Management 2025 Paper I 50 marks Explain

Q2

(a) In what ways can ethical behaviour in management contribute to a company's social responsibility efforts ? How can ethical decision-making improve a company's social responsibility outcomes ? (15 marks) (b) How do advanced technologies like Artificial Intelligence and Machine Learning support Knowledge-Based Enterprises ? (20 marks) (c) What are the challenges an organization faces in integrating human resource information system with other business systems, e.g. CRM, ERP, Payroll, etc. ? Suggest probable solutions to overcome these challenges. (15 marks)

हिंदी में प्रश्न पढ़ें

(a) किन तरीकों से प्रबंधन में नैतिक व्यवहार, कंपनी के सामाजिक उत्तरदायित्व के प्रयासों में योगदान दे सकता है ? नैतिक निर्णयन एक कंपनी के सामाजिक उत्तरदायित्व के परिणामों में कैसे सुधार ला सकता है ? (15 अंक) (b) उन्नत प्रौद्योगिकियाँ जैसे कृत्रिम बुद्धिमत्ता और मशीन लर्निंग, ज्ञान-आधारित उद्यमों का समर्थन कैसे करती हैं ? (20 अंक) (c) मानव संसाधन सूचना प्रणाली को अन्य व्यावसायिक प्रणालियों जैसे सी आर एम, ई आर पी, वेतन-पत्रक आदि के साथ एकीकृत करने में किसी संगठन को किन चुनौतियों का सामना करना पड़ता है ? इन चुनौतियों पर विजय प्राप्त करने हेतु संभाव्य समाधानों का सुझाव दीजिए। (15 अंक)

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Directive word: Explain

This question asks you to explain. The directive word signals the depth of analysis expected, the structure of your answer, and the weight of evidence you must bring.

See our UPSC directive words guide for a full breakdown of how to respond to each command word.

How this answer will be evaluated

Approach

The directive 'explain' demands conceptual clarity with cause-effect linkages across all three parts. Allocate approximately 30% time/words to part (a) on ethics-CSR nexus, 40% to part (b) on AI/ML in knowledge enterprises as it carries highest marks, and 30% to part (c) on HRIS integration challenges. Structure with a brief integrated introduction, three distinct sectional bodies with clear sub-headings, and a synthesizing conclusion that connects technological ethics with systemic integration.

Key points expected

  • Part (a): Ethical behaviour as foundation for CSR—linking stakeholder theory, triple bottom line, and ethical decision-making frameworks (Kohlberg, Trevino) to CSR outcomes like reputation, trust, and license to operate
  • Part (a): Distinction between compliance-based and integrity-based ethics programs; how ethical leadership cascades to social responsibility through value chain decisions
  • Part (b): AI/ML enabling knowledge creation, codification, and transfer in KBEs—expert systems, predictive analytics for decision support, and organizational learning loops
  • Part (b): Specific applications—knowledge discovery in databases, natural language processing for tacit knowledge capture, and AI-driven innovation ecosystems
  • Part (c): Technical challenges of HRIS integration—data standardization, interoperability, legacy system compatibility, and real-time synchronization with ERP/CRM
  • Part (c): Organizational challenges—change management, data privacy concerns, cost-benefit analysis, and solutions like middleware, cloud-based HCM suites, and phased implementation
  • Cross-cutting: Digital ethics in AI deployment and responsible automation balancing efficiency with human-centric values

Evaluation rubric

DimensionWeightMax marksExcellentAveragePoor
Concept correctness20%10Precisely defines ethical behaviour (De George), CSR (Carroll's pyramid), Knowledge-Based Enterprises (Drucker/Nonaka), and HRIS architecture; for (a) distinguishes moral management from amoral/immoral management; for (b) correctly identifies machine learning types (supervised/unsupervised/reinforcement) relevant to knowledge work; for (c) accurately describes API-based integration vs. ETL processesBasic definitions provided but conflates ethics with legality in (a), treats AI/ML generically without knowledge-specific applications in (b), and describes HRIS only as record-keeping without integration architecture in (c)Fundamental conceptual errors—equates ethics entirely with compliance, confuses AI with simple automation, or describes HRIS as standalone system without integration requirements
Framework citation20%10For (a): cites Carroll's CSR pyramid, stakeholder theory (Freeman), or ethical decision models (Rest's four-component model); for (b): references SECI model (Nonaka), knowledge management frameworks, or AI maturity models; for (c): applies enterprise architecture frameworks (Zachman/TOGAF) or change management models (Kotter/Lewin)Mentions frameworks without systematic application—names CSR models but doesn't link to ethical mechanisms, cites AI generally without knowledge management specificity, or lists integration challenges without theoretical groundingNo recognizable management frameworks; relies on generic descriptions or misattributes concepts (e.g., calling Porter's value chain a CSR framework)
Case / Indian example20%10For (a): Tata Group's ethical governance enabling community development, or Infosys's foundation work linked to corporate values; for (b): Indian IT services firms (TCS, Wipro) using AI for knowledge management, or startups like Zoho/Freshworks; for (c): Indian public sector HRIS integration (e.g., NIC's e-HRMS with PFMS), or private sector examples like Reliance's integrated digital platformsGeneric international examples (Google's AI ethics, SAP HRIS) without Indian specificity, or Indian examples mentioned superficially without analytical depthNo concrete examples, or irrelevant/inaccurate examples (e.g., citing manufacturing cases for knowledge enterprises, or confusing HRIS with basic payroll software)
Multi-perspective analysis20%10For (a): balances instrumental, relational, and moral perspectives on ethics-CSR linkage; for (b): examines AI/ML through efficiency, innovation, and ethical/risk lenses including job displacement concerns; for (c): analyzes technical, organizational, and human perspectives on integration; identifies synergies across parts (e.g., ethical AI governance in HRIS)Single-dominant perspective per part—primarily economic view of ethics, purely technological view of AI, or technical-only view of integration without organizational considerationsDescriptive listing without analytical depth; no recognition of tensions or trade-offs (e.g., between AI efficiency and knowledge worker autonomy, or integration costs versus benefits)
Conclusion & recommendation20%10Synthesizes three parts into coherent argument about responsible digital transformation—ethical AI governance as bridge between knowledge management and HRIS; offers specific, actionable recommendations (e.g., ethical AI committees, phased cloud migration with change management); forward-looking on emerging challenges like algorithmic bias in HR analyticsSummarizes each part separately without integration; generic recommendations (train employees, invest in technology) without specificity to question contextMissing conclusion, or abrupt ending; recommendations contradict earlier analysis or are entirely impractical; no connection between ethics, AI, and systems integration themes

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