How wise are onlineprocrastinators? A scale

developmentAnissa Negra and Mohamed Nabil Mzoughi

MaPReCoB Research Unit, University of Sousse, Sousse, Tunisia

Abstract

Purpose – Online purchases might be delayed. In some cases, this postponement could be aprivileged, an adequate, or an efficient strategy. Online consumer procrastination is the voluntary andrational delay of a planned online purchase. The purpose of this research is to develop a measure ofthis behavior.Design/methodology/approach – The Churchill’s paradigm adapted by Roehrich was adopted.A total of 77 items were generated from 27 interviews. This set of items was reduced to 23 afterdropping out redundant or not representative items. In a pilot study, factor analysis on the 23-itemscale yielded a two-factor structure scale of five items with a reliability ranging from 0.715 to 0.809.The Online Consumer Procrastination Scale (OCPS) was statistically confirmed and validated, in asubsequent investigation.Findings – Findings revealed a reliable and valid five-item scale. Its dimensions are online deal-proneness and online rationality.Research limitations/implications – This research allows a better conceptualization of the onlineconsumer procrastination. Future research should assess the OCPS validity across different productcategories.Practical implications – OCPS will make easier the recognition of e-shoppers who delay theachievement of online purchase intentions.Originality/value – OCPS is the first scale measuring the reasonable delay in an online purchasecontext.

Keywords Wise procrastination, Online consumer procrastination, Scale development,Construct validity, Consumer behaviour, Electronic commerce

Paper type Research paper

1. IntroductionOnline retailers invest considerable amounts of money and effort in order to enticecustomers into visiting their web-based stores (Cho, 2004; Ilfeld and Winer, 2001).Nevertheless, getting a person to visit an online site is not enough; visitors are notconverted systematically into purchasers. Most firms report to be able to transformonly 2-3 percent of web site traffic to purchase (Li and Chatterjee, 2005). The mostsuccessful convert only 8 percent (Li and Chatterjee, 2005). E-shoppers may initiatethe checkout process, but leave before completing their purchase. Even with highpurchase intention, online shoppers abandon their shopping cart and exit the web sitejust prior to checkout (Cho et al., 2006; Cho, 2004; ScanAlert, 2005). Shopping cartabandonment rate across various e-commerce web sites ranges from 20 to 90 percent(ScanAlert, 2005; Silverstein et al., 2001). Mzoughi et al. (2007) explain this issue withrespect to online consumer procrastination. This latter refers to the voluntary andrational delay of a planned online purchase. The researchers noticed that some onlineshoppers are not in any rush to click the “buy button.” Instead, they spend daysdigitally window-shopping before making a purchase or abandoning the startedtransaction (ScanAlert, 2005). These e-buyers are likely to checkout more than ten web

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Received 22 September 2011Revised 20 January 2012Accepted 20 January 2012

Internet ResearchVol. 22 No. 4, 2012pp. 426-442r Emerald Group Publishing Limited1066-2243DOI 10.1108/10662241211250971

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sites to make a decision weeks later (ScanAlert, 2005). The longer the “actual decisiontime” is, the less likely the purchase will occur (Kukar-Kinney and Close, 2010; Negraet al., 2008; Cho et al., 2006; Greenleaf and Lehmann, 1995). However, Mzoughi et al.(2007) failed to provide any statistical support to this proposition because of theabsence of a reliable and valid scale of online consumer procrastination. Severalprocrastination scales were identified in psychology literature, but they admitserious deficiencies (van Eerde, 2003b). Except for Choi and Moran’s (2009) activeprocrastination scale, almost all of procrastination instruments focussed on theundesirable side of the phenomenon. Procrastination has been generally consideredas the chronic and irrational tendency of delaying a task or a decision (Lay, 1986;Solomon and Rothblum, 1984). In purchase context, Darpy (2000) proposed theConsumer Procrastination Scale (CPS) to measure the chronic and conscious tendencyto slow down or hold down a planned purchase. In online purchase context, Mzoughiet al. (2007) judged inappropriate the use of this scale to assess online consumerprocrastination with respect to the specificity of the web-based shopping environment.Moreover, the CPS does not provide the ability to assess the functional facet ofprocrastination (Negra et al., 2008; Mzoughi et al., 2007).

This research offers the opportunity to study the intentional postponement ofplanned online purchases. It aims to develop a reliable and valid Online ConsumerProcrastination Scale (OCPS). This instrument should contribute to the advancementof the assessment of online consumer behavior. OCPS would provide the ability toidentify procrastinators with rational motivations in a web-based context. E-tailerswould, then, be able to cope with the delay and the abandonment of online purchases.The study follows the guidelines of measurement development proposed by Churchilladapted by Roehrich (1993).

2. Procrastination: a multiform concept2.1 DefinitionDefining procrastination is difficult since it is an intra-individual process regulated byinternal norms of postponement (van Eerde, 2003a, b). It is generally considered as thetendency of delaying a task or a decision without a good reason (Steel, 2003). Accordingto Milgram et al. (1998), procrastination refers to “a trait or as a behavioral dispositionto put off performing a task or making decisions.”

Although most investigators (e.g. Steel, 2010; van Eerde, 2003b; Lay, 1986) assertthat procrastination is irrational, researchers like Knaus (2000) and Ferrari (1992)consider it as a wise strategy. Accordingly, there are two types of procrastination:functional and dysfunctional procrastination (Chu and Choi, 2005; Darpy, 2000; Ferrariand Emmons, 1994).

The first is also called active procrastination (Steel, 2003, 2007, 2010; Ferrari andEmmons, 1995). It is occasional and viewed as a wise course of (in)action (Steel, 2003;Ferrari and Emmons, 1994). It refers to the useful habit of avoiding unnecessary tasksand maximizing the likelihood of task success (Corkin et al., 2011; Ferrari, 1993, 1994,2010; Chu and Choi, 2005; Bernstein, 1998). The second, also called chronic or passiveprocrastination (Ferrari, 1991, 1994), is the “purposive and frequent delay in a person’sstart or completion of a task to the point of experiencing subjective discomfort”(Ferrari, 1991). Lay (1986) evokes an “irrational tendency to postpone things thatshould be done.” Solomon and Rothblum (1984) underline a “chronic habitual delay inthe start and/or completion of tasks.”

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Active procrastinators differ from passive procrastinators in their level ofself-efficacy, time control and in various personal outcomes (Corkin et al., 2011; Choiand Moran, 2009; Chu and Choi, 2005). According to Choi and Moran (2009), activeprocrastinators prefer pressure for the accomplishment of the task on time. Peopleactively delaying tasks preplan their activities without adhering to a rigid schedule.They are able to properly estimate the minimum amount of time required to finishsuccessfully a task, even with last minute pressure (Choi and Moran, 2009).

Procrastination has been also investigated in purchase context. Consumerprocrastination is the chronic and conscious tendency to slow down or holddown a planned purchase (Darpy, 2000). In a web-based shopping context, Negra et al.(2008) and Mzoughi et al. (2007) found that contrary to consumer procrastination,e-procrastination is functional. According to Darpy (1999), functional procrastination,in a conventional purchase environment, is the current tendency to prioritize urgentneeds and tasks, to gather additional information and to put the negotiator (seller)under pressure. It is also reasonable in case of tiredness and when anticipatinga price decrease (Darpy, 1999). If the completion of the task could be excessivelyexpensive, the task would be delayed (Akerloff, 1991). Online procrastinators keeppostponing purchases because they are expecting better offer. Negra et al. (2008)and Mzoughi et al. (2007) define online consumer procrastination as “the purposiveand rational tendency to delay the accomplishment of a planned online purchase.”Extending this definition, we propose to define the concept as the occasional andwise predisposition to put off making online purchase decisions in waiting foradditional information (about the web site, prices, products, security of the transactionand so forth) to be available and in maximizing the likelihood of having thebest deal.

2.2 Procrastination scalesMost researchers have focussed on the undesirable side of procrastination (van Eerde,2003b) and proposed several scales pertaining to the assessment of the dysfunctionalprocrastination like General Procrastination Scale (Lay, 1986), Adult Inventory ofProcrastination (McCown and Johnson, 1989), Decisional Procrastination (Mann, 1982;Mann et al., 1997), Procrastination Assessment Scale – students (Solomon andRothblum, 1984), Tuckman Procrastination Scale (Tuckman, 1991) and ConsumerProcrastination (Darpy, 1999). Only Choi and Moran (2009) proposed a valid andreliable scale of active procrastination. The scale is four dimensional. Preferencefor pressure, intentional decision to procrastination, ability to meet deadlines andoutcome satisfaction are its main dimensions (Choi and Moran, 2009).

These measures were found to present serious defects (van Eerde, 2003b; Milgramet al., 1998). van Eerde (2003a, b) noticed that the roles of the context and the tasknature have been neglected (van Eerde, 2003b). As shown in Table I, the majority ofprocrastination scales has never been specific in content. Both the Adult Inventoryof Procrastination and the General Procrastination scales measure the frequency ofputting off everyday tasks (Milgram et al., 1998). Moreover, we noticed that these twoscales have the same object as the Decisional Procrastination Scale. Procrastination ofAcademic Student Scale (Solomon and Rothblum, 1984) measures procrastinationwithin a student population (Alexander and Onwuegbuzie, 2007; Howell and Watson,2007). This 44-item scale has been rarely used because of its length. Even thoughTuckman’s scale makes no reference to academic work, it has been widely used toassess academic procrastination (Howell and Watson, 2007).

428

INTR22,4

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429

How wiseare online

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430

INTR22,4

In purchase context, Darpy (1999, 2000, 2002) developed the CPS to measure thechronic and conscious tendency to delay a planned purchase (Darpy, 2000). Negra et al.(2008) and noticed that extending Darpy’s (2000) instrument from the traditionalto the internet purchase context to measure online consumer procrastination isinappropriate. Besides, Mzoughi et al.’s (2007) findings revealed that online consumerprocrastination is active. Although Active Procrastination Scale measures functionalprocrastination, we noticed that it is not specific to the online purchase context.With regard to the particularities of the web-based environment as the informationrichness and the interactivity (Brock and Zhou, 2005), traditional instruments are notalways suitable for explaining online purchase behaviors (Yu, 2011; Negra et al., 2008;Mzoughi et al., 2007). Accordingly, the operationalization of consumer procrastinationmust be rethought in online purchase context.

3. Scale developmentChurchill’s approach is suitable for marketing studies where the object or unit of studyis the person (Rossiter, 2002; Finn and Kayande, 1997). In order to develop a scale tocapture Online Consumer Procrastination (OCPS), Churchill’s (1979) paradigm adaptedby Roehrich (1993) was followed. This procedure ensures that the results are reliableand valid in representing the studied concept. Roehrich (1993) improves Churchill’sprocedure. He checks the content validity in addition to the convergent, discriminantand nomological validities. He also added the composite reliability (CR) to Cronbach’sa. The item generation, the purification of the inventory, and the assessment of thescale reliability and validity are the main followed steps.

3.1 Items generation and content validityAn initial pool of items was first developed on the bases of the proposedconceptualization and a qualitative study. To identify and capture the specific facets ofonline consumer procrastination, 27 in-depth interviews were conducted with peoplewho have delayed online purchases. Respondents were first asked to focus on an onlinepurchase they delayed during the past semester. They were then requested to providedetails about the nature of the product, the price, in how many days they accomplishthe purchase and what were their excuses for the delay. They were conducted inEnglish and in French with tourists from countries like France, the UK and Germanywhere e-commerce is a common practice. The interviews were held in a number ofMarhaba Hotel lobbies. They lasted from 20 to 30 minutes.

Using a procedure similar to Bearden et al.’s (2001), respondents’ answers wereconverted to statements reflecting e-procrastination. A total of 66 items were finallygenerated in this first step. After eliminating redundant, ambiguous and poorlyworded statement (Churchill, 1979) 37 items remained.

The generated items were then submitted to seven expert judges (marketingprofessors) in order to assess its content validity (Pons et al., 2006; Bearden andHardasty, 2001; Churchill, 1979). The experts checked the scale items for ambiguity,clarity, triviality, sensible construction and redundancy, as well as to make sure thatthe items reflected the definition of online consumer procrastination. Judges rated eachitem on the following scale: clearly representative of online consumer procrastination,somewhat representative of online consumer procrastination, and not representative ofonline consumer procrastination. The decision to keep an item in the scale was contingenton having the majority of the judges agreeing with the item representativeness of theconstruct (Zaichkowsky, 1985). Items that were not representative of online consumer

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How wiseare online

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procrastination (i.e. the average score for the item was below four) were dropped fromthe pool (e.g. “Delaying online purchases is sometimes a wise decision” and “I thinkthat postponing online purchases is a smart decision”) or not representative ones (e.g.“When purchasing over the internet, I generally hesitate a lot”). After the elimination of14 redundant items or “not representative” ones, the experts agreed that the scale itemsof OCPS adequately represented the construct. It should be mentioned that minorwording adjustments and clarification of the statements were undertaken in order torespond judges’ comments. For instance, according to some of them, using “put off,”“delay,” “postpone” may be confusing and proposed to use one verb to talk about delay.The remaining 23 items ranging from 1 “strongly disagree” to 7 “strongly agree” werethen administrated at the purification stage.

3.2 Scale purificationThe purpose of this stage is to condense the pool of items by purifying the scale on thebases of its psychometric properties, examine the dimensionality of the construct andidentify the fundamental dimensions of OCPS, and assess its reliability (Churchill,1979). At the end, only the most adequate items would remain in the scale for theconfirmatory analysis.

Sample and procedure. A questionnaire of the remaining 23 items, rated on a seven-point Likert scale from 1 (strongly disagree) to 7 (strongly agree), was answered by 206tourists familiar with e-shopping. Foreign tourists were questioned because Tunisianpeoples interest in e-shopping is still limited (Belkhiria, 2006). The sample size exceedsthe conventional condition that five observations per item are needed for performingfactor analyses (Hair et al., 2010). The respondents were 108 males (52.4 percent)and 98 females (47.6 percent). The majority was in the 21-29 and 30-39 age groups(52.4 percent).

Exploratory factor analysis (EFA). Series of principal component analyses wereperformed. Items that had a factor loading below 0.50 (i.e. Epro7, Epro8, Epro10,EproDarpy 2000, Epro18 and Epro23) were eliminated from the scale after eachfactor analysis, until satisfactory psychometrics properties were achieved (Pons et al.,2006; Bearden et al., 2001). Items were retained only if they loaded 0.50 or moreon a factor; did not load 40.50 on two factors; if the reliability analysis indicatedan item to total correlation 40.40 (Hair et al., 2010; Roman, 2006); and if theCronbach’s a of the component o0.7 (Darpy, 1999). Overall, 18 items were eliminated(Table II).

As shown in Table III, coefficient a of these two factors has acceptable levelsranging from 0.715 to 0.809 indicating good internal consistency among the itemswithin each dimension.

Online deal proneness: is the first factor (Epro1 and Epro2). It refers to the items thatare mainly related to the postponement of carrying out an online transaction to getbetter deal.

According to Lichtenstein et al. (1993), deal-prone consumers perceive a highervalue of the product when it is in sale. They are likely to wait for promotion toaccomplish a given purchase to take advantage of low prices.

Online rationality, the second factor (Epro16, Epro17 and Epro18): encompassesitems that are related to delaying online purchase decisions in order to get moreinformation. Rationality implies different meanings in different disciplines (Shugan,2006). In the economic literature, rationality is usually associated with the utilitymaximization (Shugan, 2006; Jacoby, 2000; Sanstad and Howarth, 1994). Being rational,

432

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consumers try to satisfy as fully as possible their needs with respect to their incomesand the market conditions (Sanstad and Howarth, 1994). In marketing literature,consumer rationality refers to “choosing the best procedure for deciding” (Shugan,2006). According to Zwick et al. (2003), “the optimal search behavior and the size of the

PCA KMO

Bartlett’stestsignificance

Totalvariance

explained ComponentsItemsdeleted Reason for deletion

PCA1 0.845 *** 58.129 6 Epro8Epro18Epro23

Factor loading o0.5

PCA2 0.836 *** 62.220 6 Epro7 Factor loading o0.5PCA3 0.826 *** 63.695 5 Epro3

Epro12Epro14Epro19

Load 40.50 on two factors

PCA4 0.777 *** 63.243 5 Epro10EproDarpy2000

Factor loading o0.5

PCA5 0.765 *** 69.214 5 Epro6 Cronbach’s a isconsiderablybetter by deleting the item

Epro4Epro5Epro9Epro13Epro20Epro21Epro22

Cronbach’s a o0.7

PCA6 0.624 *** 71.999 2 – –

Note: ***po0.001

Table II.Exploratory factor

analysis result

Items Factor1 Factor2 Communality

When I have the intention to buy things over the internet,I voluntarily delay the purchase 0.907 0.841I sometimes delay a purchase I have planned to perform overthe internet to maximize the likelihood of having the best deal 0.910 0.839When shopping online, I tend to voluntarily delay the purchaseto get more information 0.757 0.593When I have the intention to buy things over the internet,I spend a lot of time comparing web sites and shops 0.843 0.716I spend a lot of time searching for additional information tomake an online purchase decision 0.773 0.611’tVariance (%) 38.261 33.738Cronbach’s a 0.809 0.715

Table III.Principal component

analysis result

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How wiseare online

procrastinators?

consumer consideration set are properties of rationality.” Rational customers engage ininformation (price) search to decrease their level of uncertainty (Mehta et al., 2003).Some online shoppers decide to accumulate price knowledge for future purchase( Jiang, 2002). Hence, we propose that online rationality is the postponement of aplanned electronic purchase in order to gather more information for an optimal choiceand a better decision.

3.3 Scale refinementSample and procedure. In order to confirm the dimensional structure of onlineconsumer procrastination, a second survey was conducted for data collection.A similar procedure, as in the exploratory study, was performed. The investigationwas implemented through the assistance of Marhaba Hotels. In total 306 touristsanswered the questionnaire. A French language version of the five-scale items wasadministered in parallel with the English. The sample had more females (55 percent)than males (45 percent). The respondents’ ages had a concentrated distributionextending from 40 to 49 years (25.8 percent). The next largest category comprised therespondents from 21 to 29 years of age (25.2 percent).

Confirmatory factor analysis (CFA). A CFA using AMOS 16.0 was undertaken onthe data to verify the dimensionality and reliability of the OCPS.

The last version of the OCPS was first assessed using EFA. This latter wasemployed to make sure that the online deal proneness and the online rationality itemsare distinct constructs. Nevertheless, results showed that all items loaded on one factorand revealed a unidimensional structure of online consumer procrastination. Followingthe method utilized by Dabholkar et al. (1996), Doll et al. (1994) and Gerbing andAnderson (1988) two CFA analyses comparing two possible factor structures wereperformed (Table IV). A one-factor model and a two-factor model, designed toinvestigate how well online consumer procrastination holds up as a separate factorwere tested. The first model assumed the two constructs are distinct and allowed thecorrelation between online deal proneness and online rationality to be determined. Thesecond model forced the correlation between dimensions to be equal to one, combiningthe two into a single construct.

The measurement models were assessed by the maximum likelihood method. Toevaluate the fit of models, parsimony (w2/df ), absolute (RMSEA) and incremental (CFI,NFI, TLI) indices were used. Table IV illustrates the results of the analyses for the finalset of five items that determined online consumer procrastination.

In general, model fit is considered to be adequate if CFI, NFI and TLI are 40.95, andRMSEA is smaller than 0.07 (Hair et al. 2010). As shown in Table IV, the w2/df ratio has,respectively, a value of 3.056 for the first model, which falls within the suggested value

Indices w2/df w2 RMSEA CFI NFI TLI

M1 3.056 12.225 0.082 0.998 0.997 0.993(0.016) [0.032, 0.137]

M2 7.482 37.411 0.146 0.992 0.991 0.977(0.000) [0.104, 0.191]

Notes: M1: bidimensional (two distinct constructs); M2: unidimensional (forces the correlationbetween the two constructs to be equal to 1)

Table IV.Overall model fit of OPS

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of 5 or below, and a value of 7.482 for the unidimensional model. In addition, the otherindices show that the bidimensional model is to be considered because the incrementalindices of M2 are lower than those of M1. The RMSEA of the unidimensional modelexceeds the recommended value. M1 satisfied the recommended values. Therefore,OCPS is bidimensional.

Reliability assessment. It is a measure of the internal consistency of the constructindicators (Roussel et al., 2002). To verify the reliability for each dimension of theOCPS, Cronbach’s a was assessed and CR was calculated. As shown in Table V, theobtained coefficients satisfy the recommended criteria for confirmatory research:40.80 for established scales and 40.70 for new measures (Roussel et al., 2002). Thereliabilities of the two dimensions of online consumer procrastination range from 0.73to 0.796, and exceed the recommended level of 0.7. Besides, Cronbach’s a scores of eachconstruct show strong internal reliability.

3.4 Validity assessmentDrawing on Churchill’s (1979) procedure, construct validity is addressed by analyzingthree types of validity: convergent, discriminant and nomological.

Convergent validity. It is “the degree to which two measures of the same conceptare correlated” (Hair et al., 2010, p. 126). Convergent validity was assessed based onthe correlation between the OCPS factors and another measure supposed to betheoretically similar to online consumer procrastination. High correlations between thetest scores would be evidence of a convergent validity (Hair et al., 2010). Because thereis no previous scale of the concept, we proposed a one-item question measuring e-procrastination inspired of its definition. Results show that the Online ProcrastinationScale measures the intended concept (Table VI). Online deal proneness and onlinerationality are highly and significantly correlated with e-procrastination. Theconvergent validity of the OCPS is established.

Discriminant validity. It assesses “the extent to which a concept and its indicatorsdiffer from another concept and its indicators” (Hair et al., 2010). The empirical test ofdiscriminant validity is again the correlation between two conceptually similarconcepts, in this case, the OCPS and the CPS . The low correlation between the twoconcepts (Table VI) shows that they are sufficiently distinct. The overall resultsindicate that the two considered concepts should be considered separately.

ReliabilityDimensions Items SRW CR a

Onlinedeal-proneness

When I have the intention to buy things over theinternet, I voluntarily delay the purchase

0.820 0.75 0.796

I sometimes delay a purchase I have planned toperform over the internet to maximize thelikelihood of having the best deal

0.807

Online rationality When shopping online, I tend to voluntarily delaythe purchase to get more information

0.780 0.73 0.770

When I have the intention to buy things over theinternet, I spend a lot of time comparing websitesand shops

0.646

I spend a lot of time searching for additionalinformation to make an online purchase decision

0.759 Table V.OPS reliability

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Nomological validity. In accordance with Churchill’s procedure (1979, 1995),nomological validity requires a statistical validation of a well-established theoreticalrelationship between the measured construct and other constructs. According toMzoughi et al. (2007), online consumer procrastination should positively influenceabandonment. Consequently, in order to establish the nomological validity of the OCPS,online purchase abandonment was used as the dependent variable, whereas the OCPSdimensions were the independent variables.

An online survey was used to collect data. In addition to the OCPS, thequestionnaire also included a three-item scale of online purchase abandonmentborrowed from the Kurt et al.’s research (2007). This scale presents satisfactoryinternal consistency (Cronbach’s a 40.7) and validity (Matzler et al., 2007).Respondents were recruited through the Marhaba Hotels maintaining a mailing listof over 450,000 tourists from all over the world. Useable responses were received from1,224 online buyers, including 523 males (42.7 percent) and 701 females (57.3 percent).Approximately 42 percent of the respondents hold the Bachelor’s degree. A total of33.2 percent of them are British, slightly higher than 17 percent of the respondentsare German, around 10 percent are Russian and nearly 10 percent are French. Themajority of the sample is familiar with internet, online shopping and e-reservation.

In accordance with Mzoughi et al.’s (2007) researches, findings show that onlineprocrastination explains online purchase abandonment (Table VII). Results alsocorroborate with Kukar-Kinney and Close’s (2010) findings stipulating that the moreonline shoppers tend to wait for a sale or lower price, the more likely they are toabandon their online cart.

4. DiscussionResults confirmed the bidimensionality of the construct. Online procrastinators arerational and deal-prone online buyers. In line with Choi and Moran’s (2009) thoughts,our study shows that procrastination leads to positive outcomes. Online consumerprocrastination is the purposive and rational tendency to delay the accomplishment of

Dimensions bs t p

Online deal-proneness 0.427 16.531 ***Online rationality 0.394 15.256 ***

Note: ***po0.001

Table VII.Regression resultson predicting onlinepurchase abandonment

Dimensions E-procrastination Avoidance IndecisionOnline

rationalityOnline dealproneness

E-procrastination 1.000Avoidance 0.130* 1.000Indecision 0.191*** �0.052 1.000Online rationality 0.460*** 0.100* 0.288*** 1.000Online deal proneness �0.906*** �0.108* �0.187** �0.506*** 1.000

Notes: ***po0.001; **po0.01; *po0.05Table VI.Correlation

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a planned purchase over the internet. In accordance with Choi and Moran’s (2009)study, we found that online procrastinators prefer pressure, when they voluntarilypostpone ending up started transactions. They also judiciously prioritize tasks andsuccessfully complete transactions at the last minute. They deliberately postponedecisions in waiting for additional information to be available and to maximizethe likelihood of having the best deal. Nevertheless, Simpson and Pychyl (2009)believe that active procrastination, called also “arousal procrastination,” is anintriguing oxymoron. The researchers do not support the existence of people believingworking better under pressure. They think that these individuals are only providingthemselves with excuses for their procrastinatory behavior.

From a managerial point of view, this study provides the e-tailers with suggestionsfor improving online purchase rates. They can rely on the OCPS to identify and targetonline buyers who are more likely to put off the accomplishment of a planned onlinepurchase. Online procrastinators tend to spend long time looking for better offerand additional information, which could enhance the possibility to be exposed tounexpected events. This research provided empirical evidence that e-procrastinationincreases online purchase abandonment.

In order to identify online procrastinators, e-tailers may use the clickstream dataanalysis. Clickstreams are visitors’ path through one or more web sites (Nakataniand Chuang, 2011; Senecal et al., 2005; Lee et al., 2001). Clickstream data analysisshows how web sites are navigated and used by visitors (Lee et al., 2001). Serfer logfiles record the visitor’s ID and reveal information on visited pages as well as the timespent on each page and between-site information (Nakatani and Chuang, 2011; Senecalet al., 2005; Chatterjee et al., 2003; Bucklin et al., 2002). Thanks to these electronic files,it is possible to distinguish between browsers, buyers and e-procrastinators. Accordingto Nakatani and Chuang (2011), collecting data using such web-analytic instrumentsallow the development of competitive strategies.

It is then suggested to provide the web site visitors with full, detailed and clearinformation (Bellman et al., 1999). Online vendors should finally create, launch andadjust personalized web campaigns to target online procrastinators (Mzoughi et al.,2007). They could put into practice viral marketing strategies by delivering e-mailcampaigns that highlight relevant offers likely to motivate online procrastinators tomake immediate purchases. Deadlines, competitive prices and full information aboutthe product should be emphasized.

This research is subject to some limitations that should be taken into consideration.The first one is associated with the sample. The survey was answered by real touristscoming from different nationalities. The results could vary according to the customer’scultural background. The second limitation regards the number of items. The scaleis short. Future studies may think to develop more precise description of the onlineconsumer procrastinators by integrating Choi and Moran’s (2009) activeprocrastination scale. It would be also useful to assess the OCPS validity acrossdifferent product categories and contexts. It would be very interesting to identifythe relationship between online consumer procrastination and other variables like theimpulsive purchase and regret.

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About the authors

Anissa Negra is a Lecturer at the Higher Institute of Management, University of Sousse(Tunisia). She has her PhD in Marketing from the Higher Institute of Management, University ofTunis (Tunisia). She is a member of the research unit MaPReCoB. Her research interests includeonline consumer behavior, e-marketing, e-commerce and tourism. She has published in journalssuch as Journal of Internet Business, Journal of Customer Behaviour and International Journal of

e-Business Management.Mohamed Nabil Mzoughi is Professor of Marketing and Director of the research unit

MaPReCoB at the Higher Institute of Management, University of Sousse (Tunisia). His researchon consumer behaviour and online consumer behaviour have been presented in several nationaland international conferences and have been published in various journals such as Journal of

Internet Business, Journal of Customer Behaviour and Journal of Global and Information

Technology Management. He is in charge of PhD projects dealing with banking, e-banking ande-commerce. Mohamed Nabil Mzoughi is the corresponding author and can be contacted at:teachershello@yahoo.fr

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