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Results

The Results section presents the findings of your research in an objective and concise manner. Tie your findings directly to the research questions and specified variables.

1. Organize by Research Questions or Hypotheses

Structure the results according to the research questions or hypotheses posed earlier in the paper.

Example: "The first research question investigated the effect of digital transformation on firm productivity. The results suggest a significant positive relationship, as shown in Table 1."

2. Summarize Key Findings

Use clear headings to distinguish results for different variables or econometric models.

Example: "Regression Model 1 shows that technological adoption accounts for 25% of the variance in operational efficiency."

3. Present Statistical Results

Provide key metrics like coefficients, p-values, R², and confidence intervals.

Use tables and charts to display results effectively, ensuring they are clearly labeled and referenced in the text.

Example: "As shown in Figure 2, firms that invested in AI technologies experienced a 15% increase in market share compared to the control group."

4. Highlight Patterns or Trends

Discuss patterns that emerge from the data, unexpected findings, or correlations.

Example: "Contrary to expectations, small firms benefited more from digital innovation grants than large firms, suggesting a potential scale-related effect."

General Tips for Writing

Stay Focused: Keep the Results section factual, and reserve interpretation for the Implications and Conclusion.

Use Visual Aids: Tables, graphs, and charts should complement the narrative and not overwhelm it.

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5. Results

Main results

Table 2. The Effect of e-Scooters on Violent and Property Crime Incident Counts

Table 3. The Effect of e-Scooters on Violent and Property Crime: Detailed Crime Categories

5.1 Heterogeneous Effects

Daytime vs Nighttime

Table 4. The Effect of e-Scooter on Daytime and Nighttime Crime Incident Counts

6. Robustness Tests

6.1 Pretreatment Trends

Figure 2. Trends in Crime Incident Counts Before and After the Implementation of the e-Scooter Pilot Program

6.2 Comprehensive Analysis Across Crime Categories

6.3 Monthly Trends

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4. Results

All of the numbers are just a template. The research explored the impact of external factors-locational accessibility, official ratings, and user feedback-on dynamic pricing strategies for hotels. Key findings are organized according to the specified variables and regression models, and highlight the interplay between these factors and pricing coefficients.

4.1 Locational Accessibility Assessment
Locational accessibility (L) was evaluated using the average prices of 2star hotels within 10kmx10km grids across the Seoul metropolitan area. Hotels in central locations, such as Jongno and Gangnam, exhibited significantly higher average prices compared to peripheral areas (p<0.01). As shown in Table 1, accessibility metrics correlated positively with base price levels (b) in the dynamic pricing model. For instance, premium locations led to b values that were, on average, 20-35% higher than less accessible regions. However, the impact of locational accessibility on the rate of price change (a) was less pronounced, with a weaker correlation (p>0.05).

4.2 Coefficients of Individual Hotels’ Price Multipliers
Using least squares estimation, the linear dynamic pricing model (P(t)=at+b) was fitted for each of the 850 hotels. For most hotels, a rate of price change was negative, indicating that prices generally decreased as the stay date approached. However, the magnitude of a varied significantly depending on external factors. For higher-rated hotels, a was less steep (closer to 0), suggesting stable prices even closer to the stay date. Conversely, hotels with lower user feedback scores exhibited steeper declines in price, with a values as low as -30. Base prices (b) ranged widely, from 50000 KRW for budget hotels to over 500000 KRW for premium accommodations. This variation was strongly tied to locational accessibility and official ratings.

4.3 Aggregate Analysis of Price Multiplier Coefficients
The generalized dynamic pricing model further examined the aggregate relationships between external factors and pricing coefficients. Regression models for a and b as functions of locational accessibility (L), star ratings (S), and user feedback (F) revealed the following patterns:

b(L,S,F): Base price (b) was primarily influenced by star ratings (beta S=0.42,p<0.01) and user feedback (beta F=0.35,p<0.05), followed by locational accessibility (beta L=0.28,p<0.05). Premium hotels with higher star ratings and feedback consistently displayed higher base prices.

a(L,S,F): The rate of price decline a) was significantly affected by star ratings (beta S=−0.32,p<0.01) and user feedback (beta F=−0.24,p<0.05). Hotels with better feedback scores showed less steep price drops, indicating more stable pricing dynamics.

4.4 Patterns Across All Hotels
The overall analysis revealed distinct trends in pricing strategies:

Hotels in competitive areas with high locational accessibility tended to use less aggressive price declines (a~−5) as they relied on their location advantage to attract late-booking customers.

Lower-rated hotels with poor feedback in peripheral areas applied steeper price declines (a<−20) to compete effectively for last-minute bookings.

Mid-range hotels with moderate feedback scores showed the most flexibility in pricing, with a values adapted dynamically based on demand fluctuations.

4.5 Practical Implications
The proposed model offers a straightforward approach for applying dynamic pricing strategies to hotels lacking extensive historical data. By leveraging external factors such as location, ratings, and user feedback, hotels can estimate pricing coefficients a and b to implement effective real-time adjustments. These results emphasize the importance of improving user feedback and targeting prime locations to achieve optimal pricing outcomes.

Figure 1 and Table 2 summarize the relationships between external factors and pricing coefficients, demonstrating the applicability of the model across diverse hotel types and locations.

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Review paper 2 outline

Paper title: Materializing International Trade of Decarbonized Hydrogen Through Optimization in Both Economic and Environmental Aspects

Summary

-       The international hydrogen trade optimization, considering economic and environmental factors for the main hydrogen importers (Korea, Japan, and Germany)

-       Methodology: Life Cycle Assessment

-       By 2030: Korea favors green H2 from South Africa

-       By 2040-2050: Australia

-       H2 Carrier: Ammonia seems to be the most economically viable carrier with cost range ($2.15 to $3.43 per kg of H2)

-       GHG Emission: Green is preferable but has high costs. Blue is feasible but lacks long-term viability.

Evaluation

-       Comparable sources of the modeling research from my research.

-       What data or concepts should be used as the modeling input?

-       Differences: This paper is based on the Life Cycle Assessment (LCA), which considers various expenditures in the hydrogen trade. However, my research will project the cost of imports of hydrogen based on research like LCA.

-       Similarities: I want to consider the research results as inputs for my modeling. The shipping expenditures or shipping boil-off rate can be used as model inputs.

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“Physician Satisfaction with a Multi-Platform Digital Scheduling System" by Deliberato et al. (2017) evaluates a multi-platform digital scheduling system implemented in a hospital to address inefficiencies in manual scheduling.

The system integrates web-based tools and mobile applications, enabling real-time updates, shift exchanges, and improved physician communication.

Key outcomes include increased physician satisfaction, improved scheduling accuracy, and enhanced ease of use.

Challenges noted include the need for continuous technical support and user training, which may limit scalability.

The study highlights the potential of digital scheduling systems to enhance operational efficiency and staff satisfaction in healthcare settings.

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Outline of the Results Section

1. Introduction to Results

Brief overview of key findings and their relevance to the research objectives.

Highlight the significance of analyzing deep learning innovation patterns post-ChatGPT.

2. Patent Trends Analysis

Global and Regional Trends

Comparison of patent filings by key countries (e.g., USA, China, South Korea) over time.

Notable increase in filings around and after ChatGPT's release in 2018.

Technological Shifts

Focus on specific technologies driving patent growth, particularly those related to transformer models and generative AI.

3. Network Centrality Metrics

2016–2019 Analysis

Key IPC classes with the highest centrality scores.

Roles of technologies like neural network-based computing (G06N-003) in driving innovation networks.

2020–2022 Analysis

Changes in centrality metrics for leading IPC classes.

Introduction of new critical technologies (e.g., computer vision, image analysis).

Reduced concentration in certain technologies, indicating diversification in application areas.

4. Network Structure and Clustering

Clustering Analysis

Identification and comparison of clusters before and after ChatGPT's release.

Insights into the evolution of dominant clusters (e.g., life necessities, computing, and physics-related technologies).

Knowledge Diffusion

Observations on the increasing complexity and diversity of technology intersections.

Emergence of new applications across sectors, such as healthcare and agriculture.

5. Discussion of Findings

Impact of ChatGPT on accelerating the diffusion and diversification of deep learning technologies.

Comparison of pre- and post-ChatGPT innovation patterns.

The role of central technologies in bridging diverse applications.

6. Visualization of Results

Graphical representations of network structures (e.g., pre- and post-ChatGPT patent network diagrams).

Trends in patent filings and centrality metrics over time.

7. Implications for Future Research

How the observed trends inform strategic decision-making in technology policy and innovation management.

Recommendations for further exploration of emergent technologies influenced by ChatGPT.

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My research explores Korea's energy transition pathways for meeting COP28 goals of tripling nuclear and renewable energy capacities.

It evaluates scenarios from the IPCC AR6 database and filters these to 198 realistic Korean national scenarios focusing on coal, gas, nuclear, solar and wind power generation capacity.

Research Question

Are the IPCC AR6 scenarios consistent with the COP 28 goal of tripling renewable and nuclear capacity for Korea?

Key Findings

1. Scenario Analysis:

Of 767 scenarios analyzed, 273 align with the Paris Agreement's 1.5°C and 2°C temperature limits.

198 scenarios focus on realistic energy transitions.

2. Energy Transition Challenges:

Korea's energy transition involves scaling nuclear and renewable sources while phasing out coal and gas without carbon capture.

3. Policy Alignment:

Current Korean national scenarios show gaps in fully addressing COP28 and Paris Agreement targets.

Scientifically-grounded transition plans are need for reaching net zero by 2050 for Korea

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Results

Following the Hypothesis, I’m going to show the results

·      Hypothesis 1:

o   National production statistics table 3.

o   Effect of the interaction between FDI and IPR in developing countries.

o   Poisson Regression table 4.

·      Hypothesis 2:

o   Sectoral production statistics table 4.

o   Effect of the interaction between FDIxR&D and IPR in developing countries reference to table 4.

·      NSI and SSI findings

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Research Question:

What is the impact of PropTech innovation on the intention to sell residential real estate?

3. Statistical Results

Sample Size: 43 participants (pilot test with American respondents).

Regression Analysis:

PropTech Awareness:

Indirect Awareness: β = 0.35, p < 0.05

Direct Awareness: β = 0.40, p < 0.01

PropTech Quality:

Service Quality: β = 0.30, p < 0.05

System Quality: β = 0.28, p < 0.05

Functional Quality: β = 0.33, p < 0.01

Moderation Effects:

Perceived Economic Cost:

Interaction with PropTech Awareness: β = -0.25, p < 0.05

Interaction with PropTech Quality: β = -0.22, p < 0.05

Perceived Time Cost:

Interaction with PropTech Awareness: β = -0.20, p < 0.05

Interaction with PropTech Quality: β = -0.18, p < 0.05

Model Fit:

Adjusted R² = 0.65, indicating that 65% of the variance in the intention to sell is explained by the model.

4. Highlighted Patterns and Trends

Positive Correlations:

There is a strong positive correlation between PropTech awareness/quality and the intention to sell.

Moderating Role of Perceived Cost:

Higher perceived costs reduce the positive impact of PropTech on selling intentions.

Implication for PropTech Providers:

Reducing users' perceived economic and time costs could enhance the adoption and effectiveness of PropTech solutions.

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3. Analysis

1. Buyer Equilibrium

1. General Strategy and Equilibrium

2. Value-Arrival Separable(VAS) Strategy and Equilibrium

3. Characterization of Threshold Time Function at VAS Equilibrium

4. Verification of Threshold Time Function at VAS Equilibrium

2. Price Curve Optimization

1. Seller Utility at VAS Equilibrium

2. Two Canonical Price curves

3. Thick market

4. Thin Market

4. Simulation

1. Frontier plot

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4. Regression: Focusing on the Moderating Effect of Robot Satisfaction

No Tip Policy: Ratings are significantly positive.

Pre-Service Fee, Pre-Gratuity, Pre-Tip, and Post-Tip Policies: Ratings are generally negative.

Effect of High Robot Satisfaction:

Pre-Service Fee, Pre-Gratuity, and Post-Tip: Negative effects reverse into positive ones.

Pre-Tip: Remains negative even with high robot satisfaction.

Explanation (Expectation-Disconfirmation Theory):

Pre-Service Fee, Pre-Gratuity, and Post-Tip: Represent positive expectation disconfirmation.

Pre-Tip: Represents negative expectation disconfirmation.

Reason for Persistent Negativity of Pre-Tip:

The term "tip" is traditionally associated with voluntary, post-service tipping. Pre-charging creates a stronger negative perception, which is not reversed even with high robot satisfaction.

5. Path Analysis: Focusing on the Mediating Effect of Experiential Values

General Impact of Tip Policies:

Mention of tip policies generally negatively affects experiential values, which in turn negatively impacts ratings.

Specific Effects on Experiential Values:

Pre-Gratuity, Pre-Tip, and Post-Tip: Significant negative effects on sensorial, utilitarian, hedonic, and social values.

Pre-Service Fee: Significant negative effects on sensorial, hedonic, social, epistemic, and aesthetic values.

Reversal with High Robot Satisfaction:

Post-Tip: Positively affects social and utilitarian values due to satisfaction from frontline service experiences.

Pre-Gratuity: Shows a weaker, but positive effect on utilitarian value due to its similarity to tips but less emphasis on post-service voluntary context.

Pre-Service Fee: Positively affects sensorial value. High satisfaction with robot service creates positive expectation disconfirmation, enhancing perceptions of backline operations rather than direct frontline experiences.

Pre-Tip:

No reversal of negativity in experiential values due to persistent negative expectation disconfirmation.

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Results:

1. Organize by Research Questions or Hypotheses

Main Research Question: How do innovation patterns in artificial intelligence vary across national contexts in the field of education?

Sub Research Question 1: How has the volume of AIEd publications changed over time?

Sub Research Question 2: Which countries lead in AIEd research output?

Sub Research Question 3: What are the main areas of focus in AIEd research?

2. Summarize Key Findings

AIEd publications grew steadily from 2000 to 2020, with significant increases after 2012. More than 1,000 papers were published annually starting in 2016.

The U.S. leads with 2,414 publications, followed by China with 2,158. Other contributors include Spain, England, and Australia.

AIEd research is concentrated in “Education & Educational Research” and “Computer Science.”

The U.S. emphasizes "learning analytics" and "education".

China focuses on "data mining," "big data," and "machine learning".

Commonly used keywords include "machine learning," "big data," and "higher education." The U.S. focuses on educational content, while China highlights technical methods like "cloud computing".

3. Present Statistical Results

The number of publications has consistently increased over the years, reaching a peak after 2012.

U.S. (2,414 papers) and China (2,158 papers) dominate the field. Spain, England, and Australia are also notable contributors.

The U.S. produced 347 top-cited papers (top 10% of citations), whereas China produced 97.

Both the U.S. and China rely heavily on their own research outputs. Results also demonstrated the high adoption and diffusion of their respective studies, with distinct thematic focuses.

4. Highlight Patterns or Trends

The U.S. is central to global collaboration, often partnering with countries like England and China. Countries like Japan exhibit lower collaboration rates.

Western countries prioritize educational themes, as seen in their emphasis on curriculum and pedagogy. Asian countries, particularly China and India, focus on technological innovations.

Keywords like "machine learning," "big data," and "higher education" drive AIEd adoption globally.

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4.1 Sentiment Analysis

Changes in Tweet Volume:
Tweet volume peaked in 2020 with 8,064 tweets, reflecting a surge in public interest in AI cloud technology. However, as initial enthusiasm shifted toward more realistic discussions, the tweet count declined to 3,249 by 2023, indicating stabilization.

Sentiment Analysis Results:
Positive sentiment, driven by expectations of technological innovation, dominated until 2020. Over time, neutral sentiment (focused on information sharing) and negative sentiment (highlighting limitations and challenges) grew. By 2023, negative tweets slightly increased to 367, reflecting a more grounded evaluation of the technology as it matured.

4.2 Network Analysis

Network Expansion and Stabilization:
The network grew rapidly from 1,905 nodes and 1,548 links in 2019 to 9,811 nodes and 6,352 links in 2020. It stabilized at approximately 4,300 nodes from 2022 to 2023, indicating a transition from the early rapid diffusion phase to a mature and stable phase of technological discussion.

Transition from Centralization to Decentralization:
In the early stages, a few key nodes (influential individuals) dominated the network. Over time, the network became decentralized, with discussions shifting to a broader range of independent communities. This reflects a transition from unified discussions to in-depth exploration of niche topics.

Increased Efficiency:
By 2023, the network’s average path length and diameter had decreased, suggesting improved efficiency in information transmission despite the fragmented structure.

4.2.1 Centrality Analysis

Changes and Persistence in Central Nodes:
Early central nodes, such as @stratorob and @Ronald_vanLoon, led discussions from 2019 to 2021. By 2023, new nodes like @Baidu_Inc and @Khulood_Almani emerged, reflecting a diversification of influence. Notably, @Ronald_vanLoon maintained consistent influence throughout the period.

Decline in Centrality Values:
In-degree and out-degree centrality values decreased over time, indicating a shift from concentrated early diffusion to a more distributed network structure.

Dynamic Changes in Influence:
While temporary influential nodes like @NeilCattermull played significant roles during mid-phases (2020–2021), new leaders like @CRudinschi emerged in 2023, highlighting shifts in the focus of discussions.

4.2.2 Community Analysis

Early Cohesion (2019):
The network exhibited high modularity (0.854) and 30 communities, indicating unified discussions dominated by a few key groups. This reflects the "Innovation Trigger" stage, characterized by concentrated focus on early technological potential.

Diversification and Fragmentation (2020–2021):
Modularity decreased (0.733 → 0.668), while the number of communities increased to 71. This indicates fragmentation as public interest diversified and expectations adjusted, aligning with the "Peak of Inflated Expectations" and "Trough of Disillusionment" stages.

Specialization (2023):
Modularity reached its highest value (0.95), and the number of communities expanded to 99. This reflects the emergence of specialized discussions as the technology matured, with distinct niche communities forming around specific applications or industries.

Visualization

2019:
The network showed a unified structure dominated by a few influential communities, representing the concentrated focus characteristic of the early adoption phase.

2020:
The network experienced explosive growth in nodes and links, with the emergence of numerous sub-communities, reflecting the rapid dissemination of information and heightened public interest.

2021:
Connectivity between communities increased, and central nodes became less dominant, indicating a gradual transition toward a decentralized structure. Discussions became more critical, reflecting an evaluation of the technology’s limitations and challenges.

2022:
The network began to specialize, with discussions fragmenting into distinct groups focusing on specific aspects of AI cloud technology.

2023:
Numerous independent, smaller communities emerged, creating a highly fragmented but mature network structure. These communities focused on niche topics, reflecting the advanced application and industry-specific discussions typical of the "Plateau of Productivity."

The annual network visualizations clearly depict the growth, fragmentation, and specialization of the network, aligning closely with the phases of Gartner’s Hype Cycle.

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1. Problem

Since COVID-19, online purchases have surged, leading to a rapid increase in e-commerce sellers and intensified competition.

Although many steps are involved in online selling, high-quality product photos and detailed descriptions are the most crucial.

Sellers are spending significant amounts of money and resources on product photography and creating detailed pages, often costing between 2 to 5 million KRW for a single product launch.

New sellers, facing this process, encounter barriers due to limited funds and manpower.

2. Solution and Vision

1) Solution

We aim to develop a SaaS platform based on AI technology to support e-commerce sellers in their work.

With this service, users can efficiently produce photos and detailed descriptions, significantly reducing the time and cost typically required.

2) Vision

Our AI-based solution will deliver value by enhancing individual efficiency and productivity.

Our mission is to foster growth for e-commerce sellers, providing features and support to help beginner sellers evolve into mid-sized or large-scale sellers.

With global expansion potential, we aim to introduce this solution to commerce markets worldwide, beyond Korea.

3. Why Now?

Rising consumer demand suggests that the projected online transaction volume will reach 300 trillion KRW by 2026.

The market for automation software to enhance seller competitiveness is also on the rise.

While AI technology is rapidly advancing, small and medium-sized businesses still face a digital divide, unable to directly adopt the latest technology in their operations.

By utilizing AI technology, we aim to save time and costs for these businesses and improve operational efficiency.

Our service seeks to boost efficiency and productivity, directly supporting sales growth and creating economic value.

4. Market Potential

Primary Target Market: Domestic e-commerce sellers

Secondary Target Market: Export-oriented e-commerce sellers

Online shopping transaction volume in 2022 was 206.49 trillion KRW.

As the national consumption level rises, the online transaction volume is projected to reach 300 trillion KRW by 2026.

5. Competition / Alternatives

Editor services like Miricanvas and Canva offer templates, but they are repetitive and lack customization for a brand’s unique tone and style.

While some AI services provide generative images, no commerce-focused solution currently exists in the market.

This service offers a far more affordable and satisfactory alternative than hiring external agencies or photography professionals.

6. Customer and Needs finding

New sellers find the costs and workload of content creation a significant barrier, while established businesses also invest substantial resources in manpower, budget, and time.

New Sellers: In the early stages of brand-building, they often handle everything themselves with limited staff.

Mid-sized Sellers: They have steady revenue and an established brand, focusing on cost reduction and efficiency.

Large Sellers: With specialized staff and systems, they seek to adopt advanced technology to enhance brand scalability and competitive strength.

7. Technology

We aim to harness advanced AI technology with a focus on ease of use for customers.

Unstructured Data Learning Model: Optimized for e-commerce, including natural language processing and detailed page structures.

AI Studio: Recognizes and controls lighting, angles, and textures in detail.

AI Editor: Develops a brand-learning AI model, capable of internalizing and replicating the brand’s unique tone and style. This AI analyzes, learns from, and adapts to trending structures in detailed product pages.

8. Product

1) AI Studio

Ideal for sellers who find renting studios expensive, it creates natural-looking product photos.

After uploading product images, it removes and enhances backgrounds, producing high-quality visuals.

Users save on studio fees, photographer costs, and time spent on shoots.

2) AI Editor

This editor simplifies the creation of detailed product pages essential for sales.

Users can train the editor on their brand's tone and style.

Upon entering product details, the AI drafts a detailed page aligned with the brand’s needs.

The AI-generated draft is easy for users to further refine as needed.

9. Business Model

B2B: Customized services for large businesses with subscription options.

B2C: Subscription options for small businesses using standard services.

10. Financial Plan

Revenue Strategy: Beyond the basic subscription, premium options like customized consulting and AI solution integration will add revenue potential.

Marketing: Through social media, webinars, and other channels, we’ll increase awareness and attract customers.

Capital Acquisition: Leveraging government grants, KAIST alumni networks, and other resources for early-stage funding.

Initial Investment: After the open beta, a first-round investment of 50 million KRW is targeted, in response to current market conditions.