1494 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies of date, and some URLs represented replications of those that were already considered for the study. Finally, the data set consisted of complete REVHUYDWLRQVIRUEXVLQHVV¿UPVRSHUDWLQJ on the Web. Out of the 375 Web sites, 310 were retail Web sites and 65 were service Web sites. The retail industry contained Web sites on mer- chandize stores; apparel and accessory stores; furniture; household appliances; electronics; and so forth, where as the service industry consisted of Web sites on hotels and motels; rooming and boarding houses; sporting and recreational camps; RV parks; software services; and so forth. The collected data provided rich description of the typi- cal features, their level of support for consumers’ non-compensatory strategies, and their level of support for consumers’ compensatory strategies and preferences. To give further assurance of accuracy and validity of data collection, a second author ran- domly gathered data about some companies in the sample to compare to the other author’s data collection. There was almost perfect agreement between the two authors. Results 2XU RYHUDOO ¿QGLQJV DUH GLVSOD\HG JUDSKLFDOO\ in Figure 1. Typical Web site features are shown ¿UVW2IRX UVDPSOHRI:HEVLWHVDOOJLYHJHQ- eral company information and about two-thirds (68.5%) support online purchasing of products or services. Most of the Web sites that support online purchases display the privacy policy and inform that cookies can be loaded to the consumer’s computer. Most of the Web sites that support Figure 1. The percentage of web-retailers’ web sites investigated (375 total) having various web site features, including features that would support consumers’ decision strategies and preferences 100 68.5 68.5 68.5 48 39.2 63.2 43.5 30.7 4 51.2 28 16 12.3 14.7 0 0 0 3.7 0.5 0 0 102030405060708090100 Provides company information Provides product information Allows online purchase Provides price information Website com municates privacy policy Privacy policy informs that cookies can be loaded Home page is organized by category Seller recommends products User is shown related products Other customer's ratings are show n User can enter text for search User can choose from list of keywords User can provide or select a single search criterion User can sort products by attributes User can provide or select multiple search criteria User preferences betw een attributes are elicited User can indicate the weighting of each attribute User can specify which attributes are important User can create side-by-side comparison External ratings are shown Products are scored, screened, or ranked based on user-specified model Typical web site features Supports non- compensatory strategies Supports compensatory strategiesand user preferences 1495 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies RQOLQHSXUFKDVHVDOVRHQDEOHFRQVXPHUVWR¿QG VSHFL¿FFDWHJRULHVZKLFKIDFLOLWDWHVFRQVXPHUV¶ search. About half of the Web sites recommend products in some way, about a third show related products. Only 4% of the Web sites surveyed show other customers’ ratings. In the middle of Figure 1, the results are shown for features that would be helpful to consumers Table 4. Survey of WebDSS attributes Attributes All (N=375) Industry Sales Volume Retail (N=310) Service (N=65) Above (N=188) Below (N=187) Typical Web Site Features Provides company information 100.0 (%) 100.0(%) 100.0(%) 100.0(%) 100.0(%) Provides product information 68.5 72.3 50.8 69.7 67.4 Allows online purchase 68.5 72.3 50.8 69.7 67.4 Provides price information 68.5 72.3 50.8 69.7 67.4 Website communicates privacy policy 48.0 51.7 29.2 59.6 36.4 Privacy policy informs that cookies can be loaded 39.2 42.6 23.1 53.7 24.6 Home page is organized by category 63.2 66.1 49.2 63.8 62.6 Seller recommends products 43.5 47.7 23.1 50.0 36.9 User is shown related products 30.7 36.8 1.5 39.9 21.4 Other customers’ ratings are shown 4.0 4.8 0.0 7.4 0.5 Web Site Features Supportive of Non-Compensatory Decision Strategies User can enter text for search 51.2 60.0 9.2 51.6 50.8 User can choose from list of keywords 28.0 25.5 40.0 30.9 25.1 User can provide or select a single search criterion 19.2 15.2 38.5 20.7 17.6 User can sort products by attributes 12.3 13.5 6.2 16.0 8.6 Web Site Features Supportive of Compensatory Decision Strategies or User Preferences User can provide or select a multiple search criterion 14.7 10.0 36.9 13.8 15.5 User preferences between attributes are elicited 0.0 0.0 0.0 0.0 0.0 User can indicate the weighting to each at- tribute 0.0 0.0 0.0 0.0 0.0 User can specify which attributes are impor- tant 0.0 0.0 0.0 0.0 0.0 User can create side-by-side comparison 3.7 4.5 0.0 5.3 2.1 External ratings are shown 0.5 0.6 0.0 0.5 0.5 Products are scored, screened, and ranked EDVHGRQXVHUVSHFL¿HGPRGHO 0.0 0.0 0.0 0.0 0.0 1496 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies desiring to execute non-compensatory strategies. Most of the Web sites that supported selling had at least one feature that would enable the consumer WR ¿QG SURGXFWV EDVHG RQ D FHUWDLQ FULWHULRQ such as entering text for a search, choosing from a list of keywords, or providing a single search criterion. Nonetheless, only 12.3% of the Web sites enable the sorting of products based on an attribute value. At the bottom of Figure 1, the results are shown for features that would be helpful to consumers desiring to execute compensatory strategies. When we considered the support for compensa- tory strategies that incorporated consumer pref- erences, we found almost no support. Just 14.7% of the Web sites supported searches based on multiple criteria. Only 3.7% displayed side-by-side comparison. Only .5% showed external ratings of products or services. NONE of the Web sites assisted the consumers by allowing the users to give weights of attributes or specify which weights are important. NONE of the Web sites provided IRUVFRULQJEDVHGRQXVHUVSHFL¿HGPRGHOV To gain further insight into the breakdown of the Web sites in our sample, we subdivided our sample two ways: retail versus service, and sales volume above or below average. These results are shown in Table 4. Inspection of these breakdowns reveals several patterns. First, the typical Web site features are provided more often for retail products than for services. Service industry Web sites are more prone to just give company information and not try to sell directly on the Web site. On the other hand, company size did not appear to affect the extent of online selling, perhaps because there are few ¿ Q DQFLDORUWHFK Q RORJ LF D O E D U U LHUVWRDVPDOOEXVL- ness that wants to begin selling on the Internet. The larger companies appear to attempt to market their products somewhat more by recommending products, showing related products, and showing other customer ratings. Retailers of products more frequently allowed users to enter text for a search, while service companies more frequently allowed a choice of keywords or provision of a single search criterion. Since these features are merely different ways of achieving the same objective, we do not see sellers of products or services as dominating in supporting ways of specifying criteria. For the few Web sites that supported sorting of products by attributes, this feature was more frequently provided by retailers of products than by service ¿UPV7KHVRUWIHDWXUHZDVDOVRPRUHIUHTXHQWO\ SURYLGHGE\ODUJH¿UPVWKDQVPDOO¿UPV For compensatory strategies, the main result is that Web sites gave little support at all. For VRPHUHDVRQVHUYLFH¿UPVJDYHPRUHVXSSRUWLQ searching multiple criteria than sellers of prod- ucts. Of the few Web sites showing side-by-side comparisons, all were retailers of products (rather than services) and most were large companies. External ratings were all of products rather than services. This may be due to a lack of available external ratings of services. MANAGERIAL IMPLICATIONS 7KHPDLQ¿QGLQJRIRXULQYHVWLJDWLRQRIHFRP- merce Web sites is a complete absence of support for consumers’ compensatory strategies based on their own preferences. Given the results of academic research that compensatory WebDSS provide better decision quality, satisfaction, and FRQ¿GHQFHWRFRQVXPHUDQGUHGXFHHIIRUWDQRS- portunity is waiting for managers to start looking for ways to implement such tools. The purpose of a DSS is to help a customer pick the best possible choice in all situations. The use of non-compensatory DSS is not associated with better decision quality (Fasolo et al., 2005). However, managers have to make sure that com- pensatory WebDSS are easy to use. Most of the compensatory WebDSS implemented in research experiments typically have two screens. In the real world, as the number of screens used to capture consumer preferences increases, the longer it takes 1497 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies for customers to make a decision. Such design may discourage users. Therefore, to the extent that compensatory WebDSS are easy to use, they are likely to be used by consumers. The execution of compensatory strategies requires users to submit weights to attributes and then the DSS recommends products with high- est expected values. But, how does a user know what algorithm is being used to come up with the results? Therefore, it is recommended that managers provide information concerning how WKH¿QDOVFRUHVH[SHFWHGYDOXHVDUHFDOFXODWHG from the user supplied weights. It is also possible that the lack of expertise DQGGHYHORSPHQWDOFRVWVPD\LQÀXHQFHPDQDJHUV not to implement compensatory WebDSS. We EHOLHYHWKDWWKHH[WHQWWRZKLFKWKHEHQH¿WVRI implementing such WebDSS outweigh the costs implies that it would be a worthwhile proposition for managers to consider developing compensa- tory based decision support tools. Directions for Future Research While our study results showed absence of support for executing compensatory strategies in e-com- merce Web sites based on consumer preferences, with some additional research, we were surprised WR¿QGVRPHWKLUGSD U W \:HEVLWHVSURYLGLQJVXFK support. Examples of such third party sites include My product advisor (http://www.myproductadvi- sor.com), Select smart (http://www.selectsmart. com), and Yahoo! shopping smart sort computer and electronic recommendations (http://shopping. yahoo.com/smartsort). Future research could investigate two research questions. First, what are the factors that inhibit e-commerce Web sites from providing support for compensatory-based strategies based on consumer preferences? Sec- ond, what are the implications for e-commerce Web sites with third party Web sites providing such support when consumers expect such support from the Web retailers themselves? A second area of research could look into the issues surrounding consumers’ adoption of deci- sion technology implemented to support individu- als’ decision-making processes. Research shows that less than 10% of home users visit shopbots (Montgomery, Hosanagar, Krishnan, & Clay, 2004). Therefore, future research could look into various factors that would improve the consumer adoption of decision technology. Furthermore, additional research is needed to understand how individual differences in decision makers affect adoption and usage of decision technology on e-commerce Web sites. The present survey considers only compensa- tory and non-compensatory based systems, and the results suggest that an important gap exists between theory and practice. Future studies could conduct similar kinds of studies to investigate how well e-commerce Web sites provide support concerning content, collaborative, and hybrid WebDSS as well as the feature- and need-based WebDSS. It is our hope that as with our study, im- portant insights could be brought out by conduct- ing studies that investigate the extent of Web site support concerning other types of WebDSS. Compensatory decision tools that are imple- mented in the experiments may face challenges when extended to the real world. For example, most of the compensatory WebDSS designed in experiments contain all the attribute values for a given alternative set. However, in the real world, attributes values may be missing for some alternatives, and therefore computing expected values for such alternatives could be problematic. Therefore, future research could look at the effects of missing information on consumer choices in online decision support environments. Future research could also look at measuring WKHPRQHWDU\EHQH¿WWRDQRUJDQL]DWLRQLPSOH- menting a Web-based decision support tool on its Web site. The existing research so far has focused on decision outcome variables such as satisfac- tion, decision quality, effort, and so forth. Of 1498 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies interest to managers could be whether improved WebDSS tools augment the user’s willingness to purchase. CONCLUSION Research conducted by decision scientists over the last few decades has examined the normative way of decision making (how decisions must be PDGHDQGLGHQWL¿HGVHYHUDOGHFLVLRQVWUDWHJLHV individuals use to make a decision. These decision strategies are compensatory and non-compensa- tory in nature. After the advent of the Internet and the subsequent growth of the e-commerce market, most Web sites are implementing Web- based decision support tools to help consumer make their choices. One category of Web-based decision tools uses decision strategies to provide consu mer support. I n this st udy, we focus on Web site support for executing consumers’ compensa- tory and non-compensatory strategies. The study makes two contributions. By syn- thesizing the existing literature concerning the effectiveness of implementing compensatory versus non-compensatory WebDSS, we found that a majority of the evidence favors implement- ing compensatory WebDSS. If compensatory WebDSS are so effective, one would expect to observe e-commerce Web sites increasing the level of support for executing consumers’ compensatory strategies. Based on a study of 375 U.S. company Web sites, we found that very little support exists for features that support compensatory strategies (such as side-by-side comparison of alternatives) and no support exists for executing compensatory strategies based on consumer preferences. We also note several limitations of our study. As far as we are aware, there is no study that explored how well Web sites provide support for compensatory and non-compensatory based strategies. Though it is problematic to generalize WKH¿QGLQJVRI86EDVHGFRPSDQLHVWRFRPSDQLHV worldwide, a future study could look into how well such strategies are supported in Web sites worldwide. Secondly, choosing 25% of U.S based companies is purely arbitrary. However, we believe that the results of our study are representative of the current situation on e-commerce Web sites. )RUH[DPSOH)DVRORHWDOVWDWHWKDW³DO- though we have no precise data to support it, we are under the impression that real World Wide Web compensatory sites are having rougher and shorter lives than non-compensatory sites….We have anecdotal evidence that transparency and length might be a reason for the lack of success of compensatory ones” (p. 341). The results of this study open up an opportu- nity for managers to start providing more support for compensatory-based decision strategies, and at the same time begs the question of the lack of popularity of such tools. A number of potential reasons have been presented and a host of research questions have been raised. It is our hope this attempt fuels further research in improving the GHVLJQRI:HE'66DQG¿QGLQJIDFWRUVWKDWDIIHFW the adoption of WebDSS, ultimately contributing WRWKHEHQH¿WRIERWKWKH:HEVLWHVDQGXVHUV REFERENCES Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing Research, 37(3), 363-375. Edwards, W., & Fasolo, B. (2001). Decision technology. Annual Review of Psychology, 52(1), 581-606. Fasolo, B., McClelland, G. H., & Lange, K. A. (2005). The effect of site design and interattribute correlations on interactive Web-based decisions. In C. P. Haugtvedt, K. Machleit, & R. Yalch (Eds.), Online consumer psychology: Understanding and LQÀXHQFLQJ EHKDYLRU LQ WKH YLUWXDO ZRUOG (pp. 325-344). Lawrence Erlbaum Associates. 1499 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies Garrity, E. J., Glassberg, B., Kim, Y. J., Sanders, G. L., & Shin, S. K. (2005). An experimental investigation of Web-based information systems success in the context of electronic commerce. Decision Support Systems, 39(3), 485-503. Grenci, R. T., & Todd, P. A. (2002). Solutions- driven marketing. Communications of the ACM, 45(2), 64-71. Haubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 14-21. Hauble, G., & Murray, K. (2003). Preference con- struction and persistence in digital marketplaces: The role of electronic recommendation agents. Journal of Consumer Psychology, 13(1), 75-91. Hogarth, R. (1987). Judgment and choice (2nd ed.). New York: John Wiley and Sons. Jedetski, J., Adelman, L., & Yeo, C. (2002). How Web site decision technology affects consumers. IEEE Internet Computing, 6(2), 72-79. Jinling, C., & Guoping, X. (2005). Comprehen- sive evaluation of e-commerce Websites based on concordance analysis. Proceedings of the 2005 IEEE International Conference on E-Business Engineering (pp. 179-182). Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management Science, 31(4), 394-414. Jones, D. R., & Brown, D. (2003). The division of labor between human and computer in the pres- ence of decision support system advice. Decision Support Systems, 33(4), 375-388. Larrick, R. P. (2004). Debiasing. In D. J. Koe- hler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making. Oxford, UK: Blackwell. Montgomery, A. L., Hosanagar, K., Krishnan, R., & Clay, K. B. (2004). Designing a better shopbot. Management Science, 50(2), 189-206. Olson, E. L., & Widing, R. E. (2002). Are interac- tive decision aids better than passive decision aids? A comparison with implications for information providers on the Internet. Journal of Interactive Marketing, 16(2), 22-33. 3HUHLUD5(,QÀXHQFHRITXHU\EDVHG decision aids on consumer decision making in electronic commerce. Information Resources Management Journal, 14(1), 31-48. Pew Internet and American Life. (2006). Internet penetration and impact.Retrieved November 9, 2007, from http://www.pewinternet.org/PPF/ r/182/report_display.asp Simon, H. A. (1955). A behavioral model of ra- tional choice. Quarterly Journal of Economics, 69(1), 99-118. Song, J., Jones, D., & Gudigantala, N. (2007). The effect of incorporating compensatory choice strategies in Web-based consumer decision sup- port systems. Decision Support Systems, 43(2), 359-374. 7RGG 3 %HQEDVDW , 7KH LQÀXHQFH of decision aids on choice strategies: An ex- perimental analysis of the role of cognitive effort. Organizational Behavior and Human Decision Processes, 60(1), 36-65. U.S. Department of Commerce. (2004). A nation online, entering the broadband age. Retrieved November 9, 2007, from http://www.ntia.doc. gov/reports/anol/ Widing, R. E., & Talarzyk, W. W. (1993). Elec- tronic information systems for consumers: An evaluation of computer-assisted formats in mul- tiple decision environments. Journal of Marketing Research, 30(2), 125-141. 1500 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies Xiao, B., & Benbasat, I. (2007). E-commerce prod- uct recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137-209. ENDNOTES 1 http://www.forrester.com/Research/Docu- ment/Excerpt/0,7211,34576,00.html 2 Please visit http://www.galegroup.com/ pdf/facts/bcrc.pdfWR¿QGPRUHDERXWWKLV database 3 The questionnaire captures general details, support for user to locate a product, evalu- ate individual products, support in terms of others ratings, support to compare products, support for multi-attribute models, and infor- mation about cookies. The only place where the researcher’s perceptions could bias the results is the section on support provided to XVHUWRVHOHFWDVSHFL¿FSURGXFW7KLVSDUW is not used in the analysis. The rest of the variables are binary in nature. For example, a Web site can provide a keyword-based search or not. Similarly, a Webs ite can let the users pick important attributes or not, weight the attributes or not. Therefore, we believe that what is needed from a data col- lector is general observation skills and since perceptions are not recorded, we believe that use of one of the authors to collect data is reasonable. 1501 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies APPENDIX A. URL: __________________________________ SIC Code: __________________ Preparer __________ Name of Business ___________________________________________________________ Date__________ Types of Products Offered _____________________________________________________ Circle all that apply: shows company info, shows product info, shows prices, allows online purchase Support that Helps User Locate a Product: Y N Home page is organized by category to assist with product search Y N User can enter text for search Y N User can choose from list of keywords for search Y N User can provide or select a single search criterion (e.g., homes with 3 bedrooms, < $200,000) Y N User can provide or select multiple search criteria Y N User is shown related products Support that Helps User Evaluate Individual Products: BA A AA Products are described in detail (Below average, average, above average) BA A AA Products are shown in high quality pictures Special features (pictures): ____________________________________________________ 6XSSRUWWKDW3URYLGHV8VHUZLWK2WKHUV¶5DWLQJVRID6SHFL¿F3URGXFW Y N Other customers’ ratings or comments are shown for products Y N External ratings (e.g. Consumer Reports ratings) are shown for products Source: _________________________________________________ Y N 6HOOHUUHFRPPHQGVVRPHSURGXFWVHJ³EHVWYDOXH´ Verbiage: _________________________________________________ Support that Helps User Compare Products: Y N User can sort products by an attribute: _______________________________________ Y N User can create side-by-side comparison of products on a single web page Support that Creates Multi-Attribute Model of Elicited User Preferences: Y N User can specify which attributes are important and system picks products for user to review Explain: ______________________________________________________________ __ Y N User preferences between attributes are elicted by system (e.g., providing user with pairs of product attributes and asking user which is more important). Y N User can indicate how much weight should be given to each attribute. 1502 How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies Y N Products are scored, screened, or ranked (indicate which) based on multi-attribute model of user preferences Explain: ______________________________________________________________ ___ System Informs of Cookies in Privacy Policy: Y N Website communicates a privacy policy Y N Privacy policy informs that cookies might be loaded onto user’s computer Other Type of Support: Please describe in detail any other type of decision support provided for the consumer ________________________________________________________________________ _________________________________________________________________________ ________________________________________________________________________ This work was previously published in the International Journal of E-Business Research, edited by I. Lee, Volume 4, Issue 4, pp. 43-57, copyright 2008 by IGI Publishing (an imprint of IGI Global). 1503 Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 5.8 The Human Face of E-Business: Engendering Consumer Initial Trust Through the Use of Images of Sales Personnel on E-Commerce Web Sites Khalid Aldiri University of Bradford, UK Dave Hobbs University of Bradford, UK Rami Qahwaji University of Bradford, UK ABSTRACT Business-to-consumer (B2C) e-commerce suf- fers from consumers’ lack of trust. This may be partly attributable to the lack of face-to-face in- terpersonal exchanges that provide trust behavior in conventional commerce. It was proposed that initial trust may be built by simulating face-to- face interaction. To test this, an extensive labora- tory-based experiment was conducted to assess the initial trust in consumers using four online vendors’ Web sites with a variety of still and video images of sales personnel, both Western and Saudi Arabian. Initial trust was found to be enhanced for Web sites employing photographs and video clips compared to control Web sites lacking such images; also the effect of culture was stronger in the Saudi Arabian setting when using Saudi photos rather than Western photos. INTRODUCTION The beginning of the 21st century brought rapid G H YH O RSP H QWW RW K H¿HOG RI H FR P P H UF H D Q GP D Q\ enterprises in Western developed countries found success in this area. According to emarketer.com, total online retail sales for 2005 were $144,613 million. In 2001 Internet sales to households from WKH8.QRQ¿QDQFLDOVHFWRUVWRRGDWELOOLRQ . Web sites and 65 were service Web sites. The retail industry contained Web sites on mer- chandize stores; apparel and accessory stores; furniture; household appliances; electronics; and so forth,. industry consisted of Web sites on hotels and motels; rooming and boarding houses; sporting and recreational camps; RV parks; software services; and so forth. The collected data provided. consumers’ non-compensatory strategies, and their level of support for consumers’ compensatory strategies and preferences. To give further assurance of accuracy and validity of data collection,