Organizational Process & Change: Models, Culture, Tech & Decision-Making

TAKEAWAYS:

• Organizational processes (planning, decision-making, change) follow structured models like Lewin’s Change Model or the Rational Decision-Making Process, but real-world constraints (time, data, politics) often force contingency approaches.
• Organizational culture (values, norms, rituals) acts as the "software" of an organization—visible in Kathmandu’s traffic chaos (informal norms) or Nabil Bank’s customer service (formal values)—and directly impacts performance and change success.
• Technology-driven management (e.g., eSewa’s blockchain, Daraz’s AI logistics) disrupts traditional processes, requiring systems thinking (inputs → processes → outputs → feedback) to manage complexity.
• Change management isn’t just about tools or strategies; it’s about people—Lewin’s Unfreeze-Change-Refreeze model shows why resistance (e.g., Pathao drivers protesting new app rules) must be managed through communication and participation.
• Decision-making in organizations is rarely rational: bounded rationality (Herbert Simon) explains why NTC’s fiber-optic expansion used satisficing (good-enough choices) due to limited data.
• The contingency theory (no one-size-fits-all) is why Toyota’s lean manufacturing works in Nepal but fails in chaotic traffic—context (culture, tech, environment) dictates the best process.

1. Organizational Processes: The Engine of Business

Organizational processes are the structured, repeatable workflows that turn inputs (resources, ideas, people) into outputs (products, services, decisions). They include:

• Planning processes (strategic, tactical, operational)
• Decision-making processes (rational, bounded, intuitive)
• Change management processes (adaptation to internal/external shifts)

A. The Rational Decision-Making Process (How "Perfect" Decisions Should Work)

The 7-step model (Simon, 1947) assumes full information and logic—but real-world constraints (time, politics, data limits) make this idealized. Here’s how it works:

flowchart TD
    A["1. Problem Identification"] --> B["2. Objective Setting"]
    B --> C["3. Criteria Development"]
    C --> D["4. Alternative Generation"]
    D --> E["5. Evaluation of Alternatives"]
    E --> F["6. Decision Implementation"]
    F --> G["7. Feedback & Evaluation"]
    G -->|"Loop"| A

Worked Example: NTC’s Fiber-Optic Expansion

• Problem: Low internet penetration in rural Nepal.
• Objective: Increase coverage by 30% in 2 years.
• Criteria: Cost, speed, government subsidies, technical feasibility.
• Alternatives:
  • Expand existing towers (cheap, slow).
  • Partner with private ISPs (fast, risky).
  • Use satellite (expensive, reliable).
• Decision: NTC chose a hybrid model (satisficing), balancing speed and budget—not the "optimal" choice but the best given constraints.

Why This Fails in Reality:

• Bounded rationality: Managers don’t have all data (e.g., NTC lacked exact rural demand forecasts).
• Political factors: Government pressure to prioritize certain regions.
• Time pressure: NTC had to act before competitors (e.g., Ncell) entered the market.

2. Organizational Culture: The Invisible Glue

Definition: The shared values, beliefs, norms, and rituals that shape how employees behave and interact. It’s visible in:

• Symbols (e.g., Daraz’s orange branding = energy, trust).
• Rituals (e.g., Nabil Bank’s annual "Customer Appreciation Day").
• Stories (e.g., "How Himalayan Java survived the 2015 earthquake").

How Culture Affects Processes

Why Culture Matters for Change

• Resistance to change often stems from cultural clashes. Example:
  • Case: eSewa’s Blockchain Adoption
    • Old culture: Government employees used manual processes (slow, error-prone).
    • New tech: Blockchain for secure transactions.
    • Challenge: Employees feared job loss or complexity.
    • Solution: eSewa trained staff in small batches and framed blockchain as a career upgrade, not a threat.

Lewin’s Change Model Applied to eSewa:

flowchart LR
    A["Unfreeze: Break old habits"] --> B["Change: Train & pilot blockchain"]
    B --> C["Refreeze: New norms (e.g., 'All transactions must be digital by 2025')"]

3. Change Management: Why 70% of Projects Fail (And How to Avoid It)

Definition: The structured approach to transitioning individuals, teams, and organizations from current states to desired future states.

Key Models

1. Lewin’s 3-Step Model (Unfreeze → Change → Refreeze)

   • Unfreeze: Create urgency (e.g., "Competitors like Daraz are eating our market share").
   • Change: Pilot new processes (e.g., test AI chatbots for customer service).
   • Refreeze: Reinforce new behaviors (e.g., reward teams that adopt the chatbot).

2. Kotter’s 8-Step Model (More detailed, used by Nabil Bank for digital transformation)

   • Step 1: Create a sense of urgency.
   • Step 2: Build a guiding coalition (e.g., CEO + IT + customer service teams).
   • Step 3: Develop a vision ("Become Nepal’s #1 digital bank by 2026").
   • ... (Steps 4–8 focus on communication, short-term wins, and anchoring change).

Why Change Fails in Nepal:

• Lack of leadership buy-in: Middle managers resist (e.g., bank clerks ignoring digital loan systems).
• Poor communication: Announcements without training (e.g., NTC’s fiber rollout with no user guides).
• Underestimating culture: Ignoring informal networks (e.g., "bhai-bhai" systems in SMEs).

Case Study: Daraz’s Supply Chain Overhaul

• Problem: Slow delivery times due to inefficient logistics.
• Change Process:
  1. Unfreeze: Daraz highlighted delivery delays in ads ("Your order in 24 hours—guaranteed!").
  2. Change:
     • Partnered with local couriers (e.g., Ncell’s "Daraz Express").
     • Implemented real-time tracking (tech-driven transparency).
  3. Refreeze: Made tracking mandatory for all sellers; rewarded fast-delivery partners.

4. Technology-Driven Management: The Digital Disruption

Definition: Using technology (AI, blockchain, IoT, cloud) to automate, optimize, or transform organizational processes.

How Tech Impacts Processes

Worked Example: Kathmandu Traffic Routes as a "System"

• Inputs: Vehicles, roads, signals, drivers.
• Processes:
  • Feedback loops: Traffic jams create delays → drivers take alternate routes → new jams.
  • Technology fix: Smart traffic lights (IoT sensors + AI) adjust signals in real-time (used in Lalitpur’s pilot project).
• Output: Faster commutes, reduced emissions.

5. Contingency Theory: No One-Size-Fits-All

Definition: The best management approach depends on context (industry, size, culture, environment).

Key Contingency Factors

Example: Why Toyota’s Lean Manufacturing Works in Factories but Not in Kathmandu Traffic

• Factory (High Structure):
  • Predictable inputs (materials, workers).
  • Just-in-time delivery reduces waste.
• Traffic (Chaotic):
  • Unpredictable inputs (accidents, protests, monsoon floods).
  • Lean principles (e.g., "eliminate waste") would increase congestion if applied literally.

Contingency Theory in Action: Nabil Bank’s Digital Loan System

• Context:
  • High tech: Online applications.
  • Customer-focused culture: Fast approvals.
  • Regulatory environment: Strict banking laws.
• Approach:
  • Hybrid model: AI for initial risk assessment + human review for edge cases.
  • Result: 40% faster approvals with lower fraud.

6. Systems Theory: The Big Picture

Definition: Organizations are interconnected systems where outputs of one process become inputs for another.

The Systems Approach

flowchart LR
    A["Inputs"] --> B["Processes"]
    B --> C["Outputs"]
    C --> D["Feedback"]
    D -->|"Loops back to"| A
    subgraph System
        A -->|"Resources, Info, People"| B
        B -->|"Products, Decisions, Change"| C
        C -->|"Customer Data, Market Trends"| D
    end

Example: Daraz’s Order Fulfillment System

1. Inputs: Customer order, inventory data, weather (affects delivery).
2. Processes:
   • AI predicts demand.
   • Warehouse robots pick items.
   • Courier assigns routes.
3. Outputs: Delivered package + customer review.
4. Feedback: Review data → AI adjusts future predictions.

Limitations of Systems Theory:

• Over-simplification: Real systems are non-linear (e.g., a small protest can shut down Kathmandu’s entire supply chain).
• Data dependency: Requires accurate feedback (e.g., Daraz’s AI needs real-time delivery tracking).

In the Real World

1. eSewa’s Blockchain for Payments

   • Idea Used: Technology-driven process change + contingency theory.
   • How: Replaced manual transaction records with blockchain to reduce fraud. Contingency: Adapted to Nepal’s low digital literacy by adding SMS-based verification.
   • Impact: 30% drop in payment disputes; now used by 60% of Nepali households.

2. Pathao’s Dynamic Pricing Algorithm

   • Idea Used: Rational decision-making model (but with bounded rationality).
   • How: Surge pricing during peak hours (e.g., 7–9 PM in Kathmandu) to balance supply-demand.
   • Real-World Twist: Drivers protested when fares spiked during protests (unpredictable demand). Pathao had to adjust the algorithm—showing how culture (driver unions) and environment (political instability) override pure logic.

3. NTC’s Fiber-Optic Expansion (Failed Change Management)

   • Idea Used: Lewin’s change model (but poorly executed).
   • What Went Wrong:
     • Unfreeze: NTC announced expansion without explaining why or how it benefited users.
     • Change: Rolled out in phases without training local partners (e.g., internet cafes).
     • Refreeze: No incentives for businesses to adopt fiber.
   • Result: Slow uptake; competitors like Ncell filled the gap.

Exam Tip

How to Score Full Marks in TU/PU Exams

1. For Case Studies (e.g., Terminal 5, TGSS):

   • Step 1: Identify the process (planning? change? decision-making?).
   • Step 2: Map it to a model (e.g., Lewin’s change model for TGSS’s profitability shift).
   • Step 3: Critique using contingency theory (e.g., "Terminal 5’s failure was due to over-reliance on classical management (rigid planning) ignoring behavioral factors (employee resistance)").
   • Step 4: Link to Nepal (e.g., "Like Terminal 5, NTC’s fiber project faced cultural resistance from traditional telecom operators").

2. For Short Definitions:

   • Organizational culture: "The collective values, norms, and rituals that guide behavior in an organization" + 1 example (e.g., "Nabil Bank’s ‘customer-first’ culture is evident in its 24/7 helpline").
   • Contingency theory: "No universal management approach; effectiveness depends on context" + 1 comparison (e.g., "Toyota’s lean works in factories but not in Kathmandu traffic").

3. For Process Diagrams:

   • Always draw a flowchart (even in text exams, describe it step-by-step).
   • Example for "Rational Decision-Making":

     "Step 1: Problem identification (e.g., Daraz’s slow deliveries). Step 2: Objective (reduce delivery time by 50%). Step 3: Criteria (cost, tech, customer satisfaction). Step 4: Alternatives (AI routing, more couriers, drone delivery). Step 5: Evaluate (drone delivery failed due to regulations). Step 6: Implement AI routing. Step 7: Feedback (track success via customer surveys)."

4. Avoid Common Mistakes:

   • ❌ Saying "change management is easy" → ✅ Always mention resistance and culture.
   • ❌ Ignoring Nepali context → ✅ Tie every example to eSewa, Daraz, NTC, etc.
   • ❌ Describing models without criticism → ✅ Add limitations (e.g., "Lewin’s model ignores political factors like union protests").

Pro Tip: Memorize these 3 cases—they cover all subtopics:

1. eSewa’s blockchain (tech + culture).
2. Pathao’s surge pricing (decision-making + contingency).
3. NTC’s fiber failure (change management + systems theory).