Management Science (journal)

Management Science is a peer-reviewed academic journal that covers research on all aspects of management related to strategy, entrepreneurship, innovation, information technology, and organizations as well as all functional areas of business, such as accounting, finance, marketing, and operations. It is published by the Institute for Operations Research and the Management Sciences and was established in 1954 by the Institute's precursor, The Institute of Management Sciences. C. West Churchman was the founding editor-in-chief.

Management Science
DisciplineManagement
LanguageEnglish
Edited byDavid Simchi-Levi
Publication details
History1954-present
Publisher
FrequencyMonthly
4.219 (2018)
Standard abbreviations
ISO 4Manag. Sci.
Indexing
CODENMSCIAM
ISSN0025-1909 (print)
1526-5501 (web)
LCCN56021107
JSTOR00251909
OCLC no.01641131
Links

According to the Journal Citation Reports, the journal has a 2018 impact factor of 4.219.[1]

Editors-in-chief

The following persons are, or have been, editors-in-chief:

Notable papers

According to Google Scholar, the following three papers have been cited most frequently:

  • Sharpe, William F. (1963). "A Simplified Model for Portfolio Analysis". Management Science. 9 (2): 277. doi:10.1287/mnsc.9.2.277.
  • Harsanyi, John C. (1967). "Games with Incomplete Information Played by "Bayesian" Players, I–III: Part I. The Basic Model&". Management Science. 50 (12_supplement): 1804. doi:10.1287/mnsc.1040.0270.
  • Bass, Frank M. (1969). "A New Product Growth for Model Consumer Durables". Management Science. 15 (5): 215. doi:10.1287/mnsc.15.5.215.
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gollark: ·Oh, `bot.run`, right.
gollark: ```pythonfrom transformers import GPT2LMHeadModel, GPT2Tokenizerimport discord.extfrom discord.ext import commandsTOKEN = 'NOT TELLING YOU'bot = commands.Bot(command_prefix='$')@bot.eventasync def on_ready(): print("done!")@bot.command()async def test(ctx, arg): inputs = arg # initialize tokenizer and model from pretrained GPT2 model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') outputs = model.generate( inputs, max_length=200, do_sample=True, temperature=1, top_k=50 ) response = (tokenizer.decode(outputs[0], skip_special_tokens=True)) await ctx.send(response)client.run(TOKEN)```
gollark: Yes, you can just use `@bot.event` or something.

References
  1. "Management Science". 2018 Journal Citation Reports. Web of Science (Social Sciences ed.). Clarivate Analytics. 2019.
  2. "Stats & History". Management Science. Institute for Operations Research and the Management Sciences. Retrieved 18 October 2014.
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