Internet Dependency Relations and Online Consumer Behavior

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Journal of Interactive Advertising, Volume 3, Number 2, Spring 2003

Internet Dependency Relations and Online Consumer Behavior: AMedia System Dependency Theory Perspective on Why People

Shop, Chat, and Read News Online

Padmini Patwardhan

School of Mass CommunicationsTexas Tech University

Jin Yang

College of Mass Communication & Media ArtsSouthern Illinois University Carbondale

Table of Contents

AbstractIntroductionMedia System Dependency TheoryInternet ActivitiesOnline Chatting

Online News ReadingMeasurementMethodFindingsDiscussionContributions and LimitationsReferencesAppendix

Abstract

This study introduces Internet Dependency Relations (IDR) as a predictor of onlineconsumer activities. IDR is based on the theoretical perspective of Media SystemDependency theory, which postulates dependency relations between individuals andmedia based on the perceived helpfulness of media in meeting understanding(social/self), orientation (action/interaction) and play (social/solitary) goals. Using across-sectional email survey of 166 respondents randomly drawn from the faculty,staff, and student population at a large mid-western university in the United States,the predictive influence of IDR on online shopping, chatting, and news reading wasempirically tested. On average, consumers in the survey had bought eight products

online in the last six months, spent twenty-one minutes daily reading news online,and chatted ten minutes daily on the Internet. They also displayed moderate, though
http://jiad.org/vol3/no2/patwardhan/index.htm#author%23authorhttp://jiad.org/vol3/no2/patwardhan/index.htm#author%23authorhttp://jiad.org/vol3/no2/patwardhan/index.htm#Abstract%23Abstracthttp://jiad.org/vol3/no2/patwardhan/index.htm#Introduction%23Introductionhttp://jiad.org/vol3/no2/patwardhan/index.htm#Media%23Mediahttp://jiad.org/vol3/no2/patwardhan/index.htm#Internet%23Internethttp://jiad.org/vol3/no2/patwardhan/index.htm#Chatting%23Chattinghttp://jiad.org/vol3/no2/patwardhan/index.htm#Reading%23Readinghttp://jiad.org/vol3/no2/patwardhan/index.htm#Measurement%23Measurementhttp://jiad.org/vol3/no2/patwardhan/index.htm#Method%23Methodhttp://jiad.org/vol3/no2/patwardhan/index.htm#Findings%23Findingshttp://jiad.org/vol3/no2/patwardhan/index.htm#Discussion%23Discussionhttp://jiad.org/vol3/no2/patwardhan/index.htm#Contributions%23Contributionshttp://jiad.org/vol3/no2/patwardhan/index.htm#References%23Referenceshttp://jiad.org/vol3/no2/patwardhan/index.htm#Appendix%23Appendixhttp://jiad.org/vol3/no2/patwardhan/index.htm#author%23authorhttp://jiad.org/vol3/no2/patwardhan/index.htm#author%23authorhttp://jiad.org/vol3/no2/patwardhan/index.htm#Abstract%23Abstracthttp://jiad.org/vol3/no2/patwardhan/index.htm#Introduction%23Introductionhttp://jiad.org/vol3/no2/patwardhan/index.htm#Media%23Mediahttp://jiad.org/vol3/no2/patwardhan/index.htm#Internet%23Internethttp://jiad.org/vol3/no2/patwardhan/index.htm#Chatting%23Chattinghttp://jiad.org/vol3/no2/patwardhan/index.htm#Reading%23Readinghttp://jiad.org/vol3/no2/patwardhan/index.htm#Measurement%23Measurementhttp://jiad.org/vol3/no2/patwardhan/index.htm#Method%23Methodhttp://jiad.org/vol3/no2/patwardhan/index.htm#Findings%23Findingshttp://jiad.org/vol3/no2/patwardhan/index.htm#Discussion%23Discussionhttp://jiad.org/vol3/no2/patwardhan/index.htm#Contributions%23Contributionshttp://jiad.org/vol3/no2/patwardhan/index.htm#References%23Referenceshttp://jiad.org/vol3/no2/patwardhan/index.htm#Appendix%23Appendix


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positive dependency relations with the Internet. IDR significantly explained onlineshopping activities and online news reading, but did not predict online chatting. Interms of specific IDR goal dimensions, the predictive influence of action orientationon online shopping, solitary play on online chatting, and social understanding ononline news reading was confirmed.

Introduction

Whether dealing with the consumption of goods, news, or other types of onlinecontent, it has been suggested that consumer activities in online environmentsindicate a more instrumental than ritualistic use of media. Even more so than anyother medium, the Internet anticipates an active rather than passive audience,implying that, at the present time, its use is more purposive and goal-directed.Therefore, it is possible that the personal and social goals that people seek to meetthrough the Internet may be important motivating factors in the activities that theypursue online. In this study we attempt to tie goal-directed motivations of Internet

users with online shopping, chatting, and news reading.

Shopping, chatting, and news reading are fast proliferating activities among U.S.users in today's online environment. In March 2001 alone, more than 100 million U.S.consumers shopped online, collectively spending over $3.5 billion(Nielsen/NetRatings and Harris Interactive 2001). Similarly, thousands of chat roomsof every nature report hosting over a million chatters daily (Palm Coast/FlaglerInternet 2000), testifying to the growing popularity of instant messaging and relatedchat forms (Pastore 2001). And recent research from Pew Internet & American Life(2000) rated online news reading as the third most popular daily Internet activity inthe United States, after sending email and surfing the Web for fun.

Two theoretical approaches available to study how individual goals are met throughmedia (including Internet) resources are Uses and Gratifications and Media SystemDependency theory. Unlike Uses and Gratifications research, which is premised onconsumer control over accessing media content according to their goals/needs, wefocus on consumer dependency on Internet resources to satisfy goals. We believethat such a dependency on the Internet leads, over time, to the development of aconsumer-Internet dependency relationship, which, in turn, may likely affect thenature and extent of consumers online activities.

In this study, we propose -- and test -- the multidimensional construct of InternetDependency Relations (IDR) as a possible predictor of online activities.Conceptualized as the extent to which people depend on the Internet to meet theirsocial and personal goals, IDR is derived from Media System Dependency theory(Ball-Rokeach 1985, 1998; Ball-Rokeach and DeFleur 1976; DeFleur and Ball-Rokeach 1982, 1989), which defines individual-media relations in terms of bothoverall intensity of the dependency relationship, as well as the extent to whichindividuals relate to a medium to meet specific goals. MSD goal dimensions includeunderstanding (self and social), orientation (action and interaction), and play (solitaryand social) goals that individuals seek to meet through media resources (Figure 1).

Figure 1Goal Dimensions of Media System Dependency Relations


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The central issue this paper addresses is the extent to which Internet behaviors canbe explained by IDR both as a summed intensity, and as the intensity of six specific

goal dimensions. We argue that overall IDR intensity will significantly influenceInternet users online shopping, news reading, and chatting experiences. Theseactivities were selected both due to their growing popularity among Internet users,and their intuitive corresponding match with MSD goal dimensions: shopping withorientation, news reading with understanding, and chatting with play. We furtherhypothesize connections between the intensity of specific goal dimensions andspecific online activities, and examine predictive linkages between action orientationand online shopping, social understanding and online news reading, and solitary playand online chatting.

Media System Dependency Theory

According to MSD theory, a media dependency relationship is one "in which thesatisfaction of needs or the attainment of goals by individuals is contingent upon theresources of the other party (Ball-Rokeach and DeFleur 1976, p. 6). MSD suggeststhat in todays society individuals have to rely on media information resources inorder to attain their various goals. Information resources include all media products(Loges and Ball-Rokeach 1993), including commercial and advertising information.The intensity of media dependency relations depends on the perceived helpfulnessof the media in meeting goals. The goal scope (dimensions) of these relations(Figure 1) covers a wide range of individual goals -- understanding (social and self),orientation (interaction and action) and play (social and solitary) -- that may be met

through media resources (Loges 1994). Understanding goals deal with peoplesneeds to understand the world and themselves; orientation goals focus on the needto behave effectively in interactions with others as well as in personal behavioraldecisions; and play goals deal with the need for entertainment and escapism (Mortonand Duck 2000). While these goal dimensions are exhaustive, they are not mutuallyexclusive -- and more than one kind of goal can be activated (and satisfied) by thesame medium (DeFleur and Ball-Rokeach 1989). Both intensity and goal scope maybe determined by how exclusive media resources are perceived to be in attainingthese goals, and vary for different individuals as well as for the same individual overtime (Ball-Rokeach 1985, 1998; Ball-Rokeach and DeFleur 1976; DeFleur and Ball-Rokeach 1982, 1989).

Internet Activities


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The incorporation of the Internet into daily lives is reflected in the kinds of activitiesmany Americans pursue online. On a typical day in March 2000, 58 millionAmericans logged on to the Internet (Pew Internet & American Life 2000) to sendemail, surf for fun, get news, buy a product, or chat in a chat room or a discussionforum, among other things. Internet users surveyed in a recent study said the Internet

had improved their connection to family and friends, the way they pursue hobbies,and their ability to learn new things. Many found the Internet helpful in doing jobs,getting information on health care, shopping and managing personal finances(Howard, Rainie, and Jones 2001). The diversity and intensity of online activitiespoint to the need to investigate what factors might intervene in the activities. Recentmodels of media selection and use have suggested that, in addition to demographicsand media attributes, factors such as assessment of needs fulfillment,appropriateness, social norms, and peer evaluations are important in determining thenature of media use (Flanagin and Metzger 2001). Therefore, from an MSDperspective one might argue that individual goals --and the Internet's ability to meetthem -- may exert some influence on consumer activities in the online environment.

Online Shopping

A few years ago, shopping or purchase was rated among the least prolific uses ofthe Web (Katz and Aspden 1997; Poindexter 1999). However, most marketersbelieve it is only a matter of time before the majority of consumers shop in theirvirtual storefronts. A March 2001 survey of U.S. users found that e-commerce has hitmainstream, with 48.2% of all Americans over 18 years old --100.2 million people --having bought products online. Despite downturns in the dotcom boom, consumersonline spending has steadily increased. In March 2001 alone, more than $3.5 billionwas spent online, a jump of 35.6% from $2.6 billion in April 2000 (NielsenNetratings/Harris Interactive 2001). A Yahoo!/ACNielsen Internet Confidence Indexreport found that US consumers planned to spend at least $10 billion online betweenJuly-September, 2001 (Cyberatlas 2001).

Even so, generally speaking, buying online still does not appear to be one of theprimary reasons why people visit web sites, despite the overall increase incommercial activities on the Internet. Poindexters 1999 study found this to be true ofboth Baby Boomers and Generation Xers, even though youngsters spent more than10% of their disposable income on purchasing diverse products through the Web(Forrester Research Report 2000). While GVUs 10th WWW User Survey (1998)

found that quality information, easy ordering, and reliability were more important torespondents than security, Korgaonkar and Wolin (1999) found that, among otherthings, security concerns and transaction anxiety appeared to be the most prevalentcauses for not buying on the Web.

Studies using demographic variables to explain online shopping behavior have oftenreported conflicting or confusing results. While Li, Kuo, and Russell (1999) found ageand education level played an important role in online shopping, as did consumersshopping orientation, Bellman, Lohse, and Johnson (1999) considered demographicsan imperfect surrogate to explain online purchasing. They found that whiledemographics explained why people were online in the first place when compared to

the national U.S. population, they did not significantly predict online purchasebehavior. Donthu (1999) observed that distinction was often not made between


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online users and online shoppers. His study found online shoppers to be older, moreaffluent, with a positive attitude towards advertising and direct marketing, less priceand brand conscious and largely convenience seekers. A Forrester Research Report(1999) suggested otherwise: younger consumers (40%) bought more frequently onthe Internet as compared to more mature adults (30%); and fully 62% of all young

U.S. consumers were likely to shop online by 2003. A variety of studies have alsopointed out the increasing online shopping sophistication of todays 16-22 year olds,as evidenced by their use of various aids such as price comparison web sites andonline coupons to buy a wide variety of products on the Internet.

In terms of motivational variables found to influence online shopping, the WhartonVirtual Test Market results reported Wired Lifestyle--characterized by yearsexperience with the Internet, reception of large amounts of emails and work on theInternet in the office every week, and Time Starvation--a result of the increasingnumber of hours worked by members of a household especially in dual-incomehouseholds, as predictive of online shopping (Bellman, Lohse, and Johnson 1999).

While Media Dependency Relations has been previously used in purchase contexts(Grant, Guthrie, and Ball-Rokeach 1991; Skumanich and Kintsfather 1998), it hasbeen studied only in television shopping environments. Grant, Guthrie, and Ball-Rokeach (1991) modeled relationships between viewers/buyers, the televisionshopping program, and the television medium by extending MSD theory todependency on the television shopping genre. Skumanich and Kintsfathers (1998)study found viewer relationship with the medium, the genre and the genre personae(i.e. the tele-shopping host) highly predictive of purchase behavior.

Our research examines the relationship between Internet Dependency Relations andonline shopping. Since it is evident that the online shopping experience involves arange of diverse activities like conducting product information searches, price andbrand comparisons, searching for discounts, as well as actual online productpurchase, this study conceptualizes online shopping as both a range of activities, aswell as the actual number of products bought online.

In view of the mixed findings related to the use of demographics as online shoppingpredictors, we include demographic variables. Specifically, we ask the followingresearch questions:

RQ1: To what extent do intensity of Internet Dependency Relations, age, gender,and income influence consumers online shopping activities?

RQ2: To what extent do intensity of Internet Dependency Relations, age, gender,and income influence the number of products bought online?

Based on definitions of goals dimensions provided in MSD theory, we furtherhypothesize a connection between individuals' action oriented goals and onlineshopping:

H1: Stronger action orientation goal dimension will positively predict consumers

overall online shopping activities.


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H2: Stronger action orientation goal dimension will also positively predict actualonline product purchase.

Online Chatting

As an easy way to get instant answers to messages and to carry on conversationswith friends, colleagues, and strangers around the globe, online chatting is one of thefastest growing activities on the Internet (Pastore 1999). Almost every portal or onlinecommunity on the Internet today hosts some type of chat activity. The chat statisticsreleased by America Online indicated that there were more than 40 million registeredusers of its Buddy List and Instant Messenger services, and more than 750 milliondaily messages were sent through Buddy List and ICQ services. A NetValue surveyin 2001 found that online chatters as a group were among the heaviest users of theInternet; they generated twice as many online sessions as non-chatters (Pastore2001). The study found that women were more likely to chat online than men, andspent two more days per month on online chatting than did males (Pastore 2001).

The paucity of research on this fast proliferating online consumer activity, andindustry-reported evidence of its growing popularity among Internet users, leads us toinvestigate the connection between Internet Dependency Relations and time spentchatting on the Internet. Considering that demographics provided someunderstanding of online chatting, we ask:

RQ3: To what extent do intensity of Internet Dependency Relations, age, gender,and income influence time spent chatting online?

Again, drawing upon the goal scope of MSD theory, we propose the followingpredictive relationship:

H3: Stronger solitary play goal dimension will positively predict time spentchatting online.

Online News Reading

Practically every major mainstream newspaper or magazine in the United States isavailable in an online edition; the same holds true for broadcast news networks. Inaddition, most Internet portals themselves incorporate online news services. Millions

of other official and unofficial news-based sites are online, providing collectiveinformation resources that appear to be virtually limitless.

Market Facts/MSNBC reported in 1998 that 20.1 million U.S. residents used theInternet as a source for news (Levins 1998). A Pew Research Center biennial newsconsumption survey revealed that there was a jump in online news activities between1996-1998, from 6% of Americans to 20% searching for news at least once a week.For these users, science, health, finance and technology were big news draws (PewResearch Center for People and Press 1998). More recently, ScarboroughResearchs first National Internet Study, surveying more than 2000 U.S. adultInternet users, found that more than two out of five Internet users (45%) had read an

online newspaper in the last 30 days. Half (55%) had logged on to a nationalnewspaper web site like the New York Times, Wall Street Journal, and USA TODAY


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(Scarborough Research 2001). Scarborough Research (2001) also indicated that,generally, online news readers tend to be younger (41% were between the ages of18-34) as compared to traditional newspaper readers (only 23% in the same agecategory). Hence, it appears that online editions of newspapers intentionally orunintentionally target a new younger audience. Even though it is too early to claim

that online news has entered the mainstream, Noack (1999) argued that the obviousadvantages of Internet-based news (accessibility, convenience, in-depth research,and information) would be key in attracting readers, encouraging them to spend moretime reading news online.

Along with increased usage of online news, research has also found an increasinglypositive attitude toward news as well. For instance, among 550 Internet users polledby ScreamingMedia, more than half believed that the Internet had the mostinteresting information and provided in-depth, accurate, up-to-date information (Astor2000). Examining electronic newspaper usage, Weir (1999) concluded that mediaconsumption was purposeful and adopters of electronic newspapers used them to

get information important to them.

Prior research exploring the connections between media dependency relations andnews in a print newspaper context indicated that intensity of dependency relationsadded a significant amount of explanation in newspaper reading variance whendemographic variables were controlled; social understanding, self understanding andaction orientation were important dimensions of newspaper dependency relations(Loges and Ball-Rokeach 1993). In another study on television media dependencyrelations, a linkage between dependency and news was also investigated andconfirmed (Ball-Rokeach, Rokeach, and Grube 1984).

No prior studies have examined the connections between online news behavior andmedia dependency relations. Following Loges and Ball-Rokeachs (1993) suggestionto consider both media dependency relations as well as demographic factors inanalyzing media use, we ask the following question:

RQ4: To what extent do intensity of Internet Dependency Relations, age, gender, andincome influence time spend reading news online?

Based on MSDs identification of social understanding goals as leading to individualsinformation seeking behavior, we also hypothesize its predictive relationship with

online news reading.

H4: Stronger social understanding goal dimension will positively predicttime spent reading news online.

Measurement

To measure intensity of Internet Dependency Relations (IDR), the 18 item MSD scaledeveloped and refined by Ball-Rokeach, Rokeach, and Grube (1984), Grant, Guthrie,and Ball-Rokeach (1991), and Ball-Rokeach, Grant, and Horvath (1995) is used. IDRis thus operationalized as respondents composite mean score on the 18 item MSD

scale (See Appendix). To measure action orientation, solitary play and socialunderstanding, composite mean scores for three items each on the MSD scale were


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used. Hence, mean scores for each of the three goal dimensions used in this studyare subsets of the overall dependency mean score.

The dependent variable online shopping was conceptualized both as a range ofshopping-related activities that consumers engaged in online, as well as the number

of actual products they bought online. Operationally, a five-item interval scaledeveloped by Patwardhan (2001) was used to measure respondents self-reportedonline shopping activities, while actual purchase was measured at the ratio level asthe number of products bought online in the last six months.

Online news reading was conceptualized as the extent to which respondentsaccessed and read news on any news-based web site, including web sites ofnewspapers, broadcast media, or other news-based organizations. It isoperationalized as a ratio level measure of the amount of time spent reading newsonline every day.

Online chatting was conceptualized as respondents use of chat and instantmessaging services available on the Internet, and measured at the ratio level as theaverage amount of time spent daily chatting with others via the Internet.

Demographic variables were measured as follows: data on age were collected byasking for the year of birth; income was measured categorically (Please select theappropriate range to reflect your annual income from all sources including salary,and/or parental support if students. Unemployed students were advised to selecttheir parents income range); Gender was the only dichotomous (nominal) variable.

Method

The study used a cross-sectional email survey. The population of interest wasstudents, faculty, and administrative staff at a large mid-western university in theUnited States. The sampling frame was the university email directory. This samplewas particularly desirable for this theory testing study because universitycommunities are known to have a high proportion of Internet users. Future researchwill build on the foundations of this study, surveying national/international Internetuser populations.

Sampling

Respondents were selected using multi-stage stratified random sampling. Stratifiedrandom sampling is a superior method to simple random sampling and ensuresrepresentativeness in terms of variables important to the study. The universitypopulation was first stratified into three groups -- students, faculty, and staff -- toensure age and income variability. Each population group was further stratified onthe basis of gender, to ensure gender representation. Five percent of students, (sincestudents were a much larger group than the others), and 10% of faculty and 10% ofstaff were sampled. Thus the total sample size selected for the study was 1,462respondents (1,200 students 127 faculty and 82 staff). While exploring differences bygroup was not the primary purpose of this study, this sampling method ensured

variability on demographic factors (age, income, and gender) important to this study.


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Reliability analysis was conducted for IDR and online shopping scales. MultipleRegression was used to answer RQ1 through RQ3. Bivariate regression was used totest Hypotheses 1 through 4.

Survey Administration

After creating an email address list for respondents in the selected sample, thesurvey questionnaire was delivered via email. Conforming to research netiquette,the survey was accompanied by a cover letter explaining purpose and nature, timerequired to complete the survey, and the researchers academic affiliation. The optionto opt out was offered, as were confidentiality assurances. The letter andquestionnaire were sent out as inline text. Respondents were requested to hit thereply button, respond to questions, and send the survey back to the researchers.Two mailings were done, with some textual adjustments made for the second mailingto overcome problems in administration. For example, a respondent reported that themessage was truncated when the reply button was hit, so the revised cover letter

offered suggestions for other return routes like cutting and pasting the survey into thereply, or sending via campus mail.

Reliability and Validity

Post-test reliabilities for the scales were tested using Cronbachs alpha. While scalereliability for the overall 18-item IDR scale was fairly high (.88), it was somewhatlower for each of the six individual goal dimensions self understanding (.74), socialunderstanding (.67), action orientation (.64), interaction orientation (.59), self play(.85), and social play (.65). This was probably because only three items were used tomeasure each dimension (larger number of items generally increases reliability). Forthe dependent variable online shopping, reliability for the five-item scale was .90,similar to pre- and post-test reliabilities reported for this scale (.91) in previousresearch (Patwardhan 2001).

Despite an inability to generalize the results of this study beyond the population ofinterest (university faculty, students, and staff), it may be argued that universitypopulations are likely to reflect many of the characteristics of Internet users in theUnited States. The study does have high external validity in terms of generalizingfrom the sample to the university population, since probability sampling was used.

Findings

A total of 1,462 questionnaires were emailed over a 10-day period. Four hundred andeight emails were returned as failed deliveries; and there were twelve refusals toparticipate. [The high number of failed deliveries were mostly from the student groupin the sample, suggesting that student email addresses with the university are notnecessarily current]. Hence for a total of 1,001 emails successfully delivered, 176responses were received, a response rate of 17.6%. Subsequently, ten incomplete(truncated) replies were discarded, leaving 166 usable sample questionnaires.

We acknowledge that low response rate is a major limitation, and offer two possible

defenses. First, many online and email surveys (including the GVU Surveys) usenon-random sampling methods since it is difficult to obtain a sampling frame of all


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Internet users in a particular population. A review of the literature also suggests thatresponse rate for email and online surveys is generally much lower than mail ortelephone surveys. Our response rate falls well within the range reported byresearchers using probability sampling in email surveys. However, like otherresearchers conducting surveys using these methods, we would caution against

generalized interpretations of our results.

As a precaution against sampling error, we conducted a check for non-responsebias, and found a good match. We also compared the sample demographic profilewith the population profile obtained from university sources. There was a good matchon age for all three groups (faculty, students, and staff). In terms of gender, amoderate skew toward female respondents was observed among student and staffrespondents but no skew was detected in the faculty group. For the faculty group,higher income was over- represented and lower income was under-represented.However, the overall match between the sample and the population increasesconfidence in the generalizability of our results to the university population.

Demographic Profile

In terms of demographics, a little more than half the respondents (55%, n = 92) werestudents, 24% (n = 40) were faculty, and 21% (n= 34) were administrative staff.Representation of females was slightly higher (56%) than males (44%). Over half therespondents (52%, n = 78) were in the age group of 1834, 40% (n = 59) werebetween 3554 years old, and only eight percent (n = 12) were more than 55 yearsold. In terms of income distribution, over 61% of respondents (n = 89) had an annualincome below $50,000: of these, half (n = 45) earned below $25,000 and half (n =44) above. Twenty eight percent of the respondents (n = 41) earned between$50,000 to $100,000 annually, and a smaller number (10%, n = 15) earned morethan $100,000.

Mean IDR and Online Behavioral Activities

The mean intensity of overall Internet Dependency Relations among respondentssuggested a positive but somewhat restrained dependence on the Internetsresources to satisfy individual goals (mean = 3.1 on a scale of 1 to 5, with higherscore indicating greater dependency). Similarly positive but moderately intensedependency for understanding (mean = 3.0), orientation (mean = 3.2) and play

(mean = 3.1) were also observed (Table 1). Interestingly, the highest means werefound among the three specific goal sub-dimensions used in this study: actionorientation (mean = 3.6), solitary play (mean = 3.3) and social understanding (mean= 3.6), when compared to other social and self dimensions in the MSD goal scope.

Table 1Mean Internet Dependency Relations and Online Behavioral Activities


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In terms of Internet-based activities, most respondents in the survey engaged inonline shopping activities fairly frequently on the Internet (mean = 2.2 on a scale of 1

to 5 running from very frequently to never), and had bought an average of eightproducts online in the last six months. While there were no significant differences bygroup in the use of the Internet for online shopping activities in general, differenceswere observed in the number of products bought online by staff (15 products in sixmonths) and students (6 products in six months) (F = 4.3, p = .02) (Table 1).

Respondents also spent about 10 minutes daily chatting online. Students and facultydiffered significantly in the time they spent chatting online (F = 3.6, p = .03). Onaverage, students spent the most time chatting online daily (mean = 15 minutes),followed by staff (mean = 6 minutes), and faculty (mean = 3 minutes) (Table 1).

On average respondents spent about 21 minutes daily reading news online, and nostatistically significant differences by group were observed in time spent readingnews online, though faculty spent the most time on this daily activity (mean = 26minutes), followed by students (mean = 20 minutes) and staff (mean = 18 minutes).

RQ1: IDR and Online Shopping

RQ1 investigated the extent to which intensity of Internet Dependency Relations, anddemographic factors, affected consumers online shopping activities. Linear multipleregression was used to check the relationships. The overall model found that eightpercent of variance in online shopping was explained by IDR and demographicvariables (R square = 8.3, F = 2.75, p = .03) (Table 2). However, none of thedemographic factors (age, gender, income) were significant predictors. IDR was the


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only factor that significantly explained almost all the variance in online shopping (8%,t = 3.22, p = .00).

Table 2 Multiple Regression Analysis of Age, Gender, Income and InternetDependency Relations to Predict Variance in Online Shopping

RQ2: IDR and Number of Products Bought Online

RQ2 examined the extent to which the intensity of Internet Dependency Relations,and demographic factors, affected number of products bought online. Linear multipleregression was used to test the relationships. It was found that the regression model

did not significantly predict the number of products bought online.

H1: Action Orientation Goal Dimension and Online Shopping

H1 expressed a relationship between action orientation goal dimension and theonline shopping experience. Bivariate linear regression was used to test thehypothesized relationship. Action orientation was found to be a strong, significantpredictor (R square = .26, F = 54.05, p = .00) (Table 3). H1 was, therefore,supported.

Table 3 Linear Regression Using Specific IDR Dimensions to Predict Variance

in Online News Reading, Online Chatting, Online Shopping and Number ofProducts Bought Online


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H2: Action Orientation Goal Dimension and Number of Products PurchasedOnline

H2 asked whether action orientation goal dimension also predicted the number ofproducts bought online. Surprisingly, despite moderate significant correlationbetween online shopping activities as measured on the scale, and the number ofproducts bought online (r = .493, p = .02, one tailed), the predictive relationshipbetween action orientation and the actual number of products bought online was notsignificant (R square = .02, F = 3.38, p = .07) (Table 3). H2 was, therefore, notsupported.

RQ3: IDR and Time Spent Chatting Online

RQ3 investigated the extent to which IDR, and demographic factors, influenced timespent chatting online. The overall multiple regression model explained 18% ofvariance in online chatting (R Square = .18; F = 6.57, p = .00) (Table 4). However,the variance in time spent chatting online was not explained by IDR, but bydemographic variables (age and income). Of the total amount of variance in onlinechatting, age had a unique contribution of six percent, income explained 11%, andthe rest was shared.

Table 4 Multiple Regression Analysis of Age, Gender, Income and InternetDependency Relations to Predict Variance in Time Spent Chatting Online


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H3: Solitary Play Goal Dimension and Time Spent Chatting Online

H3 investigated the predictive relationship between solitary play and time spentchatting online. Bivariate regression analysis found that solitary play significantlypredicted the time spent chatting online (R square = .05, F = 7.6, p = .00) (Table 3).Hypothesis 3 was, therefore, supported. However, only five percent of variance in thedependent variable was explained by solitary play, suggesting that the predictiverelationship between the two was not very strong.

RQ4: Demographics, IDR and Time Spent Reading News Online

RQ4 examined the extent to which IDR intensity, and demographic variables,affected the time spent reading news online. The linear multiple regression modelaccounted for 19% of variance in the dependent variable (R square = .19, F = 6.69, p= .00) (Table 5). Interestingly, the two significant predictors were gender (T = -4.35, p= .00) which explained 11% of variance in the dependent variable, and InternetDependency Relations (T = 2.85, p = .00) which explained five percent of variance.The negative relationship in the case of gender suggested that male Internet userswere more likely to read news online than female Internet users.

Table 5 Multiple Regression Analysis of Age, Gender, Income and InternetDependency Relations to Predict Variance in Time Spent on Online News

Reading


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H4: Social Understanding Goal Dimension and Time Spent Reading NewsOnline

H4 hypothesized a predictive relationship between social understanding and timespent reading news online. Bivariate regression analysis indicated that socialunderstanding was significant in predicting time spent reading news online (R square= .08, F = 13.39, p = .00) (Table 3). Hence, H4 was supported.

Discussion

As the fastest growing communication medium of all times, the Internet is not onlychanging peoples personal lifestyles but also reshaping the interdependencebetween individuals, media, and society. Dependency is the flip side of control. Aswe argue for greater consumer empowerment and control over what media contentwe consume in Internet environments, we are also more likely to grow increasinglydependent on its resources to meet our goals. In terms of individual-mediarelationships that develop over time, our study suggests tenable connectionsbetween individual goals and dependency on Internet resources. On average,Internet users did display moderately intense Internet Dependency Relations,indicating that the medium has become an integral part of individuals mediaenvironments. IDR intensity appears to be strongest among younger people. In the

case of different IDR goal dimensions, students, more than faculty or staff, appear tobe more strongly motivated to seek out Internet resources to meet their overall play and solitary play goals, emphasizing the entertainment value of media to theyounger generation.

This study also finds support for previous research attesting to the growing popularityof online shopping, chatting, and news reading activities among Internet users.Consumers in our study had bought an average of eight products online in the lastsix months, spent at least thirty minutes per day reading news online, and chatted tenminutes daily on the Internet. Differences by group were, however, evident in the factthat faculty spent the most time reading news online, students spent the most time

chatting online, and staff did the most shopping online.


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Our study also focused on the extent to which Internet Dependency Relationsinfluenced online shopping, chatting, and news reading. At this stage of the Internetsdevelopment, IDR appears to be a moderate determinant of behavioral responses. Inthe case of online shopping, the study is consistent with previous research findingsthat suggest demographic variables are not significant in explaining online shopping

variance. However, statistical significance alone is not sufficient to draw conclusionsabout the predictive strength of IDR, considering the low R square, and futurereplications are necessary to investigate the impact of IDR intensity on onlinebehaviors, considering the criticality of the Internet-user interface in the commercialworld.

We also hypothesized a predictive link between specific goals and online activities.Examining the connection between action orientation and online shopping, we foundthat individuals who depended on the Internet to meet their action orientation goalswere also more likely to engage in shopping-related activities online. This suggeststhat greater consumer dependence on Internet resources to help make personal

behavioral decisions (action orientation) does indeed influence the online shoppingexperience. However, action orientation did not predict the number of productsactually bought online. A possible explanation might be provided by the differencesbetween groups in the number of products bought online. Since staff bought the mostproducts online as compared to students and faculty, it suggests to us that many ofthe purchases were work-related. If the above conjecture is correct, it is possible thatInternet use in work-related shopping contexts may differ from use in personalshopping contexts, and we may argue that media dependency relations, based onthe satisfaction of individual personal goals, may not influence purchase behavior inthe workplace. In future research, clearer distinction should be made between work-related and personal online shopping. Questions related to the kinds of productsbought online may also be included.

While demographics did not affect online shopping, supporting previous researchfindings, this study indicated that they still have potential to predict other types ofInternet use. Age and income are important predictors in online chatting at present;and it appears that online chatting is an activity that younger people with associatedlower incomes engage in for longer periods of time than others. The significantcorrelation of age and play goals also makes sense in the light of the greater intensityof the overall play -- as well as the more specific solitary play -- dimension amongyounger people. Hence younger people, who are more dependent on the Internet to

meet their play goals, were also the ones more likely to chat online for longer periodsof time.

IDR was a significant predictor of the amount of time spent reading news online. Asignificant gender difference was also observed, with males spending more time thanfemales on this activity. The strong predictive correlation between socialunderstanding and online news reading indicated that people do depend on theInternets information resources to understand the world around them. Previousanalysis of newspaper readership and dependency relations theorized that socialunderstanding was linked to newspaper reading because a readers goals ofincreasing integration in the community were addressed by newspaper content

(Loges and Ball-Rokeach 1993). The same appears to hold true in the Internet-basednews environment as well.


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Contributions and Limitations

By introducing IDR and its goal dimensions as a possible source of variance in onlineconsumer behavior, we hope discussion of its importance and relevance will befurther stimulated. Because of its relational aspect, IDR is potentially a better

measure than a simple quantification of the extent of Internet use. The Internet itselfis inherently more consumer-involving, increasing the likelihood of developing arelationship with it, which in turn is likely to influence the nature and extent of onlineactivities. At present, this relationship appears to be of moderate intensity, but webelieve it will strengthen over time. However, the findings in this study areexploratory, and need to be further validated through future research with moregeneral populations.

Our research has some implications for industry as well. E-commerce companies, forexample, are strongly motivated to discover reasons that drive shoppers online.Internet portal companies are anxious to increase web site traffic by uncovering

motivations that lead people to use chat and instant messaging features. And onlinenewspapers and news web sites are keen to understand how news readers/viewerscan be attracted to content on their web sites. Though variance in online shoppingand news reading explained by IDR was small, and IDR did not explain variance inonline chatting, significant linkages between specific IDR goal dimensions and onlineactivities were observed. Therefore, it is suggested that online purchase action couldbe made easier, convenient, and action-oriented to serve online shoppers better;facilitating understanding goals could be the strategic focus to serve online newsreaders; and chat sites can increase traffic by focusing on meeting play goals bymaking sites fun and entertaining to use (for example, the use of emoticons, viewcams, and other devices to make the online chatting experience multi-dimensional).

This study has some limitations. Email surveys generally result in lower responserate than those of telephone or mail surveys. Exploring Shaeffer and Dillmans (1998)suggestion of using a multi-method approach (combining email with other surveyingmethods like mail surveys, for example) and initiation of multiple contacts (this studyused just two mailings) to improve response rate, may provide some solutions in thefuture.

Second, the speed of technological advances constantly alters the nature and scopeof Internet activities; this may in turn alter the nature and scope of dependency

relations as well. Hence, tracking relations through longitudinal analysis may providea more consistent understanding of the development of individual-Internet relationsover time than the cross-sectional approach adopted in this study.

The use of the MSD theoretical perspective in this research may also invite somecriticism, due to its limited use in media effects research. In our considered opinion,despite its complex conceptualization, MSD provides a comprehensive andorganized conceptual framework to explore individual-media relations. In terms ofoperationalization, MSD measurement allows cross-media as well as cross-genrecomparisons, making it a strong and stable measuring instrument in media analysis.

Future research could examine not just overall Internet dependency, but alsodependency on specific types of Internet content, for example online advertising,


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political information, commercial information, or health/medical information.Replication with national and international Internet user populations could provideanother perspective on the development of Internet Dependency Relations and itseffects on online consumer behavior. Comparative studies of dependency on Internetand other media, and the extent to which the Internet is/is not affecting dependency

on other media or information sources also offer exciting possibilities for futureinvestigation.

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Appendix

Scales

Individual Media Dependency Relations Scale(Five point scale from not at all helpful to very helpful)

In your daily life, how useful is the Internet to
http://cyberatlas.internet.com/big_picture/traffic_patterns/print/0,,5931_1625http://cyberatlas.internet.com/big_picture/traffic_patterns/print/0,,5931_582491,00.htmlhttp://cyberatlas.internet.com/big_picture/traffic_patterns/print/0,,5931_582491,00.htmlhttp://www.pewinternet.org/reports/chart.asp?img=6_daily_activities.jpghttp://www.people-press.org/med98rpt.htmhttp://www.scarborough.com/scarb2000/press/pr_internetstudy1.htmhttp://cyberatlas.internet.com/big_picture/traffic_patterns/print/0,,5931_1625http://cyberatlas.internet.com/big_picture/traffic_patterns/print/0,,5931_582491,00.htmlhttp://cyberatlas.internet.com/big_picture/traffic_patterns/print/0,,5931_582491,00.htmlhttp://www.pewinternet.org/reports/chart.asp?img=6_daily_activities.jpghttp://www.people-press.org/med98rpt.htmhttp://www.scarborough.com/scarb2000/press/pr_internetstudy1.htm


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Self understandingGain insight into why you do some of the things you doImagine what you'll be like when you grow olderObserve how others cope with problems or situations like yours

Social understandingStay on top of what is happening in the communityFind out how the country is doingKeep up with world events

Action orientationDecide where to go for services such as health, financial, or householdFigure out what to buyPlan where to go for evening and weekend activities

Interaction orientation

Discover better ways to communicate with othersThink about how to act with friends, relatives, or people you work withGet ideas about how to approach others in important or difficult situations

Solitary playUnwind after a hard day or weekRelax when you are by yourselfHave something to do when nobody else is around

Social playGive you something to do with your friendsHave fun with family or friendsBe a part of events you enjoy without having to be there

Online Shopping Scale(Five point scale from Very Frequently to Never)I shop on the InternetI buy many different products on the InternetI make use of online discounts on goods and servicesI follow up on good deals on the InternetI buy a product online even if other buying options are available

About the Authors

Padmini Patwardhan is an Assistant Professor in School of Mass Communicationsat Texas Tech University.

Jin Yang is a Doctoral Student in College of Mass Communication & Media Arts atSouthern Illinois University Carbondale.

*This is an invited article


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URL: jiad.org/vol3/no2/patwardhanCopyright 2003 Journal of Interactive Advertising

Documents

Internet Dependency Relations and Online Consumer Behavior