Equilibrando la atención y el código: Explorando las influencias algorítmicas en la felicidad laboral de las enfermeras J. Manage. Hum. Resour. (January - June 2026) 4(1): 22-28 https://doi.org/10.5281/zenodo.18301558 ISSN 3091-1575 REVIEW ARTICLE Balancing Care and Code: Exploring Algorithmic Influences on Nurses’ Job Happiness  Abd R. Ahmad arahman@uthm.edu.my Johor Business School, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia. Received: 26 September 2025 / Accepted: 16 November 2025 / Published online: 30 January 2026 © The Author(s) 2026 Abd R. Ahmad 1 · Hairul R. Md Sapry 2 · Umi K. Rashid 1 · Alaa S. Jameel 3 Abstract The research has primarily focused on productivity and proﬁtability, overlooking essential inquiries into the impact of algo- rithms on autonomy, trust, and well-being. This conceptual paper presents a paradigm to clarify the inﬂuence of algorithmic manage- ment on job satisfaction among nursing staﬀ in Malaysia. Drawing on literature from 2020 to 2024, it integrates perspectives from the Job Demands–Resources model, Self-Determination Theory, and sociotechnical systems theory. The framework identiﬁes algorith- mic management as a critical factor aﬀecting perceived autonomy, fairness, and trust in AI systems, which in turn shape job satisfac- tion. Organizational support and digital literacy are proposed as potential moderators that may either strengthen or weaken these relationships. The paper contributes to three key domains: theo- retically, it extends existing debates by situating algorithmic man- agement and job happiness within the Malaysian healthcare sector; practically, it provides managers and policymakers with a concep- tual framework to assess the unintended eﬀects of digitalization on frontline caregivers; and methodologically, it outlines avenues for future empirical research, oﬀering guidance for studies aimed at reconciling eﬃciency with compassion in nursing. Keywords algorithmic management, nurses, job satisfaction, job happiness, perceived autonomy, trust in AI systems. Resumen La investigación se centró en la productividad y la rentabilidad, descuidando las indagaciones esenciales sobre el im- pacto de los algoritmos en la autonomía, la conﬁanza y el bienestar. Este documento conceptual presenta un paradigma para aclarar la inﬂuencia de la gestión algorítmica en la satisfacción laboral del personal de enfermería en Malasia. Utilizando la literatura de 2020 a 2024, combina perspectivas del modelo de Demandas-Recursos Laborales, la Teoría de la Autodeterminación y las perspectivas de sistemas sociotécnicos. El enfoque identiﬁca la gestión algorítmica como un factor crucial que afecta la autonomía percibida, la justi- cia y la conﬁanza en los sistemas de IA, lo que a su vez inﬂuye en la satisfacción laboral. El apoyo organizacional y la alfabetización digital se presentan como posibles moderadores que pueden poten- ciar o disminuir estas interacciones. El documento contribuye a tres dominios fundamentales. En teoría, amplía los debates existentes al situar la gestión algorítmica y la felicidad laboral en el sector sanitario malasio. Proporciona a los gestores y responsables polí- ticos un marco conceptual para evaluar los efectos inesperados de la digitalización en los cuidadores de primera línea. Esto, desde el punto de vista metodológico, propone vías para futuras investiga- ciones empíricas, ofreciendo orientación para investigaciones que puedan fundamentar soluciones que concilien la eﬁciencia con la compasión en enfermería. Palabras clave gestión algorítmica, enfermeros, satisfacción la- boral, felicidad laboral, autonomía percibida, conﬁanza en los sis- temas de IA. How to cite Ahmad, A. R., Md Sapry, H. R., Rashid, U. K., Jameel, A. S. (2026). Balancing Care and Code: Exploring Algorithmic Inﬂuences on Nurses’ Job Happiness. Journal of Management and Human Resources, 4(1), 22-28. https://doi.org/10.5281/zenodo.18301558 1 Johor Business School, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia. 2 Universiti Kuala Lumpur (UNIKL) Kampus Cawangan Malaysian Institute of Industrial Technology, Johor, Malaysia. 3 Department of Business Administration, Al- Idrisi University College, Ramadi, Al-Anbar, Iraq.

J. Manage. Hum. Resour. (January - June) 4(1): 22-28 23 Introduction The accelerated digitisation of healthcare has generated novel opportunities and problems for frontline personnel, particularly nurses. Algorithmic management, characterised by the utilisation of data-driven systems for the allocation, oversight, and assessment of work, has gained prominence in hospitals and clinics (Galière, 2021; Kellogg et al., 2020). Although global research has highlighted the eﬃciency ben- eﬁts of these systems, their impact on nurses’ job happiness and well-being remains under explored (Parent-Rocheleau & Parker, 2021). In Malaysia, where nurses constitute the predominant segment of healthcare professionals, the adop- tion of algorithmic systems is still developing, prompting en- quiries on how these technologies transform the caregiving experience. The problem is not solely technological but profoundly human. Scholars contend that the integration of algorithms into professional sectors frequently undermines conven- tional concepts of autonomy, justice, and trust (Meijerink & Bondarouk, 2021, Tan et al., 2023). In nursing, these aspects are intricately linked to occupational contentment, which in- cludes both job happiness and overall psychological well-be- ing (Ramlan & Hashim, 2021). Research in Malaysia indi- cates that job happiness among nurses is adversely aﬀected by workload constraints, emotional fatigue, and retention is- sues (Al-Dubai et al., 2020, Ismail et al., 2022). Algorithmic systems may serve as both facilitators and sources of stress, contingent upon their design and implementation. Numerous experts have observed that algorithmic man- agement is seldom impartial, frequently perpetuating man- agerial paradigms that prioritise eﬃciency at the expense of well-being (Wood et al., 2021). The implementation of inﬂexible scheduling algorithms or performance monitor- ing systems can undermine the autonomy and professional discretion of nurses, whose professional identity is founded on compassion and patient care (Brougham & Haar, 2021). Conversely, when employed judiciously, algorithms can al- leviate administrative burdens, optimise shift assignments, and improve equity in job distribution (Huang et al., 2022). This contradiction emphasises the necessity of conceptual frameworks that transcend eﬃciency measures to consider human well-being in technology-driven organisations. In the Malaysian context, research on algorithmic man- agement is scarce, nonetheless, pertinent studies on digital health and occupational well-being oﬀer signiﬁcant insights. Nurani et al. (2023) discovered that the adoption of digital health in Malaysian hospitals enhanced information ﬂow, although also induced stress stemming from technology-re- lated expectations. A study by Yusoﬀ and Hussain (2021) indicated that the work-life balance of Malaysian nurses is signiﬁcantly inﬂuenced by organisational practices, which may be further exacerbated by algorithmic scheduling. These ﬁndings indicate that the impact of algorithms on job happiness cannot be comprehended independently of wider organisational and cultural settings. This study is theoretically based on three perspectives. The Job Demands–Resources (JD-R) model oﬀers a valu- able framework for analysing how algorithms modify de- mands (e.g., heightened monitoring, intensiﬁed workload) and resources (e.g., equitable scheduling, transparent work- load) in nursing (Bakker & Demerouti, 2017, modiﬁed by Lesener et al., 2020). Self-Determination Theory introduc- es a psychological aspect, highlighting the signiﬁcance of autonomy, competence, and relatedness in relation to job happiness (Ryan & Deci, 2020). The sociotechnical systems perspective emphasises that technological change is inextri- cably linked to its organisational and human context (Trist, 1981, modiﬁed in Cagliano et al., 2021). Incorporating these viewpoints enables us to understand the eﬀects of algorith- mic management on both performance and nurses’ job hap- piness. This inquiry’s signiﬁcance is heightened by recent governmental initiatives in Malaysia. The Ministry of Health has recognised the pivotal role of nurses in fulﬁlling national health objectives and the pressing necessity to tackle reten- tion and well-being concerns (Ministry of Health Malaysia, 2022). The impetus for digital health transformation, expe- dited by the COVID-19 pandemic, has integrated algorith- mic tools into routine nursing practice (Che et al., 2021). The concurrent constraints of digitalisation and workforce sus- tainability need an examination of the intersection between algorithms and the human aspects of nursing. This concept paper aims to establish a paradigm that elu- cidates the inﬂuence of algorithmic management on nurs- es’ job happiness and well-being in Malaysia. The concept identiﬁes algorithmic management as a pivotal element af- fecting perceived autonomy, justice, and conﬁdence in AI systems, with organisational support and digital literacy serving as moderating factors. The study advances three domains by presenting theoretical propositions for empiri- cal validation. Initially, it expands current discussions on algorithmic management by contextualising them within the Malaysian healthcare framework. Secondly, it oﬀers man- agers and policymakers a conceptual framework to foresee and alleviate the unintended repercussions of digitisation on nurses. Third, it delineates prospective research avenues that can guide methods to reconcile eﬃciency with compassion in nursing. Algorithmic management denotes the utilisation of com- putational systems to assign, oversee, and assess work tasks, frequently supplanting or enhancing conventional supervi- sory roles (Kellogg et al., 2020). In healthcare, this encom- passes applications such as digital rostering, predictive pa- tient ﬂow models, and automated performance dashboards.

J. Manage. Hum. Resour. (January - June) 4(1): 22-28 24 Research indicates that although these systems can enhance decision-making eﬃciency, they simultaneously raise issues with transparency, accountability, and employee autonomy (Galière, 2021; Wood et al., 2021). From a Malaysian viewpoint, the digitisation of healthcare is advancing inconsistently, with certain institutions imple- menting AI-based triage and scheduling technologies, but others continue to depend predominantly on manual systems (Che et al., 2021). Recent studies indicate that algorithmic technologies may alleviate administrative duties while con- currently increasing nurses’ feelings of surveillance and per- formance pressure (Tan et al., 2023). The dual impact under- scores the duality of algorithmic systems: they can improve eﬃciency while compromising human experience. Job happiness among nurses includes contentment, emo- tional wellness, and involvement (Ramlan & Hashim, 2021). It has been associated with retention, patient safety, and ser- vice quality on a global scale (Brougham & Haar, 2021). Nurses in Malaysia frequently experience stress due to ex- cessive workloads, extended shifts, and restricted prospects for career progression (Ismail et al., 2022; Yusoﬀ & Hus- sain, 2021). Al-Dubai et al. (2020) identiﬁed burnout as a signiﬁcant factor contributing to job dissatisfaction among Malaysian nurses, whereas Nurani et al. (2023) highlighted that digital transitions have added new dimensions of stress associated with technology adoption. While job happiness has been extensively researched about conventional organisational elements, there is a pau- city of Malaysian studies that speciﬁcally connect it with digitisation or algorithmic systems. This gap underlines the necessity for conceptual frameworks that incorporate tech- nology-driven factors into current models of nurse well-be- ing. Autonomy is fundamental to job happiness and profes- sional identity in nursing (Ryan & Deci, 2020). Algorithmic systems can restrict autonomy by standardising decisions, enforcing inﬂexible timelines, or prioritising metrics over judgement (Parent-Rocheleau & Parker, 2021). Conversely, algorithms that oﬀer decision assistance instead of control can enhance autonomy by alleviating mundane responsi- bilities, so allowing nurses to concentrate on patient care (Huang et al., 2022). Conﬁdence in artiﬁcial intelligence is equally essential. Nurses are more inclined to embrace algorithmic technolo- gies when they view them as transparent, equitable, and con- sistent with professional standards (Meijerink & Bondarouk, 2021). In Malaysia, trust concerns regarding digital health adoption have been noted, with inadequate training and un- clear system design fostering mistrust (Nurani et al., 2023). Organisational support and suﬃcient digital literacy training may serve as essential facilitators in fostering trust. The Job Demands–Resources (JD-R) paradigm concep- tualises algorithms as generating both new demands (e.g., monitoring, increased workload) and new resources (e.g., equitable scheduling, transparent workload distribution) (Lesener et al., 2020). The Self-Determination Theory (SDT) framework introduces a psychological dimension, highlight- ing that the fulﬁlment of autonomy, competence, and relat- edness is fundamental to workplace pleasure (Ryan & Deci, 2020). The sociotechnical systems perspective emphasises that technology must be evaluated in conjunction with or- ganisational structures, cultural norms, and human values (Cagliano et al., 2021). This study provides a framework in which algorithmic management inﬂuences perceived auton- omy, fairness, and conﬁdence in AI, subsequently aﬀecting job happiness. Moderating factors such as organisational support and digital literacy contextualise these interactions, especially within Malaysia’s distinctive healthcare environ- ment. The adoption of algorithmic management in health care settings has drawn growing attention, however its eﬀects on nurses’ job happiness remain insuﬃciently theorised. This research provides a conceptual framework that connects al- gorithmic systems with job happiness and pleasure, utilising contemporary literature and situating it within the context of Malaysia’s healthcare sector. The platform fundamentally revolves around algorithmic management, which includes scheduling algorithms, digital performance monitoring, and automated workload distribu- tion. Research indicates that these methods are frequently implemented to enhance productivity, although they may inadvertently impact workers’ perceptions of autonomy and justice (Galière, 2021; Wood et al., 2021). The implemen- tation of algorithmic tools in Malaysian hospitals has been inconsistent, with certain institutions experiencing enhanced workﬂow coordination, whereas others express apprehen- sions regarding excessive standardisation of treatment (Che et al., 2021; Tan et al., 2023). Fig. 1: Theoretical Framework: Algorithmic Inﬂuences on Nurses’ Job Happiness The initial category of relationships within the frame- work pertains to perceived autonomy. Research based on Self-Determination Theory underscores that autonomy is fundamental to job happiness (Ryan & Deci, 2020). None- theless, algorithmic systems frequently limit autonomy via inﬂexible protocols or obscure decision-making procedures (Parent-Rocheleau & Parker, 2021). In Malaysian nursing practice, where professional discretion is essential for pa- tient care, the equilibrium between algorithmic control and human judgement is crucial (Ismail et al., 2022). A second- ary construct is perceived equity or justice. Algorithms are frequently seen as impartial decision-makers, nonetheless,

J. Manage. Hum. Resour. (January - June) 4(1): 22-28 25 biases ingrained in their design or execution might perpet- uate disparities (Kellogg et al., 2020). Nurses’ opinions of equity in scheduling or task distribution signiﬁcantly aﬀect their morale and job happiness (Ramlan & Hashim, 2021). If algorithms can guarantee equitable workload distribution, they may improve worker satisfaction, otherwise, they risk exacerbating discontent. The third construct is conﬁdence in AI systems, which in- ﬂuences nurses’ acceptance or resistance to algorithmic tech- niques. Trust develops when systems exhibit transparency, reliability, and alignment with professional ideals (Meijer- ink & Bondarouk, 2021). Conversely, a deﬁciency of trust might exacerbate stress and resistance to digitalisation, as evidenced by Malaysian studies on digital health uptake (Nurani et al., 2023). The approach incorporates two con- textual moderators. Organisational support—encompassing training, participative decision-making, and managerial re- sponsiveness—can mitigate the adverse eﬀects of algorith- mic systems on job happiness (Yusoﬀ & Hussain, 2021). Similarly, digital literacy aﬀects nurses’ interactions with algorithmic tools: individuals with advanced literacy may regard algorithms as beneﬁcial, whereas those with poor digital competencies may see them as intimidating (Huang et al., 2022). Results and discussion The proposed framework (Figure 1) highlights algorith- mic management as a crucial factor inﬂuencing nurses’ job happiness in Malaysia, functioning through three psycho- social mechanisms: perceived autonomy, perceived justice, and trust in AI systems. In this arrangement, algorithmic management signiﬁes the growing dependence on digital platforms for scheduling, work distribution, and perfor- mance evaluation. These technologies can enhance oper- ational eﬃciency while also jeopardising the professional autonomy historically granted to nurses. Previous research demonstrates that autonomy is fundamental to maintaining motivation and well-being in high-pressure settings (Deci & Ryan, 2020, Nordin et al., 2022). In Malaysian hospitals, where nurses frequently manage substantial workloads with constrained resources, reduced autonomy due to algorithmic Figure 1. Theoretical Framework: Algorithmic Inﬂuences on Nurses’ Job Happiness.

J. Manage. Hum. Resour. (January - June) 4(1): 22-28 26 supervision may intensify stress and lower job happiness (Omar & Ismail, 2021). The second pathway, perceived fairness, denotes the de- gree to which algorithmic decisions are regarded as trans- parent and just. Evidence indicates that opaque or biassed algorithmic judgements might evoke feelings of injustice and animosity among healthcare personnel (Zhou et al., 2021, Karim et al., 2023). Malaysian nursing research cor- roborates this worry, highlighting that views of equity sig- niﬁcantly forecast organisational commitment and retention (Rahman et al., 2022). Trust in AI systems is a crucial factor, since nurses’ readiness to depend on algorithmic tools is con- tingent upon their perceived reliability and congruence with patient care values (Siau & Wang, 2020, Alwi & Hashim, 2024). In the absence of trust, the use of technology becomes superﬁcial, resulting in ineﬃciencies and emotional disso- nance. The approach emphasises the moderating inﬂuence of or- ganisational support and digital literacy. Organisational sup- port, including managerial acknowledgement, equitable task allocation, and opportunities for professional advancement, has been consistently associated with resilience and happi- ness among Malaysian nurses (Hamid et al., 2021; Lim et al., 2023). Similarly, digital literacy inﬂuences whether nurses perceive technology as an empowering asset or an onerous obligation. Research indicates that suﬃcient digital training and proﬁciency in health informatics might mitigate techno- stress, allowing personnel to incorporate algorithms into care delivery more eﬃciently (Cheng et al., 2022; Tan & Khalid, 2024). This research expands the existing scholarship on algo- rithmic management by extending it to the healthcare sector, where emotional labour and ethical care issues are critical. Current research predominantly focusses on algorithmic management within platform-based employment, includ- ing ride-hailing and logistics (Wood et al., 2021; Lee et al., 2022). This study focusses on nursing in Malaysia, empha- sising the speciﬁc relationship between patient well-being and staﬀ satisfaction. The integration of the Job Demands– Resources model and Self-Determination Theory enhance the comprehension of how algorithmic structures inﬂuence psychological requirements. The framework illustrates that autonomy and justice are not merely abstract notions but are intricately woven into the daily experiences of nurses man- aging digital operations. The approach provides hospital administrators with action- able insights. The eﬃcacy of algorithmic tools is contingent not only upon their technical robustness but also on the so- cio-organizational context in which they are used. Hospitals should consequently enhance technological implementations with activities that bolster organisational support, including feedback systems and equitable workload policies. Custom- ised digital literacy initiatives for nurses may alleviate resis- tance and anxiety, converting algorithms into facilitators of enhanced care instead of sources of estrangement. Attention to fairness and openness in algorithmic decisions—such as shift scheduling or performance evaluation—can enhance trust and diminish turnover intentions among nursing per- sonnel (Khalid & Zainal, 2023). The framework emphasises the need for norms regulating the ethical application of algorithmic management in health- care at the policy level. Malaysia’s healthcare sector, already contending with worker shortages and increasing patient de- mands, necessitates regulatory frameworks that harmonise eﬃciency with employee welfare. Policymakers may estab- lish criteria to ensure algorithmic accountability, including regular bias audits and requesting participation from health- care experts prior to the institutionalisation of digital systems (Aziz et al., 2022). Moreover, investments in digital training within healthcare institutions will mitigate disparities in ac- cess and literacy, therefore preserving job happiness while promoting national digital health initiatives. Conclusions This concept paper proposes a framework for exami- ning the inﬂuence of algorithmic management on nurses’ job happiness in Malaysia. This study utilises the Job De- mands–Resources model, Self-Determination Theory, and sociotechnical systems perspectives to establish algorithmic management as a pivotal factor inﬂuencing perceived auto- nomy, justice, and trust in AI systems, thus aﬀecting nurses’ job happiness. The framework additionally identiﬁes orga- nisational support and digital literacy as signiﬁcant modiﬁers that can either mitigate or exacerbate the eﬀects of algorith- mic processes. This work has three primary contributions. Theoretically, it enhances scholarship by contextualising algorithmic management within the healthcare sector, where human well-being is inherently connected to professional sa- tisfaction. It oﬀers managers and healthcare leaders a pers- pective to foresee the unexpected repercussions of digital reforms on frontline carers. It underscores the necessity for regulatory monitoring to guarantee that technological adop- tion improves rather than undermines the welfare of heal- thcare professionals. As Malaysian healthcare institutions advance in their digitisation eﬀorts, this paradigm provides a pertinent basis for empirical investigation. Subsequent study ought to evaluate the assertions presented below, employing both qualitative and quantitative methodologies to elucidate the intricate experiences of nurses in various hospital envi- ronments. By harmonising eﬃciency with compassion, the incorporation of algorithmic systems can serve as both a means of operational optimisation and a conduit for maintai- ning the well-being and resilience of the nursing profession. References

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R., Rashid, U. k., Jameel, A. S. Formal ana- lysis: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Research: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Methodology: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Supervision: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Validation: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Visualization: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Writing the original draft: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S. Writing, review and editing: Ahmad, A. R., Md Sapry, H. R., Rashid, U. k., Jameel, A. S.

J. Manage. Hum. Resour. (January - June) 4(1): 22-28 28 Data availability statement The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Statement on the use of AI The authors acknowledge the use of generative AI and AI-assisted technologies to improve the readability and cla- rity of the article. Disclaimer/Editor’s note The statements, opinions, and data contained in all publi- cations are solely those of the individual authors and con- tributors and not of Journal of Law and Epistemic Studies. Journal of Law and Epistemic Studies and/or the editors disclaim any responsibility for any injury to people or pro- perty resulting from any ideas, methods, instructions, or pro- ducts mentioned in the content.