Information & Management 49 (2012) 240–247

Signaling theory and information asymmetry in online commerce

Tamilla Mavlanova a,*, Raquel Benbunan-Fich b, Marios Koufaris b

a Baruch College, The City University of New York (CUNY), United Statesb Zicklin School of Business, Baruch College, The City University of New York (CUNY), United States

A R T I C L E I N F O

Article history:

Received 15 November 2010

Received in revised form 21 November 2011

Accepted 6 April 2012

Available online 29 May 2012

Keywords:

Signaling theory

Information asymmetry

Adverse selection

Moral hazard

Website signals

A B S T R A C T

An e-business environment results in information asymmetry because buyers cannot physically evaluate

the quality of products and easily assess the trustworthiness of sellers. Product and seller quality are

communicated through website signals. Using signaling theory, we developed a three-dimensional

framework to classify website signals. We empirically tested the framework with a comparative content

analysis of websites from a sample of online pharmacies. We found that low-quality sellers were likely to

avoid costly and easy-to-verify signals and used fewer signals than did high-quality sellers, who used

costly and difficult-to-verify signals and displayed more signals. These results provide information to

online buyers and regulatory institutions in charge of online retailer evaluation.

� 2012 Elsevier B.V. All rights reserved.

Contents lists available at SciVerse ScienceDirect

Information & Management

jo u rn al h om ep ag e: ww w.els evier .c o m/lo c ate / im

1. Introduction

The Internet is a major sales channel and has resulted in fiercecompetition; it is therefore important to examine strategies thatonline retailers use to run their business successfully. However,there is little research that provides a way of evaluating a strategicwebsite: most evaluation studies have centered on user-basedsurveys, while issues regarding strategic assessments from a sellerpoint of view have been overlooked [4]. With the growingimportance of the Internet as a shopping channel, examiningonline signals has important managerial implications [3].

The focus of our research was to evaluate retail websites fromthe sellers’ perspective. We analyzed actual websites andexamined website features provided to signal quality to buyers.While honest sellers used truthful signals, deceptive sellers mayhave behaved opportunistically and manipulated website featuresto fraudulently signal quality.

Our objective was to examine specific signals that sellers, bothlegitimate and fraudulent, use to encourage online buying. Usingsignaling theory we developed a framework with three dimensions– purchase time continuum, ease of verification, and signaling cost.We examine how these dimensions influence the e-business todisplay signals on their website. In addition, we developed aclassification that categorizes website signals by examining their

* Corresponding author at: Baruch College, City University of New York (CUNY), 1

Baruch Way, B11-220, New York, NY 10010, United States. Tel.: +1 646 3123417;

fax: +1 646 3123351.

E-mail address: tamilla.mavlanova@baruch.cuny.edu (T. Mavlanova).

0378-7206/$ – see front matter � 2012 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.im.2012.05.004

specific characteristics. We then demonstrated how our frame-work and signal classification could be used by conducting acontent analysis of existing pharmaceutical websites and testingour hypotheses. In our study, we focused on observable websitesignals that are provided by sellers pre-contractually (i.e. beforeactual purchase). By focusing on the sellers’ perspective, wecompared signals that high- and low-quality sellers are likely touse.

2. Theoretical background

2.1. The role of signals in information asymmetry

Signaling theory helps to explain the behavior of two partieswhen they have access to different information [5]. Strategicsignaling refers to actions taken by a signaler to influence viewsand behaviors of receivers [18]. In e-commerce, signaling isdisplaying of certain website features that carry information fromsellers to buyers.

The core of signaling theory consists of the analysis of varioustypes of signals and the situations in which they are used [15].Signals convey information about seller characteristics and buyersexamine them to evaluate the credibility and validity of a seller’squalities. Signaling theory explains the relationship betweensignals and qualities, showing why some signals are reliable andothers are not, and that the costs of deceptively fabricating a signalmust surpass the benefits of falsifying it [6].

In traditional stores, the quality of a product is generallyobservable during the selection process. In contrast, e-stores arecharacterized by a time lag between product selection and the

PurchasingPre-Purchase Post-PurchaseSignals Signals Signals and Incentives Delivery

Pre-Contractual Problems Post-Contractual ProblemsAdverse Selection Moral Hazard

Purchasing

Fig. 1. Purchase time continuum in online shopping.

1 The placement of signals was verified by a group of experts from industry and

academia.

T. Mavlanova et al. / Information & Management 49 (2012) 240–247 241

purchase and product delivery, and there is a distance gap thatprevents buyers from directly examining products; this separationmeans that the buyer lacks information about the product until it isdelivered. Online sellers control the information that they provideand are able to exaggerate or overstate quality. Thus, some lowquality online offerings may be almost indistinguishable from highquality ones.

Uncertainty associated with online purchases leads to twoinformation asymmetry problems: adverse selection (the distortionof information that results in pre-contractual misrepresentation ofthe seller’s true characteristics) and moral hazard (arising post-contractually when sellers do not fulfill their promises or engage inactivities that benefit them at the buyer’s expense) [14]. The firstare resolved by signals, while the second are resolved byincentives. Signals, such as signs and logos, allow high-qualitysellers to disclose their identity to buyers. Incentives motivatesellers to provide high quality. The underlying principle of usingsignals is that a seller spends money on signals in anticipation offuture revenue, and buyers expect that quality-related claimsmade by a seller will be true; otherwise upfront expenditures areunwise.

To evaluate the properties of website signals, we developed aframework with three dimensions: the purchase time continuum,ease of verification, and signaling cost. The framework was appliedto compare the quality and quantity of signals provided by high-and low quality sellers: specifically, we examined online sellers ofpharmaceutical products. The quality of these sellers had beendetermined by the definitions of the National Association of Boardsof Pharmacy (NABP), which maintains a database of recommendedand not recommended sellers. According to them, low qualitysellers are fraudulent, do not comply with patient privacyregulations, have been subject to prior disciplinary action, or selllow quality products, such as expired items, those that have beenstored incorrectly, are from unidentified sources, or are notdistributed in accordance with U.S. law, etc. High quality sellers aresellers that follow all NABP regulations and distribute pharmaceu-tical products of high quality that have been stored correctly.

2.2. Three-dimensional framework of website signals

In brick and mortar stores, signals are either sale-independent(pre-purchase) or sale-contingent (post-purchase) [16]. Pre-

purchase signals involve expenditures even when the sale doesnot occur. Post-purchase signals involve expenditures only after thesale has taken place.

E-commerce stores are different from traditional ones: theyoffer an additional shopping phase that takes place after theselection of the products but before their actual purchase. This,which we call the purchasing phase, occurs after products havebeen added to the shopping cart. In an e-commerce environment,the buyer does not have immediate access to physical products forinspection; instead, the buyer is sent to web pages on whichdifferent signals convey product, billing, and delivery information.

Thus, the first dimension in our framework was the online

purchase time continuum, which has three phases: pre-purchase,purchasing, and post-purchase. Pre-purchase signals appear pre-contractually (before payment is made) and can reduce the chanceof adverse selection. Some purchasing signals and all post-purchase signals and incentives appear post-contractually andreduce problems of moral hazard (see Fig. 1).

In an e-commerce environment, due to virtual representation,businesses can easily manipulate how they present themselves [8].Thus, buyers may have difficulty in differentiating between highand low quality sellers and may ignore or misinterpret signals. Tounderstand the true nature of a signal, buyers must verify thewebsite signals; for some, this is relatively easy (e.g., an incorrect

contact address can be validated via online directories or a third-party seal can be verified on the seal provider’s website) but othersare difficult to impossible to verify (e.g., a privacy policy may signalquality but a seller may not keep its promises).

If there is a high cost of punishment when a seller is caughtdisplaying a false signal, signals are likely to be more reliable. Thus,we introduced ease of verification as the second signalingdimension. Since displaying fake signals that are easily verifiedas false may negatively affect buyers’ purchasing decisions, initialinvestment in such signals is not justified and sellers are likely torefrain from using them.

Signaling cost is the third dimension of our framework. Somesignals are costly to produce. For instance, the introduction of livechat software or a real-time product comparison chart featuremight be expensive and time consuming. Although the use of suchtechnology may improve the seller’s reputation and increase salesin the long term, low-quality sellers may avoid costly signals asthey do not plan to stay in business for long and thus will not makesignificant investments. In addition, some signals are difficult toobtain. For example, displaying an authentic third-party seal suchas that of the Better Business Bureau requires accreditation andcompliance with its Code of Business Practices.

We therefore propose a three-dimensional framework forsignals in e-commerce: time, cost and ease of verification. Eachdimension in the framework is a continuous variable that can takeany value within a specified range. Although the proposeddimensions are continuous, they can be split into distinctcategories when applied in a specific context. In our example,we split the time continuum into three stages (pre-, during-, andpost-purchase), while signaling cost and ease of verification weresplit into two states each (low/high and easy/difficult respective-ly).

Depending on the structure of the website, the positioning ofsignals may appear in more than one phase. Table 1 displayssignals based on their typical appearance during the purchase timecontinuum.1

Purchase time and signaling cost can be derived from observingthe positioning of signals on websites and comparing the cost ofobtaining a signal. However, ease of verification is a less objectivedimension. Thus, we asked a group of experts to evaluate the easeof verification of proposed signals. The experts were InformationSystems, Information Management, and Digital Forensics profes-sionals (from legal, banking, and design industries) and PhD andMaster students in Information Systems and Information Technol-ogy. The percentage of expert agreement regarding signalplacement in the pre-purchase and purchasing phases rangedfrom 67% to 100%. Signals with lower agreement percentage werearbitrated by an independent expert.

2.3. Pre-purchase signals

Pre-purchase signals include spending on website features thatmight increase the customers’ willingness to transact. The purposeof pre-purchase signals is to show that the seller incurs

Table 1Characteristics of signals.

Pre-contractual (adverse selection) Post-contractual (moral hazard)

Pre-purchase During purchase Post-purchase

Easy-to-verify Difficult-to-verify Easy-to-verify Difficult-to-verify Easy-to-verify Difficult-to-verify

Low cost � Contact

Information

� Privacy Policy

� Return Policy

� Security Policy

� Credit Card Logos

� Secure Transaction

(Secure Socket Layer

Encryption)

� Delivery Date Claim

� In Stock Availability

Claim

� Product Quality Claim

� Shipping Methods

� Email Confirmation

� Coupons (promo codes

as incentives to buy again)

� Actual Delivery

Date

High cost � Third-party Seals

� Live Chat

� Regulatory

Compliance

� Store Locator

� Consumer Feedback

� Domain Specific

Content

� Alternative Electronic

Payment Mechanisms

� Cash Back

� Coupon Redemption

� Order Status (tracking

information)

� Actual Product

Quality

Note: Incentives are shown in italics.

T. Mavlanova et al. / Information & Management 49 (2012) 240–247242

expenditures now and expects to recover expenses in the futurethrough sales: the sellers convey information that signals theirquality as merchants, the quality of their products, and theirfairness in managing private information about buyers.

Low-cost, Easy-to-verify Signals include contact information bydisplaying the seller’s physical address, phone, and emailinformation on the website. Buyers can potentially verify thisinformation by conducting a search or consulting directories.

High-cost, Easy-to-verify Signals include physical store presenceinformation, third-party seals, and regulatory compliance state-ments. Physical store signals suggest longevity and stability of awebsite as they show that an investment has been made indeveloping these stores. Live chat features demonstrate that realpeople work at the site [13].

Third-party seals are provided by independent certifying bodiesthat prove that the seller’s behavior is consistent with e-commercestandards [11]. Two types of seals may be provided: a general

verification seal, such as Verisign which proves secure e-commercetransactions and a domain specific seal, such as VIPPS (VerifiedInternet Pharmacy Practice Sites) seal to confirm that a pharmacyis licensed and complies with federal regulations. Third-party sealsrequire payment of membership fees. Buyers can easily verify theauthenticity of such signals by checking shopping directories andthe seal websites.

Regulatory compliance signals include conformity with federal orstate regulations for a specific industry. For example, websites withrated content may require user confirmation of their age.Pharmaceutical websites must request a prescription filled byan appropriate health care provider [7]. If no prescription isrequired, the website may be acting illegally, and a buyer maysuspect that the seller is not acting ethically.

Low-cost, Difficult-to-verify Signals include the seller’s Privacy,Return and Security Policy statements [10]. These are mechanismssupporting information flow from the time of actual purchase tothe time a product is received by the customer, as well asinformation regarding the security of transaction and informationsharing. In the pharmaceutical industry, legitimate pharmaciesmust provide access to the Health Insurance Portability andAccountability Act or HIPAA privacy statement on their websites.Buyers cannot easily verify if these statements are true before thepurchase, as the credibility of these statements is not observableuntil after the purchase, and it is unlikely that the seller will behaveaccording to the policies if a violation has occurred.

Credit card logos are often displayed on retail websites as signalsthat a seller accepts payments from particular merchant services.Although credit card logos are universally recognized andrepresent security associated with merchant services, the cost ofproducing such a signal is low and such logos are not linked to a

credit card provider’s website. It is not easy to verify that a linkexists between a logo displayed on a seller website and the securecredit card service.

High-cost, Difficult-to-verify Signals include feedback andwebsite content signals. Consumer feedback is a signaling mecha-nism established to discourage opportunistic behavior in uncertainmarkets; it refers to comments about buying and sellingexperience as well as evaluation of sellers [12]. It is difficult toverify whether the posted feedback corresponds to opinions of realbuyers, as feedback may have been fabricated by sellers.

Domain Specific Content refers to the availability of specificproduct information that is useful for consumers [9]: this mayinclude expert product reviews, press releases, FAQ sections, andnews. Richer content provided by a seller leads to a bettercustomer’s perception of the website. Creating, updating andmodifying content requires effort and is time consuming for sellersand the verification of content by buyers can be difficult asexpertise is required.

2.4. Purchasing signals

Purchasing signals are observable after product selection butbefore the purchase is completed. They provide information aboutpayment mechanisms, product delivery date, and claims about theactual quality and quantity of the product being delivered.

Low-cost, Easy-to-verify Signals include Secure Transactions (SSL

Encryption) to encrypt private information during online transac-tions [17]. As the cost of SSL technology is reduced, it becomesmore affordable for all sellers. Buyers can easily identify thepresence of secure transaction mechanisms by looking at the URLin the browser location bar. Browsers also provide informationabout the website identity through its digital certificate.

High-cost, Easy-to-verify Signals include payment options such ascredit card payments, money transfer, or alternative paymentmechanisms (e.g. PayPal, Google Checkout, etc.). Their presenceprovides a convenient and secure way to pay for online purchases[1]. Participation in electronic payment programs is costly formerchants as they have to pay transaction fees. Buyers candetermine whether the seller participates in such a program bychecking the electronic payment providers’ website.

Low-cost, Difficult-to-verify Signals are sellers’ claims madeduring the checkout process; they include delivery date, in-stock

availability, product quality, and shipping method. These claims arenot expensive for a seller to display but are difficult to verifybecause delivery date, actual shipping methods, and actual productquality are not verifiable until after the product is delivered.

High-cost, Difficult-to-verify Signals may include cash back andcoupon redemption mechanisms. These are costly for a seller.

T. Mavlanova et al. / Information & Management 49 (2012) 240–247 243

Money savings promises are not easily verifiable during apurchase, because time is required for savings or discounts tobe shown on credit card statements.

2.5. Post-purchase signals

Such signals are observable only after the purchase hasoccurred. They usually include final confirmation of the orderand details for tracking the product while in transit. There are fewsignals in this category because buyers in this phase mainly facemoral hazard, which is mitigated by incentives.

Low-cost, Easy-to-verify Signals include E-mail confirmation asproof of the order. The cost of the e-mail is low and the order can beeasily verified by buyers. Although it may not be possible to verifythe identity of the sender or the accuracy of the message, the merepresence or absence of confirmation may serve as a signal of sellerquality: its presence shows that the seller has provided a receipt,while its absence leads to a suspicion that the sale was improper.

High-cost, Easy-to-verify Signals include Order tracking informa-

tion that allows buyers to track a package from purchase todelivery. To use this signal, sellers have to invest in applicationsthat integrate the seller’s and a shipment company’s websites.Buyers are able to verify the validity of the tracking number onlyafter a product has been shipped, so this signal only works post-purchase.

3. Empirical investigation

To limit the scope of our investigation, we focused on pre-contractual signaling that mitigates problems of adverse selection.

3.1. Pre-contractual signaling

Pre-contractual signals that include pre-purchase and somepurchasing signals require expenditure even if the sale does notoccur; they are designed to alleviate the problem of adverseselection. When the information about quality is transparent, high-quality sellers gain from the provision of pre-contractual signals, andlow-quality sellers do better by not signaling. However, in situationswhen buyers cannot easily discern seller quality, low-quality sellersdo better by signaling (benefits from false claims may exceed losseswhen exposed). As the Internet intensifies information asymmetryby anonymity and physical and temporal distances, we expectedthat both high- and low-quality sellers would rely on pre-contractual signals to indicate quality and to motivate buyers totransact. High-quality sellers may invest in costly signals in order toprofit from future sales, and display signals as the information theyprovide is likely to be true. If signals are costly, mimicking high-quality sellers is unattractive to low-quality sellers. Furthermore,mimicking high quality signals imposes costs on low-quality sellersif a false signal is discovered. Thus, low-quality sellers have fewerchoices of signals to display and therefore we believed:

H1. Pre-contractually, low-quality online sellers will display few-er signals than high-quality sellers.

3.2. Ease of verification

Signals can be modified and thus are subject to manipulation.While high-quality sellers may use signals to inform buyers of thequality of their products, low-quality sellers may misinform buyersin order to make a profit and leave the market. Few signals areimpossible to fake and the virtual representation of e-business sitesprovides an opportunity for sellers to display false signals, whichmay be misinterpreted as genuine, but only legitimate signals can

alleviate true uncertainty in adverse selection problems. For a signalto be reliable, it should be interpreted as a commitment that cannotbe easily imitated by a low-quality seller. When signals are easilyidentified as false, buyers refrain from purchasing. Thus, low-qualitysellers may omit signals that are subject to easy verification iffraudulent, and this may limit the number of signals they can use.High-quality sellers have an advantage as they can use easy-to-verify signals which are likely to be true. Thus, we expected:

H2. Low-quality online sellers will display fewer easy-to-verifysignals than high-quality sellers.

3.3. Signaling cost

A signal should be costly enough to differentiate between high-and low-quality sellers. To promote quality, low-quality sellersmay try to mimic high-quality sellers. However, the cost ofexpensive signals may exceed the benefits of having them.Therefore, high-cost signaling is more profitable for high-qualitysellers. As a signaling party selects signals to maximize thedifference between future profits and signaling cost, we expectedthat low-quality sellers will refrain from investing in high-costsignals as profits from future sales may not be enough to recouptheir investment. Therefore, we hypothesized,

H3. Low-quality online sellers will display fewer high-cost signalsthan high-quality sellers.

3.4. Grouping of signals

Since most signals are intentional, they must be beneficial to aseller. If a seller invests in high-cost signals, we assume that thisseller intends to provide higher quality offerings, or that suchexpenditures are unwise. If sellers intend to provide higher quality,it is likely that they will provide accurate information aboutthemselves that can be easy-to-verify. Thus, we expected thatsellers who invest into high-cost signals will also display easy-to-verify signals to correctly inform buyers about their quality.

H4. Online sellers that display high-cost signals will also displayeasy-to-verify signals.

Conversely, sellers who refrain from displaying signals that areeasy to verify may have something to hide and do not correctlyinform buyers about the actual quality of their offerings. If theseller seeks to conceal some aspect of its operation, theexpenditure on high-cost signals may be lost if the true qualityof offerings is disclosed. Thus, for this category of sellers, it makeslittle sense to invest into high-cost signals. Therefore, we expected:

H5. Online sellers that display difficult-to-verify signals will alsodisplay low-cost signals.

4. Methodology

In order to test our hypotheses, we analyzed pharmaceuticalwebsites for signals that high- and low-quality sellers in thepharmaceutical industry were likely to use. Pharmacies selectedfor the analysis were taken from North American directories ofrecommended and not recommended e-pharmacies.

4.1. Content analysis

Content analysis is often used to code and examine the contentof written communication. Because it is based on explicit content,it offers advantages over surveys and interviews that provide

Table 3aDescriptive statistics by seller.

Signals by cost Signals by ease of verification

High cost signals Low cost signals Total Difficult to verify signals Easy to verify signals Total

High quality sellers 185 215 400 144 256 400

Low quality sellers 100 200 300 182 118 300

Table 2Variables and definitions.

Signaling cost Ease of verification

High cost signals Low cost signals Difficult to verify signals Easy to verify signals

Third Party Seals

Domain Specific Seals

Live Chat

Store Locator

Prescription Requirements

Consumer Feedback

Electronic Payments

Health Content

Contact details

Credit card logos

Privacy Policy

Security Policy

Return Policy

HIPAA privacy policy

Secure Transactions

Credit card logos

Privacy Policy

Security Policy

Return Policy

HIPAA privacy policy

Consumer Feedback

Health Content

Contact details

Third Party Seals

Domain Specific Seals

Live Chat

Store Locator

Prescription Requirements

Secure Transactions

Electronic Payments

Note: Only signals that appear in pre-purchase and purchasing phases are listed.

Table 3bDescriptive statistics by signals.

N Minimum Maximum Mean Std.

deviation

High cost signals 120 0 6 2.38 1.28

Low cost signals 120 0 6 3.46 1.35

Difficult to verify signals 120 0 6 2.72 1.44

Easy to verify signals 120 0 6 3.12 1.54

Total signals 120 0 11 5.83 2.06

T. Mavlanova et al. / Information & Management 49 (2012) 240–247244

subjective perceptions of artifacts. In our research, the unit of datacollection is a website, and the unit of analysis is a website signal. Itis recommended that variables, coding rules, and measurementsbe specified in advance as this ensures a consistent collection ofrelevant data. Therefore we identified 15 distinct variables thatbelong to the pre-contractual phase of the online purchase timecontinuum (see Table 2). Consistent with our focus on adverseselection, we considered only observable signals that alleviatedadverse selection problems, and ignored signals that appearedpost-contractually. All variables were identified by reviewing theliterature on e-commerce and studies of online pharmacies. Somevariables, such as brand name were not included due to theirsubjective nature.

4.2. Sample

We compiled a directory of online pharmacies from differentsources such as pharmacy.org,2 Google’s listing of online pharma-cies,3 and PharmacyChecker.4 We eliminated duplicates from thedirectory and designated each pharmacy as high- or low-qualitybased on guidelines from the National Association of Boards ofPharmacy (NABP), which accredits online pharmacies: as of May2009, NABP published a list of 1873 online pharmacies that do notmeet their standards though they acknowledged that some non-accredited pharmacies may operate legitimately and be approvedby other agencies. One such alternative agency was LegitScript,which, as of May 2009, approved 254 online pharmacies, identified745 pharmacies as candidates for approval, and classifiedthousands as unsatisfactory. We used LegitScript’s validationfeature that allowed us to check the status of pharmacies.Additionally, the directory was checked against the list of roguepharmacies at pharmacychecker.com.5 The quality of onlinepharmacies was based on recommendations of NABP and Legit-Script, both of which assign pharmacies into one of the groups:recommended or not recommended. Thus, the quality of an onlinepharmacy in our study was a binary variable provided by a thirdparty.

We then divided the sampling frame into two lists according tostatus (high or low quality) and selected a random sample of 60

2 http://www.onlinepharmacydirectory.org/.3 http://www.google.com/Top/Shopping/Health/Pharmacy/Online_Pharmacies.4 http://www.pharmacychecker.com/.5 http://www.pharmacychecker.com/rogue-pharmacies.asp.

pharmacies from each list; 120 in total. The web pages of theselected pharmacies were coded based on the 15 signals of Table 2.To perform a content analysis consistent with our theoreticalframework, we developed a coding scheme based on the featuresof interest and coding rules. These steps were intended to ensure aconsistent coding of relevant data. Three independent coders,trained on the coding scheme and rules, analyzed each pharmacywebsite in terms of its signals. Each variable in Table 2 was codedwith a categorical descriptor to indicate the presence (1) orabsence (0) of that particular variable on the website. The codingwas conducted using only explicit content (i.e. visible presence ofthe signal on the website).

5. Results

5.1. Inter-coder reliability

Three independent researchers coded the sample of onlinepharmacies. The inter-coder reliability, computed as the percent ofagreement obtained for all variables, ranged from 73% to 100%,showing a high level of reliability for all variables. Codingdisagreements were adjudicated by discussion and consensuswas reached after a joint examination of the feature in question.

5.2. Hypotheses testing

Descriptors (1) or (0) were assigned to indicate the presence orabsence of each of the variables and to compute the total numberof signals, total number of easy-to-verify signals and total numberof high cost signals. Descriptive statistics in Table 3a show theresults.

The maximum number of signals for a single web site was 15,the range of signals for the online pharmacies in our sample variedfrom 0 to 11 (see Table 3b).

Table 4Results of hypotheses testing.

H# Hypothesis Low-quality

seller mean rank

High-quality

seller mean rank

Mann–

Whitney z

Hypothesis

supported

H1 Low-quality sellers display fewer signals than high-quality sellers do 46.2 74.8 �4.58*** Y

H2 Low-quality sellers display fewer easy-to-verify signals than high-quality sellers do 33.7 87.3 �8.64*** Y

H3 Low-quality sellers display fewer high-cost signals than high-quality sellers do 41.3 79.7 �6.26*** Y

*** p < 0.0001.

T. Mavlanova et al. / Information & Management 49 (2012) 240–247 245

In order to compare signal usage of high- and low-qualitysellers, the Mann–Whitney test was conducted on the mean scoresof the coded website signals. Non-parametric tests, such as Mann–Whitney, are helpful for determining whether or not the values ofan ordinal variable differ between two groups. Table 4 showsresults of the hypotheses 1–3 testing with mean ranks, z-scoresand p-values. All hypotheses were supported.

To test Hypotheses 4 and 5 we conducted a cluster analysis todetermine whether the data contained naturally occurringhomogeneous subsets of observations. Each subset, or cluster, isinternally homogenous and externally heterogeneous. Thus, eachcluster depicts classes to which its members belong based on thedata collected, and these depictions can be abstracted to create ataxonomy [2]. A hierarchical clustering method with standardizedvariables produced a two-cluster solution. Based on the groupingsprovided by the cluster analysis it appeared that, as predicted in H4and H5, high-cost and easy-to-verify signals form one cluster(coefficient 37.6), and low-cost and difficult-to-verify signals forma second cluster (coefficient 42.1). The dendrogram depicted inFig. 2 illustrates the solution in graphical form. The dendrogram isa tree-like representation of the cluster arrangement, where thenumber of clusters and its characteristics are visible.

Agglomeration schedule

Stage Cluster combined Coefficients Stage cluster first

appears

Next stage

Cluster 1 Cluster 2 Cluster 1 Cluster 2

1 1 4 37.6 0 0 3

2 2 3 42.1 0 0 3

To count the number of cases in each cluster, we ran a k-meansanalysis. The results are shown in Table 5.

Fig. 2. Dendrogram.

We also computed F-ratios to describe the difference betweenclusters. The significance levels reported in Table 6 demonstratethat selected variables contribute to the separation of clusters.Thus, the results of cluster analysis support Hypotheses 4 and 5.

6. Discussion

We developed a framework and a classification scheme forsignaling in situations of information asymmetry in e-commerce.To demonstrate the applicability of our framework and classifica-tion scheme, we conducted an empirical study to compare signalsprovided by high- and low-quality sellers in the online pharmacyindustry. We were able to shed light on the actual usage of signalsin an online environment. Apparently there is a difference in choiceof signals between low-and high-quality sellers.

Our framework consisted of three dimensions: purchasing timecontinuum, ease of verification and signaling cost. We demon-strated that website signals can be successfully classified inaccordance with the proposed dimensions. In addition, weinvestigated how the proposed framework and signal classificationcan be used as a theory-testing tool.

We found that the proposed signaling dimensions influence thechoice of signals for sellers. According to H1, low-qualitypharmacies will use fewer signals in the pre-contractual phase.While high-quality pharmacies may display all signals, includingcostly and easy-to-verify ones, such choices are not attractive tolow-quality pharmacies.

In accordance with H2, low-quality pharmacies try to avoidsignals that are easily verifiable. On the other hand, high-qualitypharmacies do not refrain from displaying signals that are easilyverifiable as they are likely to be true.

Consistent with H3, we also found that low-quality pharmaciesavoid high-cost signals. High-cost signals are not always profitablefor low-quality pharmacies due to their initial cost. In addition,while coding and analyzing low-quality pharmacies we noticedthat the longevity of such pharmacies is short: they enter and exit

Table 5Number of cases in each cluster.

Cluster Number of cases

1 61

2 59

Total 120

Table 6ANOVA results for clusters.

Cluster Error

Mean

square

df Mean

square

df F Sig.

Low cost 102.0 1 1.03 118 99.5 0.000

Difficult to verify 215.0 1 1.08 118 199.0 0.000

Easy to verify 10.7 1 1.47 118 7.3 0.008

High cost 45.8 1 1.71 118 26.8 0.000

T. Mavlanova et al. / Information & Management 49 (2012) 240–247246

the market rapidly and reappear with new domain names. Incontrast, high-quality pharmacies stay on the market and thusbenefit from displaying high-cost signals.

In line with Hypotheses 4 and 5, sellers that invest in high-costsignals are likely to display easy-to-verify signals. These findingsare important as they show that signals are not used in isolationbut clustered into groups with high-cost and easy-to-verify signalsforming one, and low-cost and difficult-to-verify signals forming asecond group.

7. Contributions

Our research was different from other studies because wefocused on the role of IT (website and its characteristics), its usage,and its impact on a seller’s decision to display a signal in situationsof information asymmetry. To our knowledge, this is the first studythat combines three distinct signaling dimensions into a frame-work to examine the actual usage of signals by online sellers ofvarying quality.

We extended our framework by introducing a classification ofactual website signals, and we demonstrated how they correspondto the three dimensions. By using the framework, we were able toexplain signal usage of low- and high-quality sellers in a sample ofreal online pharmacies. We found that certain groups of signals arelikely to be used together, with high-cost and easy-to-verify signalsforming one cluster, and low-cost and difficult-to-verify signalsforming another. Although signaling has been used in studies of e-commerce, extensive frameworks offering explanations of onlineseller behavior have not previously been created. Our frameworkcan therefore help address existing challenges for the continueddevelopment of online marketplaces.

As more people purchase products online, it is important toidentify signals that merchants use to induce shopping behavior.Buyers will benefit from their inspection of signals offered bysellers. Our results might be useful for regulatory institutions intheir quest to educate online consumers and evaluate onlinepharmacies.

8. Limitations

Our study showed that the proposed framework is useful inevaluating e-commerce signals. However, there are certainlimitations to our study.

First, only pre-contractual signals alleviating adverse selectionproblems were analyzed.

Second, the focus of the study was on signals that are directlyobservable and cannot be subjectively interpreted.

Third, we examined only the presence of actual signals onwebsites and did not consider user perception of the signals.

In addition, we demonstrated the use of the framework in onlyone context: online pharmacies in the heavily regulated NorthAmerican marketplace.

9. Conclusion

We examine the role of website signals as a way for onlineretailers to signal quality. Drawing upon signaling theory,proposed and validated a three-dimensional framework forwebsite signals. Our study confirmed that there is a difference insignal usage between low-and high-quality online sellers: low-quality sellers are likely to avoid expensive, easy-to-verifysignals and tend to use fewer signals than do high-qualitysellers.

We showed that signaling theory can be successfully used in e-commerce research. Furthermore, awareness of signal usageenhances the buyers’ ability to differentiate between e-commercesellers.

Acknowledgements

This research is a part of the first author’s dissertation titled‘‘Signaling Theory and Information Asymmetry in Online Com-merce: Seller and Buyer Perspectives.’’ The dissertation was fundedin part by a PSC-CUNY grant #63611-00-41.

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Tamilla Mavlanova is a Ph.D. candidate in Information

Systems at Baruch College, City University of New

York. She holds MS in Library and Information Science

from Syracuse University. Her research interests

include e-business strategy, issues of trust and

deception in e-commerce, and cross-cultural research.

Her work has been published in International Journal of

Electronic Commerce, ACM Transactions on Human-

Computer Interaction and presented at The International

Conference on Information Systems (ICIS), Americas

Conference on Information Systems (AMCIS), Hawaii

International Conference on System Sciences (HICSS) and

other conferences.

T. Mavlanova et al. / Information & Management 49 (2012) 240–247 247

Raquel Benbunan-Fich is an associate professor of

Information Systems at the Zicklin School of Business,

Baruch College, City University of New York (CUNY).

She received her Ph.D. in Management Information

Systems from Rutgers University. Her research interests

include User Behavior and Multitasking, Virtual Teams

and Virtual Collaboration, IT Usage and Usability,

Systems Development and Faculty Productivity. She

has published articles on related topics in ACM

Transactions on Human-Computer Interaction, European

Journal of Information Systems, Information & Manage-

ment, International Journal of Electronic Commerce,

Journal of Strategic Information Systems and other

journals.

Marios Koufaris is an associate professor in Computer

Information Systems at the Zicklin School of Business of

Baruch College, CUNY in New York City. He received a

Ph.D. in Information Systems from the Stern School of

Business of New York University, as well as a B.Sc. in

Decision Sciences from the Wharton School of Business

and a B.A. in Psychology from the College of Arts and

Sciences, both at the University of Pennsylvania. His

research interests include consumer behavior in online

commerce, end-user behavior, and the social impact of

IT. His work has been published in Information Systems

Research, MIS Quarterly, Journal of Management Infor-

mation Systems, International Journal of Electronic Commerce, Information &

Management, DATA BASE for Information Systems, and Communications of the ACM.