Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2026A regulated many-to-one two-sided matching model for emergency teams: a university case study
Şimşek, Ahmet Bahadır; Göktekin, Zekiye; Şimşek, Tuğçe; Tunçer, Dilara
In workplaces, emergency teams comprised of employees play a critical role in minimising damage during emergencies through rapid response. This study introduces a multi-objective employee-team matching model that optimally assigns employees to emergency teams, balancing stakeholder preferences and physical requirements. Using a regulated many-to-one matching framework, this model accounts for employee preferences, decision-maker evaluations, and team-specific physical standards, addressing limitations in current manual matching practices. Tested through a real case study in a university setting, the model demonstrates its efficacy by achieving stable, satisfactory matches that outperform traditional methods. Sensitivity analysis across multiple scenarios further illustrates the model’s adaptability, providing decision-makers with a systematic tool for improving team stability, performance, and flexibility in various emergency preparedness contexts. The main contribution of this study is the reconciliation of preference-based assignments with performance requirements through a tuneable objective function. By jointly considering soft (preferences) and hard (physical) constraints in a single framework, the model allows decision-makers to balance satisfaction and operational feasibility–an integration that existing approaches typically overlook. This approach presents a robust, data-driven solution to enhance organisational resilience and efficiency in critical situations.
- Kitap BölümüYalnız künye2026Prioritising Digital Talent Initiatives in Universities: Evidence From Turkey
Şimşek, Tuğçe; Şimşek, Aybala; Şimşek, Ahmet Bahadır
Universities are under growing pressure to advance digital transformation, yet priorities in digital talent management often remain unclear. This chapter develops a document-based multi-criteria decision framework to identify and compare such priorities across universities of different institutional scale in Türkiye. Drawing on public strategic plans, quality reports, distance education documentation, and staff development texts from Anadolu, Sakarya, and Tarsus Universities, the study constructs a decision matrix covering three initiatives and six evaluation criteria. Criterion weights are calculated through entropy, and initiative rankings are generated with TOPSIS. The findings show that digital infrastructure support has the strongest discriminating power. Strengthening distance education and LMS infrastructure emerges as the top priority, followed by a needs-based in-service training system and a digital competence development programme. The chapter offers a transparent and transferable framework for setting scale-sensitive digital transformation priorities in higher education.
- MakaleYayıncıda açık erişim2026A data-driven decision-support framework for prioritizing retention interventions among high attrition-risk employee segments
Şimşek, Tuğçe; Şimşek, Aybala
Predicting employee attrition risk provides a useful signal for human resource decision-making. However, this signal alone does not determine which employee groups should be targeted first for retention interventions. This study develops a data-driven decision-support framework for prioritizing retention interventions among employees at risk of attrition. The framework uses the IBM HR Analytics Employee Attrition and Performance dataset and integrates calibrated machine learning, SHAP-based explanatory analysis, Entropy-CRITIC weighting, and PROMETHEE II ranking. Machine-learning models were first used to estimate employee-level attrition probabilities. These probabilities were then aggregated into segments defined by combinations of Department, JobRole, and OverTime, from which the high-risk segment pool was identified for multi-criteria analysis. Eight high-risk segments were detected. The segment ranked first by PROMETHEE II was not the segment with the highest attrition risk, indicating that intervention priority and attrition risk represent distinct decision outputs. Sensitivity analyses further showed that the segment-ranking structure remained stable under alternative weight-shift and threshold scenarios. The proposed framework is therefore not intended to identify individual employees at risk; rather, it supports transparent, auditable, and ethically compliant segment-level retention decisions.
- Kitap BölümüYalnız künye2026Privacy, Fairness, and Model Risk Governance in Sustainable People Analytics
Şimşek, Tuğçe; Şimşek, Ahmet Bahadır
Whilst sustainability targets are driving organisations to make more intensive use of human data, this trend is transforming HR analytics from a reporting tool into a component of decision-making architecture. This transformation is expanding the scope of data collection, increasing the visibility of employees, and intertwining privacy, fairness, perceptions of surveillance, and model risk within the same process. This study addresses the question of how this interplay can be managed at the project level. A literature review has been combined with an analysis of publicly available corporate documents, from which a compact toolkit for sustainable HR analytics has been derived. The toolkit is structured around defining project boundaries, documenting data and model assumptions, matching risks with controls, and ensuring transparency and human oversight. In this way, the chapter provides a practical framework that ensures analytical capacity is considered alongside organisational legitimacy and employee trust.
- Kitap BölümüYalnız künye2025Strategic Performance Metrics and Innovative Measurement Methods in Public Personnel Management
Şimşek, Tuğçe
Public administration increasingly demands strategic, data-oriented personnel management, yet traditional hierarchical metrics miss dynamic performance and innovation. This chapter examines the limits of conventional measurement in public personnel management and proposes a data-driven, dynamic governance framework. Its aim is to link digitalization with accountability and citizen orientation by specifying strategic indicators and technology-enabled methods. It outlines core concepts, then elaborates on strategic performance indicators and comparative analyses of metrics. It explores advanced methods, including AI-supported tools and continuous learning models, alongside blockchain, big-data analytics, sentiment analysis, and remote-work assessment. Findings show these tools improve transparency and continuous feedback while enabling ethical, auditable evaluation; policy implications for sustainable, evidence-based decision-making are provided.
- Kitap BölümüYalnız künye2025Human-machine collaboration: How do employees interact with artificial intelligence?
Şimşek, Tuğçe
This chapter examines the conceptual framework of artificial intelligence (AI) systems in the organisational context in terms of human-machine collaboration in three dimensions: supportive, collaborative, and autonomous AI models. A literature review integrates theoretical approaches on employee perceptions, acceptance and resistance processes, and the role of organisational culture in adaptation, establishing connections between performance, motivation, and psychosocial effects. Ethical dimensions are evaluated in the context of autonomy, privacy, and fairness, and the potential consequences of AI on work-life balance and role ambiguity are revealed. The findings point to a lack of empirical research despite conceptual richness and suggest the development of measurement tools with both descriptive and experimental designs. The chapter provides a roadmap for future applied research while emphasising the importance of clarifying strategic decision-making processes related to AI adaptation in an organisational context.
- MakaleYalnız künye2025Prioritisation of Human Resource Strategies in the Digital Transformation Process of SMEs
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Şimşek, Tuğçe; Şimşek, Ahmet Bahadır; Olubiyi, TO; Longe, OB; Devi, KSS; Sinha, M
This chapter focuses on the importance of human resource (HR) management strategies in the digital business strategy for small- and medium-sized enterprises (SMEs). With the increasing influence of digital transformation that alters organisational structures and implements new technologies, SMEs have no other choice but to evolve. However, due to the scarcity of resources, it becomes very important for SMEs to allocate its HR appropriately and effectively. To support decision-making on strategies like employee reskilling, recruitment of digital talent, leadership development, and promoting a digital culture, this chapter presents a multi-criteria decision analysis (MCDA) framework. The prioritisation is crucial because not all the strategies can be executed at once, and SMEs should target the most effective ones in the long run, affordable, and relevant to their digital transformation agenda. This chapter illustrates methods for how SMEs can use the proposed prioritisation framework effectively. The hypothetical case study demonstrates the real challenges faced by SMEs during digital transformation and how MCDA assists leaders in selecting the most beneficial HR strategies. The case highlights the necessity of fitting strategies to organisational challenges to allow the customisation of training and leadership to align with business demands and maximise effectiveness while minimising costs. Upon use, this framework enables SMEs to comprehend and direct their digital transformation path more effectively.
- Kitap BölümüYayıncıda açık erişim2025Challenges of adopting artificial technology in human resource management practices
Şimşek, Tuğçe; Şimşek, Ahmet Bahadir
In this study, the challenges faced by organisations in integrating artificial intelligence (AI) into human resource management (HRM) practises are examined. For that, AI can bring significant benefits, includ¬ing increasing efficiency and decision-making in the HR processes, namely recruitment, performance evaluation and talent development. Its adoption, however, presents many challenges in technological infrastructure, data privacy, ethical, and organizational culture. This chapter investigates these barriers, particularly the importance of proper management changes and the trade-off between human element and AI's capabilities. The article discusses strategies for overcoming these challenges, the importance of practising ethical AI, data security and employee development. This study offers actionable insights for organisations attempting to leverage AI in HRM in a way that can succeed at both the operational and ethical dimensions.