Appendix
Measures
Motivation. Items generated for the motivation scale were based on the qualitative study by Wolfinbarger and Gilly. The item reduction process involved the following procedure. First, 10 e-shoppers (that had used internet for shopping for at least one year) were asked to assign each item to one of the two dimensions identified by Wolfinbarger and Gilly, goal oriented and experiential motivation. At the end, they were instructed to discard the items that did not fall into any of these categories. Anderson and Gerbing’s substantive validity coefficient ( Csv = (nc- no)/N where nc is number of respondents that assign the item to the posited dimension, no is the highest number of assignments of the item to any of the 2 identified dimensions and N is the total number of respondents) was used to establish which items will be retained .40
All items were analysed using Anderson and Gerbing procedure.41 In particular, we examined the dimensionality of each dimension by examining the pattern of standardised residuals and modification indices. Purification of the scale following the Kaplan’s procedure led to a reduction of items to 10, listed below. 42
Goal oriented items
I am really specific when I’m shopping on-line; anything I’ve ever purchased is something that I have planned beforehand
I have a purpose in mind
I am looking for a specific product
I try to save time
There are no queues/crowds
I want to get in-and-out quickly (fewest clicks)
I want ease of use
Experiential oriented items
I use it for recreational purposes, its fun, because it’s out there in the world
I constantly browse out of curiosity, just to see if there’s anything that takes my fancy
I enjoy the ‘thrill of hunt’ above all other things that the internet provides
The purified model had a satisfactory fit (χ2 (34)= 61.13, p=0.004, RMSEA=0.063, GFI=0.94, CFI=0.91, N=202). 43 Reliability alpha for the two dimensions was 0.73 (goal oriented) and 0.66 (experiential), respectively. Composite reliability of each scale was above the threshold of 0.6. 44 Specifically, composite reliability was 0.73 and 0.61 for goal oriented and experiential dimensions.
Convergent validity is evidenced by highly significant t-values. All the reported t-values were above 5.5. Discriminant validity was established by checking the magnitude of correlation coefficient (phi) between the goal orientation and experiential orientation measures. The low correlation coefficient (-0.25) indicated that the two measure are distinct. Additionally, discriminant validity was tested by using the single degree of freedom test that compares the two structural equation measurement models, one with the correlation between the two constructs fixed at 1, and a second with this correlation free. 45 The difference in resulting chi-squares was significant (Δχ2 (1) =48.7, p=0.000), which supports the claim of discriminant validity.
Web service attributes of the e-retailer were measured using an abbreviated version of 8Cs framework by Srinivasan et al. The 8C variables were validated by using confirmatory factors analysis. Two factors (community and cultivation) were eliminated because of poor fit. After purification using Kaplan’s procedure the model’s fit was acceptable (χ2(50)=77.26, p = 0.008, RMSEA=0.052, GFI=0.94, CFI=0.93). The following items remained after the purification: This website makes purchase recommendations that match my needs; This website enables me to order products that are tailor-made for me (customisation); The return policies laid out in this website are customer friendly; I believe that this website takes good care of its customers (care); This website provides a “one-stop shop” for my shopping; This website does not satisfy the majority of my online shopping needs (reversed) (choice); This website design is attractive to me; For me, shopping at this website is fun; I feel comfortable shopping at this website (character); The site doesn’t waste my time; This website is very convenient to use (convenience); This website has a search tool that enables me to locate products easily; This website makes product comparisons easy (interactivity). The composite reliabilities (CR) for most of the variables were above or close to the 0.6 cut-off point: customisation (CR=0.588); care (CR=0.597); choice (CR=0.467); character (CR=0.601); convenience (CR=0.623); and interactivity (CR=0.634). Choice was eliminated from the analysis due to the low value of CR.
Store attributes. Given the nature of the e-tailer three measures from store image inventory of Chodhudy et al were used: 46 “product quality”, “product assortment” and “value for money”. After the appropriate modifications the 3 factor CFA model indicated a good fit (χ2(17)=23.37, p = 0.137, RMSEA=0.043, GFI=0.97, CFI=0.97). The following items were used: I like XYZ brand products; XYZ.com only sells high quality products; I can count on products I buy at XYZ.com to be excellent (product quality); XYZ.com has a large variety of products; Everything I need is at XYZ.com; XYZ.com carries a wide variety of national brands (assortment); The prices at XYZ.com are fair; I get value for money at XYZ.com (value for money). Composite reliabilities for the three constructs were: 0.674, 0.718, and 0.785 respectively. The corresponding Cronbach’s alpha for the two 3-item constructs that could be calculated were: 0.686 and 0.723, respectively.
Loyalty measures were adapted from Zeithaml et al. 47 After the appropriate modification CFA indicated a good fit for the one factor model (χ2(5)=10.69, p = 0.058, RMSEA=0.076, GFI=0.98, CFI=0.95). The following items were included: I seldom consider switching to another website; I say positive things about XYZ.com to other people; I will continue to do business with XYZ.com if its prices increase; I will pay a higher price at XYZ.com relative to the competition for the same benefit; I will stop doing business with XYZ.com if its competitors’ prices decrease somewhat (reversed). The composite reliability was 0.619 and Cronbach’s alpha 0.694.
Satisfaction was measured on an abbreviated version of Oliver’s scale. 48 The following items were included: I have truly enjoyed purchasing from XYZ’s; My choice to purchase from XYZ.com was a wise one; If I had to do it again, I would do my purchase at XYZ.com again. Composite reliability was CR=0.790 and Cronbach alpha 0.780.
Cluster analysis method
Initially shoppers’ clusters were formed using Ward’s method of hierarchical clustering. A three-cluster solution resulted based on an examination of the Variance Ratio Criterion (VRC) proposed by Milligan and Cooper. 49 Then, a K-means clusters procedure with the initial seeds (centroids) provided by the hierarchical analysis solution was conducted to obtain the final clusters. To validate the cluster solution, a split-sample procedure as recommended by Huberty et al was used. 50 This procedure involved the splitting of the sample in half 3 consecutive times. For each of the 3 pairs of sub-samples a matching of the correlation of the Linear Discriminant Functions (LDF) structure coefficients was estimated. In this case this calculation was difficult due to the small number of cluster variables (only two), however, in all three pairs the LDF coefficients were close to each other. The overall correlation coefficient for all three pairs of LD coefficients was high (0.920). The second validation test (cross-typology clustering) involved the use of the final cluster means for the first half as a seed for the second half (sub sample) in a K-means analysis. At the end the average hit rates of all pairs are estimated and should be higher than those expected by chance. In that case the average hit rate was 93.14%, which is much higher than that of chance (33.33%).
References
E. Brynjolfsson, E., and M. Smith, A comparison of Internet and conventional retailers, Management Science, 46 (4), 563–585 (2000). A. L. Lederer, D. A. Mirchandani and K. Sims, The search for strategic advantage from the World Wide Web, International Journal of Electronic Commerce, 5 (4), 117–133 (2001).
M. E. Porter Competitive Strategy. New York: Free Press (1980).
M. E. Porter. Strategy and the Internet, Harvard Business Review, 79(3), 62-79 (2001).
C. Ellis Lessons from on-line groceries, MIT Sloan Management Review, Winter, 8 (2003); M. Zeng, W. Reinartz, Beyond online search: The road to profitability, California Management Review, 45(2) 107-130 (2003).
E. Kim, D. Nam, and J. L. Stimpert, .The Applicability of Porter's Generic Strategies in the Digital Age: Assumptions, Conjectures, and Suggestions. Journal of Management, 30 (5), 569-590 (2004).
Y. J. Bakos, Reducing buyer search costs: Implications for electronic marketplaces, Management Science, 43(12) 1676-1693 (1997).
M. Brengman, M. Geuens, B. Weijters, S.M. Smith, W.R. Swinyard, Segmenting Internet shoppers based on their Web-usage-related lifestyle: a cross-cultural validation. Journal of Business Research, 58 79-88 (2005); S Chang Internet segmentation: state of the art marketing applications. Journal of Segmentation Marketing, 2(1) 19-34 (1998).
D. Reed Optimising marketing data: Mapping a way forward. Precision Marketing, 14 October 2005.
G. Allenby, G. Fennell, A. Bemmaor, V. Bhargava, F. Christen, J. Dawley, P. Dickson, Y. Edwards, M. Garratt, J. Ginter, A. Sawyer, R. Staelin, and Y. Sha. Market segmentation research: Beyond within and across group differences. Marketing Letters, 13(3), 233-245 (2002).
M. Greenberg and S. S. McDonald, Successful Needs/Benefits Segmentation: A User's Guide, Journal of Consumer Marketing, 6(3), 29-37 (1989).
on page 9 of P. R. Dickson, and J. L. Ginter, Market Segmentation, Product Differentiation, and Marketing Strategy, Journal of Marketing, 51(2), 28-45 (1987).
E. Kim, Nam, Dae-il and J. L. Stimpert, Testing the applicability of Porter's generic strategies in the digital age: A study of Korean cyber malls. Journal of Business Strategies, 21(1) 19-56 (2004). C. M. Koo, C. E. Koh, and K. Nam An examination of Porter’s competitive strategies in electronic virtual markets: A comparison of two on-line business models, International Journal of Electronic Commerce 9(1) 163–180 (2004).
H. Mintzberg, Generic strategies: Towards a comprehensive framework, Advances in Strategic Management, 5, 1-67 (1988).
F. Reichheld and P. Schefter, E-loyalty, Harvard Business Review 78(4), 105-113 (2000). Also, Reichheld links loyalty to growth in F. Reicheld, The one number you need to grow, Harvard Business Review 81(12), 46-54 (2003).
B.W. Wirtz and N. Lihotzky, Customer retention management in the B2C electronic business, Long Range Planning 36, 517-532 (2003).
A good recent example of a study from a customer perspective, is the one by L. Molteni & A. Ordanini, Consumption patterns, digital technology and music downloading, Long Range Planning 36, 389-406 (2003). However, their results are somehow restricted to cultural industries.
see reference in note 10.
E.M. Tauber, Why do people shop? Journal of Marketing 36 (4), 46–49 (1972).
R. A. Westbrook and W.C. Black, A motivation-based shopper typology, Journal of Retailing 61 (1), 78–103 (1985).
A.J. Rohm and V. Swaminathan, A typology of online shoppers based on shopping motivations, Journal of Business Research, 57 (7), 748-757 (2004).
M. Wolfinbarger and M. Gilly, Shopping online for freedom, control, and fun, California Management Review 43(2), 34-55 (2001).
R. L. Oliver, Whence consumer loyalty? Journal of Marketing 63, 33-44 (1999).
T.O Jones and W.E. Sasser, Jr., Why satisfied customers defect. Harvard Business Review, 73(6), 88-99 (1995); M.A. Jones, D.L Mothersbaugh and S.E. Beatty, Switching barriers and repurchase intentions in services, Journal of Retailing. 76(2), 259-274 (2000); M.A. Jones, D.L Mothersbaugh and S.E. Beatty, Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes, Journal of Business Research 55(6), 441-450 (2002).
R.E. Anderson and S.S. Srinivasan, E-satisfaction and e-loyalty: A contingency framework, Psychology & Marketing 20(2), 123-138 (2003).
For a synthesis see A. Dick and K. Basu, Customer Loyalty: Towards an Integrated Conceptual Framework, Journal of the Academy of Marketing Science 22 (2), 99-114 (1994).
H.S. Bansal, G.H.G. McDougall, S.S Dikolli and K.L. Sedatole, Relating e-satisfaction to behavioral outcomes: an empirical study, Journal of Services Marketing 18(4), 290-302 (2004).
D.M. Szymanski and R. Hise, E-satisfaction: an initial examination, Journal of Retailing 76(3): 309 – 322 (2000).
M. Gommans, K.S. Krishman and K.B. Scheffold, From brand loyalty to e-loyalty: a conceptual framework, Journal of Economic & Social Research 3(1): 43-58 (2001).
E.R. Smith, The seven steps to designing an e-loyalty strategy, Brandweek, 41(45): 30-35 (2001).
S.S Srinivasan, R. Anderson and K. Ponnavolu, Customer loyalty in e-commerce: an exploration of its antecedents and consequences, Journal of Retailing 78(1), 41-50 (2002).
N. Sirohi, E.W. McLaughlin and D.R. Wittink, A model of consumer perceptions and store loyalty intentions for a supermarket retailer, Journal of Retailing 74(2), 223-245 (1998).
E.g. V.Souitaris and M. Cohen, Internet business or just business? European Management Journal, 21 (4), 421-437 (2003).
E.g. A.G. Parsons, Non-financial motives for online shoppers: Why we click, Journal of Consumer Marketing 19(4/5), 380-393 (2002).
M. Roberts, X.M Xu and N. Mettos, Internet shopping: The supermarket model and customer perceptions, Journal of Electronic Commerce in Organizations 1(2), 32-44 (2003).
W.B. Dodds, In search of value: How price and store name information influence buyer's product perceptions, Journal of Services Marketing 5 (3), 27-36 (1991).
R. Hallowell, The relationship of customer satisfaction, customer loyalty, and profitability: An empirical study, The International Journal of Service Industry Management, 7 (4), 27-42 (1996); J.J. Cronin Jr., M.K. Brady and G.T.M. Hult, Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environment, Journal of Retailing 76(2), 134-155 (2000).
D.N. Bellenger and K.P. Korgaonkar, Profiling the recreational shopper, Journal of Retailing 56(3), 77-93 (1980).
C. Mathwicka, N. Malhotra and E. Rigdon, Experiential value: Conceptualization, measurement and application in the catalog and Internet shopping environment, Journal of Retailing 77(1), 39-56 (2001).
J. Forsyth, S. Gupta, S. Haldar, A. Kaul, and K. Kettle A segmentation you can act on. McKinsey Quarterly, 3, 6-16 (1999). A. M. Krieger and P. E. Green Modifying cluster-based segments to enhance agreement with an exogenous response variable. Journal of Marketing Research 33(3), 351-364 (1996)
J.C. Anderson and D.W. Gerbing, Predicting the performance of measures in a confirmatory factor Analysis with a pretest assessment of their substantive validity.” Journal of Applied Psychology 76(5), 732-740 (1991).
J.C. Anderson and D.W. Gerbing, Structural equation modelling in practice: A review and recommended two-step approach, Psychological Bulletin 103(3), 411-423 (1988).
D. Kaplan, Evaluating and modifying covariance structure models: A review and recommendations, Multivariate Behavioral Research l25(2), 137-155 (1990).
M.W. Browne and R. Cudeck, Alternative ways of assessing model fit, In K.A. Bollen Testing structural equation models. Newbury Park (CA), SAGE Publications (1993).
Composite reliability as described by C. Fornell and D.F. Larker, Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research 18, 39 – 50 (1981); The 0.6 threshold was proposed by R.P. Bagozzi and Y. Yi, On evaluation of structural equation models, Journal of the Academy of Marketing Science 16(1), 74-94 (1988).
R.P. Bagozzi and L.W. Phillips, Representing and testing organizational theories: A holistic construal, Administrative Science Quarterly 27 (3), 459–89 (1982).
J. Chodhudy and J. Reardon, Alternatives modes of measuring store image: An empirical assessment of structured vs. unstructured measures, Journal of Marketing Theory and Practice 4, 134-155 (1998).
V.A. Zeithaml, L.L Berry, and A. Parasuraman, The behavioral consequences of service quality, Journal of Marketing, 60 (2), 31–46 (1996).
R.L. Oliver, Satisfaction: A Behavioral Perspective on the Consumer. New York: McGraw-Hill (1997).
G.W. Milligan and M.C. Copper, An examination of procedures for determining the number of clusters in data set, Psychometrica 50, 159-179 (1985)
C.J. Huberty, C. DiStefano and R.W. Kamphaus, Behavioral clustering of school Children, Multivariate Behavioral Research 32(2), 105-135 (1997)
|
