The Impact of Electronic Commerce Environment on User Behavior:The Case of a Complex Product

Jahng, Jungjoo.Jain, Hemant.Ramamurthy K.

e-Service Journal, Volume 1, Number 1, Fall 2001, pp. 41-53 (Article)

Published by Indiana University PressDOI: 10.1353/esj.2001.0003

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The Impact of ElectronicThe Impact of ElectronicThe Impact of ElectronicThe Impact of ElectronicCommerce Environment onCommerce Environment onCommerce Environment onCommerce Environment on

User BehaviorUser BehaviorUser BehaviorUser BehaviorThe Case of a Complex Product

Jungjoo Jahng Rensselaer Polytechnic Institute

Hemant Jain University of Wisconsin-Milwaukee

K. RamamurthyUniversity of Wisconsin-Milwaukee

Authors’ names in alphabetical order; all have contributed equally. An early version of this manuscript appeared in AMCIS2000Conference Proceedings, Long Beach, CA from August 11 to 13, 2000.

ABSTRACT

There is no doubt that growth of electronic commerce (EC) on the Internet/Web is only going to accelerate even more. However, as the EC technology matures and attracts a larger number of mainstream consumers, the virtuality of the EC environment will become an important issue. Many consumers feel uncomfortable with this virtuality. A compelling issue facing the designer/developer of an EC environment is how the EC environments can be made more acceptable to consumers by approximating their real-world physical store purchase experience. Drawing upon previous literature, this study uses the theme of “¤t” between EC environment and the product type. Four different prototype systems depicting different EC environments for a complex product were developed. Using laboratory-based experiments, the influence of “¤t” between EC environment and product type on user outcomes was examined. Signi¤cant support to the “¤t” theory in the context of a complex product is obtained.

© 2001. e-Service Journal. All rights reserved. No copies of this work may be distributedin print or electronically without express written permission from Indiana University Press.

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Key Words: Electronic Commerce, e-Commerce Environmental Richness, ConsumerBehavior, Business-to-Consumer, e-Commerce Site Design

INTRODUCTION

Electronic commerce (EC) environments are by nature virtual. Many mainstream consum-ers are likely to feel uncomfortable with this virtuality. When a customer visits a physicalstore, they can touch and feel the product and talk to a “real person” right away for addi-tional product information or consultation. However, they cannot directly do that in a vir-tual store such as an EC site. This dif¤culty is exacerbated in a poorly designed ECenvironment. EC environments lack the environmental richness that physical stores enjoy.This de¤ciency might result in lack of decision con¤dence on the part of consumers, andlead to reduced user satisfaction and reduced purchase intention. Furthermore, a poorlydesigned EC environment may cause a lack of motivation on the part of consumers toaccept EC technology as a regular transaction mode. In this paper, we argue that an ECenvironment should be rich enough to compensate for this de¤ciency to have favorableinfluence on user attitude and behavioral intentions. To validate this argument, we investi-gate the impact of richness of EC environment on user attitude/behavior in the context ofa complex product. The paper is structured as follows: the next section discusses the con-cepts of social presence and product presence. In Section 3 the theory of “¤t” is used todevelop the study’s research model and proposition. In Section 4 the research methodologyand data analyses are presented. Discussions, implications and limitation of the study fol-low in Section 5.

CONCEPT OF SOCIAL PRESENCE AND PRODUCT PRESENCE

Social Presence and Product Presence in Electronic Commerce Systems

Focusing on user-computer interaction in the EC environment, EC systems envi-ronments can be characterized using two major dimensions: social presence (SP) andproduct presence (PP) (Jahng, Jain and Ramamurthy, 2000). Social presence in EC envi-ronments deals with the interaction between sellers and consumers or among consumersand can be de¤ned as the degree to which a buyer psychologically perceives the sellers tobe physically present when interacting with them (Short, Williams and Christie, 1976).In a similar vein, product presence addresses the interaction between buyers and productsand can be de¤ned as the extent to which a buyer psychologically perceives the productsto be physically present when interacting with them. These are also consistent with thekey characteristics of hypermedia computer-mediated environments identi¤ed by Hoff-man and Novak (1996) using two types of interactivity: “person interactivity” and“machine interactivity”. They de¤ne person interactivity as the interactivity betweenpeople through the medium and machine interactivity as the interactivity between usersand the medium or the mediated environment.

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The Impact of Electronic Commerce Environment on User Behavior

From a technology perspective, a fairly wide range of SP can be realized by the useof e-mail, on-line chatting, audio conferencing, audio/video conferencing, etc., whereasPP can be realized in varying degrees from text only, static picture, animation, 3–Dview, virtual reality, etc. In a richly represented virtual EC environment (e.g., real-timeinteraction enabled by online chat with fellow online buyers on their experiences) agreater level of social presence is likely to be perceived by the buyers. Likewise, in a richEC environment (e.g., enabled by interactive multimedia technology) where all nec-essary product aspects are fully represented in a multi-faceted fashion, a buyer maypsychologically perceive a higher level of physical presence of products when interactingwith them (Klein, 1998; Schloerb, 1995).

SP and PP are continuums, but in this study, we dichotomize each dimension forthe sake of simplicity and illustration. Using these as the primary dimensions we use atypology of EC environments, as displayed in Figure 1, that can be used to classifymost, if not all, of the virtual EC environments.

This typology characterizes EC environments in terms of social presence andproduct presence. EC environments can be classi¤ed into four categories: simple en-vironments, social environments, experiential environments, and rich environments(Jahng et al., 2000). Simple environments can be characterized in terms of low SP andlow PP, social environments as high SP and low PP, experiential environments as low SPand high PP, and rich environments as high SP and high PP.

Product Classification in a Virtual Environment

Products have been widely discussed in EC literature as an important contingent factor

Figure 1: EC Environment Characterization

SocialEnvironments

RichEnvironments

SimpleEnvironments

ExperientialEnvironments

Low ← → HighProduct Presence

High

↑

Social Presence

↓

Low

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of EC systems design. Many schemes for product characterization exist in the tradi-tional marketing literature—convenience goods, shopping goods and specialty goods(Copeland, 1923), low involvement and high involvement products (Krugman, 1965),search goods and experience goods (Nelson, 1970), and durable and non-durable goods(Norton and Norton, 1988). Similarly, EC literature has classi¤ed products as digitiz-able, low-priced and easily described products (Strader and Shaw, 1997), informationgoods (Bakos, 1998), and based on information intensity (Palmer and Grif¤th, 1998).

The above classi¤cation schemes, although important for marketing and deliveryof products through EC channels, do not directly contribute to the design of EC en-vironments. Along the dimensions of the EC environment characterization presentedabove, we use the following two dimensions to characterize products in EC envi-ronments: SP requirements of the product and PP requirements of the product. The SPrequirement of a product is de¤ned as the degree to which the product choice taskrequires a sense of presence of a seller. On the other hand, the PP requirement of aproduct is de¤ned as the extent to which the product choice task requires a sense ofpresence of the target product. We argue that the amount of SP requirements of aproduct is determined by the product complexity (a function of number of productattributes, variability of each product attribute, and interdependence among productattributes). Similarly the PP requirement of the product is related to the extent to whichsatisfying the ¤ve senses (of seeing, touching/feeling, smelling, tasting and hearing) isimportant to the product choice decision making. The current EC technology has beenfocusing on and been able to realize reasonable success in addressing two of the ¤vesenses (see, hear). The requirement to “see” th y experientiality (the extent to which theability to experience the product, e.g., listen to the music, video clips etc.) is critical toconsumer decision making. Based on this characterization scheme, we use a typology ofproduct characterization, as shown in Figure 2, that would be appropriate for ECenvironments (Jahng et al., 2000).

This typology suggests that all products in EC environments can be classi¤ed intofour categories: simple products, social products, experiential products, and complexproducts. Low SP requirement and low PP requirement characterize simple products.On the other hand, high SP requirement and low PP requirement characterize socialproducts. Low SP and high PP requirements characterize experiential products, andhigh SP and high PP requirements characterize complex products.

An example of a social product could be ¤nancial or insurance products that oftenrequire the expert advice of consultants and may thus require a higher level of socialpresence. An example of experiential products could be a music CD or a book where thepotential consumers want to be able to try out and evaluate product features by them-selves thus requiring a greater level of product presence. Complex products could be

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The Impact of Electronic Commerce Environment on User Behavior

complex software packages that might require higher levels of both social and productpresence; for instance, not only would a potential customer want to try out the featuresof software packages but also be able to discuss with the software expert about speci¤cquestions and advice. Finally, simple products could be commodity types such asstationery that may not need high levels of either presence. An EC environment thatcan provide a greater level of visualizability or experientiality or both in instances suchrequirements are needed could be deemed to be a “richer” environment. In this study,we examine a product that requires high SP and high PP to investigate the impact of ECenvironmental richness on user attitude/behavior.

THE RESEARCH MODEL

Fit between the Electronic Commerce Environment and Products

The above view of an EC environment and product characterization allows us to drawupon the notion of task-technology ¤t that has generated considerable attention inrecent times (Goodhue and Thompson, 1995; Goodhue, 1998; Mathieson and Keil,1998; Zigurs and Buckland, 1998). Focusing on the linkage between information sys-tems and individual performance, Goodhue and Thompson (1995) propose a compre-hensive model by incorporating insights from two complementary research streams:Utilization focus stream and Fit focus stream. The former suggests that user’s (favorable)beliefs of and attitudes toward technology characteristics lead to utilization and ulti-mately to better performance, whereas the latter argues that task-technology ¤t impactsperformance. The authors propose a comprehensive model where they suggest that

Figure 2: Product Characterization

Social Products(e.g., Financial/Insurance

Plan)

Complex Products(e.g., Software package)

Simple Products(e.g., Regular Commodity

Experiential Products(e.g., Music CD, Book)

Low ← → HighProduct Presence Requirement

High

↑

Social PresenceRequirement

↓

Low

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task, technology and individual characteristics in¶uence the task-technology ¤t (TTF)which influences utilization, and that both ¤t and utilization lead to better perfor-mance. They conceptualize the task-technology ¤t measure as a composite of eightmajor factors such as data quality, data compatibility, data locatability, production time-liness, systems reliability, etc. Instead of testing the entire model, they derive a reducedmodel to test three relationships (between task/technology characteristics and user eval-uation of ¤t, between ¤t and utilization, and between ¤t and performance). The study’sresults show that task-technology ¤t is influenced by both task and systems characteris-tics and that this ¤t (and utilization) lead to better job performance of IS users withintheir organizations. However, the study ¤nds that task-technology ¤t is only weaklyrelated to utilization.

Extending on this prior work, Goodhue (1998) develops a more comprehensiveinstrument for TTF. They compared the utility of the instrument in being used to mea-sure user evaluation of information systems vis-à-vis two other popular measures in IS lit-erature—namely, user information satisfaction—UIS (Ives, Olsen and Baroudi, 1983)and end user computing—EUC satisfaction (Doll and Torkzadeh, 1988). This measureconsists of 12 dimensions of task-technology ¤t—level of detail, data accuracy, compati-bility, locatability, accessibility, meaning, assistance, ease of use, system reliability, cur-rency, presentation, and confusion. Based on the task-technology ¤t theory, the authorasserts that the correspondence between information systems functionality and taskrequirements would lead to positive user evaluation and positive performance impacts.Results from a ¤eld study of 10 Companies show that the TTF instrument demonstratessatisfactory reliability and discriminant validity for the 12 separate dimensions of thetask-technology ¤t. This study claims that TTF is an attractive alternative (to UIS andEUC Satisfaction) to evaluate IS effectiveness within the context of managerial decisionmaking in organizations. This study acknowledges that given the rapid technologychanges (e.g., explosive growth of the Internet/Web) and sourcing options (e.g., out-sourcing) this instrument would need modi¤cation/ adaptation if it is to be used in otherresearch.

Mathieson and Keil (1998) show, through a laboratory-based experimental study,that perceived ease of use (EOU), an important factor for voluntary use of an informationsystem, is not just a function of the user-computer interface but that it is also a functionof task/technology ¤t. This study implores that developers need to consider deeper task-technology ¤t issues that are not corrected by merely changing the user interface.

Within the context of the Internet and Web, a recent study examines in detailtechnological impediments to business-to-consumer EC application and proposes “inter-face limitations” as one of the top six key impediments (Rose, Khoo and Straub, 1999).This study discusses limitations of the interface (in EC) within the context of its inabil-

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The Impact of Electronic Commerce Environment on User Behavior

ity to satisfy the ¤ve senses of humans—(3D) sight, sense of touch/feel, smell, hear, andtaste. Notwithstanding recent developments in new sensory-capturing and sending devices,their widespread commercial use may be constrained at the current time due to band-width and security/privacy issues among others.

To recap (and as would be clearer from the material presented next), our conceptualframework (involving the SP and PP dimensions) captures the key theme emerging fromthe above discussion that the interface of the EC environment must be designed to have a“¤t” with the product being sold to generate favorable user outcomes. In our study, thisinvolves an interaction with a product and a product decision-choice making task.

User Attitude and Behavior

The importance of well-de¤ned outcome variables has been emphasized in informationsystems (IS) research for a long time (DeLone and McLean, 1992). User outcome is amultifaceted construct that can be de¤ned in different ways depending on research pur-poses and contexts. Within the context of effective EC environments, outcomes in gen-eral exist from two perspectives: the provider (or seller) and the consumer. From aprovider/ seller standpoint, outcomes may include enhanced sales, pro¤ts, and marketshare through electronic business transactions. From a consumer’s standpoint, outcomesinclude various attitude and behavioral factors. Drawing upon previous literature inMarketing and IS, some of these outcomes include user attitude toward the productsand EC environments, satisfaction with product-choice and EC systems, acceptabilityof EC environments as a transaction mode, con¤dence in product-choice, ease ofobtaining/processing information, etc. (Davis, 1989; Doll and Torkzadeh, 1988; Goslaret al., 1986; Mehta, 1994). Given the consideration of research questions raised earlierand resource constraints, we chose the following four variables to be relevant user out-comes within the context of successful EC environments: users’ satisfaction with the ECenvironments, decision con¤dence, acceptance of an EC environment as a transactionmode, and purchase intention.

The FIT Model

Based on the foregoing discussions, we present our research model as illustrated in Fig-ure 3 and propose to test the following hypothesis:HYPOTHESIS: A congruence between system richness (in terms of SP and PP) and productcharacteristics (in terms of SP/PP requirement) leads to favorable user outcomes.

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RESEARCH METHODOLOGY

In order to validate the ¤t model described above, we employed a controlled laboratoryexperiment using a complex product (digital camera) that requires high SP/PP. Productcomplexity is the extent to which a product is perceived to be complex for consumerproduct-choice decision making. Perceived product complexity refers to the consum-ers’ understanding of the number, variability, and inter-dependence of product attrib-utes that they consider for their product-choice decision making (Schmidt and Spreng,1996). Even if all necessary product information is available and interpretable, whenthe number of product attributes is too many to consider for product choice decisionmaking, consumers might need to seek help or advice from experienced users orexperts to reduce their perceptual complexity. Perceptual complexity is closely relatedto lack of information processing ability. Digital camera was determined to fall intothe category of “high complexity” in light of the fact that there are over 25 productattributes to consider many of which are not only interdependent but also take onmultiple values. We developed four different prototypes (i.e., four combinations ofhigh and low social and product presence) and one of these four prototypes was ran-domly assigned to each subject. Since this experiment compares four groups (one ¤tgroup and the other three non-¤t groups), a sample size of 45 for each group wasdetermined to provide adequate statistical power (i.e., .80) for hypothesis testing(Cohen, 1969). Subjects for this experiment were recruited from an undergraduateintroductory information systems course in the business school of a major mid-westernuniversity. The participation by subjects was voluntary; there was no compensationfor participating in the study.

The four prototype EC environments were created to provide different amounts

Figure 3: EC Technology/Product Fit Model

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The Impact of Electronic Commerce Environment on User Behavior

of social presence and product presence. An EC environment with low PP was repre-sented by a textual description of product information on each attribute accompaniedby a static picture of the product (digital camera). High PP was represented by an inter-active multimedia presentation of the product information accompanied by an ability/opportunity for the user to virtually try out various features of the product (camera). AnEC environment with low SP was represented as one with e-mailing capability to obtainclari¤cations on the product from a (seller’s) representative while high SP provided real-time one-way video and two-way audio communication.

Social presence and product presence offered by the four prototype EC environ-ments were assessed with a multi-item scale to validate the perception of participants inthe experiments. Short et al.’s (1976) social presence measure was adapted to develop SP/PP measures of EC environments. User outcomes were measured by adapting previouslyvalidated scales from the literature. The adapted scales for outcome variables from the lit-erature are: user satisfaction (Doll and Torkzadeh, 1988), user decision con¤dence (Gos-lar et al., 1986), user acceptance (Davis, 1989), and purchase intention (Mehta, 1994).The propositions were tested using analysis of variance (ANOVA) technique to detectdifferences among the treatments.

DATA ANALYSIS AND RESULTS

As indicated in Table 1, four hundred and eight subjects participated in the experiment.We collected more than 100 data points in each group.

Table 1: Distribution of Subjects by Group

First, we measured social/product presence as perceived by subjects in each group.Results emerged as expected as seen from Table 2. For example, P4 (prototype used bygroup 4) has a signi¤cantly higher value of SP than P1 (prototype used by group 1) andP2 (prototype used by group 2), whereas P4 has a signi¤cantly higher value of PP thanP1 and P3 (prototype used by group 3).

Group type Prototype ID # of Subjects

Group 1

Group 2

Group 3

Group 4

P1 – Low SP & Low PP

P2 – Low SP & High PP

P3 – High SP & Low PP

P4 – High SP & High PP

103101101103

Total 408

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Table 2: Mean SP & PP Differences across Prototypes

We compared the four prototypes in terms of the four user-outcome variables. As dis-played in Table 3, there are signi¤cant differences across the four EC environments forthe same product. Group 4 using prototype 4 (the one that we manipulated to have thebest ¤t with the product type examined in this study) has the highest values on each ofthe four outcome variables. We can also see the differences among the three non-¤tgroups. For example, group 1 using prototype 1 has lower values on these four user-outcome variables than group 2 or group 3.

Table 3: Mean User-Outcome Values across Prototypes

Note: **** p <.001; *** p <.01; ** p <.05; * p <.1; ns—not signi¤cant

DISCUSSION AND CONCLUSIONS

It can be noted from the above results of the experiment that for a complex product thatrequires a high degree of social presence (e.g., high level of interaction with the sales per-son) and a high degree of product presence (e.g., ability to visualize the product andexperience its use), we need a rich EC environment that provides these high levels ofsocial and product presence. Results indicate that the group using prototype 4 (P4) hada signi¤cantly higher level of user satisfaction than the group using prototype 1 (P1).This indicates that in the case of complex products it is desirable to provide a richer rep-

P1 P2 P3 P4 F- Value (Sig. Level)

SP: Social Presence PP: Product Presence

3.4274.102

3.9554.768

4.4164.594

4.5345.017

26.30 (.0000)14.72 (.0000)

P1 P2 P3 P4 F-Value(Sig. Level)

Scheffe’s test ofPairwise comparison

EC Satisfaction

3.75 4.11 4.17 4.25 12.83 (0.000)

1–2****; 1–3****; 1–4****;

2–3ns; 2–4ns; 3–4ns

Decision Con¤dence

3.73 3.87 3.79 4.02 3.050 (0.026)

1–2ns; 1–3ns; 1–4***;

2–3ns; 2–4ns; 3–4**

EC Acceptance

3.71 4.55 4.62 5.03 8.906 (0.000)

1–2***; 1–3***; 1–4****;

2–3ns; 2–4*; 3–4ns

Purchase Intention

4.20 5.37 5.32 5.61 12.47 (0.000)

1–2****; 1–3****; 1–4****;

2–3ns; 2–4ns; 3–4ns

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The Impact of Electronic Commerce Environment on User Behavior

resentation of the product including the capability to try out some of the features of theproduct and have an expert sales person available to answer questions in real-timethrough video/ audio conferencing capability. However, the experiment was conductedin a laboratory environment with high-speed access to Internet. If high-speed access isnot available, the impending delay may result in frustration on the part of users. Withthe availability of DSL lines and cable modems a signi¤cant number of consumers arestarting to have high-speed access to the Internet. The results of this study indicate thatcompanies selling complex products need to seriously consider providing a rich environ-ment for its users in the form of higher product and higher social presence. If the ECenvironment is not rich enough for a complex product, users’ level of satisfaction, deci-sion con¤dence in the purchases, acceptance of EC as a future transaction mode, andultimately intention to purchase the product are adversely affected.

Thus, the results show that the congruence between EC environments andproducts (at least for complex product and rich environment) is critical for favorableoutcomes. This line of thought is consistent with the previous theories such as task-technology ¤t theory (Goodhue and Thompson, 1995; Zigurs and Buckland, 1998),cognitive ¤t theory (Vessey and Galletta, 1991), and media choice theories (e.g., mediarichness theory and social presence theory—Daft and Lengel, 1986; Short et al., 1976;Trevino et al., 1990).

While a “rich” EC environment as depicted by High SP/PP is perhaps the mostdesired, it is possible that there are both internal constraints (resource availability) andexternal constraints (e.g., lack of high speed networks). The results in Table 3 indicatethat signi¤cantly favorable results can be obtained by providing either a high level ofproduct presence or a high level of social presence. As can be seen from Table 3, by pro-viding a high level of product presence (P2) user satisfaction and the all important pur-chase intention can be very much improved. Similarly, high level of social presence withlow level of product presence (P3) also results in signi¤cantly favorable user outcomes.The designers of online commerce environments may have a choice—they can either optfor P2 (low SP/high PP) or P3 (high SP/low PP) each of which appears to result inequally good user outcomes on most dimensions. Choice may be based on economic con-siderations. The high level of product presence may have higher initial cost and low on-going cost while a high level of social presence may have relatively low initial cost but highoperating cost. The expected transaction volume may be an important factor to considerin this decision.

It may be noted that this study only looked at a complex product that requires ahigh amount of SP and PP. Obviously, no generalizations can be made based on a singlestudy on just one (complex) product. It is necessary to not only replicate such studies onother, similar complex products but also examine the three other categories of product

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belonging to High SP/Low PP (e.g., retirement investment plan), Low SP/High PP(e.g., music CD), and Low SP/Low PP types (e.g., blank videotape). Additionally, theEC environment is just one of many consideration affecting the purchase behavior ofthe customers. Other factors such as cost, product availability, time to shipment, returnpolicy, etc. may also signi¤cantly influence the purchase decision of the consumers.

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