Construction and validation of ane-lifestyle instrument

Chian-Son YuDepartment of Information Technology and Management,

School of Management, Shih Chien University, Taipei, Taiwan

Abstract

Purpose The purpose of this paper is to construct and validate an e-lifestyle scale.

Design/methodology/approach Through a two-step approach of exploratory factor analysis(EFA), the generated two EFA solutions reveal the adequacy of the generated seven componentsunderlying the 1,135 responses. By using the other 793 respondents sampling from the samepopulation, confirmatory factor analysis (CFA) examines and supports the fitness of the overallstructure.

Findings The empirical results show that the 39 items of the e-lifestyle scale were grouped intoseven distinct components. These components represented seven principal factors that significantlyinfluence and shape individual e-lifestyles.

Research limitations/implications This investigation merely represents a starting point ine-lifestyle research. To enhance the validity and generalization of the scale proposed in this study,further cross-cultural validation is necessary.

Practical implications Beyond constructing and validating an e-lifestyle instrument, this studycould provide marketers with insights about how to integrate e-lifestyles into marketing strategies.

Originality/value This research contributes to advance current knowledge on what factorsinfluence e-lifestyle and relative influences of main factors shaping e-lifestyle, and pave a way formarketers to execute more elaborate marketing research with the proposed e-lifestyle scale.

Keywords Lifestyles, Communication technologeis, Information Technology, Internet, E-lifestyle

Paper type Research paper

1. IntroductionThe convergence of the internet and mobile communications has stimulatedphenomenal influence of information and communication technology (ICT) andproliferation of ICT-enabled services/products. This has significantly impacted andchanged the context and the way people live in recent years. Since understandingindividual lifestyles has long been considered quite useful in tailoring and deliveringsuitable services/products to specific target segments, there is a potential need toconstruct an e-lifestyle instrument that could offer marketers a useful basis tomarketing/designing ICT-enabled services/products, commented by somepractitioners such as Mary Modahl (Vice President, Forrester Research Inc.) andJason Chian (CEO, InsightXplorer Co.) (Chen and He, 2006). Besides, previous researchhas argued that the extant lifestyle instruments almost developed in the 1970s and1980s (Lin, 2003) may not effectively capture consumers time-conscious (i.e. web-based

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The author would like to thank the anonymous reviewers for their editorial and constructivecomments. This paper is supported by National Science Council of The Republic of China underContact Number: NSC 97-2416-H-158-010.

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Received 16 October 2010Revised 16 February 2011Accepted 22 February 2011

Internet ResearchVol. 21 No. 3, 2011pp. 214-235q Emerald Group Publishing Limited1066-2243DOI 10.1108/10662241111139282

services) and technology-conscious (i.e. MP3 players) lifestyle (Swinyard and Smith,2003; Brengman et al., 2005; Allred et al., 2006; Chen and He, 2006).

Motivated by the above and based on the idea that the more you know andunderstand about consumers, the more effectively you can communicate and market tothem (Plummer, 1974; Brengman et al., 2005), the primary goal of this study is todevelop and validate an e-lifestyle instrument that could provide marketers someinsights of what triggers peoples e-lifestyles. Accordingly, Section 2 reviews thedominant lifestyle instruments, and Section 3 constructs an e-lifestyle scale based onlifestyle theories and related lifestyle rating statements. Given that the extant literaturedirectly assessing e-lifestyle is absent, this study conducts a panel discussion and apre-test group interview to check and revise the initially constructed e-lifestyle scale.Section 4 performs sampling and data collecting, while Sections 5 and 6 executeexploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Section 7discusses research implications, and Section 8 addresses limitations of the presentstudy and further research suggestions.

2. Literature review in lifestyle instrumentsThe theory-based lifestyle works emerged in the early 1950s (Havinhurst andFeigenbaum, 1959; Lazar, 1963; Ansbacher, 1976; Anderson and Golden, 1984), thelifestyle concept was first introduced to help marketers understand consumer behaviorin the late 1950s (Havinhurst and Feigenbaum, 1959) and inaugurated to marketingresearch in the early 1960s (Lazar, 1963). Since then, studies have proposed numerousworks on assessing lifestyle. Among various lifestyle scales, two well-known andwidely used lifestyle instruments are activities, interests, opinions (AIO) rating scale,originally presented by Wells and Tigert in the beginning of 1970s (Wells and Tigert,1971) and the value, attitude, and life styles (VALS) rating scale, initially developed byMitchell in 1983 (Mitchell, 1983).

In an original AIO study profiling individual lifestyles, Wells and Tigert (1971)defined activities as actual observable behaviors, interests as the continuous paying ofattention to certain objects, and opinions as responses to specific events. Since then,AIO-based studies have extensively conducted to help marketers deliver specificservices/products to different targeted segments (Wells and Tigert, 1971; Plummer,1974; Gutman, 1982; Soutar and Clarke, 1983; Bowles, 1988; Thompson and Kaminski,1993; Bates et al., 2001; Lin, 2003; Swinyard and Smith, 2003; Brunso et al., 2004;Brengman et al., 2005; Green et al., 2006; Hsu and Chang, 2008; Kumar and Sarkar,2008; Hur et al., 2010). Literature review indicates the current widely used AIOinstrument, developed by Plummer (Plummer, 1974), consists of 300 rating statements.

By conducting a study assessing the values and lives of Americans in the early1980s Mitchell and Spengler at the Stanford Research Institute developed an800-question VALS instrument (Mitchell, 1983, 1994), which covers backgroundinformation (i.e. demographics), personal life (i.e. financial issues, habits and activities),and perceived value (i.e. attitudes and beliefs). Through observing the relations amongindividual values, lives, beliefs, and actions, Mitchell discovered that a mixture ofpersonal life and perceived value determine individual behavior, while perceivedvalues is a synthesis of individual attitudes, beliefs, hopes, prejudices, and demands(Mitchell, 1983, 1994). Accordingly, except for activities, interests, and opinions, manyresearches (Mitchell, 1983, 1994; Lin, 2003) argued that value is one of the necessary

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constructs to assess individual lifestyle. In January 1989, the Stanford ResearchInstitute introduced a new VALS instrument named VALS2, which comprised only 400questions reduced from 800 in VALS. After continuously adapting VALS, currently theextensively adopted VALS2 questionnaire only contains 35 psychographic questionsand four demographic questions (Riche, 1989; Lin, 2003), which is available on theofficial web site of the Stanford Research Institute.

Except for VALS, the Rokeach Value Survey (RVS) proposed by Rokeach (1973) andthe List of Value (LOV) developed by the Survey Research Center at the University ofMichigan (Veroff et al., 1981) are two widely used instruments to assess individualvalues. RVS requires respondents to rank 18 terminal values and 18 instrumentalvalues. The terminal values are considered to be either self-centered orsociety-centered, and intrapersonal or interpersonal in focus, while the instrumentalvalues are moral values and competence. LOV asks respondents for their ratingsregarding nine different values, including identifying which is most important to them.The comparisons regarding VALS, RVS, and LOV can be found in literature (Beattyet al., 1985; Kahle et al., 1986; Novak and MacEvoy, 1990; Wang et al., 1994; Johnston,1995; Lin, 2003). Although to date no conclusive empirical evidence has supportedwhich instrument is the best in assessing individual values, literature review revealsthat VALS and LOV are much popular than RVS.

3. Construction of an e-lifestyle instrumentOn entering the internet era, Malhotra (1999) employed Kellys personal constructiontheory to construct a lifestyle scale in IT adoption domain, while Lin (2003) usedhuman motivation and expectancy value theory to establish a hospitality consumerlifestyle instrument. The personal construction theory emphasizes human capacity andemotional experiences, asserting that individuals engage in a particular behavior dueto a series of corollaries, broadly grouped into those concerned with construing,personal knowledge, and social embedding of individual efforts (Kelly, 1955; Neimeyerand Neimeyer, 2002). The human motivation theory asserts that motivation largelyaccounts for individuals engaging in particular behaviors, possibly motivated frombasic needs such as food or desired objects, hobbies, goals, state of being, or ideals(Maslow, 1970; Lin, 2003). Expectancy value theory, founded by Fishbein in the 1970s(Ajzen and Fishbein, 1980), suggested that people orient themselves to the worldaccording to their expectations (beliefs) and evaluations (Palmgreen, 1984).

Recently, by furnishing the most comprehensive and up-to-date summary oflifestyle theory, Walters (2006) suggested that lifestyle is a set of behaviors initiated bymotivation, evolved by interacting with the environmental circumstance, and formedby choice, condition, cognition, and beliefs. Human motivation, the expectancy valuetheory, and the personal construction theory all originate from sociology andpsychology. Therefore, from the sociological perspective, lifestyle is motivated byexternal stimuli (Walters, 2006). From the psychology viewpoints, Walters (2006)suggested that lifestyle is initiated by internal beliefs. Walters discussion echoes theAIO studies that consider lifestyle as a set of behaviors mirroring individualpsychological consideration and sociological consequences (Plummer, 1974; Gutman,1982).

Building on the above literature review, this study found lifestyle-related theoriescommonly agreed that human behaviors can be predicted and explained by a function

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of psychological and sociological variables. That is, individual e-lifestyles are predicableand assessable by psychological and sociological constructs. Therefore, followingprevailing instruments (i.e. AIO, VALS, ROV, and LOV), this study employed fourconstructs of e-activities, e-interests, e-opinions, and e-values, shown in Table I, toevaluate peoples e-lifestyles. As the lifestyle theories suggested, individual lifestyle is aset of behaviors reflecting individual psychological concerns (internal beliefs) andsociological consequences (external stimuli). This research operationalizes the e-activitiesas observable actions in using ICT-enabled services/products, e-interests as sensibletendencies to use and know the ICT-enabled services/products, e-opinions asfundamental response to the matters of ICT-enabled services/products, and e-valuesas basic beliefs about ICT-enabled services/products. Notably, the first three constructsof e-activities, e-interests, e-opinions are based on AIO (Plummer, 1974), while theconstruct of e-values is culled from LOV, VALS, and RVS studies (Kahle and Kennedy,1989; Mitchell, 1994; Johnston, 1995; Lekakos and Giaglis, 2004; Green et al., 2006; Royand Goswami, 2007; Harcar and Kaynak, 2008; Zhu et al., 2009).

Through exhaustively reviewing past studies on lifestyle measurement during pastdecades, this study found that literature regarding lifestyle assessment was huge(Wells and Tigert, 1971; Plummer, 1974; Gutman, 1982; Mitchell, 1983; Soutar andClarke, 1983; Kahle et al., 1986; Bowles, 1988; Kahle and Kennedy, 1989; Thompson andKaminski, 1993; Grunet et al., 1997; Bates et al., 2001; Lin, 2003; Brunso et al., 2004;Green et al., 2006; Hsu and Chang, 2008; Kumar and Sarkar, 2008, Jensen, 2009), butnone of the studies directly assessed peoples e-lifestyles and only a few lifestyle-basedstudies were conducted in ICT-related domains (Damodaran, 2001; Kim et al., 2001;Swinyard and Smith, 2003; Lekakos and Giaglis, 2004; Yang, 2004; Brengman et al.,2005; Allred et al., 2006; Zhu et al., 2009; Lee et al., 2009; Ahmad et al., 2010). Amongthese few studies, Damodaran (2001) explored human factors and lifestyles in digitaltechnology world, Kim et al. (2001) proposed a 27-item internet users lifestyle,Swinyard and Smith (2003) applied 38-item statements to assess internet shopperslifestyles, Lekakos and Giaglis (2004) analyzed consumer lifestyles for deliveringpersonalized advertisements via digital interactive television, Yang (2004) constructeda 30-item statements to assess internet users lifestyle, Brengman et al. (2005) proposeda 38-item battery to assess internet shoppers web-usage- related lifestyle, Allred et al.(2006) extended and replicated the work of Swinyard and Smith (2003), Zhu et al. (2009)adopted 56-items China-VALS to survey consumer lifestyles in the mobile phonemarket, and Lee et al. (2009) adapted AIO statements and national consumer lifestyle

e-Activities e-Interests e-Opinions e-Values

Work Family Themselves RespectedHobbies Home Social issues AccomplishmentSocial events Job Politics FulfillmentVocation Community Business Relationships with othersEntertainment Recreation Economics ExpectationClub membership Fashion Education PrejudicesCommunity Food Production HopesShopping Media Future DemandsSports Achievements Culture

Table I.Constructs used tomeasure e-lifestyle

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inventories provided by Simmons Market Research to assess consumer lifestylesregarding the adoption of electronic products. Notably, Ahmad et al. (2010)summarized the recent AIO-item-based studies, but did not empirically examine theonline shopping consumer lifestyles.

Because many empirical studies have found that single set lifestyle instruments aretypically only effective to capture consumer life in specific domains (Bowles, 1988; VanRaaij and Verhallen, 1994), the above lifestyle-based studies assess individual lifestylein specific rather than general ICT-enabled services/products, and most of thesestudies adopted items from AIO, VALS, or their variants, this study thus selected thepotential items not from these studies but also from AIO, VALS, RVS, and LOV asshown in Table I. As a result, in the initial version, this study proposed 15 items tomeasure e-activities, 15 items to measure e-interests, 15 items to measure e-opinions,and 15 items to measure e-values. In the absence of empirical research directlyassessing individual e-lifestyles, the selection and rewording of items were based onfour criteria: measurability according to each constructs operationalization definition,relevance to ICT-enabled services/products, fitness for general respondents, andoverall representativeness for ICT-enabled services/products. Past studies (Wells andTigert, 1971; Wells, 1975; Lin, 2003; Swinyard and Smith, 2003) suggested that goodlifestyle items might result from not only pertinent literature, but also in-depthinterviews with professionals comments, particularly when direct empirical researchis absent (Swinyard and Smith, 2003; Ahmad et al., 2010). Consequently, this researchconducted a panel discussion by inviting two academics and two practitioners to gothrough and reword the initially constructed e-lifestyle scale.

Following the panel discussion consensus, the study removed items deemedredundant, combined/integrated items deemed similar, simplified items deemed toolengthy, and reworded items if the statement was not clearly written and easilyunderstood. Accordingly, the initial 60 items reduced to 52 items and necessaryadjustments were made based on the comments from the panel discussion. Thereafter,a pre-testing with 18 respondents was performed to check the wording, completeness,sequencing, and other possible errors in the questionnaire. Following the respondentsfeedback, the questionnaire was slightly re-edited to strength the clarity andcompleteness. As a result, the formal questionnaire was organized into two sections,comprised of 57 questions. The first section contained 52 questions used to evaluateindividual e-lifestyle as shown in Table II, while the second section involved fivequestions used to collect demographic variables of respondents. All questions in thefirst section were measured using a five-point Likert scale, ranging from stronglydisagree to strongly agree.

4. Sampling and data collectionPast research (Bhattacherjee, 2001a, b; Sax et al., 2003; Dillman et al., 2008) hasdiscussed the advantages of online surveys over paper-based mail surveys,particularly as an appropriate approach for ICT-related studies. Common problemswith questionnaire surveys are the response rate and non-response bias. Therefore, thisresearch offered monetary incentives to increase response rate, examined the IPaddresses of respondents for double submissions, verified the uniformity of theresponses in relation to the date of receipt for non-response bias, and analyzed

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Constructs Items

e-activities 1 I frequently perform my job via ICT-enabled services/products2 I frequently play games or listen to music via ICT-enabled services/products3 I frequently shop or make purchase via ICT-enabled services/products4 I frequently watch movies or sports via ICT-enabled services/products5 I frequently do my banking or finances via ICT-enabled services/products6 I frequently share my opinions via ICT-enabled services/products7 I frequently chat via ICT-enabled services/products8 I frequently arrange trips via ICT-enabled services/products9 I frequently participate in social events via ICT-enabled services/products

10 I frequently use ICT-enabled services/products at home11 I frequently use ICT-enabled services/products on vocation12 I frequently use ICT-enabled services/products to read news or get data. 1313 I frequently spend a lot of time involved with ICT-enabled services/products.

e-interests 14 I am very interested in discovering how to use ICT-enabled services/products15 I am very excited to know new ICT-enabled services/products16 I stay updated as to the latest development in ICT-enabled services/products17 Being able to use the newest ICT-enabled services/products makes me happy18 Being able to use the newest ICT-enabled services/products gives me a sense of

achievement19 I like gaining knowledge regarding ICT-enabled services/products20 Using ICT-enabled services/products really give me a lot of fun21 I like to share with people about new knowledge of ICT-enabled services/products22 I like the challenge brought by ICT-enabled services/products23 I like ICT-enabled services/products involving in my entertainment24 I like ICT-enabled services/products involving in my learning25 I used to play an active role in an ICT-enabled service/product community26 I like to participate in communities of ICT-enabled services/products

e-opinions 27 Continued development of ICT-enabled services/products is positive for oursociety

28 Continued development of ICT-enabled services/products has negative effect forour society

29 Continued development of ICT-enabled services/products is positive for our culture30 Continued development of ICT-enabled services/products has negative effect for our

culture31 Continued development of ICT-enabled services/products is positive for our

education32 Continued development of ICT-enabled services/products has negative effect for

our education33 Continued development of ICT-enabled services/products is positive for our

economy34 Continued development of ICT-enabled services/products has negative effect for our

economy35 The more the development on ICT-enabled services/products, the more the

happiness on human lives36 The more the development on ICT-enabled services/products, the more the

pressures on human lives37 Using ICT-enabled services/products is fashionable38 Keeping alerts to the latest trends of ICT-enabled services/products is very

important39 Keeping inaugurating new ICT-enabled services/products is very important

(continued )

Table II.Items used to assess

e-activities, e-interests,e-opinions, and e-values

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unanswered questions in incomplete questionnaires for item non-response bias, assuggested by the literature (Yu and Tao, 2009).

Accordingly, 1,325 online questionnaires were administered and gathered during atwo-month online field survey. After discarding invalid and incomplete questionnaires,this study collected 1,135 valid responses. Table III displays that 589 of the 1,135 validrespondents were female, while the other 546 respondents were male. Of the totalonline respondents, 4.4 percent were aged below 20 years old, 44.1 percent were 20-24years old, 22.5 percent were 25-29 years old, 11.8 percent were 30-34 years old, 8.0percent were 35-39 years old, 5.2 percent were 40-44 years old, and 4.0 percent wereabove 45 years old. Around 93.4 percent of respondents had a bachelor degree orhigher, 39.4 percent were students, and 68.5 percent had average monthly incomesbelow NT$ 35,000.

The statistics, reported by Ministry of The Interior in July 2010, indicate thepopulation proportions of age distribution are 22.97 percent of population below age20, 15.12 percent between 20-30, 16.45 percent between 30-40, 16.09 percent between40-50, 13.46 percent between 50-60, and 15.92 percent are over 60 years old. ComparingTable II and the population statistics released by Ministry of The Interior, this studydiscovered the demographic data of the collected Sample 1 did not reflect that ofcurrent population. The reason may be heavily attributed to the fact that thetwo-month online survey was conducted via http://survey.youthwant.com.tw/, afamous online survey web site in Taiwan, where most users are of a young generation.In this respect, this work next employed the shopping mall intercept method to ensure

Constructs Items

e-values 40 ICT-enabled services/products greatly enhance the convenience of my life41 ICT-enabled services/products greatly improve my job efficiency42 ICT-enabled services/products greatly expand my friends circle43 ICT-enabled services/products greatly enhance interaction among people44 ICT-enabled services/products markedly decrease face-to-face emotional

interaction among people45 I dont like my life to involve with too many ICT-enabled services/products46 The living environment has been influenced by ICT, and I have benefited from the

impact47 The working environment has been influenced by ICT, and I have benefited from

the impact48 The leisure environment has been influenced by ICT, and I have enjoyed from the

impact49 The learning environment has been influenced by ICT, and I have benefited from

the impact50 Choosing to use an ICT-enabled service/product is heavily because its market share

is the highest51 The more new knowledge regarding ICT-enabled services/products I gain, the

more advantages I take52 The more time with ICT-enabled services/products I spend, the more advantages I

take

Notes: After a two-step EFA analysis conducted in Section 5, some items of 52 original items will beremoved. Therefore, the items, finally removed, were expressed in italics to let readers easily identify themTable II.

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the respondents reflect the age distribution of the current population. Following thepast studies suggestion (De Bruwer and Haydam, 1996; Yang, 2004), this researchtrained four research assistants and dispatched them to recruit respondents in severalTaipei downtown areas in the mornings, afternoons, and evenings during tenweekdays and two weekends, to remove potential sampling biases. After a two-weeksurvey, 793 valid respondents were collected to resemble the age distribution ofTaiwanese population, as shown in Table IV.

Category Number of respondents Percentage

GenderMale 546 48.1Female 589 51.9

AgeLess than 20 years old 50 4.420-24 years old 501 44.125-29 years old 255 22.530-34 years old 134 11.835-39 years old 90 8.040-44 years old 60 5.2above 45 years old 45 4.0

OccupationManufacturing 89 7.8ICT-related service 46 4.0Banking/financial/insurance 47 4.1Media/publishing 44 3.8Retail/distribution 46 4.0Restate/construction 22 1.9Medical/hospital/bio-tech 49 4.3Education/culture 48 4.2Military/police 22 1.9Student 447 39.4Government/non-profit sector 38 3.3SOHO 72 6.3House keeping 106 9.3Others 59 5.2

EducationSenior High Diploma or Below 87 7.6Associate Bachelor Degree 169 14.9Bachelor Degree 632 55.7Master Degree 231 20.4Ph.D. Degree 16 1.4

Monthly incomeLess than NT$ 15,000 511 45.0NT$ 15,000-24,999 122 10.7NT$ 25,000-34,999 144 12.7NT$ 35,000-44,999 114 10.0NT$ 45,000-54,999 102 9.0NT$ 55,000-64,999 83 7.3Over NT$ 65,000 59 5.2

Table III.The profile of sample 1

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5. Results of exploratory factor analysisBy grouping 52 items according to the four conceptual dimensions from which theseitems were derived, as shown in Table I, this study first performed the reliabilityanalysis to examine the corrected item-to-total correlations and check which items canbe deleted if they are irrelevant factors (i.e. loading values were lower than 0.6.) orindistinct factors (i.e. associated with more than one components). Following factoranalysis, all of these items were found evidently related to their constructs and theCronbachs alpha levels for four dimensions of e-values, e-interests, e-opinions, and

Category Number of respondents Percentage

GenderMale 386 48.7Female 407 51.3

AgeLess than 20 years old 178 22.520-30 years old 125 15.830-40 years old 131 16.540-50 years old 128 16.150-60 years old 107 13.5above 60 years old 124 15.6

OccupationManufacturing 44 5.5ICT-related service 22 13.5Banking/financial/insurance 18 15.4Media/publishing 16 9.2Retail/distribution 21 4.9Restate/construction 5 4.4Medical/hospital/bio-tech 25 1.9Education/culture 27 4.3Military/police 7 3.0Student 426 30.5Government/non-profit sector 17 1.1SOHO 70 2.3House keeping 7 1.3Others 24 2.6

EducationSenior High Diploma or Below 166 21.0Associate Bachelor Degree 103 13.0Bachelor Degree 363 45.8Master Degree 152 19.2PhD Degree 9 1.1

Monthly incomeLess than NT$ 15,000 282 35.6NT$ 15,000-24,999 77 9.7NT$ 25,000-34,999 103 13.0NT$ 35,000-44,999 142 17.9NT$ 45,000-54,999 91 11.5NT$ 55,000-64,999 80 10.1Over NT$ 65,000 18 2.3

Table IV.The profile of sample 2

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e-activities range from 0.781 to 0.899 (mean 0:846, SD 0:053). Next, to extractfactors from the data set and determine the number of factors which best explain therelationships among dataset items, a two-step approach using EFA was adopted.

This research adopted a two-step approach because of the criticism that EFA is aninternally driven analysis method with few criteria for evaluating its results (Greenet al., 2006). Accordingly, this study randomly divided the collected respondents viaonline sampling into two independent samples (one is called development sample andthe other is called replication samples) using SPSS Random Selection. Thereafter, thiswork conducted EFA using the principal components method with varimax rotation onboth development and replication samples based on the assumption that the exactnumber of dimensions underlying a set of data is unknown. This study independentlyexecuted an identical series of EFA steps for each sample.

Following past research suggestions (DeVellis, 2003; Thompson, 2004; Green et al.,2006), this study adopted four criteria to evaluate the EFA principal componentsolutions. First, this work assesses the percentage variances explained by eachindividual component and the overall set of components. That is, the varianceaccounted for by each component is employed to determine whether the componentcontributes significantly to the solution. The second evaluative criterion was theoccurrence of simple structure. Simple structure means that each item loads stronglyon only one component. Items that have strong relationships with more than onecomponent are termed cross-loading items. Cross-loading item may cause problemswhen interpreting the EFA solution. In this study, items are considered as componentmarkers if their loading value was greater than 0.6. In contrast, loweritem-to-component correlations were determined if items were not closely associatedwith other components. Third, this study evaluates the solution by the absence ofspecific components. Specific components are dimensions consisting of just one or twoitems, which frequently indicate over factoring of the data set. Finally, the study judgesthe solution based on its interpretability. This criterion is arguably the most important,because for the solution to be useful it must be substantively important based onresearcher knowledge of the content area (DeVellis, 2003; Green et al., 2006).

According to the above four criteria, the study extracted seven factors from 39 itemsout of 52 items, displayed in Table V. Notably, Table V shows replication sampleloadings, eigenvalues, percentage of variance accounted by each factor, and Cronbachalpha values in parenthesis. Table V shows the generated EFA results indicate goodinter-item consistency reliability and convergent validity, since all factor loadingexceeding 0.611 and the computed Cronbach alpha values ranging from 0.728 to 0.869in the development sample data while ranging from 0.745 to 0.853 in the replicationsample data. Besides, the computed total variances explained by the generatedseven-component solution across the 568 observations in the development sample andacross the 567 observations in the replication sample are 62.842 percent and 63.591percent, respectively. Therefore, the predictive validity is supported. Overall, the twoEFA solutions revealing 39 items under the seven components are validated andreliable across the four criteria evaluating the EFA principal component solutions.

The computed EFA solutions indicate that Factor 1 (F1) contains nine items:ICT-enabled services/products greatly enhance the convenience of my life, ICT-enabledservices/products greatly improve my job efficiency, I frequently use ICT-enabledservices/products to read news or get data, I frequently shop or make purchase via

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Factor loadings Eigenvalue

Percentage ofvariance

accounted byeach factor

Cronbach alphavalues

Factor 1Q40 0.823 (0.856)Q41 0.815 (0.822)Q12 0.812 (0.765)Q03 0.798 (0.698) 7.726 19.316 0.763Q05 0.756 (0.649) (6.915) (17.288) (0.831)Q01 0.723 (0.715)Q52 0.712 (0.722)Q46 0.665 (0.617)Q47 0.615 (0.649)Factor 2Q13 0.865 (0.892)Q14 0.864 (0.877)Q16 0.829 (0.801) 4.043 10.11% 0.772Q15 0.817 (0.815) (4.357) (10.940) (0.820)Q19 0.811 (0.836)Q38 0.686 (0.712)Factor 3Q02 0.798 (0.812)Q04 0.739 (0.754) 3.988 9.973 0.782Q23 0.720 (0.803) (4.273) (10.728) (0.853)Q20 0.682 (0.793)Q48 0.672 (0.712)Factor 4Q06 0.873 (0.886)Q07 0.860 (0.824)Q43 0.778 (0.847) 3.130 7.828 0.869Q42 0.758 (0.734) (3.181) (7.998) (0.839)Q09 0.741 (0.816)Factor 5Q51 0.828 (0.877)Q18 0.812 (0.869)Q33 0.799 (0.813) 2.762 6.909 0.741Q27 0.767 (0.762) (3.357) (7.313) (0.763)Q31 0.621 (0.788)Factor 6Q45 0.813 (0.787)Q44 0.755 (0.748) 1.967 4.915 0.728Q28 0.733 (0.706) (2.047) (5.117) (0.745)Q36 0.729 (0.765)Q32 0.717 (0.764)Factor 7Q21 0.857 (0.844)Q17 0.745 (0.810) 1.515 4.136 0.801Q22 0.737 (0.865) (1.683) (4.209) (0.803)Q39 0.687 (0.753)

Notes: Replication sample loadings, eigenvalues, percentage of variance accounted by each factor,and Cronbach alpha values are provided in parenthesis

Table V.EFA results of thedevelopment andreplication samples

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ICT-enabled services/products, I frequently do my banking or finances via ICT-enabledservices/products, I frequently perform my job via ICT-enabled services/ products,The more time with ICT-enabled services/products I spend, the more advantages I take,The living environment has been influenced by ICT, and I have benefited from theimpact, and The working environment has been influenced by ICT, and I have benefitedfrom the impact. Accordingly, F1s content reflects that individual e-lifestyles aresignificantly impacted and shaped by how ICT-enabled services/products fulfillindividual needs in his/her job and life, and the closeness regarding that individual joband life needs ICT-enabled services/products. Given that F1 content mirrors the way thesepeople live using ICT-enabled services/products mainly because ICT-enabledservices/products can bring their jobs and daily life more convenient, efficient, andbenefits. To interpret F1s content analysis best, F1 is labeled as need-driven e-lifestyle.

Similarly, F2 contains six items: I frequently spend a lot of time involved withICT-enabled services/products, I am very interested in discovering how to useICT-enabled services/products, I stay updated as to the latest development inICT-enabled services/products, I am very excited to know new ICT-enabled services/products, I like gaining knowledge regarding ICT-enabled services/products, andKeeping alerts to the latest trends of ICT-enabled services/products is veryimportant. Therefore, the content of F2 is labeled as interest-driven e-lifestyle.

F3 contains five items: I frequently play games or listen to music via ICT-enabledservices/products, I frequently watch movies or sports via ICT-enabled services/products, I like ICT-enabled services/products involving in my entertainment,Using ICT-enabled services/products really give me a lot of fun, and The leisureenvironment has been influenced by ICT, and I have enjoyed from the impact. As aresult, F3 is labeled as entertainment-driven e-lifestyle.

F4 contains five items: I frequently share my opinions via ICT-enabledservices/products, I frequently chat via ICT-enabled services/products,ICT-enabled services/products greatly enhance interaction among people,ICT-enabled services/products greatly expand my friends circle, and I frequentlyparticipate in social events via ICT-enabled services/products. Consequently, F4 islabeled as sociability-driven e-lifestyle.

F5 contains five items: The more new knowledge regarding ICT-enabledservices/products I gain, the more advantages I take, Being able to use the newestICT-enabled services/products gives me a sense of achievement, Continueddevelopment of ICT-enabled services/products is positive for our economy,Continued development of ICT-enabled services/products is positive for oursociety, and Continued development of ICT-enabled services/products is positivefor our education. F5 is thus labeled as perceived importance-driven e-lifestyle.

F6 contains five items: I dont like my life to involve with too many ICT-enabledservices/products, ICT-enabled services/products markedly decrease face-to-faceemotional interaction among people, Continued development of ICT-enabledservices/products has negative effect for our society, The more the developmenton ICT-enabled services/products, the more the pressures on human lives, andContinued development of ICT-enabled services/products has negative effect for oureducation. Accordingly, F6 is labeled as uninterested or concern-driven e-lifestyle.

F7 contains four items: I like to share with people about new knowledge ofICT-enabled services/products, Being able to use the newest ICT-enabled services/

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products makes me happy, I like the challenge brought by ICT-enabled services/products, and Keeping inaugurating new ICT-enabled services/products is veryimportant. Hence, F7 is labeled as novelty-driven e-lifestyle.

6. Results of confirmatory factor analysisAfter executing EFA to extract factors with no presumption theory, this study nextapplied CFA to empirically examine the goodness of fit for the structure and propertiesof the constructed seven-factor e-lifestyle scale with 39 items through the respondentscollected by the shopping mall intercept method. As suggested by Lee et al. (2009),factor loadings, composite reliability, and the average variance extracted (AVE) wereused to assess the convergent validities, while the discriminant validity was assessedby examining whether or not the AVE exceeds the shared variance between allpossible pairs of latent variables.

After running CFA using SPSS AMOS 18.0, this work found all factor loadings weresignificant at the level of p , 0:001, the composite reliabilities (CR) exceeded theacceptable criteria of 0.6 (Fornell and Larcker, 1981), and the AVEs for all latentvariables were greater than the threshold value of 0.5 (Forrnell and Larcker, 1981).Therefore, the convergent validities and discriminant validity were evidentlysupported for all latent variables, shown in Tables VI and VII. Notably, diagonalentries are the square roots of average variances extracted by the construct, whileoff-diagonal entries are correlation coefficients representing the associations betweenconstructs. As stated in literature (Lastovicka and Bonfield, 1980; Lewis et al., 2005),nomological validity refers to the ability of a construct to predict measures of otherconstructs within a system of related constructs. Hence, nomological validity in thiswork could be assessed by the correlation analysis of Table VII. Because Table VIIdisplays all the interrelationships among and between these constructs to besignificant, the nomological validity of the developed scale is empirically supported.

As suggested by literature (Kline, 1998; Klein et al., 2005; Zhu et al., 2009), this studyemployed the ratio of X 2 to its degree of freedom (df), root mean square error ofapproximation (RMSEA), Goodness of Fit Index (GFI), and Adjusted Goodness of FitIndex (AGFI), comparative fit index (CFI), Normed Fit Index (NFI), and Non-NormedFit Index (NNFI) to evaluate the adequate fit of the constructed e-lifestyle instrument.The CFA results show the seven-component solution comprises 61.02 percent of thevariance in the sample, the value of goodness of Fit statistics x 2=df 2:97 (,3),RMSEA 0:0076 (,0.08), GFI 0:91 (.0.9), AGFI 0:86 (.0.8), CFI 0:93(.0.90), NFI 0:91 (.0.9), and NNFI 0:92 (.0.90). Overall, the constructede-lifestyle scale of consisting nine-factors with 39 items appeared to fit the data well.That is, the scale indicated a good model-data fit, and the cross-validity of the 39-itemseven- component e-lifestyle scale was supported.

7. DiscussionAbundant research has observed ICT already permeates every aspect of peoples livestoday and the key to success for ICT products/services is to know human pattern.However, through thoroughly reviewing literature, this study discovered no researchhas directly assessed e-lifestyle even though a lot of lifestyle research exists. As thepast research (DeVellis, 2003; Klein et al., 2005) pointed out that without theory-basedscales, hypothesized relationships among consumer attitudes, perceptions, opinions,

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Latentvariables Observed variables (scale items)

Loadings(l values) CR AVE

F1 I frequently perform my job via ICT-enabledservices/products 0.836ICT-enabled services/products greatly enhance theconvenience of my life 0.835ICT-enabled services/products greatly improve myjob efficiency 0.821I frequently use ICT-enabled services/products toread news or get data 0.791 0.953 0.693I frequently shop or make purchase viaICT-enabled services/products 0.784I frequently do my banking or finances viaICT-enabled services/products 0.758The living environment has been influenced byICT, and I have benefited from the impact 0.749The working environment has been influenced byICT, and I have benefited from the impact 0.664The more time with ICT-enabled services/productsI spend, the more advantages I take 0.619

F2 I frequently spend a lot of time involved withICT-enabled services/products 0.871I stay updated as to the latest development inICT-enabled services/products 0.853I am very interested in discovering how to useICT-enabled services/ products 0.815I am very excited to know new ICT-enabledservices/products 0.781 0.920 0.659I like gaining knowledge regarding ICT-enabledservices/products 0.754Keeping alerts to the latest trends of ICT-enabledservices/products is very important 0.696

F3 I like ICT-enabled services/products involving inmy entertainment 0.796I frequently play games or listen to music viaICT-enabled services/products 0.792Using ICT-enabled services/products really giveme a lot of fun 0.738 0.942 0.767I frequently watch movies or sports viaICT-enabled services/products 0.699The leisure environment has been influenced byICT, and I have enjoyed from the impact 0.664

F4 I frequently chat via ICT-enabled services/products 0.877ICT-enabled services/products greatly enhanceinteraction among people 0.857ICT-enabled services/products greatly expand myfriends circle 0.758 0.914 0.682I frequently share my opinions via ICT-enabledservices/products 0.746I frequently participate in social events viaICT-enabled services/products 0.738

(continued )

Table VI.CFA results of the

constructed seven-factore-lifestyle scale

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227

behaviors and marketing strategies cannot be asserted, which may explain why duringthe past several decades hundreds of scales have been developed across domains.Consequently, the e-lifestyle instrument constructed and validated in this work couldoffer marketers a useful basis to execute more elaborate marketing research.

The empirical results show that the 39 items of the e-lifestyle scale were groupedinto seven distinct components. These components represented seven principal factorsthat significantly influence and shape individual e-lifestyles. Table V shows that theexplained variance of peoples e-lifestyle accounted by top three factors was17.288-19.316 percent, 10.112-10.940 percent, and 9.973-10.728 percent, while theexplained variance accounted by the lowest three factors was 4.136-4.209 percent,4.915-5.117 percent, and 6.909-7.313 percent. These figures illustrated that the influenceof each of these seven factors on stimulating individual e-lifestyles is unequal. Thisoutcome is consistent with the results of past studies, which concluded that the weightof each factor in influencing individual lifestyles is different rather than similar (Wanget al., 2006).

Latentvariables Observed variables (scale items)

Loadings(l values) CR AVE

F5 Continued development of ICT-enabled services/products is positive for our economy 0.835Continued development of ICT-enabled services/products is positive for our society 0.834Continued development of ICT-enabled services/products is positive for our education 0.809 0.925 0.711The more new knowledge regarding ICT-enabledservices/products I gain, the more advantages Itake 0.787Being able to use the newest ICT-enabled services/products gives me a sense of achievement 0.759

F6 The more the development on ICT-enabledservices/products, the more the pressures onhuman lives 0.827Continued development of ICT-enabled services/products has negative effect for our education 0.813I dont like my life to involve with too many ICT-enabled services/products 0.802 0.917 0.689Continued development of ICT-enabled services/products has negative effect for our society 0.764ICT-enabled services/products markedly decreaseface-to-face emotional interaction among people 0.772

F7 I like to share with people about new knowledge ofICT-enabled services/products 0.843Being able to use the newest ICT-enabled services/products makes me happy 0.825 0.914 0.598I like the challenge brought by ICT-enabledservices/products 0.786Keeping inaugurating new ICT-enabled services/products is very important 0.766Table VI.

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228

F1

F2

F3

F4

F5

F6

F7

F1:

nee

ds-

dri

ven

e-li

fest

yle

0.83

2F

2:in

tere

st-d

riv

ene-

life

sty

le0.

389

**

0.81

2F

3:en

tert

ain

men

t-d

riv

ene-

life

sty

le0.

175

**

0.22

5*

*0.

876

F4:

soci

abil

ity

-dri

ven

e-li

fest

yle

0.48

0*

*0.

415

**

0.21

1*

0.82

6F

5:p

erce

ived

imp

orta

nce

-dri

ven

e-li

fest

yle

0.38

2*

*0.

424

**

0.12

1*

*0.

351

**

0.84

5F

6:u

nin

tere

sted

orco

nce

rn-d

riv

ene-

life

sty

le2

0.10

3*

*2

0.15

5*

*2

0.07

6*

20.

0083

*2

0.12

5*

*0.

830

F7:

nov

elty

-dri

ven

e-li

fest

yle

0.34

5*

*0.

659

**

0.20

4*

*0.

363

**

0.37

6*

*2

0.07

8*

0.77

3

Notes:

*p,

0:05

,*

*p,

0:01

;n

793

Table VII.Discriminant validity and

correlations among theconstructs

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229

From the marketing perspectives, the weight of F1, need-driven e-lifestyle, inregards to motivating e-lifestyle, was twice that of F4, sociability-driven e-lifestyle,and four times that of F7, novelty-driven e-lifestyle. This result suggests that, in theICT-context market, the more a service or product relates to fulfilling consumers needsin both daily life and work, the higher the possibility of this service or productbecoming a fast-seller. This finding may explain why some ICT-enabled products/services (i.e. iPhone and iPad) have quickly become popular once they were launchedon the market, while other ICT-enabled products/services (i.e. digital interactivetelevision and digital photo frame) have grown relatively slowly. This implies that,when designing an ICT-enabled service/product, how well the product/service meetsthe needs of consumers work and life heavily influences its success in the market, andthat the most effective strategy for promoting ICT-enabled services/products is toillustrate their usefulness with regard to consumers needs in daily work and life.

With regard to triggering individual e-lifestyle, F2, interest-driven e-lifestyle, andF7, novelty-driven e-lifestyle, ranked 2nd and 7th in terms of influences. Becauseboth interest and novelty are psychological terms that represent a tendency to becomefamiliar with, learn about, and use ICT-enabled services/products, marketers couldcombine these factors for discussion. The theory of innovation diffusion proposed byRogers (2003) suggests that both novelty and interest are personal characteristics andinnate tendency. Accordingly, marketers can categorize consumers by analyzing theirtendencies, such as observing those who continually alert others to the latest trends ofICT-enabled services/products, those who frequently spend a great deal of timeinvolved with ICT-enabled services/products, and those who like to acquire knowledgeabout ICT-enabled services/products. This implies marketers could select suchconsumers as the first priority target customers in the initial market stage, and askthem for their advice on influencing other consumers to use the new launched serviceor product.

F3, entertainment-driven e-lifestyle, and F4, sociability-driven e-lifestyle,demonstrate that the popularity of an ICT-enabled service/product and the growthrate of its popularity depend heavily on how people feel about using theservice/product as a channel for playing games, listening to music, watching sportsand movies, sharing opinions, talking, making friends, and having fun. Theimplication derived from this result is that, in the ICT-context market, the more aservice/product relates to satisfy consumers needs for both entertainment andpersonal relationship, the more quickly this service/product becomes popular andsuccessful on the market. This finding may explain why some services (i.e. Facebookand blogs) quickly became popular once launched on the market, while other services(i.e. mobile banking and online banking) have grown relatively slowly.

F5, perceived importance-driven e-lifestyle, indicates positive expectance andopinion regarding ICT-enabled services/products and their impact on lives, while F6,uninterested or concern-driven e-lifestyle, indicates negative opinion and projectionregarding ICT-enabled services/products and their influence on lives. These twofactors are contrasting rather than reciprocal terms, and imply that marketers shouldnot neglect the negative effect (i.e. technology addiction, internet addition disorder)when promoting the benefits brought about by ICT. Therefore, when promotingICT-enabled services/products, marketers must be aware that a certain portion ofconsumers dislikes an ICT-enabled service/product heavily because of their concerns

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230

on potential negative effects brought by the service/product. Consequently, the firstimplication leads to how to dissolve negative concerns is another important factor foreffectively marketing an ICT-enabled service/product. Besides, since customers e-lifephilosophies are different, it is not easy to alter their lives in a short time. As noted byZeithaml et al. (2001), marketing efforts should concentrate on the 20 percent of regularconsumers who generate 80 percent of the business of a firm. The second implicationleads to that it is better to giving most efforts and resources to highly potential andvaluable customers rather than equal efforts and resources to all customers.

8. ConclusionsAt the beginning of the twenty-first century, some researcher institutes such asInstitute for the Future (Damodaran, 2001) have observed the ICT already impacts andpermeates every aspect of human life. Hence, the need of an e-lifestyle instrument tohelp marketers examine the relationships among consumers e-lifestyle, e-needs, andpurchase behaviors in the ICT domain is increasing and aware by some practitionersand researchers. Therefore, to discover main factors motivating consumerse-lifestyles, relative weights of those factors, and e-lifestyle patterns of the mostvaluable customers is a crucial task for marketers in the ICT sector. In line with thisthinking, beyond contributing to theoretical e-lifestyle scale, this paper also contributesto advance current knowledge on what factors influence e-lifestyle and relativeinfluences of main factors shaping e-lifestyle, and pave a way for marketers to executemore elaborate marketing research and differentiated strategies to highly potential andvaluable customers.

Limitations exist in every study and this research leaves room for futureimprovements. First, although a two-step EFA approach underlying 1,135 responseshas generated two EFA solutions to assist in judging the adequacy of the generatedfactors, and CFA to 793 respondents was used to examine and supports the fitness ofthe overall e-lifestyle scale, respondents from the second sampling were selected onlyaccording to age distribution of current population. Future research could selectrespondents by using stratified random sampling to reflect all demographicsdistribution of the Taiwanese population to examine and improve the reliability andvalidity of the e-lifestyle scale.

Second, because a great deal of research underlying general lifestyle instrumentshas been conducted in various domains during the past few decades, the relationshipsamong general lifestyle, consuming needs, and purchase behavior have beencomprehensively asserted. In contrast, the relationships among individual e-lifestyle,consuming needs, and purchase behavior in the ICT context have been notcomprehensively examined. Future studies could apply the e-lifestyle scale to differentdomains (i.e. tablet personal computer, e-reader, MP4 player, iPad4, online banking,and mobile shopping).

Third, cluster analysis has been widely used to segment the market and findopportunities for new product development (Punj and Stewart, 1983; Kaye-Blake et al.,2007), and lifestyle segmentation instruments have been shown to be especially usefulwhen combined with marketing variables such as media (Kamakura and Wedel, 2000;Brengman et al., 2005). Accordingly, further study could apply the e-lifestyle scale tothe execution of more elaborate marketing research, and cluster respondents to analyzesubgroups differences regarding ICT-enabled services/products. Fourth, this

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investigation merely represents a starting point in e-lifestyle research. To enhance thevalidity and generalization of the scale proposed in this study, further cross-culturalvalidation is necessary. Finally, the questionnaire statements in this paper wereoriginally written in Chinese and translated into English. Therefore, using thequestionnaires merits caution regarding the cultural and language differences.

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Further reading

Geen, R. (1994), Human Motivation: A Psychological Approach, Wadsworth Publishing, Belmont,CA.

Corresponding authorChian-Son Yu can be contacted at: csyu@mail.usc.edu.tw

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