Added reading list.

Author: jtkiley

readings/readings.qmd

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+---
+title: "Introduction to Python for Research"
+subtitle: "Readings"
+author: "Jason T. Kiley"
+date: 2025-05-29
+date-format: long
+csl: "https://www.zotero.org/styles/journal-of-management"
+bibliography: references.bib
+format:
+    pdf:
+        mainfont: "Source Serif 4"
+        sansfont: "Source Serif 4"
+        monofont: "Source Code Pro"
+        fontsize: 12pt
+        linestretch: 1
+        pdf-engine: xelatex
+        geometry:
+            - margin=25mm
+
+---
+
+Python is a powerful programming language that is widely used in research, spanning levels, designs, disciplines, and specific methods.
+That broad reach and skill-orientation (cf. specific methods) is reflected in this reading list in that I aim to show applications of Python and thoughts about the broader role of programming in research.
+
+
+# Python in use
+
+My examples here tend to be text related, since that is what I do and also the main way Python originally became popular in organizational research.
+
+@. @doi:10.5465/amj.2013.0288: I used Python throughout this project, in a lot of the ways we discuss in this course.
+@. @doi:10.1177/01492063241313316: We used Python extensively to solve hard data challenges in this project, including gathering and analyzing with hundreds of thousands of press releases and millions of Tweets.
+@. @doi:10.5465/annals.2017.0099: Tim used Python for topic modeling is this project, which is among the earliest common use cases for Python in our field.
+@. @doi:10.1177/0049124117729703: Laura does really interesting work, and this paper is a cool example of using Python (together with R), and she provides code to reproduce the analysis [here](https://github.com/lknelson/computational-grounded-theory).
+
+
+# Python and open science
+
+@. @doi:10.1177/01492063221141861: This is an interesting editorial that discusses the opportunities and benefits of code and data sharing in organizational research.
+@. @doi:10.1177/01492063251315701: This paper focuses on authorship and, in particular, advances some ideas about how to think about authorship in the world where our research output may have multiple artifacts, including the paper itself and computational artifacts like code and data.
+
+
+# Python and data science reference
+
+@. @Lutz2025-zq: I originally learned Python with an earlier edition of this book, and I still like it for detailed coverage of language features. For our typical use, I would read with the following in mind.
+    - Don't get bogged down in Part V on Modules and Packages. It's good to build your intuition about how these work, but creating packages isn't what most researchers do.
+    - Skip Part VI on classes and OOP, Part VII on exceptions (though perhaps look at the basics and try/except), and Part VIII on advanced topics (the Unicode chapter may be helpful if you have issues with text data encodings).
+@. @McKinney2022-rd: This is a book about Pandas by its creator. I always find it interesting to read a creator's guide to their own package, because it often reveals the design philosophy and intended use cases.
+@. @kevinheaveyModernPolars: If you decide to use Polars, as we discuss at the end of the course, this is a good  introduction to Polars' design philosophy and how it differs from Pandas.
+
+
+# References

readings/references.bib

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+
+
+@article{doi:10.1177/0049124117729703,
+  author   = {Laura K. Nelson},
+  title    = {Computational Grounded Theory: A Methodological Framework},
+  journal  = {Sociological Methods \& Research},
+  volume   = {49},
+  number   = {1},
+  pages    = {3-42},
+  year     = {2020},
+  doi      = {10.1177/0049124117729703},
+  url      = {
+              
+              https://doi.org/10.1177/0049124117729703
+              
+              },
+  eprint   = {
+              
+              https://doi.org/10.1177/0049124117729703
+              
+              },
+  abstract = { This article proposes a three-step methodological
+              framework called computational grounded theory, which combines
+              expert human knowledge and hermeneutic skills with the processing
+              power and pattern recognition of computers, producing a more
+              methodologically rigorous but interpretive approach to content
+              analysis. The first, pattern detection step, involves inductive
+              computational exploration of text, using techniques such as
+              unsupervised machine learning and word scores to help researchers
+              to see novel patterns in their data. The second, pattern refinement
+              step, returns to an interpretive engagement with the data through
+              qualitative deep reading or further exploration of the data. The
+              third, pattern confirmation step, assesses the inductively
+              identified patterns using further computational and natural
+              language processing techniques. The result is an efficient,
+              rigorous, and fully reproducible computational grounded theory.
+              This framework can be applied to any qualitative text as data,
+              including transcribed speeches, interviews, open-ended survey data,
+              or ethnographic field notes, and can address many potential
+              research questions. }
+}
+
+@article{doi:10.1177/01492063221141861,
+  author  = {Timothy J. Quigley and Aaron D. Hill and Andrew Blake
+             and Oleg Petrenko},
+  title   = {Improving Our Field Through Code and Data Sharing},
+  journal = {Journal of Management},
+  volume  = {49},
+  number  = {3},
+  pages   = {875-880},
+  year    = {2023},
+  doi     = {10.1177/01492063221141861},
+  url     = {
+             
+             https://doi.org/10.1177/01492063221141861
+             
+             },
+  eprint  = {
+             
+             https://doi.org/10.1177/01492063221141861
+             
+             }
+}
+
+@article{doi:10.1177/01492063241313316,
+  author   = {Hyunjung (Elle) Yoon and Daniel L. Gamache and Michael
+              D. Pfarrer and Jason Kiley},
+  title    = {Agent-Oriented Impression Management: Who Wins When
+              Firms Publicize Their New CEOs?},
+  journal  = {Journal of Management},
+  volume   = {0},
+  number   = {0},
+  pages    = {01492063241313316},
+  year     = {0},
+  doi      = {10.1177/01492063241313316},
+  url      = {
+              
+              https://doi.org/10.1177/01492063241313316
+              
+              },
+  eprint   = {
+              
+              https://doi.org/10.1177/01492063241313316
+              
+              },
+  abstract = { In this study, we advance organizational impression
+              management research by focusing on agent-oriented impression
+              management—which reflects attempts to create value for the firm by
+              publicizing individuals or groups who are agents of the firm.
+              Although prevalent in practice, agent-oriented impression
+              management remains unexplored in scholarly research. Specifically,
+              we introduce the concept of new CEO prominence in firm
+              communication (PFC), defined as the frequency and centrality of new
+              CEO mentions in firm press releases and social media. We argue that
+              new CEO PFC is distinct from traditional impression management
+              tactics because CEOs are agents of the firm that will personally
+              benefit from these impression management strategies. Thus, our
+              research addresses the question: Who captures the value associated
+              with new CEO PFC? We theorize that firms benefit from featuring new
+              CEOs in firm communication through improved external stakeholder
+              evaluations (i.e., analyst ratings). However, these efforts may
+              also create value for the CEOs, as evidenced by increased
+              compensation, more outside directorships, and decreased turnover
+              rates. Our empirical study of efforts to publicize a new CEO
+              following 557 succession events strongly supports our theory. }
+}
+
+@article{doi:10.1177/01492063251315701,
+  author   = {George C. Banks and Lisa M. Rasmussen and Scott
+              Tonidandel and Jeffrey M. Pollack and Mary M. Hausfeld and Courtney
+              Williams and Betsy H. Albritton and Joseph A. Allen and Nicolas
+              Bastardoz and John H. Batchelor and Andrew A. Bennett and Roman
+              Briker and Christopher M. Castille and Bart A. De Jong and Elise
+              Demeter and Justin A. DeSimone and James G. Field and Maria
+              Figueroa-Armijos and M. Fernanda Garcia and William L. Gardner and
+              J. Jeffrey Gish and Laura M. Giurge and Claudia N.
+              Gonzalez-Brambila and M. Gloria González-Morales and Lorenz
+              Graf-Vlachy and Roopak Kumar Gupta and Amanda S. Hinojosa and Zion
+              Howard and Sven Kepes and Tine Köhler and Dejun Tony Kong and
+              Markus Langer and Teng lat Loi and Liam P. Maher and Chao Miao and
+              Murad A. Mithani and Lakshmi Balachandran Nair and William G.
+              Obenauer and Ernest H. O’Boyle and Jason R. Pierce and Deborah M.
+              Powell and Roni Reiter-Palmon and Deborah E. Rupp and Srinivasan
+              Tatachari and Jane S. Thomas and Tiia Vissak and Jako Volschenk and
+              Chen Wang and Christopher E. Whelpley and Hans-Georg Wolff and
+              Haley M. Woznyj and Tao Yang},
+  title    = {Women’s and Men’s Authorship Experiences: A Prospective
+              Meta-Analysis},
+  journal  = {Journal of Management},
+  volume   = {51},
+  number   = {4},
+  pages    = {1273-1287},
+  year     = {2025},
+  doi      = {10.1177/01492063251315701},
+  url      = {
+              
+              https://doi.org/10.1177/01492063251315701
+              
+              },
+  eprint   = {
+              
+              https://doi.org/10.1177/01492063251315701
+              
+              },
+  abstract = { The opaqueness of author naming and ordering, when
+              coupled with power dynamics, can lead to a number of disadvantages
+              in academic careers. In this commentary, we investigate gender
+              differences in authorship experiences in a large prospective
+              meta-analytic study (k = 46; n = 3,565; 12 countries). We find that
+              women’s and men’s authorship experiences differ significantly with
+              women reporting greater prevalence of problematic behaviors. We
+              present seven actionable recommendations for improving the receipt
+              and reporting of intellectual credit. Such actions are needed to
+              ensure fairness in authorship, which is one of the most powerful
+              factors in academics’ career outcomes. }
+}
+
+@article{doi:10.5465/amj.2013.0288,
+  author   = {Graffin, Scott D. and Haleblian, Jerayr (John) and
+              Kiley, Jason T.},
+  title    = {Ready, AIM, Acquire: Impression Offsetting and Acquisitions},
+  journal  = {Academy of Management Journal},
+  volume   = {59},
+  number   = {1},
+  pages    = {232-252},
+  year     = {2016},
+  doi      = {10.5465/amj.2013.0288},
+  url      = {
+              
+              https://doi.org/10.5465/amj.2013.0288
+              
+              },
+  eprint   = {
+              
+              https://doi.org/10.5465/amj.2013.0288
+              
+              },
+  abstract = { Drawing on expectancy violation theory, we explore the
+              effects of anticipatory impression management in the context of
+              acquisitions. We introduce impression offsetting, an anticipatory
+              impression management technique organizational leaders employ when
+              they expect a focal event will negatively violate the expectations
+              of external stakeholders. Accordingly, in these situations,
+              organizational leaders will announce the focal event
+              contemporaneously with positive, but unrelated information. We
+              predict impression offsetting will generally occur in the context
+              of acquisitions, but also more frequently for specific acquiring
+              firms and acquisitions that are more likely to lead to an
+              expectancy violation. We also posit that offsetting will
+              effectively inhibit observers’ perceptions of events as negative
+              expectancy violations by positively influencing shareholder
+              reactions to acquisition announcements. Consistent with our
+              hypotheses, in a sample of publicly traded acquisition targets, we
+              find evidence for impression offsetting, in which characteristics
+              of both acquirers and their announced acquisitions predict its
+              frequency of use. We also find evidence that impression offsetting
+              is efficacious; on average, it reduces the negative market reaction
+              to acquisition announcements by over 40\%, which translates into
+              approximately \$246 million in market capitalization. }
+}
+
+@article{doi:10.5465/annals.2017.0099,
+  author   = {Hannigan, Timothy R. and Haans, Richard F. J. and
+              Vakili, Keyvan and Tchalian, Hovig and Glaser, Vern L. and Wang,
+              Milo Shaoqing and Kaplan, Sarah and Jennings, P. Devereaux},
+  title    = {Topic Modeling in Management Research: Rendering New
+              Theory from Textual Data},
+  journal  = {Academy of Management Annals},
+  volume   = {13},
+  number   = {2},
+  pages    = {586-632},
+  year     = {2019},
+  doi      = {10.5465/annals.2017.0099},
+  url      = {
+              
+              https://doi.org/10.5465/annals.2017.0099
+              
+              },
+  eprint   = {
+              
+              https://doi.org/10.5465/annals.2017.0099
+              
+              },
+  abstract = { Increasingly, management researchers are using topic
+              modeling, a new method borrowed from computer science, to reveal
+              phenomenon-based constructs and grounded conceptual relationships
+              in textual data. By conceptualizing topic modeling as the process
+              of rendering constructs and conceptual relationships from textual
+              data, we demonstrate how this new method can advance management
+              scholarship without turning topic modeling into a black box of
+              complex computer-driven algorithms. We begin by comparing features
+              of topic modeling to related techniques (content analysis, grounded
+              theorizing, and natural language processing). We then walk through
+              the steps of rendering with topic modeling and apply rendering to
+              management articles that draw on topic modeling. Doing so enables
+              us to identify and discuss how topic modeling has advanced
+              management theory in five areas: detecting novelty and emergence,
+              developing inductive classification systems, understanding online
+              audiences and products, analyzing frames and social movements, and
+              understanding cultural dynamics. We conclude with a review of new
+              topic modeling trends and revisit the role of researcher
+              interpretation in a world of computer-driven textual analysis. }
+}
+
+@misc{kevinheaveyModernPolars,
+  author       = {Kevin Heavey},
+  title        = {{M}odern {P}olars},
+  howpublished = {\url{https://kevinheavey.github.io/modern-polars/}},
+  year         = {2024},
+  note         = {[Accessed 29-05-2025]}
+}
+
+@book{Lutz2025-zq,
+  title     = {Learning python},
+  author    = {Lutz, Mark},
+  publisher = {O'Reilly Media},
+  edition   = 6,
+  month     = mar,
+  year      = 2025,
+  address   = {Sebastopol, CA},
+  language  = {en},
+  isbn      = {978-1098171308}
+}
+
+@book{McKinney2022-rd,
+  title     = {Python for data analysis 3e},
+  author    = {McKinney, Wes},
+  publisher = {O'Reilly Media},
+  edition   = 3,
+  month     = aug,
+  year      = 2022,
+  address   = {Sebastopol, CA},
+  language  = {en},
+  isbn      = {978-1098104030}
+}