📚segment: journal characteristics

[hyunjimoon]

(no info in order)

1. Management science
2. Academy of management Journal
3. Production and Operations Management
4. Nature
5. SEJ
6. ETP, MSOM, JOM and others

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choosing evaluators cld

[hyunjimoon]

1. MS

[hyunjimoon]

based on #242, #244 i discovered evolutionary (observer) to bayesian (actor) learning is similar to bandit to thompson modeling which differs in the modeling of theta. The Modeling Technique axis in bandit problems contrasts frequentist and Bayesian approaches. Frequentist methods rely on point estimates and likelihood maximization, exemplified by the equation θ̂ = argmax_θ L(θ; D), without incorporating prior beliefs. Bayesian methods, on the other hand, compute posterior distributions by combining prior beliefs with observed data, as shown in p(θ | D) ∝ L(θ; D) × p(θ). In practice, frequentist bandits typically use sample means for action selection, while Bayesian methods like Thompson Sampling maintain and sample from posterior distributions over parameters to make decisions.

the last column generated from gpt, i need to check more .

Article	Evolutionary Learning	Bayesian Learning	Reasoning
Agrawal, Gans & Stern (2021) Enabling Entrepreneurial Choice. Management Science, 67(9), 5510-5524.		✔️	The paper models how entrepreneurs make decisions by updating their beliefs about their abilities and opportunities. It uses probabilistic reasoning and belief updating, aligning with Bayesian learning where prior beliefs are updated with new information to inform decision-making.
Arora & Fosfuri (2005) Pricing Diagnostic Information. Management Science, 51(7), 1092-1100.		✔️	This study examines how firms price diagnostic information by considering how new information updates consumers' beliefs. It relies on Bayesian principles of belief updating and probabilistic inference to inform pricing strategies, fitting within Bayesian learning.
Åstebro, Chen & Thompson (2011) Stars and Misfits: Self-Employment and Labor Market Frictions. Management Science, 57(11), 1999-2017.	✔️		The paper explores how individuals choose self-employment due to labor market frictions, emphasizing adaptation to environmental constraints. It reflects evolutionary learning as it involves trial-and-error and adaptation without explicit probabilistic modeling or prior beliefs guiding decisions.
Camuffo et al. (2020) A Scientific Approach to Entrepreneurial Decision-Making: Evidence from a Randomized Control Trial. Management Science, 66(2), 564-586.		✔️	The study advocates for a hypothesis-driven approach to entrepreneurship, where entrepreneurs use experimentation to test hypotheses and update beliefs. This systematic incorporation of new information aligns with Bayesian learning, as decisions are guided by probabilistic reasoning and belief updating.
Gans (2023) Experimental Choice and Disruptive Technologies. Management Science, 69(11), 7044-7058.		✔️	The article discusses how firms make experimental choices regarding disruptive technologies, using results from experiments to update probabilistic assessments. This approach is consistent with Bayesian learning, where decisions are informed by updated posterior beliefs based on new data.
Koning, Hasan & Chatterji (2022) Experimentation and Startup Performance: Evidence from A/B Testing. Management Science, 68(9), 6434-6453.	✔️		This paper analyzes how startups use A/B testing as an iterative, trial-and-error method to improve performance. It reflects evolutionary learning because the startups rely on frequentist methods without prior beliefs, adapting based on empirical results rather than probabilistic reasoning from posterior distributions.
Nanda & Rhodes-Kropf (2017) Financing Risk and Innovation. Management Science, 63(4), 901-918.	✔️		The study examines how financing risk affects firms' innovation choices, focusing on adaptation to financial constraints. It aligns with evolutionary learning as firms adjust their strategies through iterative adaptation in response to environmental pressures, without explicit probabilistic modeling of uncertainty.
Roach & Sauermann (2015) Founder or Joiner? The Role of Preferences and Context in Shaping Different Entrepreneurial Interests. Management Science, 61(9), 2160-2184.		✔️	The paper investigates how individual preferences and contextual factors influence the decision to become a founder or joiner. It involves assessing probabilities of success and personal fit, using probabilistic reasoning consistent with Bayesian learning to make informed career decisions based on updated beliefs about potential outcomes.
Van den Steen (2011) Overconfidence by Bayesian-Rational Agents. Management Science, 57(5), 884-896.		✔️	The study explores overconfidence among agents who update their beliefs according to Bayesian principles. It directly engages with Bayesian learning by modeling how rational agents can become overconfident through the process of belief updating, emphasizing the role of probabilistic reasoning in decision-making processes.

[hyunjimoon]

from editorial
a. Does the paper address a question of managerial importance and relevance?
b. Does it change our thinking on an important topic in entrepreneurship or innovation?
c. Does thepaper use appropriate methodology to answer the question convincingly and rigorously?
d. Does it provide improved management principles to operate new ventures or manage innovation?
e. Successful manuscripts will usually be grounded in important phenomena; purely theoretical/conceptual pieces must explain the connection to managerial or policy importance

[hyunjimoon]

2. AMJ

[hyunjimoon]

3. POMS
   evaluators, (behavioral operations (John), innovation (Ed, Geoffrey, Onesun), disruptive tech, marketing interface)

[hyunjimoon]

4. Science/Nature

[hyunjimoon]

Commercialisation and entrepreneurship – an academic viewpoint

[hyunjimoon]

A primer for academic entrepreneurs on academic-industrial partnerships

[hyunjimoon]

Josh's GershmanHorvitzTenenbaum15.pdf and Andrew's
Gelman21_bayesian statistics and modelling.pdf

[hyunjimoon]

5. Strategic Entrepreneurship Journal
   (Dan; Arnaldo, John, Susan, Rebecca, Daniel, Hyunjin, Rembrand, Ethan, Thomas)

[hyunjimoon]

6. ETP, MSOM
   ETP evaluators

[hyunjimoon]

conditional on my research vision, below five editors has highest probability of supporting my research -

1. Dimo Dimov (University of Bath, UK)
   Rationale: Dimov's work often focuses on entrepreneurial processes and decision-making under uncertainty, which aligns well with your research on probabilistic computation for entrepreneurial decision-making.

2. Denis Gregoire (HEC Montreal, Canada)
   Rationale: Gregoire has expertise in cognitive approaches to entrepreneurship, which could be relevant to your work on entrepreneurial learning and decision support systems.

3. Dean A. Shepherd (Kelley School of Business, Indiana University, USA)
   Rationale: Shepherd is known for his research on entrepreneurial decision-making, opportunity identification, and learning from failure. These areas closely align with your focus on entrepreneurial decision support and learning across startup lifecycles.

4. Roy Thurik (Erasmus University Rotterdam, Netherlands)
   Rationale: Thurik's work often involves the economics of entrepreneurship and small business, which could be relevant to your research on value creation and capture hypotheses.

5. Karl Wennberg (Linköping University, Sweden)
   Rationale: Wennberg's research interests include entrepreneurial decision-making and entrepreneurship education, which align well with your focus on developing decision support tools and frameworks for entrepreneurs.