel estimation in the multiuser multiantenna (MU-MIMO) downlink system. The sum rate performance depending on a number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. For MIMO channel simulation the QuaDRiGa channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared with DFT precoding based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder outperforms the DFT precoder in channel with less spatial correlation. The DFT precoder in spatially more correlated channel has advantage over ZF precoder.

Potentially Unwanted App Detection for Blockchain-Based Android App Marketplace

Android is a mobile operating system with a high degree of openness, which attracts an increasing number of developers. Android application (or simply, app) marketplace provides a trusted source of apps for users and a more equitable competition environment for individual developers and commercial teams. Blockchain’s advantages of decentralization and data immutability are suitable for the Android app marketplace, which is mainly characterized by openness, equality, and security. However, this may also facilitate malicious developers to publish low-quality apps to display ads or steal users’ privacy for revenue. Therefore, blockchain-based app marketplaces have a strong need to identify those potentially unwanted apps (PUAs). In this paper, we first introduce our blockchain-based app marketplace model. Then, we propose a new PUA detection method, mainly based on metadata and user ratings, and they are easily accessible from blockchain-based app marketplaces. Moreover, we introduce dynamic analysis to check whether the URLs visited by the app are in malicious URL blacklists since apps with massive access to these URLs tend to affect user experience. After that, we pre-process those complex and redundant features and represent each app as an embedding. Finally, to validate the effectiveness of our method, we utilize several clustering algorithms to represent these apps as clusters and search for suspicious PUA clusters. Our study reveals several characteristics of PUA and suggests that PUAs are still present and need to be urgently removed.

Age-specific differences in cervical cancer screening rates in women using mental health services in New South Wales, Australia.

Women living with mental health conditions have lower cervical cancer screening rates and higher mortality. More evidence is needed to target health system improvement efforts. We describe overall and age-specific cervical cancer screening rates in mental health service users in New South Wales. Cervical cancer screening registers were linked to New South Wales hospital and community mental health service data. Two-year cervical screening rates were calculated for New South Wales mental health service users aged 20-69 years (n = 114,022) and other New South Wales women (n = 2,110,127). Rate ratios were compared for strata of age, socio-economic disadvantage and rural location, and overall rates compared after direct standardisation. Only 40.3% of mental health service users participated in screening, compared with 54.3% of other New South Wales women (incidence rate ratio = 0.74, 95% confidence interval = [0.74, 0.75]). Differences in age, social disadvantage or rural location did not explain screening gaps. Screening rates were highest in mental health service users aged <35 years (incidence rate ratios between 0.90 and 0.95), but only 15% of mental health service users aged >65 years participated in screening (incidence rate ratio = 0.27, 95% confidence interval = [0.24, 0.29]). Women who use mental health services are less likely to participate in cervical cancer screening. Rates diverged from population rates in service users aged ⩾35 years and were very low for women aged >65 years. Intervention is needed to bridge these gaps. New screening approaches such as self-testing may assist.

Інтелектуальна система ідентифікації рейтингу довіри користувача

The article develops an intelligent system for identifying the user's trust rating, which allows viewing information about contacts that are more aimed at creating trust in the interlocutor or providing information that helps to identify the caller or the person with whom we are trying to contact. The k-nearest neighbours method was chosen to create the recommendation system. The main advantage of using the k-nearest neighbours method is the ability to take into account the unique trust rating of each phone number. It is important not only to find phone numbers with a similar rating, but also to take into account the approximate rating. The k-nearest neighbours method allows you to provide recommendations based on the similarity between phone numbers that have received positive feedback from users with similar preferences. Intelligent recommendation systems can provide phone numbers with similar ratings. When a user requests information about a phone number associated with fraudsters, the system instantly offers low-rated phone numbers that are therefore potential fraudsters.

Multimedia Computation Offloading for 5G Networks Using the Volcano Eruption Optimization Algorithm

The communication services of multimedia, such as live online and short videos, gained significant attention and have been prominent in daily social interactions due to the fast development of 5G technology. However, the increase in multimedia communication requirements presents further difficulties for both the communication capacity of the wireless network and its processing capability. Mobile Edge Computing (MEC) is considered an effective technology to overcome the mentioned challenges. The subject of long-term task allocation including resource coordination is the primary focus of this study, with a special emphasis on multimedia services that must be processed, uploaded, and shared in network. The optimization issue is formulated as a stochastic optimization issue to reduce the system’s time-average energy consumption. An online energy-efficient task allocation and computing offloading method is proposed to determine task allocation, coordinate and optimize wireless, computing resource allocation, considering dynamic wireless state and service delay constraints. The proposed method is implemented in MATLAB and the efficacy of the MCO-VEA framework is estimated and performance metrics, such as minimum user rate, sum rate, energy, and spectral performance are analyzed. Then, the performance of MEC-MCO-VEA provides 22.35%, 25.84% and 36.58% higher throughput and 25.45%, 41.28%, and 28.56% higher energy efficiency compared to the existing models, such as an energy-effective multimedia task assignment with computing offloading for mobile edge computing (BA-LO-EE), energy efficiency using a communication deep neural network optimized with power allocation optimization (EE-CDNN-PAO), and energy efficiency using deep transfer deterministic policy gradient with deep deterministic policy gradient algorithm (DT-DPG-EE), respectively.

Mandatory Vision Test for Driver License Renewal in Association With Traffic Fatality and Injury Among Older Road Users in Utah.

In 2011, Utah began requiring that drivers aged 65years and older pass a vision test at each license renewal. This study aims to investigate if the mandatory vision test associated with motor vehicle fatality and injury rates in older road users. We fit controlled interrupted time series analysis models to compare fatality and injury rates for older adults (65+) affected by the law to younger adults (45-64) unaffected by the law. The models yielded estimates of differential level and slope changes in fatality and injury rates, which we used to estimate policy associations. We did not find evidence that implementing an accelerated vision test for older adults in Utah was associated with a reduction in injury or fatality rates among older (65+) drivers and non-drivers relative to those aged 45-64. Other strategies might be considered to prevent fatal motor vehicle crashes (MVCs) in older adults.

Design and application of a game‐based WeChat mini‐program for screening cognitive impairments in Chinese older adults

AbstractBackgroundEarly screening for mild cognitive impairment (MCI) is the key method to prevent and delay dementia. Digital screening tools are more efficient than traditional paper‐and‐pencil cognitive assessments, and gamified versions would promote engagement and completion rates. We introduced here the design and development of the Game‐based Cognitive Assessment ‐ 3 Minute Version (G3) WeChat mini‐program for MCI screening in Chinese older adults.MethodG3 is a battery of three one‐minute digital games built within the WeChat platform. Developed based on the cognitive paradigm and classic neuropsychological tests such as Trail Making Test (TMT), the Silhouettes test, the Brief Visuospatial Memory Test‐Revised (BVMT‐R) test, and California Card Sorting Tests (CCST), the G3 mini‐games, “Number Ordering”, “Species Sorting”, and “Gold finding” would measure the computational and executive function, abstractive function and visual perception, memory, and attention, respectively. In each game, the difficulty will be automatically adjusted according to performance. G3 integrates users’ game scores, age, gender, and education levels, to generate a comprehensive assessment of cognitive function with results of the six cognitive domains (Figure 2).ResultAs of August 31, 2022, more than 11 million users have completed G3 tests, 85.38% of them are aged under 40, and 2.05% of them are aged over 60 (Figure 3). Although people in all age groups have desired to know their cognitive function, the number of users decreased as people aged. Among users aged 40‐70, more females than males participated in G3 games, indicating middle‐aged and older women paid more attention to cognitive function. Among users over 45 years old, those of lower education levels (primary school or no education), middle levels (middle or high school), and university degrees accounted for 2.93%, 32.4%, and 59.65% respectively. The completion rate of users aged over 45 is 92.28%, and those over 80 years exceeded 86%.ConclusionThe G3 was a battery of three novel 1‐minute game‐based digital cognitive tests. The process is simple, efficient, and interesting. It is a promising tool for self‐administering, advancing large‐scale community screening, and social education for MCI in Chinese older adults, warranting further clinical trials to validate its feasibility, sensitivity, and diagnostic value.

Design and application of a game‐based WeChat mini‐program for screening cognitive impairments in Chinese older adults

AbstractBackgroundEarly screening for mild cognitive impairment (MCI) is the key method to prevent and delay dementia. Digital screening tools are more efficient than traditional paper‐and‐pencil cognitive assessments, and gamified versions would promote engagement and completion rates. We introduced here the design and development of the Game‐based Cognitive Assessment ‐ 3 Minute Version (G3) WeChat mini‐program for MCI screening in Chinese older adults.MethodG3 is a battery of three one‐minute digital games built within the WeChat platform. Developed based on the cognitive paradigm and classic neuropsychological tests such as Trail Making Test (TMT), the Silhouettes test, the Brief Visuospatial Memory Test‐Revised (BVMT‐R) test, and California Card Sorting Tests (CCST), the G3 mini‐games, “Number Ordering”, “Species Sorting”, and “Gold finding” would measure the computational and executive function, abstractive function and visual perception, memory, and attention, respectively. In each game, the difficulty will be automatically adjusted according to performance. G3 integrates users’ game scores, age, gender, and education levels, to generate a comprehensive assessment of cognitive function with results of the six cognitive domains (Figure 2).ResultAs of August 31, 2022, more than 11 million users have completed G3 tests, 85.38% of them are aged under 40, and 2.05% of them are aged over 60 (Figure 3). Although people in all age groups have desired to know their cognitive function, the number of users decreased as people aged. Among users aged 40‐70, more females than males participated in G3 games, indicating middle‐aged and older women paid more attention to cognitive function. Among users over 45 years old, those of lower education levels (primary school or no education), middle levels (middle or high school), and university degrees accounted for 2.93%, 32.4%, and 59.65% respectively. The completion rate of users aged over 45 is 92.28%, and those over 80 years exceeded 86%.ConclusionThe G3 was a battery of three novel 1‐minute game‐based digital cognitive tests. The process is simple, efficient, and interesting. It is a promising tool for self‐administering, advancing large‐scale community screening, and social education for MCI in Chinese older adults, warranting further clinical trials to validate its feasibility, sensitivity, and diagnostic value.

Algorithmic Accuracy in Context: Assessing the Impact of Financially Motivated Dishonesty

Abstract Algorithms are expected to radically improve the way businesses operate. To fully realize these benefits, customers have to accept the algorithmic implementations. Accuracy is a critical component of algorithmic acceptance models. However, thus far, research into accuracy has been limited to user ratings of the accuracy of the algorithmic output despite strong evidence for customer (user) acts of embellishment and other moral hazards. This article aims to deepen the understanding of accuracy ratings by assessing the impact of variation in user input accuracy on the accuracy ratings of the algorithm’s ultimate response. Drawing on honesty, a survey was presented to 500 individuals on the Cloud Research platform. The quantitative analysis showed that the more inaccurately a user felt and behaved, the more accurately they rated the algorithmic response of ‘decline’, with contextual accuracy factors explaining up to 35% of the variation in ratings of the algorithm’s accuracy. This finding raises important implications for researchers and practitioners who want to improve algorithmic acceptance but may have limited their improvement focus to explainability or model accuracy without considering the user behavior. Algorithmic accuracy ratings and acceptance should be evaluated in the context of the user and their likelihood to provide inaccurate inputs.

Rainbow link beamforming technology and relevant analysis

In the 5G era, millimeter wave has been mentioned as an important position. Millimeter wave has some problems that cannot be ignored: the free space path loss and atmospheric attenuation. To overcome these problems, millimeter wave systems rely on beamforming to overcome losses. Traditional techniques utilize phase antenna arrays for beamforming, which can cause delays in the time consumption and beam scheduling, and due to limited beams, this technology can only be connected to a limited number of devices, and each transmission occupies the entire bandwidth. This paper introduces a new technology called rainbow link, which is implemented by a new true time delay (TTD) array structure. This technology can achieve the allocation of different subcarriers to a large number of users in the corresponding beamforming direction. Users only need to select appropriate frequency resources. In this work, we calculate the performance of rainbow link with different parameters, and our results show that given 1 GHz bandwidth using 64 element antenna arrays, a rainbow-link cell achieves 1ms latency and 0.6M symbols per second with 0.03 active user rate.

Level of Service Assessment for People with Disability in Kawo Bus Terminal Park Kaduna State, Nigeria

This study presents measurements of the overall level of service (LOS) for people with disabilities (PWDs) at public bus terminals. According to user perceptions, these metrics are helpful for evaluating the total LOS on a single scale. The process entails watching passengers and gathering a number of socioeconomic and physical factors that could affect how users rate the bus terminal. With the ability to assess and determine the LOS of transit terminals for physically challenged people, solutions to develop more empathetic transit terminals can be identified, and efforts to confirm the deficiencies can be put into place. It is possible to derive quantitative LOS ratings from survey data using a psychometric scaling technique. Following that, the relationship maps between these two categories of items were examined. The quantitative LOS ratings indices were rated using survey analysis. The findings revealed that the grand mean for expected service was 3.67 (SD = 0.77) while the mean for recorded LOS was 2.01 (SD = 0.58), indicating a significant gap between the two, thus needed attention to address LOS given to PWDs. A planning guideline for IPA is offered to design and optimise bus terminal access facilities for new or existing terminal stations, underscoring the practical usefulness of the approach. The outcome from this study evidently if incorporated in designing Bus Park will help in modelling bus terminal that will accommodate PWDs.

Penggunaan Metode Delone dan Mclean Untuk Menganalisis Kesuksesan Mulawarman Online Learning System (MOLS)

In 2019, the Mulawarman Online Learning System (MOLS) was created. All Mulawarman University students can use MOLS for support their study. The purpose of this reasearch is to determine the level of success in using the Mulawarman Online Learning System (MOLS) using the Delone and Mclean method. In this study using the Delone and Mclean method. Delone and Mclean are methods for measuring the success rate of users of an information system. Delone and Mclean have 6 (six) measurement variables including system quality, information&#x0D; User Satisfaction&#x0D; Delone and Mclean&#x0D; E-Learning quality, user satisfaction, use, service quality and net benefit. This research is quantitative data with a sample of 100 Mulawarman University student respondents. The results of this study indicate that the independent variables, namely system quality, information quality and service quality can affect the dependent variable, namely user satisfaction, use and net benefits.

Statins Effects on Blood Clotting: A Review.

Statins are powerful lipid-lowering drugs that inhibit cholesterol biosynthesis via downregulation of hydroxymethylglutaryl coenzyme-A reductase, which are largely used in patients with or at risk of cardiovascular disease. Available data on thromboembolic disease include primary and secondary prevention as well as bleeding and mortality rates in statin users during anticoagulation for VTE. Experimental studies indicate that statins alter blood clotting at various levels. Statins produce anticoagulant effects via downregulation of tissue factor expression and enhanced endothelial thrombomodulin expression resulting in reduced thrombin generation. Statins impair fibrinogen cleavage and reduce thrombin generation. A reduction of factor V and factor XIII activation has been observed in patients treated with statins. It is postulated that the mechanisms involved are downregulation of factor V and activated factor V, modulation of the protein C pathway and alteration of the tissue factor pathway inhibitor. Clinical and experimental studies have shown that statins exert antiplatelet effects through early and delayed inhibition of platelet activation, adhesion and aggregation. It has been postulated that statin-induced anticoagulant effects can explain, at least partially, a reduction in primary and secondary VTE and death. Evidence supporting the use of statins for prevention of arterial thrombosis-related cardiovascular events is robust, but their role in VTE remains to be further elucidated. In this review, we present biological evidence and experimental data supporting the ability of statins to directly interfere with the clotting system.

Recommendation system based on a compact hybrid user model

The object of research is the process of recommending items to the system user and improving the recommendation results. The principle of the recommendation system and the stages of creating recommendations are considered. The article describes various methods of filtering information to generate personalized recommendations. The advantages and disadvantages of each of the methods are revealed. Therefore, taking into account both theoretical and practical aspects, it is important to conduct research in order to correct the limitations characteristic of various filtering methods. A compact hybrid user model has been proposed for the collaborative filtering method of information. This model overcomes the limitations often encountered when using traditional approaches and allows for more efficient generation of personalized recommendations. For a visual example of the described approach, the recommender system of motion pictures was taken. This model combines user ratings with descriptions of the content of objects and uses the concept of genre interest rate, which was obtained by deriving formulas for calculating a hybrid feature, that is, an indicator of how much the user is interested in a particular genre. This facilitates the formation of a set of close associates for an active user. The proposed approach is aimed at ensuring high accuracy of recommendations in the system, reducing the requirements for computing resources and effectively using information about the content of objects. The hybrid model combines the benefits of memory-based collaborative filtering and model-based recommender systems, providing accuracy and scalability. Several examples with calculation results are given. A conclusion was drawn regarding the potential improvement of the quality of recommendations, taking into account the capabilities of the developed algorithm.

Enhancing User Engagement through Hybrid Algorithms in Movie Recommendation Systems

The Netflix Movie Recommendation System is a sophisticated algorithmic solution designed to enhance user experience by providing personalized movie suggestions. This system utilizes a combination of collaborative filtering, content-based filtering, and hybrid approaches to predict and recommend movies that users are likely to enjoy based on their viewing history and preferences. Collaborative filtering leverages the collective behavior of users, identifying patterns and similarities in viewing habits to suggest films. Content-based filtering, on the other hand, analyzes the attributes of movies, such as genre, director, and cast, to recommend titles with similar characteristics. The hybrid approach integrates both methods to overcome individual limitations and improve recommendation accuracy. Netflix employs advanced machine learning techniques, including deep learning and matrix factorization, to handle vast amounts of data and continuously refine the recommendation process. The system also incorporates user feedback, ratings, and implicit signals such as viewing time and interaction patterns to dynamically adapt to changing user preferences. This multi-faceted recommendation engine is critical in retaining user engagement, reducing churn, and enhancing the overall viewing experience on the platform. Through constant innovation and optimization, Netflix aims to deliver highly relevant and enjoyable content to its diverse global audience.

Association Between the Characteristics of mHealth Apps and User Input During Development and Testing: Secondary Analysis of App Assessment Data.

User involvement is increasingly acknowledged as a central part of health care innovation. However, meaningful user involvement during the development and testing of mobile health apps is often not fully realized. This study aims to examine in which areas user input is most prevalent and whether there is an association between user inclusion and compliance with best practices for mobile health apps. A secondary analysis was conducted on an assessment data set of 1595 health apps. The data set contained information on whether the apps had been developed or tested with user input and whether they followed best practices across several domains. Background information was also available regarding the apps' country of origin, targeted condition areas, subjective user ratings, download numbers, and risk (as per the National Institute for Health and Care Excellence Evidence Standards Framework [ESF]). Descriptive statistics, Mann-Whitney U tests, and Pearson chi-square analyses were applied to the data. User involvement was reported by 8.71% (139/1595) of apps for only the development phase, by 33.67% (537/1595) of apps for only the testing phase, by 21.88% (349/1595) of apps for both phases, and by 35.74% (570/1595) of apps for neither phase. The highest percentage of health apps with reported user input during development was observed in Denmark (19/24, 79%); in the condition areas of diabetes (38/79, 48%), cardiology (15/32, 47%), pain management (20/43, 47%), and oncology (25/54, 46%); and for high app risk (ESF tier 3a; 105/263, 39.9%). The highest percentage of health apps with reported user input during testing was observed in Belgium (10/11, 91%), Sweden (29/34, 85%), and France (13/16, 81%); in the condition areas of neurodiversity (42/52, 81%), respiratory health (58/76, 76%), cardiology (23/32, 72%), and diabetes (56/79, 71%); and for high app risk (ESF tier 3a; 176/263, 66.9%). Notably, apps that reported seeking user input during testing demonstrated significantly more downloads than those that did not (P=.008), and user inclusion was associated with better compliance with best practices in clinical assurance, data privacy, risk management, and user experience. The countries and condition areas in which the highest percentage of health apps with user involvement were observed tended to be those with higher digital maturity in health care and more funding availability, respectively. This suggests that there may be a trade-off between developers' willingness or ability to involve users and the need to meet challenges arising from infrastructure limitations and financial constraints. Moreover, the finding of a positive association between user inclusion and compliance with best practices indicates that, where no other guidance is available, users may benefit from prioritizing health apps developed with user input as the latter may be a proxy for broader app quality.

What influences users to provide explicit feedback? A case of food delivery recommenders

Although various forms of explicit feedback such as ratings and reviews are important for recommenders, they are notoriously difficult to collect. However, beyond attributing these difficulties to user effort, we know surprisingly little about user motivations. Here, we provide a behavioral account of explicit feedback’s sparsity problem by modeling a range of constructs on the rating and review intentions of US food delivery platform users, using data collected from a structured survey (n = 796). Our model, combining the Technology Acceptance Model and Theory of Planned Behavior, revealed that standard industry practices for feedback collection appear misaligned with key psychological influences of behavioral intentions. Most notably, rating and review intentions were most influenced by subjective norms. This means that while most systems directly request feedback in user-to-provider relationships, eliciting them through social ties that manifest in user-to-user relationships is likely more effective. Secondly, our hypothesized dimensions of feedback’s perceived usefulness recorded insubstantial effect sizes on feedback intentions. These findings offered clues for practitioners to improve the connection between providing behaviors and recommendation benefits through contextualized messaging. In addition, perceived pressure and users’ high stated ability to provide feedback recorded insignificant effects, suggesting that frequent feedback requests may be ineffective. Lastly, privacy concerns recorded insignificant effects, hinting that the personalization-privacy paradox might not apply to preference information such as ratings and reviews. Our results provide a novel understanding of explicit feedback intentions to improve feedback collection in food delivery and beyond.

Safeguarding trust in a digital ecosystem

PurposeThe purpose of this paper is to elaborate the significance of safeguards in digital ecosystems and their role in generating trust among participants. This paper argues that the right mix and number of safeguards are crucial for an ecosystem’s growth and success. It offers ecosystem orchestrators concrete guidelines for how to implement and monitor safeguards.Design/methodology/approachThis research is based on both consulting experience and publicly available information on several digital ecosystems.FindingsThis research conceptualizes safeguards as precautionary mechanisms that mandate or promote desirable behavior in an effort to engender trust among ecosystem participants. Safeguards can take various forms, including passwords, escrow, user privacy controls, ratings and reviews and policies and contracts. Striking the right balance of safeguards – neither too few nor too many – is crucial for ecosystem orchestrators. This paper identifies the factors that determine the optimal mix of safeguards, including the power asymmetry between sellers and buyers, the sophistication of participants, the nature of transactions, the cost of negative outcomes and the cost-benefit tradeoff.Originality/valueTo the best of the authors’ knowledge, this study is one of the first to illuminate the relationship between safeguards and trust in the context of digital ecosystem. It is also one of the few attempts to provide managerial guidance for ecosystem designers trying to structure their platform for trust.