A review of an article to evaluate one of its arguments: for Business Management students: 04

A review of an article to evaluate one of its arguments: for Business Management students [information from chatpdf.com]

The article reviewed is: "Generative artificial intelligence and evaluating

strategic decisions" Strategic Management Journal.

My question 1: "What are the research objectives of the article?"

The reply: "The research objectives of the article "Generative artificial intelligence and evaluating strategic decisions" by Doshi et al. are as follows:

1. Evaluate the Use of Generative AI: The article investigates how generative artificial intelligence, specifically large language models (LLMs), can be utilized to evaluate strategic alternatives in business decision-making processes.

2. Compare AI and Human Evaluations: It aims to compare the ranking and evaluation of business models generated by AI with those evaluated by human experts, assessing the level of agreement between the two.

3. Understand the Aggregation of AI Evaluations: The research explores the benefits of aggregating evaluations from multiple AI sources (LLMs, roles, and prompts) to enhance the quality and reliability of strategic decision inputs.

4. Contribute to Literature on Strategic Decision Making: The study seeks to fill the gap in the existing literature by focusing on the role of artificial evaluators in strategic decision-making processes and how they can complement or substitute human decision-making capabilities.

5. Identify Practical Implications for Managers: It intends to provide actionable insights for managers on how to effectively integrate generative AI into their decision-making frameworks, highlighting best practices for leveraging AI-generated evaluations alongside human inputs."

My question 2: "What are the research methods employed in the study as reported in the article?"

The reply: "The research methods employed in the study as reported in the article include:

1. Quantitative Analysis: The study uses quantitative methods to evaluate and compare the rankings of business models generated by large language models (LLMs) and those assessed by human experts. Specifically, two studies were conducted, each involving a sample of 60 business models , .

2. Comparative Evaluation: In both studies, the evaluations of business models by AI (LLMs) and human experts were compared. Study 1 involved AI-generated rankings, while Study 2 used rankings based on evaluations from judges at a business model competition , .

3. Aggregation Techniques: The researchers investigated the effectiveness of aggregating evaluations from multiple AI models, roles, and prompts. They analyzed how aggregating these AI predictions impacts the quality and consistency of the evaluations , .

4. Prompt Variations: In the comparative evaluations, two types of prompts were utilized for LLMs: a basic prompt to determine which business model is more likely to succeed, and a chain-of-thought prompt, which encouraged reasoning before arriving at a conclusion .

5. Diversity and Scale Metrics: The study explored the importance of diversity and scale in AI evaluations and how these factors affect the overall reliability and accuracy of the aggregated predictions , .

These methods together contribute to understanding the role of generative AI in evaluating strategic decisions and highlight its potential benefits and limitations in this context."

My question 3: "Could you give me one concrete example of an argument in the article, indicating its "backings", "warrants", "qualifiers", "grounds", "rebuttals" and "claims" in terms of the Toulmin argument model?"

The reply: "Certainly! Here’s an example of an argument from the article structured using the Toulmin argument model:

Claim: Generative AI can provide comparable insights to human experts when evaluating business models.

• Grounds: In the study, AI rankings showed significant correlations (0.675 Pearson and 0.463 Spearman) with human expert rankings. The AI was able to agree with human experts on the best business model in 5 out of 10 industries and on the worst business model in 6 out of 10 industries .

• Warrant: If a method (in this case, generative AI) produces evaluations that closely align with human expert evaluations, it indicates that the method is effective at assessing business models.

• Backing: Previous research has demonstrated that the aggregation of evaluations tends to produce better predictions when individual evaluations have high variability, which supports the validity of using multiple AI models to gather diverse predictions .

• Qualifier: While generative AI displayed strong performance, it should be noted that the agreement with human experts is not perfect, which suggests that while AI can be effective, it is not infallible.

• Rebuttal: There may be instances where AI underperforms compared to human experts, particularly in complex or nuanced scenarios where human judgment might incorporate more contextual knowledge or intuition than current AI models can offer .

This structured argument demonstrates how the findings of the research can be framed within the Toulmin model, highlighting the effectiveness of generative AI in strategic decision-making contexts."

My question 4: "Does the example of the argument address any of the research objectives of the article?"

The reply: "Yes, the example of the argument addresses one of the primary research objectives of the article, which is to investigate the role of generative artificial intelligence (AI) in evaluating strategic decisions, specifically in assessing business models.

The research aims to explore how generative AI can serve as a tool for evaluating strategic alternatives, alongside human expert evaluations. By arguing that generative AI can provide comparable insights to human experts when evaluating business models, the provided argument directly contributes to understanding the potential effectiveness and utility of AI in the strategic decision-making process.

Additionally, the argument reflects on the study's goal of examining both the capability of AI in generating rankings of business models and the implications for managers seeking to integrate AI into their decision-making frameworks. The findings, as expressed in the argument, illustrate the potential benefits of utilizing generative AI in conjunction with human expertise, aligning with the article's overall objective to assess and highlight the value of AI in strategic management contexts."

Reference: a collection of blog notes on using chatgpt for research purpose.