Fashion Consumers’ Channel Switching Behavior During the COVID-19: Protection Motivation Theory in the Extended Planned Behavior Framework

Abstract

The purpose of this study is to understand fashion consumers’ channel switching to online stores during the COVID-19. We proposed an extended theory of planned behavior by incorporating protection motivation theory. The results showed that consumer assessments of perceived severity and altruistic fear of COVID-19 and response efficacy and self-efficacy of channel switching increased their beliefs (i.e., attitude, perceived behavior control, subjective norm) and intentions to switch shopping channels to online. We also found that the age (young vs. old) moderated the effects of response efficacy and self-efficacy on perceived behavior control, perceived severity on subjective norm, perceived behavior control on channel switching intentions, and channel switching intention on actual switching behavior. The findings provide fashion retailers and the society with a better understanding about fashion consumers’ shopping channel switching under the pandemic.

Keywords

protection motivation age altruistic fear channel switching COVID-19

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References

Ajzen

(1985). From intentions to actions: A theory of planned behavior. In Kuhl

Beckman

(Eds.), In Action control (pp. 13–39). Springer.

Alfaro

Faia

Lamersdorf

Saidi

(2020). Social interactions in pandemics: Fear, altruism, and reciprocity (Working Paper Series, No. 27134). National Bureau of Economic Research. https://www.nber.org/papers/w27134.pdf

Arora

Sahney

(2018). Consumer’s webrooming conduct: An explanation using the theory of planned behavior. Asia Pacific Journal of Marketing and Logistics, 30(4), 1040–1063.

Center of Disease Control and Prevention. (2020). Severe outcomes among patients with coronavirus disease 201 (COVID-19)–United States, February 12–March 16, 2020. http://dx.doi.org/10.15585/mmwr.mm6912e2

Chan

D. K.

Zhang

C. Q.

Josefsson

K. W.

(2020). Why people failed to adhere to COVID-19 preventive behaviors? Perspectives from an integrated behavior change model. Infection Control & Hospital Epidemiology, 1–2. https://doi.org/10.1017/ice.2020.245

Chang

H. H.

Wong

K. H.

S. Y.

(2017). Applying push-pull-mooring to investigate channel switching behaviors: M-shopping self-efficacy and switching costs as moderators. Electronic Commerce Research and Applications, 24, 50–67.

Chen

(2020, March 27). Which retailers have been hit hardest by the COVID-19 crash? Business of Fashion. https://www.businessoffashion.com/articles/professional/retailers-coronavirus-covid-19-best-worst-stocks

Farooq

Laato

Islam

A. N.

(2020). Impact of online information on self-isolation intention during the COVID-19 pandemic. Journal of Medical Internet Research, 22(5), 1–15.

Floyd

D. L.

Prentice-Dunn

Rogers

R. W.

(2000). A meta-analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30(2), 407–429.

10.

Ifinedo

(2012). Understanding information systems security policy compliance: An integration of the theory of planned behavior and the protection motivation theory. Computers & Security, 31(1), 83–95.

11.

Johnston

A. C.

Warkentin

(2010). Fear appeals and information security behaviors: An empirical study. MIS Quarterly, 34(3), 549–566.

12.

Kansara

V. A.

(2020, June 19). Could the pandemic make retail better? Business of Fashion. https://www.businessoffashion.com/briefings/retail/could-the-pandemic-make-retail-better

13.

Kim

Karpova

(2010). Consumer attitudes toward fashion counterfeits: Application of the theory of planned behavior. Clothing and Textiles Research Journal, 28(2), 79–94.

14.

Kim

Park

(2005). A consumer shopping channel extension model: Attitude shift toward the online store. Journal of Fashion Marketing and Management: An International Journal, 9(1), 106–121.

15.

Kline

R. B.

(2015). Principles and practice of structural equation modeling. Guilford Publications.

16.

Laroche

Toffoli

Zhang

Pons

(2001). A cross-cultural study of the persuasive effect of fear appeal messages in cigarette advertising: China and Canada. International Journal of Advertising, 20(3), 297–317.

17.

Lee

Larsen

K. R.

(2009). Threat or coping appraisal: Determinants of SMB executives’ decision to adopt anti-malware software. European Journal of Information Systems, 18(2), 177–187.

18.

Madahi

Sukati

(2016). An empirical study of Malaysian consumers’ channel-switching intention: Using theory of planned behavior. Global Business Review, 17(3), 489–523.

19.

O’cass

Fenech

(2003). Web retailing adoption: Exploring the nature of internet users web retailing behaviour. Journal of Retailing and Consumer Services, 10(2), 81–94.

20.

Oberoi

(2020). COVID-19’s impact on the global and US economy. Market Realist. https://marketrealist.com/2020/05/covid-19s-impact-global-us-economy/

21.

Passyn

K. A.

Diriker

Settle

R. B.

(2011). Images of online versus store shopping: Have the attitudes of men and women, young and old really changed? Journal of Business & Economics Research, 9(1), 99–110.

22.

Pookulangara

Hawley

Xiao

(2011). Explaining consumers’ channel-switching behavior using the theory of planned behavior. Journal of Retailing and Consumer Services, 18(4), 311–321.

23.

Ramkumar

Woo

(2018). Modeling consumers’ intention to use fashion and beauty subscription-based online services (SOS). Fashion and Textiles, 5(1), 22.

24.

Reuters. (2020, May 7). Brands see an uptick in online sales during the COVID-19 crisis. Business of Fashion. https://www.businessoffashion.com/articles/news-analysis/brands-see-an-uptick-in-online-sales-during-the-covid-19-crisis

25.

Rogers

R. W.

(1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In Cacioppo

J. R.

Petty

R. E.

(Eds.), Social psychology: A sourcebook (pp. 153–176). Guilford Press.

26.

Sheth

(2020). Impact of COVID-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283.

27.

Shim

Chapman

G. B.

Townsend

J. P.

Galvani

A. P.

(2012). The influence of altruism on influenza vaccination decisions. Journal of the Royal Society Interface, 9(74), 2234–2243.

28.

Sloan

Haner

Graham

Cullen

F. T.

Pickett

Jonson

C. L.

(2020, May 3). Pandemic emotions: The extent, correlates, and mental health consequences of personal and altruistic fear of COVID-19. https://doi.org/10.31235/osf.io/txqb6

29.

Sommestad

Karlzén

Hallberg

(2015). The sufficiency of the theory of planned behavior for explaining information security policy compliance. Information & Computer Security. 23(2), 200–217.

30.

Sun

Wang

Guo

Peng

(2013). Understanding the acceptance of mobile health services: A comparison and integration of alternative models. Journal of Electronic Commerce Research, 14(2), 183–200.

31.

Tan

G. W. H.

Ooi

K. B.

(2018). Gender and age: Do they really moderate mobile tourism shopping behavior? Telematics and Informatics, 35(6), 1617–1642.

32.

Tarhini

Hone

Liu

(2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163–184.

33.

Teasdale

Yardley

Schlotz

Michie

(2012). The importance of coping appraisal in behavioural responses to pandemic flu. British Journal of Health Psychology, 17(1), 44–59.

34.

Thakur

(2018). The role of self-efficacy and customer satisfaction in driving loyalty to the mobile shopping application. International Journal of Retail & Distribution Management, 46(3), 283–303.

35.

Watanabe

Omori

(2020). Online consumption during the COVID-19 crisis: Evidence from Japan. Covid Economics, 32, 218–252.