I normally post on my blog every Friday but the last Friday was a holiday here in Catalonia so I decided to take a break. It was also my birthday week so I wanted to take the time to reflect too on my life and the things I want to achieve moving forward. Anyhow, this are my top reads of the last week:

A hypothesis is a liability – compares day science (where ideas are tested) with night science (where ideas are generated). When we focus on testing a hypothesis, we may be blinded from exploring other important facets of the data. I especially liked the experiment where they asked students to analyze a certain dataset. If you simply plot the dataset, you will see a gorilla. However, most students miss this as they are too focused on running the regressions, without adequately conducting data exploration.

Learning from nature – Biomimicry innovation to support infrastructure sustainability and resilience – fascinating paper on how innovations in the natural world can be applied in developing infrastructures. The tables showing examples of biomimicry in different engineering products, processes and systems are really interesting.

What’s Wrong with Social Science and How to Fix It: Reflections After Reading 2578 Papers – a blog post by someone who skimmed tons of social science papers to participate in a replication market. A replication market is a market where one can predict whether a paper will replicate or not. Among the different fields, economics had the highest expectations of replication. Management and marketing ranked the lowest (honestly, unsurprisingly).

Illuminating the dark spaces of healthcare with ambient intelligence – a review in Nature on how sensors and artificial intelligence can improve the health of individuals in both hospitals (ICUs, operating rooms and even lobbies) and daily living settings (elderly homes, chronic disease management and mental health monitoring).

From Necessity to Opportunity: Scaling Bricolage across Resource-Constrained Environments – bricolage refers to working with what is available. Normally, this concept is applied to startups which may not have access to many resources. In this work, the researchers explore how a social enterprise was able to scale to different countries with bricolage.

Coevolution of cyberinfrastructure development and scientific progress – explored how developing cyberinfrastructure can impact progress in research, specifically in the field of biodiversity and ecology. The researchers show that the coevolution between this data infrastructure and the field itself led to evolutionary progress in terms of increasing both collaborations and democratization in this research community. Moreover, it also led to revolutionary progress in terms of connecting this field with other scientific disciplines.

On democracy – The current issue of Science has a special section dedicated to democracy Articles cover a wide range of topics including immigration, inequality and activism. Just last week, Economist also had a plot showing the increasing support towards autocrats by people in weaker democracies. In the plot, my country the Philippines has increased support for “Having the army rule is good.” Scary times ahead. It’s difficult to not feel powerless in this situation. Yet, I’m hopeful that the brilliant social scientists working on this topic could come up with ways to better support democracy.

Is disruptive innovation in emerging economies different? Evidence from China – When we think of disruptive innovation, it is natural to think of the Ubers and the Netflixes of the world. However, disruptive innovation can manifest differently in emerging economies. The authors argue that, in China, disruptive innovations emerge by improving value propositions through cost innovation, quickly iterating to improve the quality of their offerings, launching directly to mass-market and placing efficient production processes.

Conceptualising technology, its development and future: The six genres of technology – Creates a typology of technology based on various combinations of relationships between humans and artefacts. It’s interesting how they weave the coming AI apocalypse throughout the article.

The impact of technology transfer and knowledge spillover from Big Science: a literature review – Big science refers to this phenomenon where scientific experiments require larger and larger groups of scientists, needing funding from groups of governments. The term is associated with organizations/projects like CERN, NASA and the Human Genome Project. The outcomes of these experiments impact society in real, tangible ways as documented by this review.

Does the merger of universities promote their scientific research performance? Evidence from China – universities around the world have become so conscious of their ranking. France for instance has been featured in the Economist recently for their Paris Saclay initiative which combines many of their research organizations in Paris to improve their rankings. In China, however, these mergers have not proved to be too successful due to difficulties in cultural integration and reaching economies of scale.

Confidential Gossip and Organization Studies – It still fascinates me how researchers can study phenomena of all kinds. This one is interesting considering how prevalent gossip can be yet how understudied they are in general.

Being Extraordinary: How CEOs’ Uncommon Names Explain Strategic Distinctiveness – Reminds me of this episode on Freakonomics exploring how names can influence the career paths that people take (like people named Dennis become dentists). In a similar vein, CEOs with uncommon names pursue uncommon strategies.

A history of insatiable intellectuals – A review of the book The Polymath by Peter Burke. I listened to the podcast episode where the author gives a lecture about the book. It was really fascinating to see how polymaths and the tension between specialization and being a generalist have evolved over time.

The Strategic Allocation of Inventors to R&D Collaborations – they find that companies send their inventors with strong IP protection to collaborations. These inventors serve to balance value creation with value protection.

Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies – Maps 3D printing technologies using patents. I like their path analysis showing the contributions of the different countries in the development of this technology. I explored this technique in a previous post.

Optimal distinctiveness in platform markets: Leveraging complementors as legitimacy buffers – This studied optimal distinctiveness by comparing MOOC platforms. They found that “a standard deviation increase in distinctiveness (from low to moderate) increases the expected number of platform users by 1.7 million (+55.1%) for platforms with an above-average share of high-status complementors, but decreases the number of users by 2.9 million (-53.5%) for platforms without high-status complementors in their ecosystem.”

Government royalties on sales of biomedical products developed with substantial public funding – The pharma industry has faced a lot of criticisms due to the increasing drug prices and privatization of research funded by taxpayer money. This paper proposes how royalties can be a better alternative compared to price controls by not decreasing investment in R&D.

The authenticity premium: Balancing conformity and innovation in high technology industries – A study on authenticity, putting their own spin to the idea of optimal distinctiveness. They looked at the balance between differentiation and conformity in three signals given by firms – network, governance and narrative.

Post-Failure Success: Sensemaking in Problem Representation Reformulation – failures normally spring from a faulty representation of the problem. A reformulation of these wrong assumptions is the key then to steer one’s trajectory, ultimately turning the initial failure into a success.

Organizational Resilience: A Valuable Construct for Management Research? – Resilience has become a buzzword during the pandemic. The paper clarifies a lot of things about what it really is about and how to measure it. They identify behavior, resources and capabilities as relevant components which aid to have a resilient response which then leads to organizational growth.

Managing intrapreneurial capabilities: An overview – an introduction to a special issue on intrapreneurship and dynamic capabilities. They identify different research streams in the intersection of these two topics.

I wanted to get updated with the latest trends in the technology management literature. To do this, I conducted a bibliometric review of the publications in the top innovation and general management journals.

Journals Analyzed

I searched the Web of Science for articles published from 2019 in the top technology journals (Research Policy, Journal of Product Innovation Management, Technovation, Technological Forecasting and Social Change, R & D Management, Technology Analysis & Strategic Management, Journal of Engineering And Technology Management, Industry and Innovation, Research-Technology Management, Scientometrics and Journal of Technology Transfer). I then added articles in the top general management journals as long as they contain the terms science, technology or innovation. These journals include Administrative Science Quarterly, Academy of Management Journal, Academy of Management Annals, Academy of Management Review, Academy of Management Perspectives,  Journal of Business Research, British Journal of Management, Journal of Business Venturing, Journal of Management Studies, Entrepreneurship Theory and Practice, Strategic Management Journal, Management Science, Strategic Entrepreneurship Journal,  Journal of Management and Organization Science.

Using these data collection steps, I had 2,561 articles. I used python to analyze the articles in bulk. Visualizations were carried out using VosViewer.

Top Cited Works

Title	First Author	Journal	Year	Internal Citations
Self-citations as strategic response to the use of metrics for career decisions	Seeber, M	Res Pol	2019	9
Can big data and predictive analytics improve social and environmental sustainability?	Dubey, R	TFSC	2019	8
Social media and innovation: A systematic literature review and future research directions	Bhimani, H	TFSC	2019	7
How crowdfunding platforms change the nature of user innovation – from problem solving to entrepreneurship	Brem, A	TFSC	2019	7
Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices	El-Kassar, AN	TFSC	2019	7
Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges	Appio, FP	TFSC	2019	6
Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective	Frank, AG	TFSC	2019	6
Technology Reemergence: Creating New Value for Old Technologies in Swiss Mechanical Watchmaking, 1970-2008	Raffaelli, R	ASQ	2019	6
Innovation policy for system-wide transformation: The case of strategic innovation programmes (sips) in Sweden	Grillitsch, M	Res Pol	2019	6
Collaborative modes with Cultural and Creative Industries and innovation performance: The moderating role of heterogeneous sources of knowledge and absorptive capacity	Santoro, G	Technovation	2020	6
Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies	Sepasgozar, SME	TFSC	2019	6

There are two few works not in the chosen journals but are still highly cited. The first one is Large teams develop and small teams disrupt science and technology by Wu et al in Nature, which received 9 internal citations. The only other one in the top 10 was An agenda for sustainability transitions research: State of the art and future directions by Kohler et al.

Clusters

Cluster	Top Keywords	Top Cited Works in Cluster	Count
Red (1)	innovation, technology, market, product, user, platform, venture, ecosystem, social, digital	fornell c (1981), eisenhardt km (1989), podsakoff pm (2003), davis fd (1989), eisenhardt km (2007)	596
Green (2)	firm, innovation, knowledge, performance, effect, innovation performance, capability, network, industry, collaboration	cohen wm (1990), laursen k (2006), march jg (1991), kogut b (1992), zahra sa (2002)	578
Dark Blue (3)	research, journal, citation, publication, article, science, scientific, field, paper, researcher	hirsch je (2005), van eck nj (2010), merton rk (1968), egghe l (2006), hicks d (2015)	457
Yellow (4)	innovation, policy, system, smart city, technology, transition, scenario, development, foresight, process	geels fw (2002), geels fw (2007), markard j (2012), geels fw (2004), bergek a (2008)	289
Violet (5)	patent, technology, trademark, technological, innovation, analysis, invention, firm, market, method	trajtenberg m (1990), lerner j (1994), mendonca s (2004), daim tu (2006), flikkema m (2014)	133
Light Blue (6)	university, technology transfer, research, academic entrepreneurship, innovation, knowledge transfer, entrepreneurial university, knowledge, collaboration, commercialization	perkmann m (2013), siegel ds (2003), d’este p (2011), grimaldi r (2011), d’este p (2007)	113

Reviewing the field of external knowledge search for innovation: theoretical underpinnings and future (re-)search directions – a review of how firms search for innovation outside. They introduce the term decoupled search which happens when the search process involves an assistant.

Charting a Path between Firm‐Specific Incentives and Human Capital‐Based Competitive Advantage – firms can offer unique incentives to their employees (e.g. Disney parks’ discount to employees, Google enabling employees access to interesting data, having a culture of fun at Zappos ). The paper provides a typology of these different incentives that can be unique to certain firms.

Scale quickly or fail fast: An inductive study of acceleration – accelerators have three main characteristics: investment on ventures that already reached product-market fit, focus on growth (through revenue, number of customers and even team maturity) in a short amount of time and an emphasis on aggressively testing whether the venture can succeed or fail.

Detecting academic fraud using Benford law: The case of Professor James Hunton – Benford’s law states that the first digit of datasets collected would likely to be small. For instance, the number 1 appears 30% of the time if a dataset if collected truthfully. The researchers used this idea to see whether they can detect the fraud from retracted papers.

A Quantum Approach to Paradox Entails Neither Preexisting Tensions Nor Asymmetry: Response to Li – So, this was a letter in response to a comment by Li on the original article by Hahn and Knight using concepts from quantum physics to resolve the paradoxes in management research. I wouldn’t claim that I understand anything but it’s really fascinating the extent that researchers are adapting theories from other fields.

The Role of Research in Business Schools and the Synergy Between its Four Subdomains – applies Pasteur’s quadrants to create a typology of business research. I liked the part described how famous names in management fit into the different quadrants.

Crossing the valley of death: Five underlying innovation processes – They describe five processes to cross the so-called valley of death, hindering early-stage ventures from succeeding. These include: refining the narrative for the technology concept, evaluating the technical aspects of the lab-scale models, refining how the technology will be used, assessing the comparative value and integrating the inputs of innovator actors.

ON THE THEORY OF ORGANIZATIONAL PATH DEPENDENCE: CLARIFICATIONS, REPLIES TO OBJECTIONS, AND EXTENSIONS – a follow up to the highly cited article in 2009 on organizational path dependence

The Transformation of the Innovation Process: How Digital Tools are Changing Work, Collaboration, and Organizations in New Product Development – digital tools “not only do they affect output and process efficiency, but they also increase depth and breadth of the work of individual innovators, they lead to rearrangement of the entire innovation processes, enable new configurations of people, teams, and firms”

Why ‘Doing Well By Doing Good’ Goes Wrong: A Critical Review of ‘Good Ethics Pays’ Claims in Managerial Thinking – it has been quite an accepted idea these days that companies should do good as its good for their bottom line. I remember listening to a podcast episode from Econtalk a few weeks ago that touches this (which I highly recommend). Nonetheless, this AOM review is fascinating as it forces us to revisit our assumptions.

The creative cliff illusion – people assume that their creativity will drop over time. This study however demonstrates that this is not the case and that having such negative assumptions can be detrimental to performance.

The role of immigration in innovation has gotten a lot of attention this week (ex. from In the Pipeline, Brookings). The piece that resonated a lot to me is this editorial introducing the special issue of Research Policy on immigration.

The authors introduced their article with 4 quotes, which then anchored the different perspectives to explore immigration.

Self-selection among immigrants and role in the diffusion of innovation

“Migration has one characteristic that should make it very effective as a diffusion method. The hardships occasioned with [it] will usually discourage all but the most resourceful, energetic, and courageous. Those who have the hardihood to venture in this way hence are likely to have exactly those human qualities which are most essential to innovating and diffusing”

-Warren C. Scoville (“Spread of Techniques: Minority Migrations and the Diffusion of Technology”, Journal of Economic History 1951: 11/4, p.349)

Arguments against brain-drain and for the value of choice

“… even were it possible to force the professionals to stay at home, it would be a foolish policy. Lack of congenial working conditions, absence of peer professionals to interact with, and resentment at being deprived of the chance to emigrate can lead to a wholly unproductive situation in which one has the body but not the brain. The brain is not a static thing: it can drain away faster sitting in the wrong place than when travelling to Cambridge or Paris!”

Jagdish Bhagwati (In Defense of Globalisation, Oxford University Press: 2007; p.214)

Struggles in assimilating and in being away

“Le véritable lieu de naissance est celui où l’on a porté pour la première fois un coup d’oeil intelligent sur soi-même: mes premières patries ont été des livres, à un moindre degré, des écoles.”
(The real birthplace is where you first took an intelligent look at yourself: my first countries were books, to a lesser extent, schools.)

Marguerite Yourcenar (Mémoires d’Hadrien, Plon: 1951)

Job market

“We hire from the best schools. All the people who go to those schools […] we offer jobs to American, non-American, that’s who we build these product teams around. And so, because we’re in a very competitive business, we don’t compromise on that. Wherever we can get those people, that’s where we create the jobs.”

Bill Gates (National Public Radio interview, March 12, 2008; https://www.npr.org/transcripts/88154016– last visit May 2020)

I needed to familiarize myself with the literature on science entrepreneurship (for reasons I’m going to explain soon). After delving into bibliometrics and doing literature review repetitively for my PhD, I already have a system to efficiently introduce myself to a new literature. In this post, I will explain my process, hoping it helps others who are also entering a new field.

I typically follow these steps:

1. Explore the Web of Knowledge using a keyword search
2. Explore data in Python
3. Create visualizations using VosViewer

The first step for me is usually just trying out different keywords in the Web of Knowledge. I then browse the first page of the latest articles and the top cited articles. I try to check whether these are related to my topic of interest.

For this topic of science entrepreneurship, I settled with the following keywords. I also narrowed it down to the management journals that I know are relevant to technology and innovation management and just general management. Moreover, I was just interested in the papers published from 2010. Below was my keyword search:

TS=(science OR technology ) AND TS=(startup* OR “start up” OR “new venture” OR entrepreneur* OR “new firm” OR “spin off” OR spinoff* OR SME OR SMEs) AND SO=(RESEARCH POLICY OR R D MANAGEMENT OR STRATEGIC MANAGEMENT JOURNAL OR JOURNAL OF PRODUCT INNOVATION MANAGEMENT OR ACADEMY OF MANAGEMENT REVIEW OR ACADEMY OF MANAGEMENT JOURNAL OR TECHNOVATION OR SCIENTOMETRICS OR TECHNOLOGICAL FORECASTING “AND” SOCIAL CHANGE OR TECHNOLOGY ANALYSIS STRATEGIC MANAGEMENT OR ORGANIZATION SCIENCE OR ADMINISTRATIVE SCIENCE QUARTERLY OR JOURNAL OF BUSINESS VENTURING OR INDUSTRY “AND” INNOVATION OR STRATEGIC ENTREPRENEURSHIP JOURNAL OR JOURNAL OF TECHNOLOGY TRANSFER OR JOURNAL OF ENGINEERING “AND” TECHNOLOGY MANAGEMENT OR JOURNAL OF MANAGEMENT OR JOURNAL OF MANAGEMENT STUDIES OR RESEARCH TECHNOLOGY MANAGEMENT OR ENTREPRENEURSHIP THEORY “AND” PRACTICE OR ACADEMY OF MANAGEMENT ANNALS OR ACADEMY OF MANAGEMENT PERSPECTIVES OR JOURNAL OF BUSINESS RESEARCH OR BRITISH JOURNAL OF MANAGEMENT OR EUROPEAN JOURNAL OF MANAGEMENT OR MANAGEMENT SCIENCE)

After exploring the results, I then downloaded the articles. These amounted to 1412 articles in total. Since WOS only allowed downloading of 500 at a time, I named these files 1-500.txt, 501-1000.txt and so on. I saved all the files in a folder (named Raw in this case) in my computer.

Data Exploration in Python

In the following, I show the code to import the data into Python and format the articles into a pandas dataframe.

import re, csv, os 
import pandas as pd
import numpy as np
import nltk
import math
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('white')
from collections import Counter

columnnames =['PT','AU','DE', 'AF','TI','SO','LA','DT','ID','AB','C1','RP','EM','CR','NR','TC','U1','PU','PI','PA','SN','EI','J9','JI','PD','PY','VL','IS','BP','EP','DI','PG','WC','SC','GA','UT']

def convertWOScsv(filename):
    openfile = open(filename, encoding='latin-1')
    sampledata = openfile.read()
    # divide into list of records 
    individualrecords = sampledata.split('\n\n')
    databaseofWOS = []
    for recordindividual in individualrecords:
        onefile = {}
        for x in columnnames:
            everyrow = re.compile('\n'+x + ' ' + '((.*?))\n[A-Z][A-Z1]', re.DOTALL)
            rowsdivision = everyrow.search(recordindividual)
            if rowsdivision:
                onefile[x] = rowsdivision.group(1)
        databaseofWOS.append(onefile)
    return databaseofWOS

def massconvertWOS(folder):
    publicationslist = []
    for file in os.listdir(folder):
        if file.endswith('.txt'):
            converttotable = convertWOScsv(folder + '\\' + file)
            publicationslist += converttotable
    publicationslist = pd.DataFrame(publicationslist)
    publicationslist.dropna(how='all', inplace=True)
    publicationslist.reset_index(drop=True, inplace=True)
    publicationslist['PY'] =publicationslist['PY'].fillna('').replace('', '2019').astype(int)
    publicationslist['TC'] = publicationslist['TC'].apply(lambda x: int(x.split('\n')[0]))
    return publicationslist

df = massconvertWOS('Raw')
df = df.drop_duplicates('UT').reset_index(drop=True)

I preview some of the articles that I was able to download below. I chose the relevant columns to show.

print('Number of Articles:', df.shape[0])
df.head()[['TI', 'AU', 'SO', 'PY']]

Number of Articles: 1412

	TI	AU	SO	PY
0	Non-linear effects of technological competence…	Deligianni, I\n Voudouris, I\n Spanos, Y\n…	TECHNOVATION	2019
1	Creating new products from old ones: Consumer …	Robson, K\n Wilson, M\n Pitt, L	TECHNOVATION	2019
2	What company characteristics are associated wi…	Koski, H\n Pajarinen, M\n Rouvinen, P	INDUSTRY AND INNOVATION	2019
3	Through the Looking-Glass: The Impact of Regio…	Vedula, S\n York, JG\n Corbett, AC	JOURNAL OF MANAGEMENT STUDIES	2019
4	The role of incubators in overcoming technolog…	Yusubova, A\n Andries, P\n Clarysse, B	R & D MANAGEMENT	2019

WOS is smart in the sense that even if the text does not contain the keywords you said, they still may include papers because they sense that these are relevant papers. To filter out these papers that did not contain the keywords I wanted, I further filtered the dataset by checking the title, abstract and author-selected keywords. Moreover, let’s remove articles without any citations.

df["txt"] = df["TI"].fillna("") + " " + df["DE"].fillna("") + " " + df["AB"].fillna("")
df["txt"] = df["txt"].apply(lambda x: x.replace('-', ' '))
df = df[df['txt'].apply(lambda x: any([y in x.lower() for y in ['scien', 'technolog']]))]
df = df[df['txt'].apply(lambda x: any([y in x.lower() for y in ['startup', 'start up', 'new venture', 'entrepreneur', 'new firm', 'spin off',
                                                                'spinoff', 'sme ', 'smes ']]))]
df = df[~df['CR'].isnull()] 
print('Number of Articles:', df.shape[0])

Number of Articles: 846

I can plot the number of articles over time

df.groupby('PY').size().plot(kind='bar')

I can look at the breakdown per journal

#df.groupby('SO').size().sort_values().plot(kind='barh', figsize=[5,10])
soplot = df.pivot_table(index='PY', columns='SO', aggfunc='size').fillna(0) #.reset_index()
soplot = soplot[soplot.sum(axis=0).sort_values().index].reset_index().rename(columns={'PY':'Year'})
soplot['Year'] = pd.cut(soplot['Year'], [0, 2014, 2019], labels=['2010-2014', '2015-2019'])
soplot.groupby('Year').sum().T.plot(kind='barh', stacked=True, figsize=[5,10])
plt.ylabel('Journal'), plt.xlabel('Number of Articles')
plt.show()

I can look at the top cited articles. This shows what are the foundational material that I should know before delving into the topic.

topcited = df['CR'].fillna('').apply(lambda x: [y.strip() for y in x.split('\n')]).sum()
pd.value_counts(topcited).head(10)

COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553                  115
Shane S, 2004, NEW HORIZ ENTREP, P1                                               88
Shane S, 2000, ACAD MANAGE REV, V25, P217, DOI 10.5465/amr.2000.2791611           87
Rothaermel FT, 2007, IND CORP CHANGE, V16, P691, DOI 10.1093/icc/dtm023           86
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108                81
Shane S, 2000, ORGAN SCI, V11, P448, DOI 10.1287/orsc.11.4.448.14602              78
TEECE DJ, 1986, RES POLICY, V15, P285, DOI 10.1016/0048-7333(86)90027-2           77
Di Gregorio D, 2003, RES POLICY, V32, P209, DOI 10.1016/S0048-7333(02)00097-5     77
EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557               75
Nelson R.R., 1982, EVOLUTIONARY THEORY                                            69
dtype: int64

The articles above are not really very specific to our topic of interest. These are foundational papers in innovation/management. To explore those papers that are more relevant to our topic, what I can do then is find which is the most cited within the papers in this dataset, meaning hey include the keywords that I’m interested in. This is the internal citation of the papers.

def createinttc(df):
    df["CRparsed"] = df["CR"].fillna('').str.lower().astype(str)
    df["DI"] = df["DI"].fillna('').str.lower()
    df["intTC"] = df["DI"].apply(lambda x: sum([x in y for y in df["CRparsed"]]) if x!="" else 0)
    df["CRparsed"] = df["CR"].astype(str).apply(lambda x: [y.strip().lower() for y in x.split('\n')])
    return df

df = createinttc(df).reset_index(drop=True)

df.sort_values('intTC', ascending=False)[['TI', 'AU', 'SO', 'PY', 'intTC']].head(10)

	TI	AU	SO	PY	intTC
401	30 years after Bayh-Dole: Reassessing academic…	Grimaldi, R\n Kenney, M\n Siegel, DS\n W…	RESEARCH POLICY	2011	45
301	Academic engagement and commercialisation: A r…	Perkmann, M\n Tartari, V\n McKelvey, M\n …	RESEARCH POLICY	2013	41
428	Why do academics engage with industry? The ent…	D’Este, P\n Perkmann, M	JOURNAL OF TECHNOLOGY TRANSFER	2011	32
402	The impact of entrepreneurial capacity, experi…	Clarysse, B\n Tartari, V\n Salter, A	RESEARCH POLICY	2011	26
407	ENDOGENOUS GROWTH THROUGH KNOWLEDGE SPILLOVERS…	Delmar, F\n Wennberg, K\n Hellerstedt, K	STRATEGIC ENTREPRENEURSHIP JOURNAL	2011	24
430	Entrepreneurial effectiveness of European univ…	Van Looy, B\n Landoni, P\n Callaert, J\n …	RESEARCH POLICY	2011	23
398	The Bayh-Dole Act and scientist entrepreneurship	Aldridge, TT\n Audretsch, D	RESEARCH POLICY	2011	20
400	The effectiveness of university knowledge spil…	Wennberg, K\n Wiklund, J\n Wright, M	RESEARCH POLICY	2011	19
515	Convergence or path dependency in policies to …	Mustar, P\n Wright, M	JOURNAL OF TECHNOLOGY TRANSFER	2010	19
413	Entrepreneurial Origin, Technological Knowledg…	Clarysse, B\n Wright, M\n Van de Velde, E	JOURNAL OF MANAGEMENT STUDIES	2011	19

A complementary approach is to look at the articles that are citing the most the rest of the papers in the dataset. These allows us to see which reviews already integrates the studies within our dataset. We can then start reading from this set of papers as they cover already a lot of the other papers in the dataset.

doilist = [y for y in df['DI'].dropna().tolist() if y!='']
df['Citing'] = df['CR'].apply(lambda x: len([y for y in doilist if y in x]))
df.sort_values('Citing', ascending=False)[['TI', 'AU', 'SO' , 'PY',  'Citing', ]].head(10)

	TI	AU	SO	PY	Citing
139	Conceptualizing academic entrepreneurship ecos…	Hayter, CS\n Nelson, AJ\n Zayed, S\n O’C…	JOURNAL OF TECHNOLOGY TRANSFER	2018	75
168	THE PSYCHOLOGICAL FOUNDATIONS OF UNIVERSITY SC…	Hmieleski, KM\n Powell, EE	ACADEMY OF MANAGEMENT PERSPECTIVES	2018	33
138	Re-thinking university spin-off: a critical li…	Miranda, FJ\n Chamorro, A\n Rubio, S	JOURNAL OF TECHNOLOGY TRANSFER	2018	31
122	Public policy for academic entrepreneurship in…	Sandstrom, C\n Wennberg, K\n Wallin, MW\n …	JOURNAL OF TECHNOLOGY TRANSFER	2018	28
37	Opening the black box of academic entrepreneur…	Skute, I	SCIENTOMETRICS	2019	28
166	RETHINKING THE COMMERCIALIZATION OF PUBLIC SCI…	Fini, R\n Rasmussen, E\n Siegel, D\n Wik…	ACADEMY OF MANAGEMENT PERSPECTIVES	2018	25
68	The technology transfer ecosystem in academia….	Good, M\n Knockaert, M\n Soppe, B\n Wrig…	TECHNOVATION	2019	24
40	Theories from the Lab: How Research on Science…	Fini, R\n Rasmussen, E\n Wiklund, J\n Wr…	JOURNAL OF MANAGEMENT STUDIES	2019	22
659	How can universities facilitate academic spin-…	Rasmussen, E\n Wright, M	JOURNAL OF TECHNOLOGY TRANSFER	2015	21
73	Stimulating academic patenting in a university…	Backs, S\n Gunther, M\n Stummer, C	JOURNAL OF TECHNOLOGY TRANSFER	2019	21

Bibliometric Analysis in VosViewer

To create visualizations of the paper, we do the following steps. First, we can export the filtered dataset into a text file.

def convertWOStext(dataframe, outputtext):
    dataframe["PY"]=dataframe["PY"].astype(int)
    txtresult = ""
    for y in range(0, len(dataframe)):
        for x in columnnames:
            if dataframe[x].iloc[y] != np.nan:
                txtresult += x + " " + str(dataframe[x].iloc[y]) + "\n"
        txtresult += "ER\n\n"
    f = open(outputtext, "w", encoding='utf-8')
    f.write(txtresult)
    f.close()

convertWOStext(df, 'df.txt')

We can then open the file in VosViewer. From there, we can create various visualizations. I like using bibliographic coupling to map all the papers in the dataset

I saved the file in VosViewer. This gives you two files, one has the data on each document and the second file has the network data. We modify these files to make certain changes. First, the citations above reflect their citations from all the papers outside the dataset. I want the internal citations to be shown so I replace it.

def createvosfile1(filename, df, updatecit= False, newclusters = False, newname=None):
    vosfile1  = pd.read_csv(filename, sep="\t")
    voscolumns = vosfile1.columns
    vosfile1["title"] = vosfile1["description"].apply(lambda x: x.split("Title:</td><td>")[1])
    vosfile1["title"] = vosfile1["title"].apply(lambda x: x.split("</td></tr>")[0])
    df["TI2"] = df["TI"].apply(lambda x: " ".join(x.lower().split()))
    vosfile1 = vosfile1.merge(df[[x for x in df.columns if x not in voscolumns]], how="left", left_on="title", right_on="TI2")
    vosfile1["txt"] = vosfile1["TI"].fillna(" ") + " " + vosfile1["DE"].fillna(" ") + " " + vosfile1["AB"].fillna(" ")  
    vosfile1["txt"] = vosfile1["txt"].apply(lambda x: x.lower())
    vosfile1["weight<Citations>"] = vosfile1["intTC"].fillna(0)
    vosfile1 = vosfile1.drop_duplicates('id')
    vosfile1['id'] = vosfile1.reset_index().index + 1
    if newclusters == True:
        vosfile1['cluster'] = artclusters
    if updatecit == True:
        vosfile1[voscolumns].to_csv(newname, sep="\t", index=False)
    return vosfile1

df = createvosfile1('Processed\VosViewer_1_Original.txt', df, newname='Processed\VosViewer_1_intCit.txt', updatecit= True, newclusters=False)

The above network just uses the citation data of the publications. To improve it, I like integrating the textual data from the title, abstract and keywords. I followed the steps suggested here for cleaning the text (https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/). I then combine these two measures to allow for hybrid clustering

Liu, Xinhai, Shi Yu, Frizo Janssens, Wolfgang Glänzel, Yves Moreau, and Bart De Moor. “Weighted hybrid clustering by combining text mining and bibliometrics on a large‐scale journal database.” Journal of the American Society for Information Science and Technology 61, no. 6 (2010): 1105-1119.

#Bibliometric coupling
from scipy.sparse import coo_matrix
from collections import Counter
from sklearn.metrics.pairwise import cosine_similarity

def createbibnet(df):
    allsources = Counter(df['CRparsed'].sum())
    allsources  = [x for x in allsources if allsources[x]>1]
    dfcr = df['CRparsed'].reset_index(drop=True)
    dfnet = []
    i=0
    for n in allsources:
        [dfnet.append([i, y]) for y in dfcr[dfcr.apply(lambda x: n in x)].index]
        i+=1
    dfnet_matrix = coo_matrix(([1] * len(dfnet), ([x[1] for x in dfnet], [x[0] for x in dfnet])), 
                              shape=(dfcr.shape[0], len(allsources)))
    return cosine_similarity(dfnet_matrix, dfnet_matrix)

#Lexical Coupling
from nltk.corpus import stopwords 
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
import string
from gensim.models.phrases import Phrases, Phraser

def clean(doc):
    stop = set(stopwords.words('english'))
    exclude = set(string.punctuation) 
    lemma = WordNetLemmatizer()
    stop_free = " ".join([i for i in doc.lower().split() if i not in stop])
    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)
    normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split())
    normalized = " ".join([x for x in normalized.split() if not any(c.isdigit() for c in x)])
    normalized = " ".join([x for x in normalized.split() if len(x)>3])
    return normalized

def bigrams(docs):
    phrases = Phrases(docs)
    bigram = Phraser(phrases)
    docs = docs.apply(lambda x: bigram[x])
    phrases = Phrases(docs)
    trigram = Phraser(phrases)
    docs = docs.apply(lambda x: trigram[x])
    return docs

def createtfidf(df, sheet_name):
    df["lemma"] = df["txt"].apply(lambda x: clean(x).split())
    df["lemma"] = bigrams(df["lemma"])
    vect = TfidfVectorizer(min_df=1)
    tfidftemp = vect.fit_transform([" ".join(x) for x in df["lemma"]])
    return cosine_similarity(tfidftemp) 

#Hybrid network
def createhybridnet(df, weightlex, sheet_name='Sheet1'):
    bibnet = createbibnet(df)
    tfidftemp = createtfidf(df, sheet_name)
    hybnet = pd.DataFrame(np.cos((1-weightlex) * np.arccos(bibnet) + weightlex *  np.arccos(tfidftemp))).fillna(0)
    return hybnet

from itertools import combinations
def createvosviewer2filefromhybrid(hybridlexcit, minimumlink, outputfilename):
    forvisuals = []
    for x, y in combinations(hybridlexcit.index, 2):
        val = int(hybridlexcit.loc[x,y]*100)
        if val > minimumlink:
            forvisuals.append([x, y, val])
    forvisuals = pd.DataFrame(forvisuals)
    forvisuals[0] = forvisuals[0] + 1
    forvisuals[1] = forvisuals[1] + 1
    forvisuals.to_csv(outputfilename, index=False, header=False)
    
dfhybrid = createhybridnet(df, 0.5)
createvosviewer2filefromhybrid(dfhybrid, 0, r'Processed/VosViewer_2_Hybrid.txt')

If we reimport these modified files to VosViewer. We come up with this visualization which incorporates both textual and citation data.

I can then spend tons of time just exploring the network. I look at the papers in each cluster. I check which papers have high citations. I can do this also with the help of python. We can update the clustering using the one generated by VosViewer.

df = createvosfile1('Processed/VosViewer_1_Clus.txt', df)

df[df['cluster']==1].sort_values('intTC', ascending=False)[['TI', 'AU', 'SO', 'PY', 'intTC']].head(10)

	TI	AU	SO	PY	intTC
404	ENDOGENOUS GROWTH THROUGH KNOWLEDGE SPILLOVERS…	Delmar, F\n Wennberg, K\n Hellerstedt, K	STRATEGIC ENTREPRENEURSHIP JOURNAL	2011	24
500	Cognitive Processes of Opportunity Recognition…	Gregoire, DA\n Barr, PS\n Shepherd, DA	ORGANIZATION SCIENCE	2010	11
439	Managing knowledge assets under conditions of …	Allarakhia, M\n Steven, W	TECHNOVATION	2011	10
353	Technology entrepreneurship	Beckman, C\n Eisenhardt, K\n Kotha, S\n …	STRATEGIC ENTREPRENEURSHIP JOURNAL	2012	9
484	IAMOT and Education: Defining a Technology and…	Yanez, M\n Khalil, TM\n Walsh, ST	TECHNOVATION	2010	8
343	TECHNOLOGY-MARKET COMBINATIONS AND THE IDENTIF…	Gregoire, DA\n Shepherd, DA	ACADEMY OF MANAGEMENT JOURNAL	2012	8
443	The Strategy-Technology Firm Fit Audit: A guid…	Walsh, ST\n Linton, JD	TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE	2011	8
411	The Cognitive Perspective in Entrepreneurship:…	Gregoire, DA\n Corbett, AC\n McMullen, JS	JOURNAL OF MANAGEMENT STUDIES	2011	8
596	Technology Business Incubation: An overview of…	Mian, S\n Lamine, W\n Fayolle, A	TECHNOVATION	2016	6
303	Local responses to global technological change…	Fink, M\n Lang, R\n Harms, R	TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE	2013	6

df[df['cluster']==2].sort_values('intTC', ascending=False)[['TI', 'AU', 'SO', 'PY', 'intTC']].head(10)

	TI	AU	SO	PY	intTC
410	Entrepreneurial Origin, Technological Knowledg…	Clarysse, B\n Wright, M\n Van de Velde, E	JOURNAL OF MANAGEMENT STUDIES	2011	19
461	On growth drivers of high-tech start-ups: Expl…	Colombo, MG\n Grilli, L	JOURNAL OF BUSINESS VENTURING	2010	17
387	WHEN DOES CORPORATE VENTURE CAPITAL ADD VALUE …	Park, HD\n Steensma, HK	STRATEGIC MANAGEMENT JOURNAL	2012	10
514	The M&A dynamics of European science-based ent…	Bonardo, D\n Paleari, S\n Vismara, S	JOURNAL OF TECHNOLOGY TRANSFER	2010	9
506	The role of incubator interactions in assistin…	Scillitoe, JL\n Chakrabarti, AK	TECHNOVATION	2010	9
423	EXPLAINING GROWTH PATHS OF YOUNG TECHNOLOGY-BA…	Clarysse, B\n Bruneel, J\n Wright, M	STRATEGIC ENTREPRENEURSHIP JOURNAL	2011	9
574	CHANGING WITH THE TIMES: AN INTEGRATED VIEW OF…	Fisher, G\n Kotha, S\n Lahiri, A	ACADEMY OF MANAGEMENT REVIEW	2016	9
354	Amphibious entrepreneurs and the emergence of …	Powell, WW\n Sandholtz, KW	STRATEGIC ENTREPRENEURSHIP JOURNAL	2012	8
507	A longitudinal study of success and failure am…	Gurdon, MA\n Samsom, KJ	TECHNOVATION	2010	8
324	Are You Experienced or Are You Talented?: When…	Eesley, CE\n Roberts, EB	STRATEGIC ENTREPRENEURSHIP JOURNAL	2012	8

df[df['cluster']==3].sort_values('intTC', ascending=False)[['TI', 'AU', 'SO', 'PY', 'intTC']].head(10)

	TI	AU	SO	PY	intTC
398	30 years after Bayh-Dole: Reassessing academic…	Grimaldi, R\n Kenney, M\n Siegel, DS\n W…	RESEARCH POLICY	2011	45
298	Academic engagement and commercialisation: A r…	Perkmann, M\n Tartari, V\n McKelvey, M\n …	RESEARCH POLICY	2013	41
425	Why do academics engage with industry? The ent…	D’Este, P\n Perkmann, M	JOURNAL OF TECHNOLOGY TRANSFER	2011	32
399	The impact of entrepreneurial capacity, experi…	Clarysse, B\n Tartari, V\n Salter, A	RESEARCH POLICY	2011	26
427	Entrepreneurial effectiveness of European univ…	Van Looy, B\n Landoni, P\n Callaert, J\n …	RESEARCH POLICY	2011	23
395	The Bayh-Dole Act and scientist entrepreneurship	Aldridge, TT\n Audretsch, D	RESEARCH POLICY	2011	20
397	The effectiveness of university knowledge spil…	Wennberg, K\n Wiklund, J\n Wright, M	RESEARCH POLICY	2011	19
392	What motivates academic scientists to engage i…	Lam, A	RESEARCH POLICY	2011	19
511	Convergence or path dependency in policies to …	Mustar, P\n Wright, M	JOURNAL OF TECHNOLOGY TRANSFER	2010	19
479	A knowledge-based typology of university spin-…	Bathelt, H\n Kogler, DF\n Munro, AK	TECHNOVATION	2010	18