Contemporary Research in E-Marketing_14

328 Del Giudice and Polski costs, learning costs, sunk costs). In the next section we will describe how to shift from a classical view of switching costs to a digital environment. Empirical Results The Model’s Hypotheses In a precedent study (Del Giudice & Del Giudice, 2003) we hypothesized six dimensions of possible source of switching costs on the Internet, quite similar to the classic switching costs known from off-line markets:12 cookie costs13 (digital continuity costs); interface tools costs14 (digital continuity costs); Web searching costs15 (digital learning costs); interface learning costs16 (digital learning costs); profile setup costs17 (digital learning costs); sunk costs.18 Table 1. Switching costs pattern definition in a digital environment (Del Giudice & Del Giudice, 2003) CATEGORIES E-SWITCHING COSTS E-SWITCHING COSTS PATTERN DEFINITION e-Continuity costs e-Learning costs Sunk costs Cookie costs Interface tools costs Web searching costs Interface learning costs Profile setup costs Psychological costs Customer’s perception of the benefits involved in Customer’s purchase pattern (cookie) being lost on switching Customer’s perception of the likelihood of lower performance when switching (e.g., all the filtering tools that help the Web crawler to recognise in the Website a powerful business tool) Perception of the time and effort of gathering and evaluating information prior to switching Perception of the time and effort of learning a new Web site interface and routine subsequent to switching Perception of the time, effort, and expenses required to set up a new profile with an e-business Perception of investments and costs already incurred in establishing and maintaining a business relationship Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Locked In By Services 329 In Table 1, results of the e-switching costs analysis have been summed up. Thus we hypothesize the following: H1: Each switching cost dimension relates positively with repurchase intentions (and thus negatively with customer churn rate). H2: Cookie costs, interface tools costs, and interface learning costs relate more strongly with perceived Web site service quality (through better Web site usability, better Web design, etc.) than the other switching cost dimensions. Starting from the premise that a loyal customer, being locked by his/her deep satisfaction stemming from his/her current supplier’s Web site, can be willing to pay more in order to keep alive his/her business relationship, we then hypothesize the following: H3: Each switching cost dimension relates positively with customer willingness to pay more. Research Methodology The main goal of this section is to test the hypothesized six dimensions of switching costs. Our empirical analysis followed two steps: in the first step, standard scale development procedures were followed in the development of the multidimensional switching costs scale. In the second step, we provide a more rigorous assessment of the dimensionality of the switching cost scale and we test the hypotheses. Data Collection and Sampling Procedure In-depth interviews with managers from a sample of 15 firms from the IT (B2B) sector (three e-suppliers and 12 of their e-customers [that had experienced shopping online with all of the three e-suppliers]) were conducted to define the scale items. Those interviews, our precedent study, and a review of the relevant literature allowed us to generate an initial set of nine acceptable items per switching cost dimension. A panel of five marketing faculty reviewed the items for clarity and face validity. Moreover, the original items were refined and pared Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 330 Del Giudice and Polski to six items per dimension. Item-total correlation, Cronbach’s alpha, and exploratory factor analysis were examined for each switching cost dimension (deleting the items based on low factor loadings, negative contribution to alpha, and/or low item-total correlation). After the exploratory factor analysis, we developed the confirmatory model and tested the propositions by administrating (through e-mail) the questionnaire to a sample of 180 e-customers (who had experienced shopping online from at least two of the original three e-suppliers). The following paragraphs show the result of our analyses. Exploratory Factor Analysis Item-total correlation, Cronbach’s alpha, and exploratory factor analysis were examined for each switching cost dimension.19 We calculated Cronbach’s alphas for the scale items to ensure that they exhibited satisfactory levels of internal consistency (see Appendix, Table A). We refined the scales by deleting items that did not load meaningfully on the underlying construct and those that did not highly correlate with other items measuring the same construct. We deleted the items showing low factor loadings, negative contribution to alpha, and/or low item-total correlation. Finally we got just six factors reflecting the six proposed switching cost dimensions (eigenvalue >1). Cronbach’s alpha gave positive results on all the six dimensions (see Appendix, Table A), supporting the proposed switching cost dimensions. Particularly, Cookie costs (Alpha = .92) Interface tools costs (Alpha = .83) Web searching costs (Alpha = .86) Interface learning costs (Alpha = .85) Profile setup costs (Alpha = .95) Sunk costs (Alpha = .83) Table A in the Appendix presents the meaningful items (factor loadings less than .40 are not shown) and includes Cronbach’s alphas for the hypothesized switching cost dimensions. Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Locked In By Services 331 Analyses and Results: The Test The Methodology The hypotheses were tested using multiple multivariate analysis methodologies (we used SPSS 11.0 and LISREL 8.54). The switching cost items retained from the first part of the analysis were used in order to test the hypotheses. In order to pursue this goal, repurchase intentions, perceived Web site quality, and willingness to pay more were also measured. Particularly, repurchase intentions and perceived Web site quality were assessed on a 7-point Likert scale (from “unlikely” to “likely,” from “impossible” to “very possible,” from “no chance” to “certain scales” [Oliver & Swan, 1989]). Willingness to pay more (defined as the willingness on the part of the customer to continue purchasing from the e-supplier despite an increase in price) was measured on a 5-point semantic differential scale (with anchors “not at all likely” and “very likely”), by adapting relevant scale items from Zeithaml, Berry, and Parasuraman (1996). Moreover, after the factory analysis, we were ready to administer (through e-mail) the questionnaire to a sample of 180 e-customers (who had experienced shopping online from at least two of the original three e-suppliers). The answering rate was quite high (about 86%). Confirmatory Model and Tests of Hypotheses The exploratory factor analysis conducted provided strong support for the proposed switching costs dimensions. The second part of our analysis, instead, provided a more rigorous assessment of the dimensionality of switching cost scale and allowed to test the hypotheses. We conducted a confirmatory factor analysis for the overall sample (with LISREL 8.54). Fit statistics indicated acceptable fit (Tucker Lewis Index = 0.93; Comparative Fit Index = 0.92; Bollen, 1989). Results also support the internal consistency of each switching cost dimension since composite reliabilities (a LISREL-generated measure similar to Cronbach’s alpha) were generally high (see Appendix, Table B). Moreover, estimates of variance extracted for each dimension were greater than 0.60, indicating high shared variance between indicators of each dimension (Fornell & Larcker, 1981). Propositions regarding switching cost correlates were tested using the phi estimates from the confirmatory model and chi-square difference tests of alternative models. H1 indicates that each switching cost dimension relates positively with repurchase intentions (and thus negatively with customer churn rate): it was supported since all phi estimates between switching costs and repurchase intentions were significant (phi’s range from 0.21 to 0.57; see Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 332 Del Giudice and Polski Appendix, Table B). H2 indicates that cookie costs, interface tools costs, and interface learning costs relate more strongly with perceived Web site service quality (through Web site usability, Web design, etc.) than the other switching cost dimensions: it was supported by the higher association among cookie costs, interface tools costs, and interface learning costs (phi = 0.59, phi = 0.63, and phi=0.52, respectively) and perceived service quality, than that between the other switching cost dimensions and perceived service quality (phi’s range from 0.19 to 0.32) (it was confirmed also by chi-square difference tests, all chi-square diff > 26,59, df = 1, Ps <.01). Finally, H3 indicates that each switching cost dimension relates positively with customer willingness to pay more was supported since all phi estimates between switching costs and willingness to pay more were significant (phi’s range from 0.45 to 0.69) (it was confirmed also by chi-square difference tests, all chi-square diff > 19,82, df = 1, Ps <.01). In sum, all three hypotheses were supported. Implications for Managers and Practitioners Research that contributes to the understanding of customer experiences with online shopping has important implications for researchers as well as business managers and information systems managers (Adam et al., 1999). Although marketers are beginning to understand the innovative strategies that will attract visitors to Web sites (Hoffman et al., 1995; Morr, 1997), little is known about the factors that make Web use a compelling customer experience or about the key customer satisfaction outcomes of this compelling experience. Nowadays, the high cost of attracting new customers on the Internet and the relative difficulty in retaining them make customer loyalty an essential asset for many online vendors. Attracting new customers costs online vendors at least 20% to 40% more than it costs vendors serving an equivalent traditional market (Reichheld & Schefter, 2000). To recoup these costs and show a profit, online vendors, even more so than their counterparts in the traditional marketplace, must increase customer loyalty, which means convincing customers to return for many additional purchases at their site. Customer loyalty, in general, increases profit and growth in many ways (Chow & Red, 1997; Heskett et al., 1994) to the extent that increasing the percentage of loyal customers by as little as 5% can increase profitability by as much as 30%–85%, depending upon the industry involved (Reichheld & Sasser, 1990), a ratio estimated to be even higher on the Web (Reichheld & Schefter, 2000). The reason for this is that loyal customers are typically willing to pay a higher price and are more understanding when something goes wrong (Chow & Reed, 1997; Del Giudice & Polski, 2003; Fukuyama, 1995; Reichheld & Sasser, 1990; Reichheld & Schefter, 2000; Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. ... - tailieumienphi.vn