Business Research Proposal: AI vs. Traditional Browser Impact Analysis

Recommended Framework: Technology Acceptance Model (TAM) & Multi-Criteria Decision Analysis (MCDA)

• Framework Teaching:
  • Technology Acceptance Model (TAM): This is a foundational framework for understanding how and why users adopt new technology. It posits that a user's decision to use a new system is primarily determined by two factors:
    1. Perceived Usefulness (PU): The user's belief that the technology will help them perform their tasks better or achieve their goals more effectively.
    2. Perceived Ease of Use (PEOU): The user's belief that using the technology will be free of effort.
  • Multi-Criteria Decision Analysis (MCDA): This is a method for structuring a complex problem and evaluating different options (in our case, the two browser types) against a set of key criteria. It allows for a systematic and transparent comparison, especially when there are trade-offs between criteria like user experience and privacy.
• Applicability Explanation:
  • The problem requires understanding both individual user behavior and the broader systemic impacts. TAM is perfectly suited to diagnose the 'why' behind user adoption—it will help you measure and compare the core user experience of both browsers. MCDA provides the structure to evaluate the two browsers against the specific, multi-faceted concerns you've identified: user experience, data privacy, knowledge quality, and economic impacts.
  • By combining them, you can explain why users might prefer one browser (using TAM) and simultaneously assess the wider consequences of that preference (using MCDA).

Part 1: Information Collection

1.1 Web Search Plan

• Web Search Queries & Purpose:
  • Queries: "Technology Acceptance Model for new software adoption", "user experience studies of AI search vs traditional search engines".
  • Purpose: To understand the established factors that drive user adoption. This will help you formulate specific, validated questions for your user tests regarding usefulness and ease of use. It also provides a baseline of existing public survey data.
  • Queries: "economic impact of generative AI on content creators and SEO", "Impact of AI on SEO and Content Marketing".
  • Purpose: To gather expert analysis and data on the potential economic shifts. This information is crucial for the "economic impacts" criterion of your MCDA framework, as it cannot be easily measured in a user test.
  • Queries: "filter bubble and information verification on AI search platforms", "Filter Bubbles in Recommender Systems".
  • Purpose: To collect information on the technical and societal risks of AI-driven content curation, such as the filter bubble effect and the spread of misinformation. This will inform your analysis of "knowledge quality" and "data privacy."

1.2 User Testing & Interview Plan

• Participants: Recruit a diverse sample of 15-20 users, segmented into distinct personas:
  • The "Power Researcher": (e.g., students, academics, journalists) Users who rely on search for in-depth, complex information gathering and source verification.
  • The "Everyday User": (e.g., working professionals, parents) Users who perform frequent, practical searches for quick answers, product comparisons, and local information.
  • The "Casual User": (e.g., older adults, tech novices) Users who search infrequently and may be less familiar with advanced search techniques.
• Methodology:
  1. Task-Based Testing: Prepare a set of 4-5 realistic search tasks for each participant to complete on both the traditional and AI-driven browsers. The order of browsers should be alternated between participants to avoid learning bias.
  2. Think-Aloud Protocol: Instruct users to speak their thoughts aloud as they work through the tasks. This provides invaluable insight into their expectations, frustrations, and thought processes.
  3. Post-Task & Post-Session Questionnaires: Use targeted questions after each task and at the end of the session to quantify their perceptions.
• Core Interview & Observation Areas:
  • Task Scenario Example (for a "Power Researcher"): "You are writing a paper on the ethical implications of gene editing. Use the browser to find three credible, recent academic sources that present opposing viewpoints on the topic."
    • Analysis Purpose: This task directly tests the AI's ability to provide nuanced, verifiable information versus a traditional search engine's list of direct sources. You can observe which approach is more efficient and trusted by this persona.
  • Task Scenario Example (for an "Everyday User"): "You're planning a family dinner for this weekend. Use the browser to find a well-rated, family-friendly Italian restaurant near you that is open on Sunday evening and takes reservations."
    • Analysis Purpose: This tests the ability to synthesize multiple constraints (cuisine, rating, location, hours) and perform a transactional task. It compares the AI's direct answer to the user piecing together information from multiple links on a traditional SERP (Search Engine Results Page).
  • Key Questions to Ask (Post-Task):
    • "On a scale of 1-7, how useful was this browser for completing the task?" (Measures Perceived Usefulness)
    • "On a scale of 1-7, how easy was it to use this browser to complete the task?" (Measures Perceived Ease of Use)
    • "How much did you trust the information you found? What made you trust or distrust it?" (Measures Knowledge Quality)
    • "Did you have any concerns about how your data was being used during that search?" (Measures Data Privacy)

Part 2: Information Analysis

2.1 TAM Analysis: Mapping User Adoption

• How to Execute:
  1. Quantify Perceptions: For each user persona ("Power Researcher," "Everyday User," etc.), calculate the average scores for Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) for both browsers based on your post-task questionnaires.
  2. Create a TAM Matrix: Plot the results on a 2x2 grid with PU on the Y-axis and PEOU on the X-axis. This will give you a visual map showing where each browser stands for each user group.
  3. Synthesize Qualitative Data: Use the "think-aloud" notes and interview responses to explain the "why" behind the scores. For instance, you might find that while "Everyday Users" score the AI browser high on both PU and PEOU for its speed, "Power Researchers" score it lower on PU because they distrust answers that don't show primary sources.

2.2 MCDA Analysis: Evaluating Overall Impact

• How to Execute:
  1. Build Your Decision Matrix: Create a table with "Traditional Browser" and "AI Browser" as the columns. Use "User Experience," "Data Privacy," "Knowledge Quality," and "Economic Impact" as the rows (your criteria).
  2. Score Each Criterion: Assign a score (e.g., -5 to +5, or a qualitative High/Medium/Low rating) to each browser for each criterion, justifying it with your collected data.
     • User Experience: Use the aggregate PU/PEOU scores and satisfaction ratings from your TAM analysis.
     • Data Privacy: Score based on user-reported concerns and findings from your web search about algorithmic transparency and data usage.
     • Knowledge Quality: Score based on user trust ratings, observations on source verification, and your research on filter bubbles and AI accuracy.
     • Economic Impact: Use the insights from your web search to assign a directional impact (e.g., "High potential for disruption to publisher revenue").

2.3 From Analysis to Actionable Recommendations

• How to Synthesize for the Final Report:
  1. Develop User Adaptation Profiles: Based on the TAM analysis, create detailed profiles for each user segment. Describe which browser they prefer for which types of tasks and articulate their primary drivers and barriers to adopting AI search.
  2. Present the Balanced Verdict: Use the MCDA matrix as the centerpiece of your impact assessment. Clearly present the trade-offs. For example: "While the AI browser offers a superior user experience for simple informational tasks (a 'high' score), it introduces significant risks related to knowledge quality due to opaque sourcing (a 'low' score)."
  3. Formulate Strategic Recommendations: Based on the synthesis, provide "how-to" guidance.
     • Product Strategy Example: "To capture the 'Power Researcher' segment, the AI browser must prioritize features that allow for easy source verification and link attribution within the generated answer."
     • Ethical Guideline Example: "To mitigate privacy concerns and build trust, we recommend developing a 'transparency dashboard' that explains to users why they are seeing a particular answer."
     • Ecosystem Strategy Example: "To address negative economic impacts, the company should pilot a revenue-sharing or traffic-referral program with high-quality content publishers whose data is integral to AI-generated answers."