HEDONIC SHOPPING MOTIVATIONS IN COLLECTIVISTIC AND INDIVIDUALISTIC CONSUMER CULTURES Heiner Evanschitzky* Professor of Marketing, Aston Business School, Marketing Group, Aston Triangle, Birmingham, B4 7ET, UK ([email protected]) Oliver Emrich (Assistant Professor of Distribution Management and E-Commerce. University of St.Gallen, Institute of Retail Management, Dufourstrasse 40a, CH-9000 St.Gallen. [email protected]) Vinita Sangtani (Assistant Professor of Marketing. Mike Cottrell School of Business, North Georgia College and State University. [email protected] ) Anna-Lena Ackfeldt (Lecturer in Marketing . Aston Business School, Marketing Group, Aston Triangle, Birmingham, B4 7ET, UK. [email protected]) Kristy E. Reynolds (Bruno Associate Professor of Marketing. Department of Management and Marketing, Culverhouse College of Commerce, The University of Alabama. [email protected]) Mark J. Arnold (Senior Associate Dean, Professor of Marketing. Department of Marketing, John Cook School of Business, Saint Louis University. [email protected]) *) corresponding author ========================================================== ARTICLE INFO Article history: First received in October 10, 2013 and was under review for 4½ months. Replication Editor: John G. Lynch ============================================================ Forthcoming IJRM Volume 31 #3 (2014) Replication Corner
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HEDONIC SHOPPING MOTIVATIONS IN COLLECTIVISTIC AND
INDIVIDUALISTIC CONSUMER CULTURES
Heiner Evanschitzky* Professor of Marketing, Aston Business School, Marketing Group, Aston Triangle,
χ2 = chi-square, df=degrees of freedom, Δ χ2 = chi-square difference, RMSEA=Root Mean Square Error of Approximation, CFI=Comparative Fit Index, TLI=Tucker-Lewis Index, SRMR= Standardized Root Mean Square Residual
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Appendix 2 Descriptive statistics for the country samples
U.S. Mean S.D. 1 2 3 4 5 6 7 1 Adventure 3.00 1.56 1 2 Gratification 3.91 1.61 .72 1 3 Idea 3.57 1.57 .46 .52 1 4 Role 4.83 1.56 .37 .46 .30 1 5 Social 3.41 1.56 .63 .60 .47 .43 1 6 Value 4.79 1.68 .35 .42 .18 .37 .32 1 7 Flow 3.68 1.70 .65 .62 .37 .39 .52 .32 1 Germany Mean S.D. 1 2 3 4 5 6 7 1 Adventure 2.86 1.42 1 2 Gratification 3.18 1.51 .61 1 3 Idea 2.72 1.48 .44 .44 1 4 Role 3.99 1.65 .42 .46 .30 1 5 Social 2.76 1.47 .44 .39 .44 .39 1 6 Value 3.42 1.71 .31 .29 .21 .30 .26 1 7 Flow 2.49 1.43 .56 .55 .48 .41 .45 .29 1 India Mean S.D. 1 2 3 4 5 6 7 1 Adventure 3.90 1.41 1 2 Gratification 3.69 1.60 .52 1 3 Idea 4.38 1.60 .39 .40 1 4 Role 4.95 1.47 .32 .30 .30 1 5 Social 4.00 1.46 .30 .36 .36 .37 1 6 Value 4.41 1.60 .17 .17 .22 .14 .25 1 7 Flow 4.52 1.35 .43 .33 .40 .36 .31 .24 1 Oman Mean S.D. 1 2 3 4 5 6 7 1 Adventure 3.98 1.36 1 2 Gratification 3.80 1.57 .49 1 3 Idea 4.73 1.51 .30 .36 1 4 Role 4.77 1.32 .21 .18 .36 1 5 Social 4.55 1.40 .16 .09** .22 .31 1 6 Value 4.75 1.35 .17 .12* .20 .22 .29 1 7 Flow 4.59 1.17 .24 .20 .16 .22 .13* .21 1 All correlations are significant at the p<.01 level, except * significant at the p<.05 level and ** not significant.
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WEB-APPENDIX Web-Appendix 1 Vignette
Relevance of hedonic shopping experiences in collectivistic consumer cultures
In most Eastern retail markets, organized retailing has only a market share of less than ten percent but market potential is huge such as in India with an estimated retail turnover of 350 billion US-dollar and growth rates of 15-20 percent per year (PriceWaterhouseCoopers: Winning in India’s retail sector 2011). According to retail experts, differentiation is an important competitive factor:
”Many retailers will be looking to enter the Indian market – but successful entry will require nimble adaptation to unique consumer demands” (Nathan Associates, 2011). “Shopping is all about drama. You need to create excitement” (Samar S. Sheikhawat, VP Marketing, Spencer’s Retail).
Examples:
• Indian retailer Bharti tries to enhance social shopping as a central hedonic component through wider shopping aisles, allowing more people in a group to approach the displays.
• Luxury retailer Hermes aims to cater for adventure shopping by integrating an art gallery in its retail location in Mumbai.
• Competition in India between retail chains and small mom-and-pop stores, called ‘kirana’ stores, has induced many retailers to offer bargains and large discounts, setting value shopping in the focus of most retailers.
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Web-Appendix 2 Association between flow and hedonic shopping motivations across country samples U.S. Germany India Oman Flow Single regression β (t-value) Single regression β (t-value) Single regression β (t-value) Single regression β (t-value) ADV .81
(10.11) .79
(15.17) .54
(8.77) .53
(3.38)
GRA .71 (9.03)
.57 (14.75)
.22 (6.89)
.25 (3.62)
IDE .43 (5.82)
.50 (12.69)
.28 (8.76)
.17 (2.39)
ROL .50 (5.87)
.48 (11.33)
.38 (8.54)
.21 (2.67)
SOC .63 (8.08)
.64 (12.65)
.34 (7.82)
.32 (2.43)
VAL .41 (4.68)
.38 (8.29)
.29 (6.60)
.37 (3.75)
Note: For all country samples, flow is separately regressed on each hedonic shopping motivation. All path coefficients are significant at p<.05.
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Web-Appendix 3 Partial metric invariance of samples – factor loadings Factors U.S. GER India Oman IND COL Flow Most of the time I go shopping at malls, stores, or shopping complexes I feel that I am in flow
.65 .65 .65 .65 .64 .64
I loose track of time when I am in the store, shopping complex or at the mall
1.00 1.00 1.00 1.00 1.00 1.00
Time seems to fly by when I'm shopping at the mall, retail stores or shopping complexes
1.00 1.00 1.00 .75 1.01 .96
When I leave a retail store, mall, or shopping complexes, I'm sometimes surprised to see that it is dark outside
1.03 .80 .95 .61 .87 .87
Adventure
To me, shopping is an adventure 1.00 1.00 1.00 1.00 1.00 1.00 I find shopping stimulating 1.07 1.07 1.07 1.07 1.07 1.07 Shopping makes me feel like I am in my own universe .92 .99 1.33 1.48 .98 1.39 Gratification
When I'm in a down mood, I go shopping to make me feel better 1.00 1.00 1.00 1.00 1.00 1.00 To me, shopping is a way to relieve stress .98 .98 .98 .98 .99 .99 I go shopping when I want to treat myself to something special .61 .61 .51 .61 .57 .53 Idea
I go shopping to keep up with the trends .98 .80 .98 .85 .89 .96 I go shopping to keep up with new fashions 1.00 1.00 1.00 1.00 1.00 1.00 I go shopping to see what new products are available .73 .73 .73 .73 .74 .74 Role
I like shopping for others because when they feel good I feel good
1.00 1.00 1.00 1.00 1.00 1.00
I enjoy shopping for my friends and family 1.01 1.01 1.01 1.01 1.05 1.05 I enjoy shopping around to find the perfect gift for someone .89 .98 .75 .32 1.00 .66 Social
I go shopping with my friends or family to socialize 1.00 1.00 1.00 1.00 1.00 1.00 I enjoy socializing with others when I shop .83 .83 1.09 1.30 .88 1.13 Shopping with others is a bonding experience 1.03 1.03 1.03 1.03 1.01 1.01 Value
For the most part, I go shopping when there are sales 1.00 1.00 1.00 1.00 1.00 1.00 I enjoy looking for discounts when I shop 1.29 1.29 1.29 1.29 1.25 1.25 I enjoy hunting for bargains when I shop 1.19 1.25 1.12 0.68 1.19 1.01 χ2 (df) of partial metric model 2175.760 (779) 1667.114 (383) χ2 (df) of free factor loading model 2139.416 (752) 1660.428 (376) Δ χ2(Δdf) 36.344 (27), n.s. 6.69 (7), n.s. Note: The partial metric invariance measurement model is displayed with unstandardized factor loadings. Items in italics are non-invariant across country samples. The final solution comprises 14 items with invariant factor loadings out of a total of 22 items across the four country samples. The chi-square difference (Δχ2
27=36.34, n.s.) shows that the partial metric invariance model does not significantly differ from a model with free factor loadings. Therefore, partial metric invariance exists across the four country samples. Moreover, fixing the 14 invariant items across pooled individualistic (IND) and collectivistic (COL) samples (unstandardized factor loadings are shown) also yields no significant chi-square difference (Δχ2
7=6.69, n.s.) between partial metric invariance model and free model. Partial metric invariance exists between individualistic and collectivistic samples and also within individualistic and collectivistic samples (within-samples chi-square tests are reported in Web-Appendix 4). χ2 = chi-square, df=degrees of freedom, Δ χ2 = chi-square difference
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Web-Appendix 4 Test statistics for invariance models within pooled individualistic and collectivistic samples Samples Model χ2 df Δ χ2 Δdf Within individualistic samples (U.S. and Germany)
Baseline model (free factor loadings) 1162.026 376 Test for invariance of factor loadings (partial metric invariance model)
1171.621 385 9.595 9 n.s.
Test for invariance of factor covariance and variance* (partial correlation invariance model)
1204.799 409 33.178 24 n.s.
Within collectivistic samples (India and Oman
Baseline model (free factor loadings) 977.390 376 Test for invariance of factor loadings (partial metric invariance model)
989.681 385 12.291 9 n.s.
Test for invariance of factor covariance and variance* (partial correlation invariance model)
1017.194 410 27.513 25 n.s.
Note: Comparing the partial metric invariance model with 16 invariant items out of 22 items to a model with free loadings yields no significant chi-square differences within individualistic samples (for U.S. and Germany, Δχ2
9=9.60, n.s) and within collectivistic samples (for India and Oman, Δχ29=12.29, n.s), confirming partial
metric invariance. *We test invariance of correlations within individualistic and collectivistic samples by comparing a model, where covariances and variances are fixed, to the partial metric invariance model as baseline. The covariance between idea shopping and flow is not invariant for the individualistic samples, U.S. and Germany, as well as for the collectivistic samples, India and Oman (the collectivistic sample also has a non-invariant variance of idea shopping). This non-invariant relationship suggests that cultural factors other than individualism/collectivism might be accountable for this difference. Furthermore, social and gratification shopping co-occur more often in the U.S. than in Germany and more often in India than Oman (these differences are easily detectable by inspecting the correlations in the Appendix 2; the U.S. also has higher covariance between social and adventure shopping and between value and gratification shopping than Germany). Relaxing these parameters yields a non-significant difference of chi-square within individualistic samples (for U.S. and Germany, (Δχ2
24=33.18, n.s) and within collectivistic samples (for India and Oman, Δχ2
25=27.51, n.s), confirming a partially invariant correlation model. According to Steenkamp and Baumgartner (1998), requirements for invariance testing depend on the goal of the research. The minimal requirement for the testing of associations between constructs (which is the case in this article) is partial metric invariance which is fully established (Steenkamp & Baumgartner, 1998). But also higher degrees of invariance, which are desirable, can be established for the substantial correlations in the hedonic shopping motivations model (which contains a large magnitude of relationships). Because the most strongly varying correlations involving idea and social shopping are not subject to our hypotheses, non-invariance of the five mentioned parameters does not interfere with testing our hypotheses as partial invariance of covariances and variances can be established by relaxing the non-invariant parameters. χ2 = chi-square, df=degrees of freedom, Δ χ2 = chi-square difference Fort
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Web-Appendix 5 Interactions of hedonic shopping motivations with cultural differences Differences of collectivist versus
individualist cultures Cultural difference (1=collectivistic/0=individualistic)
Differences within individualist cultures Cultural difference (1=Germany/0=U.S.)
Differences within collectivist cultures Cultural difference (1=India/0=Oman)
Adventure shopping (ADV) .21 (11.21)* .21 (7.54)* .45 (5.75)* .10 (1.97)* Gratification shopping (GRA) .12 (6.89)* .22 (8.02)* .28 (3.54)* .05 (1.05) Idea shopping (IDE) .14 (8.76)* .15 (6.53)* -.01 (-.10) .01 (.26) Role shopping (ROL) .10 (6.19)* .06 (2.67)* .12 (1.95) .10 (1.94) Social shopping (SOC) .07 (4.39)* .10 (4.22)* .10 (1.36) .02 (.33) Value shopping (VAL) .07 (5.12)* .06 (3.21)* .03 (.49) .09 (2.04)* Cultural Difference (CUL) 1.06 (20.16)* 1.60 (9.30)* -.66 (-1.97)* -1.27 (-3.62)* ADVxCUL -.01 (-.19) -.19 (-2.18)* .12 (2.11)* GRAxCUL -.17 (-4.70)* -.04 (-.44) .00 (.03) IDExCUL -.03 (-.83) .21 (3.09)* .14 (2.85)* ROLxCUL .07 (2.37)* -.06 (-.84) .04 (.66) SOCxCUL -.07 (-1.96) .04 (.48) .03 (.57) VALxCUL .02 (.70) .02 (.34) -.02 (-.36) R2 .719 .730 .626 .456 Notes: A basic model with cultural difference (collectivistic/individualistic) and hedonic shopping motivations as independent variables shows that consumers in collectivistic cultures generally report a higher shopping flow than consumers in individualistic cultures. More importantly, the differences of individualistic versus collectivistic cultures are assessed by additionally including product terms of cultural difference with each hedonic shopping motivation. Intrinsically enjoyable customer experiences in collectivistic cultures are less strongly associated with gratification shopping (β=-.17, t=-4.70, p<.05) and more strongly associated with role shopping (β=.07, t=2.37, p<.05), compared with individualistic cultures. No further cultural differences exist for the other hedonic shopping motivations. We can also rule out that country differences exist within individual and collectivistic cultures regarding our hypotheses as shown by non-significant interactions of gratification and role shopping with country-specific dummy variables ((Germany/US) and (India/Oman)). Only the association of flow with idea shopping and the association of flow with adventure shopping vary between Germany and the U.S. and between India and Oman which suggests that other cultural factors than individualism/collectivism might be accountable for these differences. * p<.05
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Web-Appendix 6 Interactions of hedonic shopping motivations, cultural differences, and income Differences of collectivist versus individualist cultures
Cultural difference (1=collectivistic/0=individualistic) Model 1 Model 2 Model 3 Model 4 Adventure shopping (ADV) .36 (8.06)* .33 (3.87)* .25 (1.51) .31 (1.44) Gratification shopping (GRA) .08 (3.27)* .24 (5.56)* .17 (2.18)* .25 (2.16)* Idea shopping (IDE) .11 (5.50)* .16 (5.08)* .27 (5.01)* .23 (2.92)* Role shopping (ROL) .11 (4.10)* .05 (1.16) .17 (1.88) .18 (1.63) Social shopping (SOC) .06 (2.20)* .07 (1.62) .08 (.96) .17 (1.49) Value shopping (VAL) .08 (4.17)* .06 (2.20)* .06 (1.21) .06 (.92) Cultural Difference (CUL) 1.01 (18.97)* .72 (6.82)* .58 (4.91)* .61 (5.10)* Income (INC) -.06 (-3.02)* -.12 (-4.14)* -.15 (-4.85)* -.18 (-5.37)* CULxINC .12 (3.11)* .18 (4.04)* .18 (4.00)* ADVxCUL .15 (1.33) .17 (1.44) .04 (.13) GRAxCUL -.30 (-5.19)* -.30 (-5.16)* -.43 (-3.04)* IDExCUL -.09 (-2.04)* -.09 (-2.04)* -.01 (-.09) ROLxCUL .17 (2.52)* .16 (2.33)* .04 (.28) SOCxCUL -.08 (-1.41) -.09 (-1.53) -.22 (-1.48) VALxCUL .04 (1.04) .05 (1.16) .01 (.14) ADVxINC .03 (.53) -.02 (-.27) GRAxINC .03 (1.37) .01 (.29) IDExINC -.05 (-2.57)* -.03 (-1.03) ROLxINC -.04 (-1.49) -.05 (-1.25) SOCxINC -.00 (-.11) -.03 (-.71) VALxINC -.00 (-.10) -.01 (-.41) ADVxCULxINC .08 (.83) GRAxCULxINC .04 (.70) IDExCULxINC -.03 (-.80) ROLxCULxINC .05 (.86) SOCxCULxINC .04 (.78) VALxCULxINC .02 (.48) Note: Model computation uses integration of latent factors based on a Monte Carlo simulation with 5,000 integration points, resulting in a replication of the results displayed in Table 3. Unstardardized path coefficients are displayed. Additionally, model 4 includes three way interactions between hedonic shopping motivations, cultural difference, and income. No three way interaction is positive. This model further supports that the association of flow with gratification shopping as well as with role shopping differs between collectivistic and individualistic cultures irrespective of income. Model 4 serves to rule out that income biases the found the cross-cultural differences in associations of hedonic shopping motivations with flow, which is confirmed as no three-way interaction is significant. Please note that path coefficients for two-way interactions and simple effects in Model 4 cannot be interpreted because the base level of income is without the range of actual scale values (in case of significant three way interactions, floodlight analysis should be used to interpret these coefficients, see Spiller et al. 2013). * p<.05