Restricted accessResearch articleFirst published online 2021-04-12
Using fuzzy set qualitative comparative analysis for evaluating the effectiveness of relationship marketing and innovation in 3D printing medical market
This study aims to explore the configurations of potential relevant antecedents in 3D printing medical Market for achieving high user satisfaction from both the suppliers’ and users’ perspectives. The important antecedents in this study include relationship marketing, innovation, 3D printing perceived values, and 3D printing perceived risk. Firstly, this study investigates the relationships among potential relevant antecedents and user satisfaction. Furthermore, to explore the gap between users’ evaluation and innovation suppliers’ perception, this study addresses this issue based on both perspectives of suppliers and buyers. To assess the applicability of the proposed model, we employed questionnaires survey and collected primary data from 3D printing suppliers and their customers. Moreover, the fuzzy set qualitative comparative analysis (fsQCA) approach has been applied for evaluating the effectiveness of relationship marketing and innovation in 3D printing medical market. Finally, the numerical results indicate that there is one causal configuration (i.e., 1A) found to be sufficient for high user satisfaction for the perspectives of 3D printing suppliers and three configurations for the perspectives of 3D printing customers. In the perspectives of 3D printing suppliers, the combination of relationship marketing, innovation, and 3D printing perceived value is sufficient conditions causing high user satisfaction. However, there are three causal configurations (i.e., 1B, 2B, and 3B) found to be sufficient for high user satisfaction for the perspectives of 3D printing customers.
AdjeiM.T., GriffithD.A. and NobleS.M., When do relationships pay off for small retailers? Exploring targets and contexts to understand the value of relationship marketing, Journal of Retailing85 (2009), 493–501.
2.
AkgünA.E., KeskinH. and ByrneJ., Organizational emotional capability, product and process innovation, and firm performance: An empirical analysis, Journal of Engineering and Technology Management26 (2009), 103–130.
BerryL.L., Relationship Marketing, in L.L. Berry, G.L. Shostack, G.D. Upah, (Eds.) Emerging Perspectives on Service Marketing 25–38 American Marketing Association, Chicago, IL. 1983
5.
BockenN.M.P., AllwoodJ.M., WilleyA.R. and KingJ.M.H., Developmentofatoolforrapidlyassessingtheimplementationdifficulty and emissions benefits of innovations, Technovation32(1) (2012), 19–31.
6.
Canalys, 3D printing market to be worth US$22.4 billion in 2020. https://www.google.com.tw/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjojYXr4uzSAhXGq5QKHdp7CNgQFggYM AA&url=https%3A%2F%2Fwww.canalys.com%2Fstatic%2Fpress release%2F2016%2Fmedia-alert-17052016-3d-printing-market-be-worth-us224-billion-2020.pdf&usg=AFQjCNE1yL7gYijPHH2ofj0cAmHSKDzj3Q. 2016
7.
ChangM.L. and ChengC.F., How balance theory explains high-tech professionals’ solutions of enhancing job satisfaction, Journal of Business Research67(9) (2014), 2008–2018.
8.
ChenC.F. and ChenF.S., Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists, Tourism Management31 (2010), 29–35.
9.
ChengC.F., ChangM.L. and LiC.S., Configural paths to successful product innovation, Journal of Business Research 66(12) (2013), 2561–2573.
10.
ChengC.F., LaiM.K. and WuW.Y., Exploring the impact of innovation strategy on R&D employees’ job satisfaction: A mathematical model and empirical research, Technovation30(7–8) (2010), 459–470.
CovinJ.G., EggersF., KrausS., ChengC.F. and ChangM.L., Marketing-related resources and radical innovativeness in family and non-family firms: A configurational approach, Journal of Business Research 69(12) (2016), 5620–5627.
13.
FissP.C., Building better causal theories: A fuzzy set approach to typologies in organization research, Academy of Management Journal54(2) (2011), 393–420.
GengD., WangL. and YinZ., Intelligent analysis on the factors influencing the innovation and development performance of high-tech industry in China, based on two-stages model, Journal of Intelligent & Fuzzy Systems35 (2018), 2703–2709.
16.
GhanbariM., A discussion on “solving fuzzy complex system of linear equations”, Information Sciences402 (2017), 165–169.
17.
HairJ.F., BlackB., BabinB. and AndersonR.E., Multivariate Data Analysis, 7th, Upper Saddle River, N.J.: Pearson Prentice Hall, 2010.
18.
HjalagerA.M., A review of innovation research in tourism, Tourism Management31 (2010), 1–12.
19.
HorstM., KuttschreuterM. and GuttelingJ.M., Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands, Computers in Human Behavior23 (2007), 1838–1852.
20.
HuY., McNamaraP. and McLoughlinD., Outbound open innovation in bio-pharmaceutical out-licensing, Technovation35 (2015), 46–58.
21.
IlbaharE. and KahramanC., Retail store performance measurement using a novel interval-valued Pythagorean fuzzy WASPAS method, Journal of Intelligent & Fuzzy Systems35 (2018), 3835–3846.
22.
ImI., KimY. and HanH.J., The effects of perceived risk and technology type on users’ acceptance of technologies, Information & Management45 (2008), 1–9.
23.
KacprzykJ., KrawczakM. and SzkatułaG., On bilateral matching between fuzzy sets, Information Sciences402 (2017), 244–266.
24.
KimD.J., FerrinD.J. and RaoH.R., A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents, Decision Support Systems44 (2008), 544–564.
25.
KrausS., MenschingH., CalabròA., ChengC.F. and FilserM., Family firm internationalization: A configuration approach, Journal of Business Research69(11) (2016), 5473–5478.
26.
KusiakA., Innovation: A data-driven approach, International Journal of Production Economics122 (2009), 440–448.
27.
LeeC.K., YoonY.S. and LeeS.K., Investigating the relationships among perceived value, satisfaction, and recommendations: The case of the Korean DMZ, Tourism Management28 (2007), 204–214.
28.
LeeC.S., Watson-ManheimM.B. and ChudobaK.M., Investigating the relationship between perceived risks in communication and ICT-enabled communicative behaviors, Information & Management51(6) (2014), 688–699.
29.
MendelJ.M. and KorjaniM.M., Theoretical aspects of Fuzzy Set Qualitative Comparative Analysis (fsQCA), Information Sciences237 (2013), 137–161.
30.
MendelJ.M. and KorjaniM.M., Charles Ragin’s Fuzzy Set Qualitative Comparative Analysis (fsQCA) used for linguistic summarizations,1–23, Information Sciences202 (2012).
31.
MiaoR., XuF., ZhangK. and JiangZ., Development of a multi-scale model for customer perceived value of electric vehicles, International Journal of Production Research52(16) (2014), 4820–4834.
32.
MurovecN. and ProdanI., Absorptive capacity, its determinants, and influence on innovation output: Cross-cultural validation of the structural model, Technovation29 (2009), 859–872.
33.
PugaD. and TreflerD., Wake up and smell the ginseng: International trade and the rise of incremental innovation in low-wage countries, Journal of Development Economics91 (2010), 64–76.
34.
RaginC.C., Qualitative comparative analysis using fuzzy sets (fsQCA). Thousand Oaks and London: Sage. 2009.
35.
RaymondL. and St-PierreJ., R&D as a determinant of innovation in manufacturing SMEs: An attempt at empirical clarification, Technovation30 (2010), 48–56.
36.
SongW., ZhuJ., ZhangS. and LiuX., A multi-stage uncertain risk decision-making method with reference point based on extended LINMAP method, Journal of Intelligent & Fuzzy Systems35 (2018), 1133–1146.
TangX., FuC., XuD.L. and YangS., Analysis of fuzzy Hamacher aggregation functions for uncertain multiple attribute decision making, Information Sciences387 (2017), 19–33.
39.
TsaurS.H., WuD.H., YenC.H. and WuM.H., Promoting relationship marketing of tour leaders’ blog: The role of charisma, International Journal of Tourism Research16(5) (2014), 417–428.
WangC.Y. and ChenS.M., Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method, Information Sciences 397–398 (2017), 155–167.
42.
WangZ.J. and LinJ., Acceptability measurement and priority weight elicitation of triangular fuzzy multiplicative preference relations based on geometric consistency and uncertainty indices, Information Sciences 402 (2017), 105–123.
43.
WatkinsA. and HillR.P., A simulation of business-to-business decision making in a relationship marketing context, Industrial Marketing Management38 (2009), 994–1005.
44.
WoodsideA.G., The general theory of behavioral pricing: Applying complexity theory to explicate heterogeneity and achieve high-predictive validity, Industrial Marketing Management47 (2015), 39–52.
45.
WuS.Q., Models for evaluating the technological innovation capability of small and micro enterprises with hesitant fuzzy information, Journal of Intelligent & Fuzzy Systems32 (2017), 249–256.
46.
YoganathanD., JebarajakirthyC. and ThaichonP., The influence of relationship marketing orientation on brand equity in banks, Journal of Retailing and Consumer Services26 (2015), 14–22.
47.
YuT.W. and TungF.C., Investigating effects of relationship marketing types in life insurers in Taiwan, Managing Service Quality23(2) (2013), 111–130.
48.
YükselA. and YükselF., Shopping risk perceptions: Effects on tourists’ emotions satisfaction and expressed loyalty intentions, Tourism Management28 (2007), 703–713.
49.
ZengZ., Model for evaluating the technological innovation capability in high-tech enterprises with fuzzy number intuitionistic fuzzy information, Journal of Intelligent & Fuzzy Systems33 (2017), 2085–2094.
50.
ZhangZ., ZhangZ. and LawR., Relative importance and combined effects of attributes on user satisfaction, The Service Industries Journal34(6) (2014), 550–566.
