Evidence–based practice in entrepreneurship requires effective communication of research findings. We focus on how research synopses can “promote” research to entrepreneurs. Drawing on marketing communications literature, we examine how message characteristics of research synopses affect their appeal. We demonstrate the utility of conjoint analysis in this context and find message length, media richness, and source credibility to have positive influences. We find mixed support for a hypothesized negative influence of jargon, and for our predictions that participants’ involvement with academic research moderates these effects. Exploratory analyses reveal latent classes of entrepreneurs with differing preferences, particularly for message length and jargon.
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References
1.
Academy of Management. (2011). Divisions & interest groups of the Academy of Management. Available at http://aom.org/DIG/, accessed 18 April 2013.
2.
AkaikeH. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317–332.
3.
AngstC.M. & AgarwalR. (2009). Adoption of electronic health records in the presence of privacy concerns: The ELM and individual persuasion. MIS Quarterly, 33(2), 339–370.
BakerM.J. & ChurchillG.A. (1977). The impact of physically attractive models on advertising evaluations. Journal of Marketing Research, 14(4), 538–555.
6.
BartunekJ.M. & RynesS.L. (2010). The construction and contributions of “implications for practice”: What's in them and what might they offer?Academy of Management Learning and Education, 9(1), 100–117.
7.
BergkvistL. & RossiterJ.R. (2007). The predictive validity of multiple–item versus single–item measures of the same constructs. Journal of Marketing Research, 44(2), 175–184.
8.
BrownR.D., BraskampL.A., & NewmanD.L. (1978). Evaluator credibility as a function of report style: Do jargon and data make a difference?Evaluation Quarterly, 2(2), 331–341.
9.
BrownT. (1972). A comparison of judgmental policy equations obtained from human judges under natural and contrived conditions. Mathematical Biosciences, 15, 205–230.
10.
CarterS. (2011). The rewards of entrepreneurship: Exploring the incomes, wealth, and economic well–being of entrepreneurial households. Entrepreneurship Theory and Practice, 35(1), 39–55.
11.
CascioW.F. (2007). Evidence–based management and the marketplace for ideas. Academy of Management Journal, 50(5), 1009–1012.
12.
ChaikenS. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766.
13.
ChaikenS. (1987). The heuristic model of persuasion. In ZannaM.P., OlsonJ.M., & HermanC.P. (Eds.), Social influence: The Ontario symposium (Vol. 5, pp. 3–39). Hillsdale, NJ: Erlbaum.
14.
ChaikenS. & EaglyA.H. (1976). Communication modality as a determinant of message persuasiveness and message comprehensibility. Journal of Personality and Social Psychology, 34(4), 605–614.
15.
ChaikenS., LibermanA., & EaglyA.H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In UlemanJ. & BarghJ.A. (Eds.), Unintended thought (pp. 212–252). New York: Guilford.
16.
ChaikenS. & MaheswaranD. (1994). Heuristic processing can bias systematic processing: Effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology, 66, 460–473.
17.
ChoiY.R. & ShepherdD.A. (2004). Entrepreneurs’ decisions to exploit opportunities. Journal of Management, 30(3), 377–395. doi:10.1016/j.jm.2003.04.002
18.
CronbachL.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
19.
DaftR.L. & LengelR.H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571.
20.
DavidssonP. (2002). What entrepreneurship research can do for business and policy practice. International Journal of Entrepreneurship Education, 1, 5–24.
21.
DavidssonP. & GordonS.R. (2012). Panel studies of new venture creation: A methods–focused review and suggestions for future research. Small Business Economics, 39(4), 853–876.
22.
DavidssonP., SteffensP., & FitzsimmonsJ. (2009). Growing profitable or growing from profits: Putting the horse in front of the cart?Journal of Business Venturing, 24(4), 388–406.
23.
de–MargerieV. & JiangB. (2011). How relevant is OM research to managerial practice? An empirical study of top executives’ perceptions. International Journal of Operations and Production Management, 31(2), 124–147. doi: 10.1108/01443571111104737
DeTienneD.R., ShepherdD.A., & De CastroJ.O. (2008). The fallacy of “only the strong survive”: The effects of extrinsic motivation on the persistence decisions for under–performing firms. Journal of Business Venturing, 23(5), 528–546.
DouglasS.C., KiewitzC., MartinkoM.J., HarveyP., KimY., & ChunJ. (2008). Cognitions, emotions and evaluations: An elaboration likelihood model for workplace aggression. Academy of Management Review, 33, 425–451.
28.
D'UrsoS.C. & RainsS.A. (2008). Examining the scope of channel expansion: A test of channel expansion theory with new and traditional communication media. Management Communication Quarterly, 21(4), 486–507.
29.
DwyerJ. (2005). Communication in business: Strategies and skills.Frenchs Forest, NSW, Australia: Pearson Education.
30.
EaglyA.H. & ChaikenS. (1984). Cognitive theories of persuasion. In BerkowitzL. (Ed.), Advances in experimental social psychology (Vol. 17, pp. 268–359). New York: Academic Press.
31.
EdmondsonA.S. & McManusS. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1155–1179.
32.
ElsbachK.D. & ElofsonG. (2000). How the packaging of decision explanations affects perceptions of trustworthiness. Academy of Management Journal, 43(1), 80–89.
33.
FlynnB.S., WordenJ.K., Yanushka BunnJ., ConnollyS.W., & DorwaldtA.L. (2011). Evaluation of smoking prevention television messages based on the elaboration likelihood model. Health Education Research, 26(6), 976–987.
GoreF.D. (2007, May 28). Business–school days. Fortune, p. 12.
36.
GreenP.E. & HelsenK. (1989). Cross–validation assessment of alternatives to individual–level conjoint analysis: A case study. Journal of Marketing Research, 26, 346–350.
37.
GreenP.E., KriegerA.M., & WindY. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3), 556–573. doi:10.1287/inte.31.3s.56.9676
38.
GreenP.E. & RaoV.R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8(3), 355–363.
39.
GreenP.E. & SrinivasanV. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.
40.
GreenP.E. & SrinivasanV. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54(4), 3–19.
41.
HaardJ., SlaterM.D., & LongM. (2004). Scientese and ambiguous citations in the selling of unproven medical treatments. Health Communication, 16, 411–426.
42.
HairJ.F.Jr., BlackW.C., BabinB.J., & AndersonR.E. (2010). Multivariate data analysis: A global perspective.Upper Saddle River, NJ: Pearson Education.
43.
HammondK. & AdelmanL. (1976). Science, values, and human judgment. Jurimetrics, 194, 389–397.
44.
HarveyC., StewartD.B., & EwingM. (2011). Forward or delete: What drives peer–to–peer message propagation across social networks?Journal of Consumer Behavior, 10(6), 365–372.
45.
HenreksonM. & SanandajiT. (2013). Billionaire entrepreneurs: A systematic analysis. IFN Working Paper No. 959. Stockholm: The Research Institute of Industrial Economics.
JoinerT.A., LevesonL., & Langfield–SmithK. (2002). Technical language, advice understandability, and perceptions of expertise and trustworthiness: The case of the financial planner. Australian Journal of Management, 27(1), 25–43.
52.
KaishS. & GiladB. (1991). Characteristics of opportunities search of entrepreneurs versus executives: Sources, interests, general alertness. Journal of Business Venturing, 6(1), 45–61.
53.
KamakuraW.A., WedelM., & AgrawalJ. (1994). Concomitant variable latent class models for conjoint analysis. International Journal of Research in Marketing, 11, 451–464.
54.
KardesF.R., FennisB.M., HirtE.R., TormalaZ.L., & BullingtonB. (2007). The role of the need for cognitive closure in the effectiveness of the disrupt–then–reframe influence technique. Journal of Consumer Research, 34, 377–385.
55.
KarmarkarU.R. & TormalaZ.L. (2010). Believe me, I have no idea what I'm talking about: The effects of source certainty on consumer involvement and persuasion. Journal of Consumer Research, 36, 1033–1049.
56.
KelemenM. & BansalP. (2002). The conventions of management research and their relevance to management practice. British Journal of Management, 13(2), 97–108.
LaczniakR.N., MuehlingD.D., & GrossbartS. (1989). Manipulating message involvement in advertising research. Journal of Advertising, 18(2), 28–38.
59.
LaneB. (2011, November 2). Let others judge research impact: Expert. The Australian. Available at http://www.theaustralian.com.au, accessed 18 April 2013.
60.
LohrkeF.T., Bugg HollowayB., & WoolleyT.W. (2010). Conjoint analysis in entrepreneurship research: A review and research agenda. Organizational Research Methods, 13(1), 16–30.
LowM.B. & MacMillanI.C. (1988). Entrepreneurship: Past research and future challenges. Journal of Management, 14(2), 139–161.
63.
LuceR.D. (1959). Individual choice behavior: A theoretical analysis.New York: Wiley.
64.
LuceR.D. & TukeyJ.W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27.
65.
MacInnisD.J., MoormanC., & JaworskiB.J. (1991). Enhancing and measuring consumers’ motivation, opportunity, and ability to process brand information from ads. Journal of Marketing, 55, 32–53.
66.
MacInnisD.J. & ParkC.W. (1991). The differential role of characteristics of music on high– and low–involvement consumers’ processing of ads. Journal of Consumer Research, 18, 161–173.
67.
McFaddenD. (1986). The choice theory approach to market research. Marketing Science, 5(4), 275–297.
68.
McGahanA. (2007). Academic research that matters to managers: On zebras, dogs, lemmings, hammers, and turnips. Academy of Management Journal, 50(4), 748–753.
69.
MitchellA.A. (1981). The dimensions of advertising involvement. In MonroeK.B. (Ed.), Advances in consumer research (Vol. 8, pp. 25–30). Washington, DC: Association for Consumer Research.
70.
MitchellR.K. (2011). Increasing returns and the domain of entrepreneurship research. Entrepreneurship Theory and Practice, 35(4), 615–629.
71.
MitraA., RaymondM., & HopkinsC. (2008). Can consumers recognize misleading advertising content in a media rich online environment?Psychology and Marketing, 25(7), 655–674.
72.
MohrmanS., GibsonC., & MohrmanA. (2001). Doing research that is useful to practice: A model and empirical exploration. Academy of Management Journal, 44, 357–375.
73.
National Science Foundation. (2012). Promoting research and innovation in methodologies for evaluation (PRIME). Available at http://www.nsf.gov/publications/, accessed 18 April 2013.
74.
O'KeefeD.J. (2002). Persuasion: Theory and research (2nd ed.). Thousand Oaks, CA: Sage Publications.
OtondoR.F., Van ScotterJ.R., AllenD.G., & PalviaP. (2008). The complexity of richness: Media, message, and communication outcomes. Information and Management, 45, 21–30. doi:10.1016/j.i.m.2007.09.003
77.
ParkD.H., LeeJ., & HanI. (2007). The effect of on–line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148.
78.
PettyR.E. (2001). Attitude change. In SmelserN.J. & BaltesP.B. (Eds.), International encyclopedia of the social and behavioral sciences (pp. 894–899). Oxford: Pergamon.
79.
PettyR.E. & BriñolP. (2012). The elaboration likelihood model. In Van LangeP.A.M., KruglanskiA.W., & HigginsE.T. (Eds.), Handbook of theories of social psychology (pp. 224–245). London: Sage.
80.
PettyR.E. & CacioppoJ.T. (1984). The effects of involvement on responses to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology, 37, 1915–1926.
81.
PettyR.E. & CacioppoJ.T. (1986). The elaboration likelihood model of persuasion. In BerkowitzL. (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123–205). New York: Academic Press.
82.
PettyR.E., CacioppoJ.T., & GoldmanR. (1981). Personal involvement as a determinant of argument–based persuasion. Journal of Personality and Social Psychology, 41, 847–855.
83.
PettyR.E., CacioppoJ.T., & SchumannD. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135–146.
84.
PettyR.E., RuckerD., BizerG., & CacioppoJ.T. (2004). The elaboration likelihood model of persuasion. In SeiterJ.S. & GassG.H. (Eds.), Perspectives on persuasion, social influence and compliance gaining (pp. 65–89). Boston, MA: Allyn & Bacon.
85.
PettyR.E. & WegenerD.T. (1998). Attitude change: Multiple roles for persuasion variables. In GilbertD.T., FiskeS.T., & LindzeyG. (Eds.), The handbook of social psychology (4th ed., pp. 323–390). New York: McGraw–Hill Companies, Inc.
86.
PfefferJ. & SuttonR.I. (2006). Evidence–based management. Harvard Business Review, 84(1), 62–74. Available at http://hbr.org/, accessed 18 April 2013.
87.
RatneshwarS. & ChaikenS. (1991). Comprehension's role in persuasion: The case of its moderating effect on the persuasive impact of source cues. Journal of Consumer Research, 18(1), 52–62.
88.
RauchA. & FreseM. (2006). Meta–analyses as a tool for developing entrepreneurship research and theory. Advances in Entrepreneurship, Innovation, and Economic Growth, 9, 29–51.
89.
Research Excellence Framework. (2012). Research excellence framework 2014. Available at http://www.ref.ac.uk, accessed 18 April 2013.
90.
RobsonC. (2011). Real world research: A resource for users of social research methods in applied settings.Hoboken, NJ: Wiley–Blackwell.
91.
RousseauD.M. (2006). Is there such a thing as “evidence–based management”?Academy of Management Review, 31(2), 256–269.
92.
RousseauD.M. & McCarthyS. (2007). Educating managers from an evidence–based perspective. Academy of Management Learning and Education, 6(1), 84–101.
93.
RynesS.L., BartunekJ.M., & DaftR.L. (2001). Across the great divide: Knowledge creation and transfer between practitioners and academics. Academy of Management Journal, 44(2), 340–355.
94.
RynesS.L., ColbertA.E., & BrownK.G. (2002). HR professionals’ beliefs about effective human resource practices: Correspondence between research and practice. Human Resource Management, 41(2), 149–174.
95.
RynesS.L., GilukT., & BrownK. (2007). The very separate worlds of academic and practitioner periodicals in human resource management: Implications for evidence–based management. Academy of Management Journal, 50(5), 987–1008.
96.
SeverinW.J. & TankardJ.W. (2001). Communication theories: Origins, methods, and uses in the mass media (5th ed.). White Plains, NY: Longman.
97.
ShannonC. & WeaverW. (1949). The mathematical theory of communication.Urbana, IL: University of Illinois Press.
98.
ShapiroD.L., KirkmanB.L., & CourtneyH.G. (2007). Perceived causes and solutions of the translation problem in management research. Academy of Management Journal, 50(2), 249–266.
99.
ShepherdD.A. & ZacharakisA. (1999). Conjoint analysis: A new methodological approach for researching the decision policies of venture capitalists. Venture Capital, 1(3), 197–217.
100.
SimmondsP.G., DawleyD.D., RitchieW.J., & AnthonyW.P. (2001). An explanatory examination of the knowledge transfer of strategic management concepts from the academic environment to practicing managers. Journal of Managerial Issues, 13(3), 360–375.
101.
SimonS.J. & PeppasS.C. (2004). An examination of media richness theory in product web site design: An empirical study. The Journal of Policy, Regulation and Strategy for Telecommunications, Information and Media, 6(4), 270–281.
TerpstraD.E. & RozellE.J. (1998). Human resource executives’ perceptions of academic research. Journal of Business and Psychology, 13(1), 19–29.
104.
ThomasK.W. & TymonW.G. (1982). Necessary properties of relevant research: Lessons from recent criticisms of the organizational sciences. Academy of Management Review, 7, 345–352.
105.
TormalaZ.L. & ClarksonJ.J. (2007). Assimilation and contrast in persuasion: The effects of source credibility in multiple message situations. Personality and Social Psychology Bulletin, 33, 559–571.
106.
TormalaZ.L. & PettyR.E. (2004). Source credibility and attitude certainty: A metacognitive analysis of resistance to persuasion. Journal of Consumer Psychology, 14, 427–442.
107.
Van de VenA.H. & JohnsonP.E. (2006). Knowledge for theory and practice. Academy of Management Review, 31(4), 802–821.
WooC.Y., CooperA.C., & DunkelbergW.C. (1991). The development and interpretation of entrepreneurial typologies. Journal of Business Venturing, 6(2), 93–114.
110.
YalchR.F. & Elmore–YalchR. (1984). The effect of numbers on the route to persuasion. Journal of Consumer Research, 11(1), 522–527.
111.
YangS., KangM., & JohnsonP. (2010). Effects of narratives, openness to dialogic communication, and credibility on engagement in crisis communication through organizational blogs. Communication Research, 37(4), 473–497.
112.
ZahraS.A. & WrightM. (2011). Entrepreneurship's next act. Academy of Management Perspectives, 25(4), 67–83.
113.
ZaichkowskyJ.L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12, 341–352.
114.
ZhouT. (2012). Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Computers in Human Behavior, 28(4), 1518–1525.
