An Exploration of a Respondent Pre-Qualifying Framework to Increase Response Rates in Social Media Initiated Online Surveys

Australasian Marketing Journal - Tập 26 - Trang 239-261 - 2018
Diana Denise F. Basa-Martinez1, Janet Y. Cabrera1, LA G. Dionaldo1, Jonathan Gavino R. Orillo1, Paul John M. Ramos1, Lanndon A. Ocampo2
1School of Management, University of the Philippines Cebu, Gorordo Avenue, Lahug, Cebu City 6000, Philippines
2Department of Industrial Engineering, Cebu Technological University, Corner M.J. Cuenco Ave. & R. Palma St., Cebu City, 6000, Philippines

Tóm tắt

The use of web-based surveys is currently increasing due to its cost-effectiveness and agility as it provides access to market researchers to web-connected populations who are unlikely to answer through traditional survey methods. However, survey response rates in market research are in general decline and among survey platforms, web-based surveys have the highest rates of non-response. Thus, there is a need to address the declining response rates for web-based surveys particularly unit response rates – the likelihood the respondent would answer the survey. This paper proposes a respondent prequalifying framework that reduces unit non-response rates of web-based non-probabilistic surveys. A checklist of respondent characteristics influencing the likelihood of unit non-response was developed. The framework was then adopted for its applicability by replicating the recruitment phase of two case studies wherein the prequalifying checklist was applied with consideration to the respondent profile requirements of each study. While this paper does not intend to provide robust empirical evidence to the proposed framework, it demonstrates a promising framework that can be used to increase unit non-response rate by comprehensively integrating the pre-qualifying factors in the domain literature and carefully developing such framework to the most plausible direction a web-survey can be implemented. Findings suggest that (1) the proposed respondent prequalifying framework increases the unit response by prequalifying the sample in the recruitment stage, and (2) increasing the threshold value may increase unit response rates with careful consideration to some significant issues such as the weights assigned to the prequalifying factors, the quality of the background information of the respondents in relation to the prequalifying factors, and the sensitivity of the survey topic. The proposed framework is developed with strong theoretical grounding and detailed discussion for its practical use is also provided. The framework benefits market researchers by reducing unit non-response costs and increasing efficiency in social media-based market surveys.

Tài liệu tham khảo

Alsnih R., 2006, Travel Survey Methods, Quality and Future Directions, 569, 10.1108/9780080464015-032 Akbulut Y., 2017, Comput. Hum. Behav., 72, 87, 10.1016/j.chb.2017.02.043 Allen J., 1981, J. Chiropr., 27, 24 Amon K.L., 2014, Acad. Pediatr., 14, 439, 10.1016/j.acap.2014.05.049 Angeliki N.M., 2016, J. Choice Model., 18, 18, 10.1016/j.jocm.2016.04.005 Armoogum J., 2015, Transp. Res. Procedia, 11, 60, 10.1016/j.trpro.2015.12.006 Askalidis G., 2017, Decis. Support Syst., 97, 23, 10.1016/j.dss.2017.03.002 Aydın G.S., 2013, Procedia – Soc. Behav. Sci., 93, 737, 10.1016/j.sbspro.2013.09.272 Batterham P.J., 2014, Int. J. Methods Psychiatr. Res., 23, 184, 10.1002/mpr.1421 Bayart C., 2015, Transp. Res. Procedia, 11, 118, 10.1016/j.trpro.2015.12.011 Berry D.M., 2012, Res. Nurs. Health, 35, 659, 10.1002/nur.21498 10.1177/089443939701500204 Bonnel P., 2015, Transp. Res. Procedia, 11, 108, 10.1016/j.trpro.2015.12.010 10.1111/j.1083-6101.2001.tb00124.x 10.1177/0190272516628298 Cerasoli C.P., 2014, Psychol. Bull., 140, 980, 10.1037/a0035661 Choi I., 2017, Internet Interv., 8, 27, 10.1016/j.invent.2017.02.002 Constantinides E., 2014, Procedia – Soc. Behav. Sci., 148, 40, 10.1016/j.sbspro.2014.07.016 Cornil Y., 2017, J. Consum. Psychol., 27, 456, 10.1016/j.jcps.2017.03.003 Couper M.P., 2001, Public Opin. Q., 64, 464, 10.1086/318641 Daniels D.P., 2017, Organ. Behav. Hum. Decis. Process., 139, 92, 10.1016/j.obhdp.2016.12.005 Davis R.E., 2010, Health Educ. Res., 25, 14, 10.1093/her/cyp046 de Abreu e Silva J., 2015, Transp. Res. Procedia, 11, 289, 10.1016/j.trpro.2015.12.025 Dillman D.A. Mail and Internet Surveys: The Tailored Design Method–2007 Update with New Internet, Visual, and Mixed-Mode Guide 2011 John Wiley & Sons Dillman D.A. Mail and Internet Surveys: The Tailored Design Method 2nd ed.2007 John Wiley New York Ertugan A., 2016, Procedia Comput. Sci., 102, 677, 10.1016/j.procs.2016.09.461 Fang J., 2012, Cyberpsychol. Behav. Soc. Netw., 15, 195, 10.1089/cyber.2011.0411 García-Domingo M., 2017, Procedia – Soc. Behav. Sci., 237, 249, 10.1016/j.sbspro.2017.02.071 10.1186/s12889-015-2350-9 Groves R.M., 2002, Survey Nonresponse, 23 Groves R.M., 2010, Public Opin. Q., 74, 849, 10.1093/poq/nfq065 Gulbahar M.O., 2015, Procedia – Soc. Behav. Sci., 195, 453, 10.1016/j.sbspro.2015.06.489 Harris M.L., 2014, Aust. N. Z. J. Public Health, 38, 495, 10.1111/1753-6405.12281 Hohwü L., 2013, J. Med. Internet Res., 15, e173, 10.2196/jmir.2595 Hoonakker P., 2009, Int. J. Hum.-Comput. Interact., 25, 348, 10.1080/10447310902864951 Jia-ming F., 2007, Proceedings of International Conference on Management Science and Engineering, ICMSE 2007, 174, 10.1109/ICMSE.2007.4421843 Jin B., 2013, Comput. Hum. Behav., 29, 2463, 10.1016/j.chb.2013.05.034 Kayrouz R., 2016, Internet Interv., 4, 1, 10.1016/j.invent.2016.01.001 Kim K.Y., 2015, Technol. Forecast. Soc. Change, 91, 78, 10.1016/j.techfore.2014.01.011 Kiran A.M., 2016, Procedia – Soc. Behav. Sci., 217, 858, 10.1016/j.sbspro.2016.02.015 Krishnan T.N., 2016, IIMB Manag. Rev., 28, 88, 10.1016/j.iimb.2016.05.001 Kuru O., 2016, Comput. Hum. Behav., 57, 82, 10.1016/j.chb.2015.12.008 Lewis E.F., 2013, Radiography, 19, 240, 10.1016/j.radi.2013.02.003 Loxton D., 2015, J. Med. Internet Res., 17, e109, 10.2196/jmir.4261 Mierzwa S., 2016, Procedia Eng., 159, 66, 10.1016/j.proeng.2016.08.065 Millar M.M., 2011, Public Opin. Q., 75, 249, 10.1093/poq/nfr003 Orr E.S., 2009, CyberPsychol. Behav., 12, 337, 10.1089/cpb.2008.0214 Peter J.P. Donnelly J.H. Marketing Management: Knowledge and Skills: Text, Analysis, Cases, Plans 2011 Business Pub., Inc. Plano Pinder W.C.C. Work Motivation in Organizational Behavior 2nd ed.2011 Psychology Press New York, NY 10.1016/j.kjss.2017.02.001 Romero D.M., 2011, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 18, 10.1007/978-3-642-23808-6_2 Sánchez-Fernández J., 2012, Comput. Hum. Behav., 28, 507, 10.1016/j.chb.2011.10.023 Schneider K.L., 2012, J. Epidemiol. Community Health, 66, 290, 10.1136/jech.2009.103861 Sauermann H., 2013, Res. Policy, 42, 273, 10.1016/j.respol.2012.05.003 Spoorena P., 2012, Procedia – Soc. Behav. Sci., 69, 990, 10.1016/j.sbspro.2012.12.025 Stoop I. Koch A. Halbherr V. Loosveldt G. Fitzgerald R. Field Procedures in the European Social Survey Round 8, Guidelines for Enhancing Response Rates and Minimising Non-response Bias 2016 ESS ERIC Headquarters LondonTechnical report Szolnoki G., 2013, Wine Econ. Policy, 2, 57, 10.1016/j.wep.2013.10.001 Taylor C.L., 1985, Paper presented at a joint meeting of the Canadian Evaluation Society Terho H., 2017, Ind. Mark. Manag., 64, 175, 10.1016/j.indmarman.2017.01.005 Tolonen H., 2015, Scand. J. Soc. Med., 43, 212 Ünlü Ince B., 2014, Internet Interv., 1, 74, 10.1016/j.invent.2014.05.003 van Voorst S.F., 2015, BMJ Open, 5, 1, 10.1136/bmjopen-2014-006284 Vivo S., 2017, Dev. Eng., 2, 53, 10.1016/j.deveng.2016.06.002 Walmsley B., 2016, Poetics, 58, 66, 10.1016/j.poetic.2016.07.001 Watanabe M., 2017, Soc. Sci. Res., 63, 324, 10.1016/j.ssresearch.2016.09.005 Watson S., 1998, Journal of Continuing Higher Education, 46, 31, 10.1080/07377366.1998.10400335 Widmar N.J.O., 2016, Prev. Med. Rep., 4, 270, 10.1016/j.pmedr.2016.06.017 10.1177/1745691612442904 Yan T., 2010, Int. J. Public Opinion Res., 22, 535, 10.1093/ijpor/edq037 Zhang X., 2017, Comput. Hum. Behav., 71, 172, 10.1016/j.chb.2017.02.006