Algorithmic Management Dilemma: The Double-Edged Sword Effect on Employee Well-Being and Task Performance
School of Economics and Management，Sichuan Agricultural University, Mianyang, 621000, Sichuan Province, China.v455921738@163.com AbstractAn algorithmic management (AM) has emerged in the rapidly changing arena of digitalworkplaces, representing a transformative — and controversial — concept. Using a survey with312 respondents from a variety of industries where work is allocated, monitored and evaluatedusing algorithmic systems, this study examines the double edge sword of AM impact onemployee well-being and task performance. The study shows that while AM significantlyincreases task performance (measured by higher efficiency and consistency; better real timefeedback), it significantly but negatively affects employee well being (higher psychologicalstress, perceived surveillance and autonomy loss). The mediating role of perceived autonomy inthe relationship between AM and well-being is further confirmed by the mediation analysis andit reveals its crucial role in human systems interaction. This is a paradox: algorithmic tools thatare built for performance optimization can cause emotional exhaustion and alienation whenimplemented without human-centered defenses. This study enlarges the ongoing discussion ofdigital labor, AI machine work systems and workers’ resiliency and provides practical insightsfor ethical design and regulation of AM technology. The need is for an ‘and’: the harmony ofautomation and empathy to guarantee that the primal essence of human experience in work is notdigested by the efficiency of technology. Keywords: Algorithmic Management, Employee Well-Being, Task Performance, Autonomy,Digital Labor, Surveillance, AI in HR, Ethical Design, Workplace Automation, Technostress al., 2021). For example, in warehouse settings algorithmic scheduling has reduced idle time andimproved throughput (Rosenblat & Stark, 2016). However, while the same systems mightchampion metrics and micro surveillance and continuous assessment they may threatencreativity, adaptability and emotional resilience (Veen et al., 2020). Particularly sharp are gigworker reports of feeling ‘trapped in metrics’, i.e., in the sense that all their actions are undersurveillance and measured against opaque performance benchmarks (Moore, Upchurch, &Whittaker, 2018). The psychological consequences of these are consistent with the technostressframework (Tarafdar et al., 2019) that frames digital technologies as both an enabler as well as astressor based on the extent of control exercised by the user, training and support systemsassociated with these technologies.Concerns have also been raised in regards to the dehumanizing components of AM by scholars.Although traditional supervisors may be empathetic, judicious and make contextual judgments,algorithmic managers inflexibly and lacking emotional nuance impose rules (ParentRocheleau &Parker, 2021). Feedback and decision making without interaction with people erodes relationaltrust leading to feelings of alienation or depersonalized work (Kellogg et al., 2020). Additionally,employees are frequently left out of the design and iterative process for these systems and, as aresult, are left powerless to participate in and bargain collectively (Galière, 2022). It adds to theethics of digital labor rights, algorithm accountability and workplace fairness particularly insettings in which the workforce is already precarious.As such, AM is a dual technical and socio-technical phenomenon and the discussion regardingits implications for STS theory resonates with broader debates regarding the nature oftechnological interventions and their direct instrumental and indirect socio-humanistic impacts(Trist & Bamforth, 1951). Rather, AM tools are not neutral, but rather form and are formed bythe organizational environment (i.e., culture, labour relations and institutional frameworks) inwhich they are situated (Zuboff, 2019). An alternative lens to pursue this inquiry is similarlyprovided by the Job Demands-Resources (JD-R) model. According to Bakker and Demerouti(2007), job demands, e.g., surveillance and inflexible metrics, deplete energy and psychologicalresources; conversely job resources, like feedback and autonomy, enhance engagement and wellbeing. Employee strain ensues and work satisfaction declines as the number of personal resources and work demands rises, but algorithmic management results in poor resourceallocation.These tensions need to be addressed and thus, we need to empirically gauge the effects ofalgorithmic management on task performance and employee well being. Despite the surge of asignificant body of work that has focused either on performance gains or stress outcomes andwithin different employment contexts, a paucity of studies considers both performance gains andstress outcomes together. In order to address this gap, this study combines quantitativeperformance metrics with qualitative insights on worker experiences to analyze the double-edgedsword effect of AM. The aim of doing so is to take part in the development of algorithmicgovernance frameworks that are balanced and ethical, while enabling productivity withoutcompromising human dignity of labor. analytics make workers more anxious, outraged and less satisfied with their jobs when they arenot allowed to understand or influence their opaque metrics. The result of this produces whatO’Neill (2016) calls ‘weapons of math destruction’, utilizing flawed or biased data to facilitateunfair outcomes that are at a high level hard to refute.Furthermore, literature has demonstrated that algorithmic control increases work intensificationand emotional labor to unsustainable degrees. According to Fleming (2017), algorithmicmanagement replicates a kind of ‘digital Taylorism’: the minutia of an employee’s workbecomes monitored and quantified and overseen in real time. In this condition, workers areexpected (although unwillingly) to carry out the performative labor, described by Diefenbachand Sillince (2011), as working tirelessly to optimise ‘visibility’ and ‘productivity’, regardless ofthe loss of authenticity and health. Leighton (2019) documents, for example, how Amazonwarehouse employees were pushed by algorithmic weights to forgo bathroom breaks, todownplay injuries and to stay in an escalated state of productivity at the expense of burnout andattrition.On the other hand, algorithmic systems also provide opportunities to reduce bias and standardize.In one vein of research, AM tools can decrease favoritism and discrimination by ensuringuniformity by utilizing well designed AM tools to enforce uniform rules and performancestandards (Binns, 2019). For example, algorithms used in HR analytics can increase diversity byreducing unconscious bias during candidate, evaluation and resume screening, according to Kim(2017). But even these benefits rely on knowing that these systems are trained with data that istransparent and fair. However, if the training data contain past inequalities or discriminatoryhiring patterns, the algorithm will replicate and perfect these patterns, as reported by Noble(2018) in his study on algorithms bias in digital platforms.The literature in the domain of employee autonomy takes a nuanced view. However, otherscholars contend that, far from diminishing agency, AM forces conformity to rigid behavioralscripts (Scholz, 2017); and yet still other scholars argue that algorithmic tools can enhanceautonomy in specific settings. Boudreau and Lakhani (2013)
A Cross-Center Risk Prediction Model for OsteoporoticFracture Under the Federated Learning Framework
First author: Yizhe Fan, drfanyizhe@163.comSecond author and co-first author: Zhongyuan Shen, zyshen@njucm.edu.cnThird author: Xiao Zhang, qucyzhang@163.comFourth Author: Zhen Han, 18168988971@163.comCorresponding author: Chengjian Wei, drweichengjiantcm@163.comAcknowledgementFundingsThe current work was supported by the National Nature Science Foundation ofChina (No.81973872); Jiangsu Provincial Medical Key Discipline (Laboratory)Cultivation Unit (No. JSDW202252); Postgraduate Research & Practice InnovationProgram of Jiangsu Province (No.SJCX24_0960).Ethics approval and consent to participateThis study was established and authorized by the Animal Care and UseCommittee of the Nanjing University of Chinese Medicine (Approval number:ACU231001). AbstractThis work sets out to build a privacy-preserving risk-evaluation engine forosteoporotic fractures, stitched together across several clinical sites rather than parkedin one central warehouse. To do so, the authors lean on a federated-learning scaffoldthat lets participating hospitals crunch their own numbers, ship only gradients back toa neutral server, and still compare performance in a meaningful way without tradingpatient identities. Researchers lifted the patient sample from NHANES 2017-2020,slicing it into three virtual centres that mimic the demographic and technicalpatchwork seen in community care; each slice held 2,743 individuals in total andcarried 42 columns describing things like age, lab values, lifestyle habits, and bonemineral density. The base learner is a compact neural net dressed with differentialprivacy, Byzantine guards, and gradient-compression tricks so the server side staysmanageable even under heavy load. Adaptive weighting schemes soften the usualaches caused by uneven data distributions, letting the architecture dodge the poorgeneralisability that plagues most single-site prototypes. Final numbers read 0.847 forarea under the curve, a shade ahead of the comparable centralised version (0.832, p =0.024) and miles better than the rough-and-ready FRAX calculator (0.734, p 0.001).Even when noise budgets tighten to ε = 1.0, leakage metrics drop by 60 and the AUCstill rests at a respectable 0.841. A post hoc consistency check shows centre-by-centrescores clustering tightly between 0.841 and 0.853, reassuring the research team thatthe framework scales well no matter whose data plug into the engine.Recentinvestigations have shown that federated learning sidesteps the privacy bottlenecksthat typically beset single-centre trials. By distributing the analytics across multiple 2 nodes rather than hauling patient records to a central vault, the approach keepssensitive information resident at its source. Researchers now regard that model as thefirst truly scalable blueprint for multi-institutional collaborations in clinical machinelearning.Keywords: Federated Learning; Osteoporotic Fracture; Risk Prediction;Differential Privacy; Machine Learning; Multi-center Collaboration 3 estimating fracture risk—rather underexplored [12] . Typical federated learningdeployments face persistent hurdles, including the stark data heterogeneity foundacross different contributing sites, the pressing need for efficient communicationprotocols, and the constant demand for privacy safeguards that match the sensitivityof medical information [13] . Merging federated algorithms with routine clinical riskworkflows also compels practitioners to reckon with dual requirements: theinterpretability of the distributed model outputs and the rigorous clinical validationthat decision-makers expect [14] .This project confronts well-documented barriers in osteoporosis research byconstructing a wide-reaching federated-learning infrastructure tailored to predictingfracture risk across separated hospital networks. Embedded within the framework arecutting-edge, privacy-minding routines, sophisticated aggregation schemes thatreconcile uneven data distributions, and interpretable algorithms that clinicians canreadily discuss with patients. Field tests draw on openly released datasets that mimicmulti-centre patient flows, offering early proof that distributed techniques can rivalconventional single-site models in clinical forecasting for bone health. By keepingraw records stationary and exchanging only numerical summaries, the methodologysafeguards personal information while still permitting large-scale learning. Outputsfrom the system not only advance the technical literature on federated health AI butalso lay groundwork for tomorrow’s collaborative decision-support tools in hospitalsettings. In practical terms, the infrastructure equips different care providers to poolinsights without crossing the regulatory red lines around data movement, thusbroadening the evidence base for everyday fracture prevention strategies. 4 standard centralised methods and common clinical scorecards; results were filed insuch a way that anyone with the public datasets could repeat the exercise.2.2 Feature Engineering and Data PreprocessingA detailed data-preprocessing pipeline was built to safeguard quality anduniformity among the various simulated centres, as shown in Figure 1. Within thatframework, engineers carried out feature extraction, filled in missing values, flaggedoutliers, and standardised the data—each step vital to the reliable training of machine-learning models. NHANES Raw Data2017-2020 Cyclesn= 15,560 subjectsFeature ExtractionDemographicsAge, Gender, BMlBMD T-scoresSpine, Hip, NeckLaboratoryCa, P, Vit DData Quality ControlMissing DataKNN lmputationk=5 neighborsMissing rate < 20%Outlier DetectionlQR MethodQ1-1.5xIQR toQ3+1.5xIQRData ValidationRange ChecksClinical rangesConsistencyFeature StandardizationZ-score NormalizationZ=(X-μ)/σPer-center basisFeature EncodingOne-hot EncodingNominal variablesRace, medication useOrdinal EncodingOrdered categoriesEducation levelProcessed Dataset89 features, Ready for FL Figure 1: Data Preprocessing and Feature Engineering Pipeline Feature extraction in the present study set out to delineate those variables moststrongly linked to the hazard of osteoporotic fracture. Basic demographicmarkers—age, sex, racial background, and body mass index—enter the analysis as afoundation. Clinical inputs then follow, notably the lumbar spine and total hip Tscores plus femoral neck T values, together with serum calcium, phosphorus, and 25-hydroxyvitamin D levels. Lifestyle contributors rely on self-reported smoking,habitual drinking, activity frequency, and daily calcium intake from food orsupplements. A final block captures patient history, listing prior fractures, a familyosteoporosis pedigree, and any medications currently in use.Missing data imputation was performed using k-nearest neighbors (KNN)algorithm to preserve the underlying data distribution. For continuous variables, theimputed value was calculated as: 11kiiimissingkiiwX X w where iwrepresents the inverse distance weight for the i -th nearest neighbor 5 and iXdenotes the corresponding feature value.Outlier detection employed the interquartile range method, identifyingobservations beyond 31.5QIQR or below 11.5QIQR , where 1Qand 3Qrepresent the first and third quartiles, respectively. Extreme outliers were winsorizedto the 95th or 5th percentiles to maintain data integrity while preserving sample size.Feature standardization was implemented using Z-score normalization to ensurecomparable scales across different measurement units: XZ where and represent the population mean and standard deviation,respectively. This standardization process was performed independently within eachsimulated center to preserve the federated learning paradigm and prevent data leakagebetween institutions.Categorical variables were encoded using one-hot encoding for nominalvariables and ordinal encoding for naturally ordered categories. Feature correlationanalysis was conducted to identify highly correlated variables (|r| > 0.8) andimplement appropriate dimensionality reduction strategies when necessary.2.3 Federated Learning Framework DesignA federated learning architecture was created to facilitate joint prediction ofosteoporotic-fracture risk among a network of virtual healthcare providers, all withoutexposing individual patient records. In building the framework, the researchersclosely
The influence of leisure sports recreation resources value on recreationers’ willingness to revisit
1.Male, school of Leisure sprots,Chengdu Sport University,Chengdu City610041,Sichuan, China2.Female,College of physical education and health ,Aba Teachers University, AbaTibetan and qiang autonomous prefecture 623002,Sichuana.Email：101054@cdsu.edu.cnb.Email：20139613@abtu.edu.cn Abstract: Leisure sports recreation resources serve as the foundation forrecreational activities, encompassing diverse categories such as natural andcultural resources. Their value is constituted by functional, experiential,emotional, and social dimensions. Revisit intention, a key indicator of tourismexperience and destination appeal, is influenced by factors like satisfactionand emotional identification. This study analyzes the components of leisuresports recreation resource value and the determinants of revisit intention,revealing that resource value exerts influence through a sequential pathway:“value perception → satisfaction enhancement → emotional reinforcement →revisit intention formation.” Based on these findings, we propose strategiesfor enhancing resource value, improving revisit intention, and achievingsynergistic optimization between the two aspects, providing actionablereferences for the sustainable development of leisure sports recreationdestinations.Key words: leisure sports recreation resources; revisit intention;influence analysis; optimization strategyforewordWith the steady implementation of China’s “Healthy China” strategy, leisuresports tourism has become an integral part of modern life. The development andsustainable management of these resources have garnered significant attention.As a key indicator of destination competitiveness, repeat visit willingnessplays a vital role in maintaining tourist retention and reducing marketingcosts. While current leisure sports tourism resources exhibit diversifiedcharacteristics, their intrinsic connection between resource value and visitorloyalty remains underexplored. Clarifying this relationship is crucial forenhancing resource appeal and driving high-quality industrial development.I. Definition and classification of leisure sports recreation resourcesLeisure sports recreation resources refer to the comprehensive collection of material and intangible elements that meet people’s needs for leisure,fitness, and entertainment. These resources form the foundational conditionsfor conducting recreational sports activities. They can be categorized based onresource attributes and functional characteristics: In terms of resourceattributes, they include natural categories such as mountains, lakes, andforests suitable for outdoor sports, as well as cultural categories like sportsvenues, fitness trails, and sports theme parks[1]. Functionally, there arefitness and leisure facilities (e.g., gyms, square dance areas), competitiveviewing venues (e.g., sports event stadiums), adventure-oriented resources(e.g., rock climbing bases, rafting rivers), and folk sports venues (e.g.,traditional martial arts training grounds, dragon boat racing waters).Together, these diverse resources establish a multifaceted leisure sportsrecreation system.Subcategorization Specific examples natural kind Mountains, lakes, forests and other natural Spacessuitable for outdoor sports Humanities Sports venues, fitness trails, sports theme parksand other artificial facilities Fitness andrecreation Gyms, square dancing venues Competitiveviewing A venue for a sporting event Outdooradventure Rock climbing base, river for rafting Folk sportscategory Traditional martial arts practice ground, dragonboat race water area Table 1 Classification of leisure sports recreation resourcesThe concept of willingness to revisitRevisit intention refers to the subjective inclination and psychologicalwillingness of tourists to return to a specific destination after completing a travel experience. As a key indicator for evaluating tourism quality anddestination appeal, it is influenced by multiple factors including touristsatisfaction, emotional identification, destination image, and service quality.Common manifestations include clear plans for future visits, willingness torecommend the destination to others, or sustained interest in the location[2].Revisit intention not only reflects tourists ‘recognition of their travelexperiences but also correlates with sustainable development of destinations.High revisit intention helps reduce marketing costs, stabilize tourist markets,and serves as a core element demonstrating a destination’s competitiveness. felt during traditional dragon boat races—strengthens their preference forreturn visits. Resource uniqueness is equally crucial: scarce naturallandscapes (e.g., exclusive hiking trails) or distinctive activities (such asintangible cultural heritage sports experiences) can reduce alternativedestination appeal[3]. Word-of-mouth recommendations from friends and family,social media reviews, and destination innovation (like new activity offeringsand seasonal events) all influence participants’ psychological expectations anddecision-making, ultimately shaping their willingness to revisit.(3) The relationship between the value of leisure sports recreation resources and thewillingness to revisitThe value of leisure sports recreation resources serves as a key driver forrepeat visitation intentions, demonstrating a strong positive correlationbetween the two. The functional value of resources directly enhances basicsatisfaction by aligning with recreational needs and practical expectations,thereby laying the foundation for repeat visits. For instance, well-equippedprofessional fitness facilities encourage regular return visits from fitnessenthusiasts. Experiential and emotional values strengthen psychologicalconnections to boost revisit loyalty. Immersive experiences like forest cyclingor emotional resonance during team-building activities create lasting nostalgiaand revisit impulses. Additionally, the social value and uniqueness ofresources reinforce destination irreplaceability, reducing visitor switching.When visitors perceive resource value as closely matching their needs, theydevelop trust and reliance on the destination, transforming resource valueperception into explicit revisit tendencies. This establishes a transmissionpath: “value perception → satisfaction enhancement → emotional reinforcement→ repeat visit intention formation”. cultivation requires cultural empowerment by exploring regional sportstraditions, transforming folk activities like dragon dance and polo intointeractive experiences, and enhancing emotional connections through narrativestorytelling. For social value expansion, establishing “sports + social”platforms is crucial. Regular community sports events and family-friendlycarnivals should be organized, while resource-sharing mechanisms like schoolsports facilities being open to the public during holidays should beimplemented. This creates a closed-loop value enhancement system characterizedby “comprehensive functionality, unique experiences, emotional bonds, andsocial recognition” [4].(2) Strategies to enhance the willingness of recreationers to revisitTo enhance visitors ‘willingness to return, it’s essential to establish amulti-touchpoint incentive system spanning the entire lifecycle. Experienceoptimization forms the foundation of this framework, covering every stage ofrecreation: Before travel, VR technology provides virtual destination tours tohelp tourists plan their itineraries. During activities, a “tailored service”mechanism is implemented – offering childcare for families and professionalcoaching for fitness enthusiasts. Post-experience, personalized reports withexercise statistics and memorable photos are emailed. For emotional retention,long-term interaction mechanisms should be built: First, create exclusiveprofiles documenting participants’ exercise preferences and activity records.Then, send customized birthday wishes and event invitations during holidays tofoster a “sports community” that enhances belonging. Organize online groups bysports type and regularly host offline events like monthly themed running clubsto build stable interest communities. Differentiated attraction is key toincreasing return visits. Develop unique experiences through resource-specificinitiatives: Lake-based projects like “Starry Night Kayaking Tours,” ormountain-themed challenges like “Seasonal Hiking Challenges” – these rareexperiences reduce the likelihood of being replaced. To enhance engagement, weimplement a tiered reward system featuring a “Revisit Points System” whereaccumulated points can be redeemed for sports gear, exclusive courses, oraccommodation discounts. High-frequency visitors receive “Honorary Membership”privileges including free parking and priority