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

Discoverers in scientific citation data – this research finds that there are a group of researchers who are good at discovering (or citing early) potentially important papers. This reminds me of the book Superforecasting which talks about how some people are better than others in forecasting the future.

Choices and Consequences: Impact of Mobility on Research-Career Capital and Promotion in Business Schools – In a study of 376 professors in European business schools, they find that mobility is useful in building research careers. At the same time, moving too much can also delay promotions.

The Art of the Pivot: How New Ventures Manage Identification Relationships with Stakeholders as They Change Direction – there is so much emphasis these days for startups to be able to pivot. The problem however is that pivoting is not so easy when you have many stakeholders to appease. This research gives insights on how to manage such relationships with important stakeholders when a startup needs to pivot.

Political skills and career success of R&D personnel: a comparative mediation analysis between perceived supervisor support and perceived organisational support – like many things, the technical superiority of an entity (whether it’s a product, firm or an employee) does not guarantee its success. In this study, they look at R&D employees and find that political skills are important for one to get ahead in one’s career.

Collaborative patents and the mobility of knowledge workers – In my field of FBDD, research mobility seems to be one of the most important mechanisms for the knowledge to spread. In this study of the European biotech sector, inventors who were previously located together are found to form collaborations faster.

Taking leaps of faith: Evaluation criteria and resource commitments for early-stage inventions – Researchers use text mining to quantify how technology transfer office evaluate and decide to financially back a new invention. They find that feasibility and desirability (expressed through words used in the examination document) are important for new inventions.

Exploration versus exploitation in technology firms: The role of compensation structure for R&D workforce – people respond to incentives. This study explores how a firm can structure its incentives as a lever to incentivize exploration / exploitation. In this study, the researchers find that firms with ” higher-powered tournament incentives in vertical compensation structure report higher fraction of innovation directed towards exploration”

Aligning technology and institutional readiness: the adoption of
innovation – It’s always exciting to explore how big firms adopt innovation. While technological readiness is important, researchers in this paper introduce that it should be complemented with the idea of institutional readiness.

Team efficiency and network structure: The case of professional League of Legends – with the amount of data generated by Esports, we should expect more management insights coming from them. In this study, they look at the effect of team interactions/centrality on team performance.

For the next month, I’ll be at the University of Cambridge to conduct a study on how fragment-based drug discovery thrived in the area.

The Legitimacy Threshold Revisited: How Prior Successes and Failures Spill Over to Other Endeavors on Kickstarter – previous outcomes in Kickstarter affect future crowdfunding efforts by “encouraging audiences to repeatedly support other related endeavors or by discouraging them from doing so.”

The Time Efficiency Gain in Sharing and Reuse of Research Data – sharing research data can yield to efficiency gains to the scientific community

Does combining different types of collaboration always benefit firms? Collaboration, complementarity and product innovation in Norway – conventional thinking dictates that firms should collaborate as much as they can to increase the chances of innovation occurring. This study however finds that pursuing all types of collaborations (in this case, scientific and supply chain) might not be useful all the time as these might interact and may negatively impact innovation.

It’s in the Mix: How Firms Configure Resource Mobilization for New Product Success – networks are always fascinating. Here, they look at the new product development through a network perspective.

Improving the peer review process: a proposed market system – Currently, reviewers do not receive any compensation given the amount of work they have to do. This is bad for science as well because papers do not get reviewed properly/fast enough. Creating a market system for the review process for better incentivization of both authors and reviewers might improve the process.

Federal funding of doctoral recipients: What can be learned from linked data – New datasets are always exciting. Researchers in this study propose linking a huge dataset on university payrolls with another huge survey about PhD graduates. It would be interesting to see how other researchers will use data to understand innovation, basic research, career development to name a few.

Universities and open innovation: the determinants of network centrality – Universities that are located centrally in their university-industry networks are also in better position to generate spinoffs and conduct projects with external funding.

The fragmentation of biopharmaceutical innovation – The pharma industry is not consolidating as much as expected, with smaller firms playing big roles. Commentary by the blog in the pipeline here.

This week seems to be a special one for the field of entrepreneurship, with some publications on the merits of studying it from an academic perspective such as this one A wakeup call for the field of entrepreneurship and its evaluators

Has the Concept of Opportunities Been Fruitful in the Field of Entrepreneurship? – In line with the previous one, this reflects on the concept of opportunities which has always been in the same conversation with entrepreneurship. I have not been able to access the article despite various searchers but I’m sure that it touches on the perennial question on whether opportunities are created or discovered. I find this discussion fascinating because by itself, entrepreneurship research is already too scholarly. Going one step backwards and reflecting on such philosophical questions, perhaps pushes this even further.

Firm Strategic Behavior and the Measurement of Knowledge Flows with Patent Citations –  Ever since I got into bibliometrics, citations have been fascinating. Instead of just a measure of paper’s worth and knowledge flow, citations also reflect other subtle things such as informal ties, cliques and prestige. In this paper, the researchers looked at patent citations and explores how it does not only reflect knowledge flow but also other other factors including firm strategy and intellectual property regime.

Predicting citation counts based on deep neural network learning techniques – in the theme apply neural networks to everything, in this paper, the researchers aimed to predict citation counts of papers. This makes me wonder whether we could reverse the process one day and design an AI that can output papers according to an input citation count.

Optimal Distinctiveness, Strategic Categorization, and Product Market Entry on the Google Play App Platform – optimal distinctiveness is really taking the management literature by storm. It will probably be the next open innovation or absorptive capacity with the growth in publications about it such as this one looking at app success.

The age at which Noble Prize research is conducted – Spoiler alert: It’s 44

Zero impact: a large-scale study of uncitedness – It’s common knowledge that a large number of scientific articles do not get cited, from this article around 20-30%. This article breaks down the level of uncitedness per subject and article type. This begs the question, if a tree falls in a forest and no one is around to hear it, does it make a sound?

How optimal distinctiveness affects new ventures’ failure risk: A contingency perspective – I really am fond of this topic optimal distinctiveness. In this study, they looked at metal bands and how much distinct they can be with their mix of sub-genres. They show that optimal distinctiveness is relevant to the failure risk of new ventures.

Is Elsevier helping or hurting scientific progress? – Fascinating podcast opening a dialogue between a scientist in Elsevier and an advocate of open science. I like how the host Julia and the participants tried to see each other’ perspective and not only discredit each other. Personally, I adore Elsevier and how it is moving towards more of a service company than a traditional journal publishing one.

The past week there has been a lot of fuss over the release of a language model by OpenAI which was supposed to be the most advanced yet. There is a lot of discussion online too because of the team’s decision to not release the full version of the model, afraid that it will be misused by Russian trolls, Chinese spies or whatever. Nonetheless, the partial model has been available for researchers to tinker with. To make things more convenient, there has also been an online implementation to skip the troubles of having to install the model on my computer.

I played with it to try to find out if it would help me write my doctoral dissertation. Maybe not, but at least give me some inspiration. These are my queries followed by the model’s continuation:

Prompt: Absorptive capacity is important for firms to gain competitive advantage.

A high birth rate reduces labor size relative to that in the United States (approximately 2.5 births per woman per 100 that would be generated in the United States in 2025) by the same method that causes individual WL to lag the rest of the world as a comparable technology (James and Wilcox 2005; Steuber and Turnbull 2006). Moreover, this concept establishes that WL may very well be a competitive advantage in conduct metrics secondary to WL. Thus, in conclusion, an incomplete equilibrium system often poses the problem of generating the next big PBE, ready for limiting these situations…

Prompt: To create disruptive innovations, companies should…

not work in the present day and if they do: “We’re not picking those lines for half-measures. ” Trust Poopmakers, Congress The hard law enforcement obligation imposed by the U. S. government on startups and small businesses should be absolute, followed by the next step. However, the United States should not violate copyright as being without regard to collateral damage and even have some credentials as a custodian of such equivalent works. Counterproductive courts should inquire into the bill from both sides and pass amendments for the immediate reporting of the U. S. to the American people…

The results have been disappointing unfortunately. We are not yet at a point that doctoral students can be replaced by actual robot slaves.