Electronic Business: Concepts, Methodologies, Tools, and Applications (4-Volumes) P157 pps

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’ compen

of date, and some URLs represented replications

of those that were already considered for the

study Finally, the data set consisted of complete

REVHUYDWLRQV IRU  EXVLQHVV ¿UPV RSHUDWLQJ

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

¿UVW2IRXUVDPSOHRI: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 10 20 30 40 50 60 70 80 90 100 Provides com pany inform ation

Provides product inform ation Allow s online purchase Provides price inform ation Website com m unicates privacy policy Privacy policy inform s that cookies can be loaded Hom e page is organized by category Seller recom m ends products User is show n related products Other custom er's ratings are show n User can enter text for search User can choose from list of keyw ords User can provide or select a single search criterion User can sort products by attributes User can provide or select m ultiple search criteria User preferences betw een attributes are elicited User can indicate the w eighting of each attribute User can specify w hich attributes are im portant User can create side-by-side com parison External ratings are show n Products are scored, screened, or ranked based on user-specified m odel

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

(N=375)

Retail (N=310)

Service (N=65)

Above (N=188)

Below (N=187)

Typical Web Site Features

Privacy policy informs that cookies can be

Web Site Features Supportive of Non-Compensatory Decision Strategies

User can provide or select a single search

Web Site Features Supportive of Compensatory Decision Strategies or User Preferences

User can provide or select a multiple search

User preferences between attributes are

User can indicate the weighting to each

User can specify which attributes are

Products are scored, screened, and ranked

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

¿QDQFLDORUWHFKQRORJLFDOEDUULHUVWRDVPDOOEXVL-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

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[SHFWHGYDOXHVDUHFDOFXODWHG

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¿QGVRPHWKLUGSDUW\: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-compensacompensa-tory 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

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

PDGHDQGLGHQWL¿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

consumer support In this study, we focus on Web

site support for executing consumers’

compensa-tory and non-compensacompensa-tory 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)DVRORHWDOVWDWHWKDW³DO-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

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 Supsup-port 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.

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.pdf WR ¿QG PRUH DERXW WKLV

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

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

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).

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 GHYHORSPHQWWRWKH¿HOGRIHFRPPHUFHDQGPDQ\ 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¿QDQFLDOVHFWRUVWRRGDW…ELOOLRQ

(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)... 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-compensacompensa-tory... and persistence in digital marketplaces:

The role of electronic recommendation agents

Journal of Consumer Psychology, 13(1), 75-91.

Hogarth, R (1987) Judgment and