Beyond Twitter: the Nye-Krauss Klout index ?

kim-kardashian-tao-silver-dress-1565676875 bill-nye Thank you so much for the wonderful comments and suggestions on my last blog! My two priorities for today are:

  • give you the much-wanted list of everybody in the NK-index data
  • get your vote on how to improve our metric of social media presence

First, please check the below list to see whether your information is correct and whether we forgot anybody in our sample (remember, we need at least 200 Twitter followers). As stated in the previous blog, we could not find reliable lists of marketing professors on Twitter and many professors may tweet under another name. Last week, we added 5 datapoints, which also change the regression line to lnFollowers = 6.18+ 0.15*lnCitations (all numbers have been updated in the table). One key question is whether we should continue to update the regression line – or keep fixed the formula to calculate expected followers.

Second, the NK index is supposed to measure the discrepancy between a scientist’s academic impact and social media presence. Much has been written about whether citations (and from which source) best represent academic impact. Should we go to the H-index, which reflects the highest number X of papers that are cited at least X times? Please let us know! However, many of you felt particularly strong that one’s Twitter following is NOT the best operationalization of a scientist’s social media presence. Instead, people proposed Facebook (professional) page likes (https://www.facebook.com/Smarter-Marketing-with-Analytics-Dashboards-586717581359393/ ), LinkedIn Connections, YouTube video views (https://www.youtube.com/watch?v=q67Qwyhx0O0) and the score on Marketingprofs.com; where free consultancy is valued with Expert points (http://www.marketingprofs.com/ea/qst_category.asp?catID=5). Others recommended metrics such as number of students taught and books sold, but these are not part of online social media presence.

A composite index appears the best way forward, but how can we decide on which platforms to include and how much weigh to give each? The Klout score (klout.com) has the advantage of (1) connecting to the pages the user (in this case the marketing scientist) indicate are most important on a given platform, and (2) weigh the platform’s impact on a continuous basis. Please vote in the comments below: should we go to the Klout score instead?

Next week, we will talk about the broader implications for the tradeoff between knowledge creation and dissemination, and about the recommendations you offered for scientists aiming to increase either their social media presence or their academic impact…Stay tuned !

Name LnGS_Cite Ln_Follow predicted followers NKindex
A Pocheptsova 5.53 6.24 1107 67.20
Adam J. Fein 6.45 7.71 1271 77.97
Adrian F. Ward 4.56 5.42 957 25.49
allen weiss 5.50 9.88 1102 17.69
americus reed II 8.11 6.66 1630 14.84
Amitava Chattopadhyay 8.17 6.15 1645 11.85
Andreas Munzel 2.94 6.46 751 17.58
angela hausman 7.90 9.23 1580 9.18
angeline close 6.86 6.81 1352 4.57
Anindya Ghose 8.45 8.27 1716 5.95
Anita Elberse 7.67 8.40 1526 2.90
ashwin malshe 3.50 10.10 816 11.56
aylin aydinli 2.40 6.07 692 7.83
aysegul toker 5.88 6.64 1167 9.68
Barbara Bickart 7.75 5.66 1545 3.94
barbara kahn 9.14 7.99 1903 3.70
behice ece ilhan 2.77 7.46 732 7.51
cait lamberton 6.21 5.83 1226 4.54
ceren kolsarici 4.50 5.70 949 7.31
Charles Hofacker 7.79 6.04 1554 3.64
christine moorman 9.80 7.46 2101 1.38
christopher lee 2.77 8.73 732 4.74
claudia townsend 5.06 6.30 1032 1.68
dan ariely 9.96 11.50 2152 1.81
dan goldstein 9.20 8.32 1920 2.14
daniel ladik 5.36 6.14 1079 2.08
Darin White 5.70 7.97 1136 1.36
David Aaker 10.99 9.33 2511 0.82
david bell 8.21 9.15 1655 1.59
david schweidel 6.04 6.09 1195 1.44
Ellen Van Kleef 7.27 7.39 1437 2.05
Eric Bradlow 8.57 6.78 1747 1.72
Frank Goedertier 5.48 6.82 1099 1.65
Gad Saad 7.48 9.49 1483 1.95
gary schirr 4.91 11.22 1009 2.53
geeta menon 8.01 7.71 1606 1.38
Gino Van Ossel 5.58 8.60 1115 1.46
gonca soysal 3.43 5.44 808 2.07
hernan bruno 4.30 5.93 921 1.17
hope jensen schau 8.29 6.79 1675 0.72
Jaideep Prabhu 8.04 7.87 1613 0.40
Jennifer Aaker 9.60 10.09 2039 0.45
John Deighton 8.87 8.64 1827 0.95
jonah berger 7.49 7.07 1485 0.79
julian villanueva 6.67 8.61 1314 0.38
june cotte 7.34 6.96 1452 0.51
katherine milkman 7.34 6.96 1451 0.73
kelly goldsmith 5.25 7.45 1062 1.11
koen pauwels 8.38 6.50 1698 0.38
Kristin Diehl 6.76 5.65 1331 0.68
kristof dewulf 8.12 9.58 1633 0.47
laurens sloot 6.24 5.82 1232 0.35
linda price 9.39 6.28 1975 0.40
Marie Taillard 4.16 7.35 901 0.81
marije teerling 6.43 5.95 1267 0.92
markus giesler 7.04 8.15 1389 0.64
Michaela Draganska 6.74 6.04 1327 0.41
minha hwang 7.17 5.58 1416 0.62
myung ja kim 6.58 6.59 1296 0.60
neil bendle 6.09 6.98 1204 0.35
nick lee 8.42 5.30 1708 0.30
Nina Mazar 7.76 6.16 1547 0.25
Nirmalya Kumar 9.57 7.97 2029 0.34
nuno camacho 4.01 6.04 881 0.53
ofer mintz 3.83 5.98 858 0.44
paul driessen 4.69 5.53 976 0.68
Peeter Verlegh 7.88 6.53 1575 0.43
pete fader 8.71 8.84 1784 0.46
Peter C. Verhoef 9.02 5.82 1869 0.24
pierre chandon 8.22 7.07 1657 0.32
Pinar Yildirim 4.53 6.46 953 0.44
puneet manchanda 7.96 5.58 1594 0.19
Raymond Fisk 8.67 6.53 1773 0.27
Rhiannon MacDonnell 6.43 9.88 1268 0.30
rob kozinets 9.43 8.01 1987 0.24
Robert F. Lusch 10.18 6.72 2224 0.10
rudy moenaert 8.21 7.42 1655 0.20
Stephen L Vargo 9.97 7.06 2155 0.16
steven seggie 6.16 7.50 1217 0.44
steven white 7.07 8.71 1395 0.30
Sunil Gupta 9.20 8.32 1920 0.13
tim smits 6.06 7.63 1199 0.19
Timothy Keiningham 8.50 5.65 1728 0.16
tolga akcura 5.04 5.38 1029 0.21
uma karmarkar 7.07 6.68 1395 0.19
Venkat Ramaswamy 9.49 5.81 2005 0.13
wendy moe 7.81 6.28 1559 0.18
william rand 7.49 7.07 1485 0.23
wolfgang weitzl 2.08 6.20 660 0.40
Yogesh Joshi 6.63 5.59 1306 0.26
yves van vaerenbergh 4.87 6.61 1003 0.28
zeynep arsel 7.00 7.10 1380 0.14
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