Social network engagementand online consumer attitudes:

a psychosocial model Karmela Arbanasić

mentor: prof. dr. psych. Andrea Guazzini

co-mentor: prof. dr. sc. Goran Milas

Social influence

• Actual od perceived influence of a person or a group on an individual’s thoughts, actions and physical states (Aronson, Wilson and Akert, 2010 )

• Normative and informational influence

Social influence: Word of Mouth

• Word of mouth

• act of exchanging marketing information among consumers (Grewal, Cline, and Davies 2003)

• opinion seeking and opinion leading

Word of Mouth on Social Networks

• Several psycho-social variables play role in this kind of interaction (Chu and

Kim, 2011)

• social capital

• tie strength

• homophily (similarity to other contacts on SNS),

• Trust (to other contacts)

• susceptibility to interpersonal influence

Online word of mouth and consumer behavior

• Consumers that search product information online:

• Are more interested in purchasing online (Bickart and Schindler, 2001)

• Have more experience in purchasing online (Goldsmith and Horowitz, 2006)

• E-WOM enhances positive attitudes about brands by (Hung andYiyan Li, 2007):

• Increasing social capital of the group

• Enhancing consumer reflexitivity and thinking about product

• Providing consumers with knowledge about products

• Creating stronger sense of belonging to community

Online consumer behavior (Teo, 2015)

• Main reasons for buying online

• Buying things that are not available in our country

• Time and cost efficiencies

• Main reasons not to buy online

• Security of financial transactions

• Quality of the products

Factors

• Usage of information communication technologies (ICT) and social networks (SNS)

• Attitudes toward social networks

• Sense of virtual community

• Susceptibility to interpersonal influence

• Self – monitoring

• Social self – efficacy

• Personality

ICT and SNS usage

• Opinion seeking

• Internet social connection was positevely related to searching information aboutproducts online (Mourali, Laroche and Pons, 2005)

• Online shopping attitudes

• Online trust and prior online purchase were found to be positively correlated withattitudes toward online purchase (Choon Ling, Teck Chai and Hoi Piew, 2010)

Sense of virtual community

• SOVC reinforces influence of the online comments within consumercommunity on purchase decisions (Huang, Hsiao and Chen, 2012)

Susceptibility to interpersonal influence

• Engagement in e – WOM

• Normative influence as significant predictor for opinion giving, opinion seeking andpass-along behavior (Chu and Kim, 2011)

• Consumer behavior

• Relationship between susceptibility and clothes purchasing decisions (Rose, Boush and Friestad, 1998)

Self – monitoring

• Opionon leading (Rose and Kim, 2011)

• Opinion leaders are motivated by social status for engaging in e-WOM and have higherresults on self-monitoring

• Consumer behavior (Cass, 2001)

• More self – aware people are more concerned about their appearance and aboutimpression that product creates about them

• Their purchase decisions are more driven by motive for social approval and pleasure consumption motives

Personality

• Willingness to buy online (Bošnjak, Galešić and Tuten, 2006)

• higher affective involvement

• lower enjoyment in cognitively demanding tasks

• higher emotional instability

• lower agreeableness

• higher openness for new experiences

• Extraversion has found to be one of strongest predictors of engaging in e-WOM (Hennig-Thurau, Gwinner, Walsh and Gremler, 2004)

Research objectives

1. Examine the relationship between engagement in online word of mouth on social networks and attitudes toward online shopping

2. Examine relationship of usage and perception of social networks with online word of mouth and attitudes toward online shopping

3. Examine relationship of social variables on social networks with online word of mouth and attitudes toward online shopping

4. Examine relationship of personal variables with online word of mouth and attitudes toward online shopping

Sample

• Non – probabilistic, voluntary sample

• 105 participants

• Italians or non – Italians with residency in Italy

• Active members of social networks

Construct Subscales Instrument Authors Year

Internet social connectionconfidence

- Sun, T., Youn, S., Wu, G., & Kuntaraporn, M. 2006.

Prior experience with socialnetworks

- Ling, K. C., Chai, L. T., & Piew, T. H. 2010.

Perception of social networks Semantic differential Osgood, C.E. 1957.

Online Word of Mouth Opinion givingOpinion seekingPass-along behavior

Opinion giving scaleOpinion seeking scaleOnline forwarding scale

Flynn et al. Flynn et al.Sun et al.

1996.1996.2006.

Attitudes toward online purchase

External search effortPerceived benefits of searchInterest in e-commerceDeal evaluationPerceived risk

Scale of attitudes towardonline shopping

Teo, T. S. 2002.

Personality ExtraversionNeuroticismAgreeablenessConsciousnessOpeness to experience

I-TIPI (Italian translation) Chiorri, C., Bracco, F., Piccinno, T., Modafferi, C., and Batin, V.

2014.

Self-monitoring Adjusted self-presentationSituation variabilitySocial appropriateness

Self-monitoring scale Synder, M. and Gangestad S.W. 1986.

Sense of virtual community scale - Sense of community scale Rovai, A. P., Wighting, M. J., & Lucking, R. 2004.

Social self-efficacy - Scale of perceived social self-efficacy

Smith, H. M., & Betz, N. E. 2000.

Consumer susceptibility to interpersonal influence

Normative influenceInformational influence

Scale of interpersonal influence Bearden et al. 1989.

Procedure

• Online survey

• 20 minutes

• Mail and Facebook

Statistical analysis

• Frequencies/percentages

• Means and standard deviations

Descriptive statistics

• Chi – square

• Correlations

• ANOVAUnivariate statistics

• Multiple regression (subscales)

• Generalized linear modelling(whole scales)Multivariate statistics

Results

Sample description - demographics

36%

64%

GENDER

Male Female

42%

9%

47%

2%0%

MARITAL STATUS

Single In a relationship Married/In cohabitation Divorced WidowedMedium age: 27,8 years

Sample description: demographics

4%

50%

15%

5%

10%

16%

EMPLOYMENT STATUS

Unemployed Student Student and working

Occasional worker Temporary worker Permanent worker

2% 3%

8%

12%

75%

INTERNET USAGE

Never Rarely Enough Frequently Always

Medium annual household income: cca 33 000€

ICT and SNS usage

* 60% of participants uses Internet for shopping, but only 13% discusses products on social networks

Most frequently used device Smartphone

Most frequently used web service e-mail, social network, search tools

Most prevalent context of usage Free time

Most popular social network Facebook

Most frequent activity on SNS reading news and comments on timeline

Most discussed topic on SNS academic topics

Average duration of membership on SNS 2 – 5 years

Average number of daily visits 1 – 10 per day

Average duration of daily visits 1 – 2 hours per day

E – WOM and OS attitudes: correlations

Search effort Perc. Benefitsof search

Interest in e-commerce

Dealevaluation

Positiveattitudes

Opinion giving .228* .452*** .200* .331***

Opinionseeking

.251** .488*** .356***

Pass-alongbehavior

.214* .385*** .263**

• Scale of positive attitudes was correlated to all three subscales of e-WOM• Subscales of search effort and interest in e-commerce were correlated to all three subscales of e-

WOM

Results: ICT/SNS usage and e – WOM: correlations

Opinion giving Opinion seeking Pass-along e-WOM total

Number of SNS .239* .288** .271**

Frequency of daily visits

to SNS.218*

Duration of daily visits

to SNS.287** .231* .323***

Frequency of

communication on SNS.280** .313***

Importance of contacts

on SNS.225* .266**

Feeling of connection on

SNS.262**

• Pass-along behavior didn’t show any significant correlations to SNS usage• Opinion giving was correlated to prion experience with social networks, and opinion

seeking was correlated with social connection confidence on social networks

*: p. < 0.05; **: p. < 0.01; ***: p. < 0.001

Psychological/psychosocial variables and e-WOM: correlations

Opinion giving Opinion seeking Pass-along behavior e-WOM total

Agreeableness -.219* -.195*

Sense of virtual community .426*** .364*** .446***

Susceptibility: normative

influence-.277**

Susceptibility: informational

influence.260** .316***

Susceptibility: total .262** -.218* .226*

SM: adjusted self-presentation .382***

SM: situational variability .334***

SM: total .638***

*: p. < 0.05; **: p. < 0.01; ***: p. < 0.001

• Susceptibility to interpersonal influence and sense of virtual community showed most connections with e-WOM subscales

• Pass – along behavior shows moderate to high correlations with self - monitoring

Results: ICT/SNS usage and OS attitudes: correlations

Search effort Benefits of search Interest in e-commerce Deal evaluation OS positive attitudes

Number of SNS .253**

Frequency of Internet usage .248*

Frequency of daily visits to

SNS.260**

Duration of daily visits to

SNS.257** .278** .236** .306**

Frequency of

communication on SNS.221*

SNS prior experience total .224* .218*

SNS social connection

confidence total.270** .216* .215* .282** .336***

*: p. < 0.05; **: p. < 0.01; ***: p. < 0.001

• Search effort was most correlated to ICT and SNS usage• Social connection confidence on social networks was correlated to all subscales of positive

attitudes about OS

Psychological/psychosocial variables and OS attitudes: correlations

Search effort Benefits of search Interest in e-commerce Deal evaluation OS positive attitudes

Agreeableness .241*

Conscientiousness .227*

Sense of virtual

community.312*** .265** .215* .321***

Susceptibility: normative

influence.222*

Susceptibility:

informational influence.348*** .446*** .305** .407***

Susceptibility: total .235* .267** .306** .307***

SM: situational variability -.252**

SM: total -.242*

*: p. < 0.05; **: p. < 0.01; ***: p. < 0.001

• Interest in e – commerce was showed most connections with psycho – social variables• Susceptibility to interpersonal influence and sense of virtual community showed most correlations to

subscales of attitudes about OS

Online word of mouth: generalized linear modelling

General model fitting

df Likelihood Ratio χ2

Online word of mouth - Model 8 89.48***

Fixed effects and parameters

Factor β Wald χ2 (F)

Topic discussed on SNS: Gossips (Yes) 0.089 3.33*

Topic discussed on SNS: Products (Yes) 0.13 3.42*

Duration of daily usage of SNS 0.069 6.17*

SNS perception: safe – dangerous 0.028 4.52*

SNS perception: unreliable - reliable 0.025 3.2*

OS: Interest in e-commerce 0.061 21.98***

Agreeableness -0.055 11.1***

Sense of virtual community 0.019 21.65***

*: p. < 0.05; **: p. < 0.01; ***: p. < 0.001

• People who engage in e-WOM: gossip more on social networks, spend more time on social networks, are more interested in e-commerce and are less agreeable

Attitudes toward online shopping: generalized linear modelling

General model fitting

df Likelihood Ratio χ2

Online shopping - Model 8 89.61***

Fixed effects and parameters

Factor β Wald χ2 (F)

Devices used: Tablet (Yes) 2.365 5.75*

Context of internet usage: Shopping (Yes) 2.931 10.41***

Daily usage of SNS: duration 1.701 11.97***

e-WOM: opinion giving 0.146 3.49*

Conscientiousness 0.548 4.17*

Susceptibility to informational influence 0.488 12.82***

*: p. < 0.05; **: p. < 0.01; ***: p. < 0.001

• People with positive attitudes abut online shopping: are more likely to possess tablet, use Internet more for online shopping, spend more time on social networks

Implications of the research

• Pointing out the possible role of Sense of virtual community in online Word of mouth that should be further investigated

• Finding that Internet social connection confidence positively correlates withattitudes to online shopping

• Finding that different kinds of social influence are related to differentaspects of attitudes to online shopping

Limitations of the research

• Small and unbalanced sample

• Dichotomization of continuous variables before conducting Chi-square andANOVA

Conclusion

• Engagement in e-WOM is positively related to positive attitudes about online shopping

• ICT and SNS usage correlated most with opinion seeking and search effort

• Sense of virtual community and susceptibility to interpersonal influence showed positiverelationships with engagement in online word of mouth and attitudes toward online shopping

• Self-monitoring didn’t show major relationships, except with pass-along behavior

• Agreeableness and conscientiousness were positively related to attitudes to online shopping

• Social self-efficacy didn’t show any significant correlations

Literature

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