Social Change & Technology: Causes, Impacts & Digital Revolution

TAKEAWAYS:

• Social change is not random: it follows patterns (invention, diffusion, cultural contact) and is accelerated by technology.
• Technology reshapes institutions (marriage, education, governance) by altering norms, roles, and power structures.
• Digital divide creates winners and losers: while urban Nepalese use eSewa for payments, rural areas lag in access.
• Cultural lag occurs when social norms (e.g., traditional marriage) struggle to adapt to rapid tech changes (online dating).
• Feedback loops exist: tech changes society, which then demands new tech (e.g., smartphones → social media → political movements).
• Ethical dilemmas arise: convenience (e.g., food delivery apps) vs. job displacement (e.g., auto-rickshaw drivers vs. Pathao).

Core Concepts: What is Social Change?

Social change refers to alterations in social structures, cultural values, or institutions over time, driven by internal (e.g., population growth) or external (e.g., globalization) factors. Technology acts as a catalyst, compressing timelines of change. For example, the shift from landline phones to smartphones in Nepal (2010–2023) didn’t just improve communication—it redefined social interactions, political organizing (e.g., #SaveTheHills protests on Facebook), and even criminal networks (cyber fraud via USSD).

How Technology Drives Change: The 3 Key Mechanisms

Factors of Social Change: The "Big 5"

Social change is multifactorial. Technology is one driver, but others interact with it. Here’s how they work in Nepal:

Caption: How tech alters social rituals: from bhatki to digital invites in 10 years.

How Technology Changes Social Institutions

Institutions are stable, enduring patterns of behavior (e.g., family, religion, economy). Tech disrupts or reinforces them:

1. The Marriage Institution: From Gai Jatra to Tinder

Traditional Model:

• Arranged marriages based on caste, family reputation, and dowry.
• Rituals like Gai Jatra (cow parade) symbolize community bonds.
• IMAGE: Gai Jatra procession in Bhaktapur | Shows pre-tech social cohesion rituals.

Tech-Disrupted Model:

Worked Example: The Daraz Delivery Driver’s Dilemma

• Scenario: A Daraz driver in Kathmandu must choose between:
  1. Traditional role: Deliver packages manually (high physical strain, low pay).
  2. Tech-enabled role: Use Daraz’s route-optimization app to save time (but risk job insecurity if replaced by drones).
• Social Impact:
  • Economic: Drivers earn ₹200–₹500/day (vs. ₹100 for rickshaw pullers).
  • Social: Stigma—many families disapprove of "delivery boy" jobs.
  • Tech Feedback: Daraz’s app creates new norms (e.g., "5-star delivery ratings" pressure).

2. Economic Systems: From Barter to Cryptocurrency

Caption: Tech replaces trust-based economies with algorithmic transactions.

Case Study: NTC Bill Payments

• Before eSewa (2018):
  • Process: Visit NTC office → bribe clerk → wait in line.
  • Social Cost: ₹50–₹200 "tea money" (informal bribe).
• After eSewa (2023):
  • Process: Scan QR → pay via app → instant confirmation.
  • Social Impact:
    • Transparency: Bribes dropped by 70% (NTC’s own data).
    • Trust: Citizens now prefer digital (90% of urban users).
    • New Problem: Digital literacy gap—elderly struggle with apps.

3. Political Participation: From Bandh to Twitter

Caption: How tech changes protest dynamics from physical to virtual.

Worked Example: The 2015 Earthquake Response

• Pre-Tech: Relief efforts relied on radio broadcasts and physical distribution centers (slow, corrupt).
• Post-Tech:
  • Google Person Finder: 50K+ missing persons located via digital reports.
  • Ushahidi Crowdmap: Volunteers reported damage in real-time → faster NGO response.
• Social Impact:
  • Trust in tech: 80% of survivors now use digital tools for emergencies.
  • New roles: Digital volunteers emerged (e.g., #NepalQuake Twitter hashtag).

The Dark Side: Challenges of Tech-Driven Change

Not all change is positive. Unintended consequences emerge:

1. Digital Divide: Who Loses?

• Urban vs. Rural: 90% of Kathmandu uses internet vs. 30% in rural Sindhupalchowk.
• Gender Gap: Women in Nepal are 30% less likely to own a smartphone (UN 2023).
• Economic Impact: Auto-rickshaw drivers in Kathmandu see 40% drop in income due to Pathao.

Caption: Tech access varies by region—Kathmandu (95%) vs. Achham (10%).

2. Cultural Lag: Norms Can’t Keep Up

• Problem: Tech changes faster than social rules.
• Example: Online dating in Nepal clashes with:
  • Caste norms: Some families reject matches outside their jati.
  • Parental control: 70% of Nepali parents monitor their child’s dating app activity (Pew Research 2022).
• Result: Hybrid marriages—some couples meet online but still follow traditional rituals.

3. Ethical Dilemmas: Convenience vs. Privacy

In the Real World

1. eSewa & Khalti (Nepal)

   • Idea Used: Diffusion of financial technology (FinTech).
   • How It Works: These apps replace cash transactions with digital wallets, reducing corruption in utility bills (NTC, NEA) and government fees.
   • Impact:
     • Before: Bribes for NTC bills cost ₹200–₹500 per month.
     • After: 90% of urban users now pay digitally—bribes dropped by 80%.
     • New Problem: Cyber fraud (e.g., fake eSewa agents stealing money).

2. Pathao & Foodmandu (Nepal)

   • Idea Used: Gig economy disruption.
   • How It Works: Apps connect drivers/delivery agents with customers via algorithms, optimizing routes.
   • Impact:
     • Economic: 50K+ drivers earn ₹200–₹800/day (vs. ₹100 for rickshaw pullers).
     • Social: Stigma—families often disapprove of "delivery boy" jobs.
     • Tech Feedback: Apps create new norms (e.g., "5-star ratings" pressure drivers).

3. WhatsApp in Nepali Politics

   • Idea Used: Digital mobilization.
   • How It Works: Political parties use WhatsApp groups to organize protests, spread propaganda, and bypass traditional media.
   • Impact:
     • 2015 Protests: 50K+ people mobilized via WhatsApp (vs. 10K via flyers).
     • 2022 Elections: CPN-UML’s viral videos got 1M+ views, influencing youth votes.
     • Dark Side: Fake news (e.g., 2019 "child trafficking" rumors led to lynchings).

Exam Tip: How to Score Full Marks

This unit is conceptual + applied. Examiners love:

1. Definitions + Examples:
   • Don’t just say "tech changes society"—explain how (e.g., "eSewa reduces corruption by eliminating middlemen in NTC bill payments").
2. Nepalese Context:
   • Always tie examples to Nepal (e.g., eSewa, Daraz, NEPSE, WhatsApp protests).
3. Pros & Cons Tables:
   • For questions like "Discuss the influence of modern technology", use a 2-column table (Impact vs. Challenge).
4. Real-World Data:
   • Mention statistics (e.g., "70% of Kathmandu couples now use WhatsApp for wedding planning").
5. Theory + Application:
   • Link concepts to sociological theories:
     • Functionalism: Tech improves efficiency (e.g., eSewa speeds up bill payments).
     • Conflict Theory: Tech creates inequality (e.g., digital divide between urban/rural).
     • Symbolic Interactionism: Tech changes symbols (e.g., bhatki → digital invites).

Common Mistakes to Avoid:

• ❌ Vague answers: "Tech is good" → ✅ "eSewa reduced NTC bribes by 80% but created cyber fraud risks."
• ❌ No examples: Always use Nepalese cases (eSewa, Daraz, WhatsApp protests).
• ❌ Ignoring challenges: Tech isn’t all positive—discuss downsides (digital divide, job loss).

Final Mermaid Summary:

mindmap
  root((Social Change & Technology))
    Factors
      Invention
      Diffusion
      Cultural Contact
      Population Growth
      Environmental
    Institutions
      Marriage
      Economy
      Politics
    Nepalese Examples
      eSewa (FinTech)
      Pathao (Gig Economy)
      WhatsApp (Politics)
    Challenges
      Digital Divide
      Cultural Lag
      Ethical Dilemmas