Importance-performance analysis (IPA) is a popular research tool to prioritize continuous improvement opportunities. To overcome issues and limitations of IPA, researchers have put forth diagonal line IPA, stated improvement analysis, and necessary condition analysis as complementary analyses. Embracing the concept of research triangulation, this study examines different variations of IPA, stated improvement analysis, and necessary condition analysis to determine if these complementary analyses deliver similar improvement priorities to IPA. The results suggest that these complementary analyses add valuable insight into prioritizing continuous improvement opportunities.
AbaloJ.JesúsV.VicenteM. (2007). Importance values for importance–performance analysis: A formula for spreading out values derived from preference rankings. Journal of Business Research, 60(2), 115–121. https://doi.org/10.1016/j.jbusres.2006.10.009
2.
AbrolR. (2024). How gen Z is reshaping the streaming landscape. NewsCast Studio, The Trade Publication for Broadcast Production; February 14.
BansalH. S.DuvergerP. (2013). Investigating the measures of relative importance in marketing research. International Journal of Market Research, 55(5), 675–694. https://doi.org/10.2501/ijmr-2013-057
5.
ChrzanK. (2023). Performance improvement experiments for CX research. Quirks Marketing Research Review; September.
6.
ChrzanK.GolovashkinaN. (2006). An empirical test of six stated importance measures. International Journal of Market Research, 48(6), 717–740. https://doi.org/10.1177/147078530604800607
7.
ColtmanT.DevinneyT. M.MidgleyD. F.VenaikS. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262. https://doi.org/10.1016/j.jbusres.2008.01.013
8.
DulJ. (2016). Necessary condition analysis (NCA): Logic and methodology of ‘necessary but not sufficient’ causality. Organizational Research Methods, 19(1), 10–52. https://doi.org/10.1177/1094428115584005
9.
GaleB. (1994). Managing customer value: Creating quality and service that customers can see. Free Press.
10.
GarverM. S. (2003). Customer-driven improvement model: Best practices in identifying improvement opportunities. Industrial Marketing Management, 32(6), 455–466. https://doi.org/10.1016/s0019-8501(02)00238-9
11.
GarverM. S. (2009). A maximum difference scaling application for customer satisfaction researchers. International Journal of Market Research, 51(4), 158–175. https://doi.org/10.2501/S1470785309200694
12.
GarverM. S. (2019). Threats to the validity of logistics and supply chain management research. Journal of Business Logistics, 40(1), 30–43. https://doi.org/10.1111/jbl.12203
13.
GarverM. S. (2024a). Examining stated improvement research methods. International Journal of Market Research. Advance online publication. https://doi.org/10.1177/14707853241280443
14.
GarverM. S. (2024b). New CX measurement scales: Examining the best ever scale and stated improvement scale. Quirks Marketing Research Review. Advance online publication.
15.
GarverM. S.WilliamsZ. (2020). Utilizing relative weight analysis in customer satisfaction research. International Journal of Market Research, 62(2), 158–175. https://doi.org/10.1177/1470785319859794
16.
GarverM. S.WilliamsZ. (2024). Key driver analysis with relative weight analysis: A two-step approach. International Journal of Market Research, 66(6), 711–728. https://doi.org/10.1177/14707853241251719
17.
HairJ. F.HultT.RingleC. M.SarstedtM. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.
18.
HauffS.RichterN.SarstedtM.RingleC. (2024). Importance and performance in PLS-SEM and NCA: Introducing the combined importance-performance map analysis (cIPMA). Journal of Retailing and Consumer Services, 78, Article 103723. https://doi.org/10.1016/j.jretconser.2024.103723
19.
HawesJ. M.RaoC. P. (1985). Using importance-performance analysis to develop health care marketing strategies. Journal of Health Care Marketing, 5(4), 19–25.
20.
JarvisC.MackenzieS.PodsakoffP. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218. https://doi.org/10.1086/376806
21.
JohnsonJ. W.LeBretonJ. M. (2004). History and use of relative importance indices in organizational research. Organizational Research Methods, 7(3), 238–257. https://doi.org/10.1177/1094428104266510
22.
LeBretonJ. M.PloyhartR. E.LaddR. T. (2004). A Monte Carlo comparison of relative importance methodologies. Organizational Research Methods, 7(3), 258–282. https://doi.org/10.1177/1094428104266017
PhadermrodB.CrowderR.WillsG. (2019). Importance-performance analysis based SWOT analysis. International Journal of Information Management, 44, 194–203. https://doi.org/10.1016/j.ijinfomgt.2016.03.009
25.
RustR.ZahorikA.KeininghamT. (1994). Return on quality (ROQ): Making service quality financially accountable. Journal of Marketing, 59(2), 58–70. https://doi.org/10.1177/002224299505900205
26.
SarstedtM.RichterN.HauffS.RingleC. (2024). Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): A SmartPLS 4 tutorial. Journal of Marketing Analytics, 12(4), 746–760. https://doi.org/10.1057/s41270-024-00325-y
SlackN. (1994). The importance-performance matrix as a determinant of improvement priority. International Journal of Operations & Production Management, 14(5), 59–75. https://doi.org/10.1108/01443579410056803
29.
TonidandelS.LeBretonJ. (2015). RWA web: A free, comprehensive, web-based, and user-friendly tool for relative weight analyses. Journal of Business and Psychology, 30(2), 207–216. https://doi.org/10.1007/s10869-014-9351-z
30.
WaddellH. (1999). A better customer satisfaction scale. Quirks Marketing Research Review; October.
