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Publishing Qualitative Research

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Primer on designing and publishing qualitative management research; presented at Baylor University Hankamer School of Business

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Publishing Qualitative Research

  1. 1. Publishing  Qualita.ve  Research   Joel  West   Keck  Graduate  Ins.tute   The  Claremont  Colleges   October  17,  2018   Research  Methods  Symposium  Series   Hankamer  School  of  Business   Baylor  University  
  2. 2. Jargon  Check   Related  (but  dis.nct)  terms   •  Qualita.ve  data   •  Case  study   •  Ethnographic  study   •  Interview  data   •  Induc.ve,  theory-­‐building,  exploratory   Either  Posi+vist  or  Intrepre+vist  
  3. 3. Qualita.ve  is  Similar  to  Quant   •  Importance  of  framing,  contribu.on   •  Arduous  review  process   •  Wide  variability  in  reviewer  opinions   •  Strong  methodological  norms  in  the  field  
  4. 4. Qualita.ve  is  Different  from  Quant   •  Different  forms  of  data   •  Different  forms  of  analysis   •  Different  standards  of  representa.veness  and   validity   •  Different  sorts  of  ques.ons   – Generally  exploratory  rather  than  confirmatory   – Richer  but  less  precise  data   – BeYer  for  "why"  and  "how"  rather  than  "how   o[en"  ques.ons  
  5. 5. Four  Phases  of  Understanding   1.  Learning  methodological  norms   2.  Research  design  for  a  specific  study   3.  Wri.ng  up  the  study   4.  Geang  it  published  
  6. 6. My  Biases   •  31  ar.cles;  only  a  few  "A*"  journals   – Mostly  innova.on,  some  management,  MIS,   entrepreneurship   – ≈25  chapters   – 4  HICSS  proceedings   – 2  edited  books   •  AE  for  Research  Policy,  journal  reviewer   •  Posi.vist  industry-­‐  or  firm-­‐level  research  
  7. 7. 1.  METHODOLOGICAL  NORMS  
  8. 8. Five  Qualita.ve  Approaches   •  Narra.ve   •  Phenomenological   •  Grounded  theory   •  Ethnographic   •  Case  study   Creswell  and  Poth,  Qualita+ve  Inquiry  and   Research  Design  4e,  Sage,  2017   Goulding,  "Grounded  theory,  ethnography  and   phenomenology,"  European  Journal  of   Marke+ng,  2005    
  9. 9. Level  of  Analysis   •  Consumer  behavior:  the  individual,   community   •  Marke.ng  strategy/MIS:  a  project   •  Org  design:  a  group/division   •  Strategy:  level  of  the  firm   •  Innova.on:  a  technology  
  10. 10. Management  Norms   •  Eisenhardt  and  Graebner,  "Theory  building   from  cases:  Opportuni.es  and  challenges,"   Academy  of  Management  Journal,  2007.   •  Eisenhardt  et  al,  "…Rigor  without  rigor   mor.s,"  Academy  of  Management  Journal,   2016.   •  Gibbert  et  al,  "What  passes  as  a  rigorous  case   study?"  Strategic  Management  Journal,  2008.  
  11. 11. Informa.on  Systems  Norms   •  Dubé  and  Paré,  "Rigor  in  informa.on  systems   posi.vist  case  research,"  MISQ,  2003.   •  Sarker  et  al,  "Qualita.ve  studies  in   informa.on  systems,"  MISQ,  2013.   •  Marshall  et  al,  "Does  sample  size  maYer  in   qualita.ve  research?"  Journal  of  Computer   Informa+on  Systems,  2013.  
  12. 12. Marke.ng  Norms   •  Belk,  Handbook  of  Qualita+ve  Research   Methods  in  Marke+ng.  Edward  Elgar   Publishing,  2007.   •  Gummesson,  "Qualita.ve  research  in   marke.ng,"  European  Journal  of  Marke+ng,   2005.   •  Goulding,  "Grounded  theory,  ethnography   and  phenomenology,"  European  Journal  of   Marke+ng,  2005.  
  13. 13. 2.  RESEARCH  DESIGNS  
  14. 14. Research  Design   Key  decisions  in  research  design:   •  Research  ques.on(s)   •  Literature/gap   •  Proof/contribu.on   •  Data  collec9on   •  Data  analysis   Some  (not  all)  can  be  changed   later  
  15. 15. Typical  Management  Designs   •  Single  case  design   – Firm,  technology,  industry   – Exemplar,  outlier,  unusual  insight  (Tripsas  &   Gavea,  SMJ  2000;  West  &  Wood,  AiSM  2013)   – Used  for  process  studies  (Tripsas,  SMJ  1997)  and   longitudinal  studies  (West,  JMS  2008)   •  Compara.ve  case  design  (Eisenhardt)   – "Theore.cal  sampling"  to  show  variance   Typically,  30-­‐50  interviews  
  16. 16. Eisenhardt  method   •  Jus.fy  theory  building   •  Theore.cal  sampling  of  mul.ple  (4-­‐12)  cases   –  Code  variables  between  cases  to  show  variance   •  Specific  approach  for  exposi.on:   –  "Sketch  emergent  theory  in  the  intro"   –  (Usually)  LiYle  or  no  lit  review   –  Present  proposi.ons  supported  by  data   –  Long  discussion  sec.on     See  Eisenhardt,  Graebner  &  Sonenshein  (AMJ  2016),  Eisenhardt  &  Graebner   (AMJ  2007),  Eisenhardt  (AMR  1989);  Graebner,  Mar.n  &  Roundy  (SO  2012)  
  17. 17. Other  Methods   1.  Eisenhardt  most  cited  but  not  only  method   2.  Gioia  method   –  Induc.ve,  grounded  theory   –  Assumes  socially  constructed  ontology   3.  Langley  method:  an  approach  for  process   (rather  than  variance)  research   Gehman  et  al,  "Finding  theory–method  fit:  A  comparison  of   three  qualita.ve  approaches  to  theory  building,"  Journal  of   Management  Inquiry  27,3  (2018):  284-­‐300.  
  18. 18. Coding  Data   How  do  interviews  get  coded?   •  Formal  coding:  grounded  theory   – Typically  with  so[ware  package  (Nvivo,  Atlas..)   – Mul.ple  levels  of  codes   •  Informal  coding   – Less  rigorous  examina.on  of  paYerns   •  Say  what  you  did   •  Don’t  claim  to  do  something  you  didn’t  
  19. 19. Evolving  Data  Collec.on   •  Interview  ques.ons  o[en  evolve  over  .me   – Some  ques.ons  don’t  work   – Others  iden.fy  completely  new  areas  of  inquiry   – Opportunity  to  fix  data  as  it’s  collected   •  O[en  possible  to  change  the  ques.ons   – Keep  core  ques.ons,  re-­‐interview  for  new  ones   – Some.mes  you  can’t  change  it  enough  
  20. 20. 3.  WRITING  UP  RESEARCH  
  21. 21. Recommended  Ar.cle  
  22. 22. Pisalls  (1)   Pisalls  are  o[en  similar  to  quan.ta.ve   •  Framing   – Confused/unclear  framing   – Framing  doesn’t  match  data   – Framing  doesn’t  match  discussion/contribu.on   •  Lit  review  vs.  findings   – Theory  develop:  bias  towards  short  lit  reviews   – What  you  learn  doing  a  study  is  a  finding,  not  part   of  the  lit  review  
  23. 23. Pisalls  (2)   •  Falling  in  love  with  the  data   – Excessive  length  or  detail   – Neglec.ng  generizability  and  the  "so  what"   •  Non-­‐standard  research  design  &  ontology   – Common:  you  can’t  test  theory  with  an  N  of  1   – Less  common:  confusing  mixture  of  data   gathering,  collec.on,  analysis  
  24. 24. Find  Journal-­‐Specific  Exempars   •  Each  field  has  its  favorite  authors,  exemplars,   methods  cita.ons   •  Each  journal  has  its  own  norms   •  Editor(s),  associate  editors,  senior  editors   •  Reviewer  pool   •  Standards  and  previously  accepted  work   •  Find  recent  exemplars  in  that  journal!   •  Supplement  with  similar  (and  "beYer")  journals  
  25. 25. Management  Exemplars   •  Academy  of  Management  Journal:  Santos  &   Eisenhardt  (2009),  Hallen  &  Eisenhardt  (2012),   Ben-­‐Menahem  et  al  (2016)   •  Strategic  Management  Journal:    Tripsas   (1997),  Bingham  &  Eisenhardt  (2011)   •  Strategic  Entrepreneurship  Journal:  Clarysse  et   al  (2011),  Bingham  &  Haleblian  (2012)   •  Research  Policy:  O’Mahony  (2003),  Jain   (2012),  Lehoux  et  al  (2014)  
  26. 26. Informa.on  Systems  Exemplars   •  MIS  Quarterly:    Kaplan  &  Ducho  (1988),   Cooper  (2000),  Levina  &  Vaas  (2005),  Markus   et  al  (2006)   •  Informa+on  Systems  Research:  Ramesh  et  al   (2012),  Germonprez  et  al  (2017)     •  Journal  of  Management  Informa+on  Systems:   Wigand  et  al  (2005)  
  27. 27. It’s  all  about  the  tables…   •  Most  qualita.ve  papers  require  tables   •  Breaks  up  text   •  Reveals  data  you  used  for  inference   •  Forces  you  to  simplify   •  Looks  more  “scien.fic”   Diagrams  are  usually  great,  but  not  required  
  28. 28. 4.  GETTING  RESEARCH  PUBLISHED  
  29. 29. Typical  Problems   Ordinary  research  problems   •  Doesn’t  deliver  on  promises  in  framing   •  Poor  execu.on  or  explana.on   •  Abstrac.on/generalizability   •  Nothing  new   •  Doesn’t  (can’t)  address  reviewer  concerns  
  30. 30. Qualita.ve  Problems   Problems  specific  to  qualita.ve  studies:   •  Confusing  mess  of  story  or  data   •  Missing  insights  from  data   •  Ontological  impossibility  (suggest,  not  prove)    
  31. 31. Theory  Building  on  the  Fron.er   •  Research  opportuni.es  on  the  fron.ers  of   science  are  like  opportuni.es  on  the  19th  century   Western  fron.er   •  Qualita.ve  researchers  are  trappers   –  They  live  off  the  land  at  at  the  edge  of  the  fron.er   –  They  work  in  a  world  without  fences   •  Quan.a.ve  researchers  are  the  farmers/ranchers   –  They  put  up  fences,  bring  order,  civiliza.on   –  Goal:  consistent,  efficient,  reliable  produc.on   •  When  the  seYlers  show  up,  a  trapper  needs  to   find  a  new  fron.er  
  32. 32. LEARN  FROM  MY  MISTAKES  
  33. 33. #1:  MISQ   •  In  2003,  MIS  student  Jason  Dedrick  &  I  conduct   11  interviews  on  Linux  adop.on  by  firms   •  Almost  no  research  on  how  firms  adopt  standards   •  Combine  org  innova.on  adop.on  literature  with   standards  literature   •  June  2003:  submit  "An  Exploratory  Study  into   Open  Source  Plasorm  Adop.on"  to  HICSS   •  Sept  2003:  submit  to  special  issue  workshop   •  March  2004:  submit  to  MISQ  special  issue  on   "Standard  Making:  A  Cri.cal  Research  Fron.er   for  Informa.on  Systems"  
  34. 34. Take-­‐away  From  Reviews   •  Fixable  problems:   •  Rushed  to  special  issue   •  Put  off  contribu.on  to  the  last  minute   •  Needed  more  Eisenhardt-­‐style  data  coding   •  Not  fixable  problem:   •  Interview  data  about  open  source,  not  standards   •  One-­‐.me  opportunity:  first  (and  only)  MISQ  special   issue  on  standards   •  Conclusion:   •  Important  research  ques.on   •  “A”  journal  pub  doomed  by  poor  design  that  didn’t  fit  (or   couldn’t  be  expanded  to  address)  special  issue  
  35. 35. #2.  SEJ  2018   •  Went  5  rounds  for  SEJ  special  issue  on  "open   innova.on"   •  Data:  interviews,  secondary  data  on  28  3D   prin.ng  entrepreneurs   – Ques.on:  what  explains  variance  on  openness?   •  Nonstandard  qualita.ve  research  design  
  36. 36. Difficulty  Finding  Exemplar   •  Iden.fied/studied  20  published  studies   –  8  AMJ;  3  SEJ;  2  JBV,  RP,  SMJ;  1  ASQ,  ISR,  JPIM   –  Typically  4-­‐10  cases,  rich  data  on  each  case   •  Bingham  &  Haleblian  (SEJ  2012):  7  cases,  45  interviews   •  Our  study   –  1  interview  for  each  of  28  cases   –  71%  <  3  years  old,  most  1-­‐3  employees   –  Who  else  do  you  interview  in  a  new  firm?   •  Secondary  data  essen.al  to  sa.sfy  reviewers  
  37. 37. MIXED  METHODS  
  38. 38. Mixed  Methods   •  Each  form  of  data  has  its  weaknesses   •  Mul.ple  data  sources  allow  for  triangula.on   Common  mixed  method  designs   1.  Quan.ta.ve  &  qualita.ve   –  Quan.ta.ve  provides  generalizability   –  Quali.a.ve  (pre-­‐  or  post-­‐)  explains  what’s  measured   2.  Qualita.ve:  interview  and  archival   –  Qualita.ve  provides  insight   –  Archival  is  objec.ve  and  o[en  longitudinal   Some.mes  includes  quan.fying  qualita.ve  data  
  39. 39. Example:  Jarvenpaa  &  Leidner  (1999)   •  Experiment:  350  students  from  six  con.nents   par.cipate  in  online  simula.on   •  Data:  individual  surveys,  email  archive   – 29/75  teams  have  complete  data   – Code  4  categories;  pick  3  exemplars  per  category   •  Content  analysis  of  email  from  12  teams   •  Surveys  quan.fy  variance,  qualita.ve  data   explains  the  “how”  and  “why”   Jarvenpaa  and  Leidner,  "Communica.on  and  trust  in  global  virtual  teams,"  Organiza+on  Science,  1999.  
  40. 40. #3.  JMS   •  Context:  Applica.on  of  1  technology  (Shannon   theory)  to  deep  space  communic.ons   •  Broad  mix  of  data   – Scien.fic  publica.ons  &  awards   – Archival  data  on  27  NASA  probes,  1960-­‐1978   – 11  primary  interviews  with  engineers   – Previous  oral  histories  with  key  inventors   – Secondary  data  on  two  key  MIT  spinoff  companies   Commercializing Open Science: Deep Space Communications as the Lead Market for Shannon Theory, 1960–73 Joel West San José State University ABSTRACT Recent research on the commercialization of scientific discoveries has emphasized the use of formal intellectual property rights (notably patents) as a mechanism for aligning the academic and entrepreneurial incentives for commercialization. Without such explicit intellectual property rights and licensing, how is such open science commercialized? This paper examines the commercialization of Claude Shannon’s theory of communications, developed at and freely disseminated by Bell Telephone Laboratories. It analyses the first 25 years of Shannon theory, the role of MIT in developing and extending that theory, and the importance of deep space communications as the initial market for commercialization. It contrasts the early paths of two MIT-related spinoffs that pursued this opportunity, including key technical and business trajectories driven by information theory. Based on this evidence, the paper provides observations about commercializing open science, particularly for engineering-related fields. INTRODUCTION Industries typically enjoy long periods of relatively predictable incremental innovation, punctuated by irregular bursts of discontinuous technological innovation. Such discon- tinuities enable new, previously unexplored trajectories for technological innovation (Dosi, 1982; Nelson and Winter, 2002). From these new technological trajectories many opportunities arise for new products, new firms and new industries (Anderson and Tushman, 1990; Nelson, 1995). In many cases, such technological breakthroughs can be traced back to basic research disseminated through the peer-reviewed process of open science, often from public research institutions such as universities. In some cases, the discontinuous improvement can be traced to a single discovery, whereas in other cases, it builds upon a stream of research in open science. Address for reprints: Joel West, San José State University, BT555, 1 Washington Square, San José, CA 95192-0070, USA (Joel.West@sjsu.edu). © Blackwell Publishing Ltd 2008. Published by Blackwell Publishing, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. Journal of Management Studies 45:8 December 2008 0022-2380
  41. 41. Review  (round  1)   •  "I  would  like  to  applaud  the  authors  for  their   detailed  data  collec.on  and  their  descrip.on   of  the  journey  between  science  à   commercializa.on  as  it  pertains  to  Shannon’s   informa.on  theory.  This  is  a  fascina.ng  case   descrip.on.  The  key  task  for  the  authors  is  to   make  the  paper  more  theore.cally  precise  so   that  its  insights  can  then  be  compared  and   contrasted  with  prior  work/alternate  models   of  technological  development.  "  
  42. 42. Typology  of  Case  Studies   Welch  et  al  (2011),   JIBS,  740-­‐762  
  43. 43. “Contextualized”  Explana.on   •  "Overall,  case  studies  that  emphasised  causal   explana.on  …  were  in  the  minority.  …  [W]e  paid   aYen.on  to  how  authors  in  this  quadrant  were   able  to  combine  the  inherent  strength  of  the  case   study  to  contextualise  with  its  explanatory   poten.al.  …   •  "In  this  quadrant,  authors  were  more  open  about   the  explanatory  aims  of  their  paper  …  what   typifies  the  authors’  language  is  a  very  par.cular   view  of  causality  as  a  complex  and  dynamic  set  of   interac.ons  that  are  treated  holis.cally.  "     Welch  et  al  (2011),  JIBS,  p.  753-­‐754  
  44. 44. West  (2008):  Final  Framing   •  "Technological  breakthroughs  can  [o[en]  be   traced  back  to  basic  research  disseminated   through  the  peer-­‐reviewed  process  of  open   science,  o[en  from  public  research  ins.tu.ons   such  as  universi.es.     •  "But  how  does  such  open  science†  get   commercialized?  In  par.cular,  absent  an  explicit   policy  to  align  the  interests  of  scien.sts  and   firms,  how  does  the  knowledge  disseminated  in   open  science  become  incorporated  into  the   offerings  of  for-­‐profit  companies?"   †  Paul  A.  David  (1998),  ‘Common  agency  contrac.ng  and  the  emergence  of  open  science   ins.tu.ons,’  American  Economic  Review,  88  
  45. 45. CONCLUSIONS  
  46. 46. Publishing  Qualita.ve  Reseach   •  Interes.ng  data  and  phenomenon   •  Important  unanswered  ques.on   •  Engage  powerful  theories   •  Legi.mate  methods  and  research  design   •  Clarity  of  exposi.on   •  Clear  alignment  of  ques.on,  framing,  data,   contribu.on  

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