9 dis

Carrying Forward the Uses and Grats 2.0 Agenda: An Affordance-Driven Measure of

Social Media Uses and Gratifications

Chamil Rathnayake and Jenifer Sunrise Winter

The notion of social media affordances has not been fully integrated into the uses and gratifications literature. Building on the MAIN (modality, agency, interactivity, and navigability) model, this study develops and tests a social media uses and gratifications scale with a sample of 393 college students. Results of the study support the MAIN model, as conceptualizing social media uses and gratifications as a second-order factor structure with 4 different types of affordances displays similar goodness-of-fit to a single-order factor structure. A confirmatory factor analysis with a second sample of 313 adults further confirms the applicability of the scale among the general population.

Uses and Gratifications (U&G) is a much-debated theoretical approach that has its origins in media effects studies (Ruggiero, 2000). This approach has direct implica- tions for understanding the dynamics of social media consumption. Examining social media uses and gratifications requires identifying unique uses and gratifications, developing and validating measures, and testing them across different user groups and platforms. Several early studies highlighted the importance of examining uses and gratifications in the context of computer-mediated communication (e.g., Ruggiero, 2000). There is a growing body of literature examining various aspects of uses and gratifications in different online settings, such as social network sites (e.g., Alhabash, Chiang, & Huang, 2014; Apaolaza, He, & Hartmann, 2014; Chen & Kim, 2013; Huang, Hsieh, & Wu, 2014; Pai & Arnott, 2013; Smock, Ellison, Lampe, & Wohn, 2011), online games (e.g., Wu, Wang, & Tsai, 2010), micro-blogs (e.g., Chen, 2011), crowd-sourced business review sites (e.g., Hicks et al., 2012), social recom- mendation systems (e.g., Kim, 2014), Web-based information services (e.g., Luo &

Chamil Rathnayake (Ph.D., University of Hawaii) is a Lecturer in Media and Communication at the University of Strathclyde, Glasgow. His research interests include cross-ideological exposure online, issue- response networks on social media, and social network analysis.

Jenifer Sunrise Winter (Ph.D., University of Hawaii) is an associate professor in the School of Communications at the University of Hawaii, Manoa. Her research addresses the Internet as a support for good governance, information policy, and digital inequalities and algorithmic discrimination in the context of big data and the Internet of Things.

© 2018 Broadcast Education Association Journal of Broadcasting & Electronic Media 62(3), 2018, pp. 371–389 DOI: https://doi.org/10.1080/08838151.2018.1451861 ISSN: 0883-8151 print/1550-6878 online

371

Remus, 2014), and social media in general (e.g., Leung, 2013; Wang, Tchernev, & Solloway, 2012).

Although the body of social media U&G literature is diverse in terms of measures used and platforms and populations examined, many studies tend to use more user- centered measures such as socializing, entertainment, information sharing, escapism, need to connect, and convenience (e.g., Apaolaza et al., 2014; Chen, 2011; Hicks et al., 2012; Huang, Hsieh, & Wu, 2014; Smock et al., 2011). Sundar and Limperos (2013) identify this issue and claim that new media studies tend to be biased towards social and psychological factors rather than medium-related factors. They suggest that researchers need to focus on the technologies themselves and the new uses and gratifications they create. They suggest 16 types of gratifications based on 4 classes of social media affordances (modality, agency, interactivity, and navigability) and items that can be used to measure those gratifications under the MAIN model. This study develops a measure of social media uses and gratifications using the

conceptualizations suggested by Sundar and Limperos (2013). The scope of the study extends beyond a mere validation of the original model and items suggested, build- ing on the original framework by refining the items, introducing new items specifi- cally tailored to social media, and testing them to develop a measure of social media uses and gratifications.

Social Media Uses and Gratifications

Social media literature includes a considerable number of studies that examine different types of uses and gratifications perceived by the users. Pai and Arnott (2013) highlight the need to differentiate social network sites from other online communities and claim that users adopt them to attain four values (belonging, hedonism, self- esteem, and reciprocity). Ancu and Cozma (2009) claim that social interaction is the primary motive for accessing profiles of political candidates, while entertainment and information-seeking are secondary. Quan-Haase and Young (2010) argue that Facebook users tend to seek enjoyment and knowledge about social activities within their networks, while instant messaging is focused more on relationship building and maintenance. Smock et al. (2011) suggest that, while only three motivations predict general Facebook use, a relatively higher number of variables can predict motiva- tions for using specific features such as status updates, comments, chat, and groups. Krause, North, and Heritage (2014) examine motives for listening to music on Facebook and identify three gratifications: entertainment, communication, and habi- tual diversion.

The Need for Platform-Oriented Measures

As social media are different from traditional media, they may be able to generate new uses and gratifications. Accordingly, social media U&G studies need to focus on

372 Journal of Broadcasting & Electronic Media/September 2018

at least two aspects. First, it is necessary to evaluate recent social media U&G literature in terms of its coverage of unique uses and gratifications. Second, uses and gratifications measures need to be evaluated in terms of their coverage of user- based and platform-based gratifications. The notion of social media affordances has recently gained more research atten-

tion. Generally defined, affordances relate to the mutuality between social media users and features of the platforms that enable or constrain behavior. Majchrzak, Faraj, Kane, and Azad (2013) identify technology affordances as “the mutuality of actor intentions and technology capabilities that provide the potential for a particular action” (p. 39). According to boyd (2011), affordances can destabilize core assump- tions related to engaging in social life and reshape publics directly and indirectly. boyd notes that affordances such as persistence, replicability, scalability, and search- ability of online content can play a significant role and help scholars understand why people engage the way they do. Sundar and Limperos (2013) stress the importance of conceptualizing affordance-

based uses and gratifications. They highlight that new media U&G studies have only slightly modified older media gratifications to suit new media, and examine 20 studies published between 1940 and 2011 to reveal overlaps in measures across different types of new media. Sundar and Limperos argue that nuanced, and perhaps new, gratifications have not been fully specified. The collection of studies they reviewed covers a broad range of technologies or media, such as radio talk shows, telephone, newspapers, TV, video games, and social network sites. While these studies help the authors to argue that affordance-based measures are necessary, they may not represent what is commonly known as social media. Social media U&G measures should take into consideration the interactive nature

of social media platforms, the ability of users to create and manage content, and the wide variety of features available. Measures such as socializing (Apaolaza et al., 2014), virtual community (Chen & Kim, 2013), socialization-seeking (Kim, 2014), interpersonal utility (Luo & Remus, 2014), reciprocity (Pai & Arnott, 2013), expres- sive information sharing, companionship, professional advancement, social interac- tion, meeting new people (Smock et al., 2011), career opportunities, global exchange (Roy, 2009), surveillance (Zhang & Zhang, 2013), self-status seeking (Park, Kee, & Valenzuela, 2009), and spiritual support, psychological support, and networking (Anderson, 2011) cover a broad range of unique gratifications. Social media uses and gratifications can be classified as either user-oriented or

platform-oriented. User-orientation puts less emphasis on the features or affordances of the platform. For instance, many of the aforementioned gratifications focus only on the user. Platform-oriented uses and gratifications take into consideration the features of the platform or the affordances they offer. For example, blog ambiance gratifica- tion (Kaye, 2010) acknowledges that the users enjoy the overall atmosphere of a blog. Although the measures used in previous studies adequately cover user-oriented uses and gratifications (e.g., socializing, psychological support, and surveillance) that have social and psychological origins, they lack coverage of social media/platform- oriented measures.

Rathnayake and Winter/CARRYING FORWARD THE USES AND GRATS 2.0 AGENDA 373

The MAIN model (Sundar, 2008) suggests that four affordances (Modality, Agency, Interactivity, and Navigability) are prevalent in contemporary digital media, and they are able to cue cognitive heuristics (i.e., judgment rules) that can shape user assess- ment of the medium. The MAIN model rejects the idea that all gratifications relate to innate needs and argues that distinctive gratifications can emerge from new media affordances. In particular, Modality, defined by Sundar and Limperos as “different methods of presentation (e.g., audio or pictures) of media content, appealing to different aspects of the human perceptual system (e.g., hearing, seeing),” acknowledges that the Internet can provide users with content in multiple modalities and some of them can be considered unique (e.g., pop-up ads) (p. 512). The Agency affordance of the MAIN model suggests that the Internet allows users to be agents or sources of information, and this view recognizes the ability of users to be gatekeepers of content, build commu- nities, and contribute. Interactivity, according to Sundar (2008), is the most distinctive affordance of digital media, and it relates to interaction and activity on a given medium. Sundar and Limperos (2013) define Interactivity as “the affordance that allows the user to make real-time changes to the content in the medium” (p. 515), and Navigability as “the affordance that allows user movement through the medium” (p. 516). Sundar (2008) notes that metaphors like “site” and “cyberspace,” the availability of naviga- tional aids and content generated—and even the mere presence of navigational aids such as hyperlinks—can trigger certain heuristics. The MAIN model is suitable to examine social media uses and gratifications for

several reasons. First, it focuses on the capacity of the Internet-based platforms to provide a range of usage or engagement options. Second, it suggests platform- oriented gratifications that have not been discussed in previous studies. Third, the model is comprehensive. Therefore, the MAIN model provides a rich approach to understand new media uses and gratifications. However, it has not yet been subject to adequate academic inquiry, validated, and tested across different populations.

Method

Item Development

For this study, social media were defined as online platforms where users can interact with each other, build networks, share and create content, publish, and make comments. Social media include a range of platforms, such as Facebook, Twitter, YouTube, Instagram, Tumblr, Reddit, and WordPress. The survey instrument provided a definition of social media to respondents to ensure that they reflected on interactive platforms without restricting their responses to a specific platform. The set of 57 items suggested by Sundar and Limperos (2013) was used as the

initial pool of items to develop the suggested scale. As these items were not targeted towards a particular technology, contain general statements, and focus on devices, they were re-written specifically to focus on social media. For instance, the item “I feel like I am able to experience things without actually

374 Journal of Broadcasting & Electronic Media/September 2018

being there” was re-worded as “Social media provide quality information that makes me feel like I am able to experience things as they are without actually being there.” In addition, 15 original items were introduced. For instance, items such as “Social media help me to have real interactions with people although I am not in physical proximity” and “Social media allow me to actively contribute to communities that make an impact on society” were added to capture the unique- ness of social media. The final set of items included 72 statements tailored to measure the constructs in the context of social media. These items were checked by three independent readers to evaluate the clarity and meaning.

Data Collection

Two online surveys were conducted to collect data for the study. The first survey was conducted among students in 15 classes at a national research university and two other four-year campuses in the State of Hawaii. The respondents were recruited through instructors who agreed to distribute the survey among their students. Surveys with missing entries were excluded, yielding a sample for analysis consisting of 393 respondents. The demographics of the sample paralleled the demographics of the universities, including undergraduate and graduate students. The sample was appro- priate for examining social media uses and gratifications, as more than 90% of the respondents indicated that they use social media at least once a day. A second survey was conducted through Qualtrics Research Services to collect data from a more diverse and representative sample. The second sample included 313 adults who represented different age groups, education levels, and ethnic groups.

Identification and Refinement of Latent Constructs

Four exploratory factor analyses (maximum likelihood with Varimax rotation) were conducted using the first sample to identify the best items to measure constructs representing each class of affordance. Factor analysis was repeated, after dropping items with low factor loadings, until an acceptable solution was reached. This analysis resulted in a reduced pool of 42 items that had high correlations (at least .5, except for a few items) with the factors that they were hypothesized to operationalize. Several items with loadings below .50 were retained as they had loadings close to .50 and at least three items were used to operationalize each construct. Items that loaded on to a different factor and had low cross-loadings were identified as new items that could operationalize the latter factor.

Rathnayake and Winter/CARRYING FORWARD THE USES AND GRATS 2.0 AGENDA 375

Assessment of the Latent Factor Structure

The items used for exploratory factor analysis (EFA) were hypothesized to exhibit a latent factor structure that corresponds to Sundar and Limperos’s (2013) conceptua- lization of affordance-based uses and gratifications. The EFA provides a preliminary test to determine the items that best reveal latent factor structure for each MAIN dimension. These results were verified by conducting a confirmatory factor analysis (CFA), ensuring that a factor structure that includes all constructs shows reasonable fit. The theoretical foundation provided by the MAIN model extends beyond a first- order factor structure, as the constructs represent four different classes of affordances. Therefore, a second-order factor structure was tested to examine whether the con- structs suggested by initial analysis correspond with the MAIN model. CFAs focused on validating the scale along two dimensions: 1) social media uses and gratifications as a first-order factor structure that examined the goodness-of-fit of the operationa- lization of constructs, and 2) a second-order factor structure that examines the goodness-of-fit of classifying constructs tested in the first model under the four classes of affordances suggested in the MAIN model. This two-step analysis allowed exam- ination of the suitability of individual items to operationalize each construct as well as constructs to represent each class of affordance. An additional CFA was con- ducted with the second sample to further test the validity of the scale.

Results and Discussion

Identification of Latent Constructs

Factor loadings provided in Table 1 show that the factors identified in each solution suggest several distinct dimensions for each class of affordance. While these models support the original conceptualization, they indicate some deviations from the original factors and items suggested by Sundar and Limperos (2013). The first factor analysis provided a three-factor solution (Realism, Coolness, and Being There) that included two newly introduced items (REAL 4: I can experience the real world through social media, and BEIN 5: Social media help me to have real inter- actions with people although I am not in physical proximity). BEIN 5 was retained despite low loading, as it can allow more modifications in the CFA. The survey included five and four items to measure Coolness and Novelty,

respectively; two Coolness items and three Novelty items loaded into one factor. While both Coolness items suggested by Sundar and Limperos (2013) and the newly introduced items capture aspects of Coolness, Novelty items triggered a very similar perception among respondents. This might result from the striking similarity between the two factors. Sundar, Tamul, and Wu (2014) note that “an interface is cool if it is novel” (p. 177). For instance, distinctiveness (COOL2) and differences in interface

376 Journal of Broadcasting & Electronic Media/September 2018

Ta bl e 1

Fa ct or

Lo ad

in g,

R el ia bi lit y,

an d D es cr ip tiv

e St at is tic

s (S am

pl e 1)

C on

st ru ct

Ite m

Ite m

m ea

n Ite

m SD

Fa ct or

lo ad

in g

C on

st ru ct

m ea

n, SD

, an

d α

EF A 1

R ea

lis m

R EA

L2 — C om

m un

ic at in g us in g so ci al

m ed

ia is no

tt ha

td iff er en

tf ro m

fa ce

-to -

fa ce

co m m un

ic at io n.

1. 92

4 .8 86

.5 80

M ea

n: 2. 03

SD : .7 38

α: .7 05

R EA

L3 — Th

e ex

pe rie

nc e in

so ci al

m ed

ia is ve

ry m uc

h lik

e re al

lif e.

1. 93

9 .9 36

.8 57

R EA

L4 — I ca

n ex

pe rie

nc e th e re al

w or ld

th ro ug

h so ci al

m ed

ia .

2. 21

5 1. 02

0 .5 56

C oo

ln es s

C O O L1

— So

ci al

m ed

ia pl at fo rm

s ar e un

iq ue

co m pa

re d to

ot he

r m ed

ia .

3. 69

7 .8 26

.8 16

M ea

n: 3. 75

SD : .5 89

α: .7 62

So ci al

m ed

ia pl at fo rm

s ar e di st in ct iv e co

m pa

re d to

ot he

r m ed

ia .

3. 71

1 0. 72

6 .7 40

So ci al

m ed

ia pl at fo rm

s ar e ne

w co

m pa

re d to

ot he

r m ed

ia .

3. 70

8 .8 21

.4 59

So ci al

m ed

ia pl at fo rm

s ha

ve in no

va tiv

e fe at ur es .

3. 84

1 .6 74

.5 08

So ci al

m ed

ia in te rfa

ce s ar e di ffe

re nt

th an

tra di tio

na l W

eb si te s.

3. 84

4 .6 84

.5 06

B ei ng

th er e

So ci al

m ed

ia he

lp m e im

m er se

m ys el f in

pl ac

es th at

I ca

nn ot

ph ys ic al ly

ex pe

rie nc

e. 3. 44

3 1. 00

9 .7 22

M ea

n: 3. 25

SD : .8 86

α: .8 02

So ci al

m ed

ia cr ea

te th e ex

pe rie

nc e of

be in g pr es en

ti n di st an

te nv

iro nm

en ts . 3. 31

4 1. 03

2 .8 11

So ci al

m ed

ia pr ov

id e qu

al ity

in fo rm

at io n th at

m ak

es m e fe el

lik e Ia

m ab

le to

ex pe

rie nc

e th in gs

as th ey

ar e w ith

ou t ac

tu al ly

be in g th er e.

2. 99

1 1. 01

6 .7 23

EF A 2

A ge

nc y

B EI N 5—

So ci al

m ed

ia he

lp m e to

ha ve

re al

in te ra ct io ns

w ith

pe op

le al th ou

gh I am

no t in

ph ys ic al

pr ox

im ity

. 3. 37

3 1. 04

9 .4 23

A G N C 1—

So ci al

m ed

ia al lo w

m e to

fre el y ex

pr es s m y op

in io ns .

3. 56

6 .9 18

.8 39

M ea

n: 3. 58

SD : .7 59

α: .8 32

A G N C 2—

So ci al

m ed

ia al lo w

m e to

fre el y as se rt m y id en

tit y.

3. 48

3 .8 97

.7 74

A G N C 5—

So ci al

m ed

ia al lo w

m e to

ha ve

m y sa y.

3. 69

6 .8 12

.6 12

(c on

tin ue

d )

Rathnayake and Winter/CARRYING FORWARD THE USES AND GRATS 2.0 AGENDA 377

Ta bl e 1

(C on

tin ue

d)

C on

st ru ct

Ite m

Ite m

m ea

n Ite

m SD

Fa ct or

lo ad

in g

C on

st ru ct

m ea

n, SD

, an

d α

C om

m un

ity bu

ild in g

C M N B 2—

So ci al

m ed

ia he

lp m e to

be pa

rt of

a co

m m un

ity th at

I w ou

ld no

t ot he

rw is e ha

ve be

en pa

rt of .

3. 71

0 .8 96

.7 15

M ea

n: 3. 68

SD : .7 12

α: .7 87

C M N B 3—

So ci al

m ed

ia al lo w

m e to

bu ild

a ne

tw or k th at

co ul d br in g m e

so ci al

su pp

or t.

3. 77

5 .8 07

.7 93

C M N B 4—

So ci al

m ed

ia al lo w

m e to

ac tiv

el y co

nt rib

ut e to

co m m un

iti es

th at

m ak

e an

im pa

ct on

so ci et y.

3. 55

4 .8 59

.5 35

B an

dw ag

on B A N D 2—

So ci al

m ed

ia co

m fo rt m e by

le tti ng

m e kn

ow th e th ou

gh ts an

d op

in io ns

of ot he

rs .

3. 39

9 .9 30

.4 95

M ea

n: 3. 36

SD : .7 49

α: .6 84

B A N D 4—

R ea

di ng

ot he

rs co

m m en

ts on

so ci al

m ed

ia be

fo re

I m ak

e co

m m en

ts he

lp s m e to

av oi d po

te nt ia l co

nf lic

ts .

3. 63

6 .8 93

.6 84

B A N D 5—

I try

to ad

ju st m y re ac

tio ns

to so ci al

m ed

ia po

st s ba

se d on

co m m en

ts m ad

e by

ot he

rs .

3. 05

6 1. 03

7 .7 23

Fi lte

rin g

FI LT

3— So

ci al

m ed

ia al lo w

m e to

so rt th ro ug

h in fo rm

at io n be

fo re

I sh ar e it

w ith

ot he

rs .

3. 68

8 .7 68

.5 60

M ea

n: 3. 83

SD : .5 45

α: .6 40

FI LT

4— So

ci al

m ed

ia al lo w

m e to

lim it th e vi si bi lit y of

in fo rm

at io n Ip

os tt o a

sm al l gr ou

p. 3. 74

1 .8 89

.7 09

O W

N N 3—

M y fri en

ds ha

ve th ei r ow

n w ay

s of

us in g so ci al

m ed

ia .

4. 06

4 .6 37

.4 84

EF A 3

In te ra ct io n

IN TR

1— So

ci al

m ed

ia re ly

on us er

in te ra ct io n.

4. 13

0 .7 55

.5 60

M ea

n: 3. 70

SD : .6 03

α: .5 70

IN TR

2— W

he n I us e so ci al

m ed

ia , I fre

qu en

tly in te ra ct

w ith

th e pl at fo rm

. 3. 47

0 .8 96

.5 36

IN TR

3— O n so ci al

m ed

ia , I ca

n sp ec

ify m y ne

ed s an

d pr ef er en

ce s on

an on

go in g ba

si s.

3. 50

0 .8 29

.3 86

378 Journal of Broadcasting & Electronic Media/September 2018

A ct iv ity

A C TV

1— I ca

n pe

rfo rm

a nu

m be

r of

ta sk s on

so ci al

m ed

ia .

3. 68

0 .7 65

.5 26

M ea

n: 3. 08

SD : .8 01

α: .7 75

A C TV

2— I fe el

ac tiv

e w he

n I us e so ci al

m ed

ia .

2. 91

0 1. 06

9 .6 79

A C TV

3— M y In te ra ct io n on

so ci al

m ed

ia is no

t pa

ss iv e.

3. 01

9 .9 12

.6 06

A C TV

4— I ge

t to

do a lo t of

th in gs

on so ci al

m ed

ia .

3. 30

9 .9 60

.6 55

R es po

ns iv en

es s

R ES

P2 — So

ci al

m ed

ia ar e re sp on

si ve

to m y co

m m an

ds .

3. 20

2 .8 56

.7 06

M ea

n: 3. 12

SD : .7 44

α: .8 35

R ES

P3 — So

ci al

m ed

ia re sp on

d w el l to

m y re qu

es ts .

3. 20

1 .8 22

.7 83

D Y N M 1—

So ci al

m ed

ia al lo w

m e to

be in

ch ar ge

. 2. 97

1 .9 47

.5 95

D Y N M 2—

So ci al

m ed

ia gi ve

m e m or e co

nt ro l ov

er in fo rm

at io n I po

st .

3. 55

0 .8 17

.5 68

D Y N M 3—

Ia m

ab le

to co

nt ro lm

y in te ra ct io n w ith

th e in te rfa

ce s of

th e so ci al

m ed

ia pl at fo rm

s I us e.

3. 55

0 .7 71

.6 05

EF A 4

B ro w si ng

B R O W

1— So

ci al

m ed

ia al lo w

m e to

ob ta in

a w id e va

rie ty

of in fo rm

at io n.

3. 77

6 .8 44

.7 82

M ea

n: 3. 80

SD : .6 29

α: .8 80

B R O W

2— So

ci al

m ed

ia ca

n lin

k m e to

si te s th at

ha ve

di ffe

re nt

ty pe

s of

in fo rm

at io n.

3. 93

3 .7 02

.7 58

B R O W

3— So

ci al

m ed

ia al lo w

m e to

su rf fo r th in gs

th at

I am

in te re st ed

in .

3. 64

5 .7 66

.7 74

B R O W

4— So

ci al

m ed

ia al lo w

m e to

br ow

se fre

el y.

3. 73

6 .8 28

.5 90

SC A F2

— So

ci al

m ed

ia al lo w

m e to

lin k to

ot he

r pi ec

es of

in fo rm

at io n.

3. 80

5 .6 78

.6 63

SC A F3

— So

ci al

m ed

ia of fe r a nu

m be

r of

vi su al

ai ds

fo r m or e ef fe ct iv e us e.

3. 81

5 .7 41

.6 82

Pl ay

PL A Y 3—

I en

jo y es ca

pi ng

in to

a di ffe

re nt

w or ld

th ro ug

h so ci al

m ed

ia .

3. 34

7 1. 00

6 .5 25

M ea

n: 3. 50

SD : .7 78

α: .8 05

PL A Y 4—

So ci al

m ed

ia pr ov

id e m or e en

te rta

in in g in fo rm

at io n th an

ot he

r m ed

ia .

3. 54

6 .8 88

.8 65

PL A Y 5—

So ci al

m ed

ia of fe r m or e en

te rta

in in g fe at ur es

th an

ot he

r m ed

ia .

3. 60

9 .8 52

.8 57

Rathnayake and Winter/CARRYING FORWARD THE USES AND GRATS 2.0 AGENDA 379

(NOVL3) are highly related so that they load into one factor. Accordingly, combining Coolness and Novelty into one factor does not jeopardize the quality of the model. To measure agency-based social media uses and gratifications, the survey gener-

ated a four-factor solution (Agency, Community Building, Bandwagon, and Filtering), which is slightly different from the original five-factor conceptualization. This solu- tion builds on the work of Sundar and Limperos (2013), as the items retained include five items that were newly introduced to the scale (AGNC1: social media allow me to freely express my opinions, CMNB3: social media allow me to build a network that could bring me social support, BAND4: reading others comments on social media before I make comments helps me to avoid potential conflicts, BAND5: I try to adjust my reactions to social media posts based on comments made by others, FILT4: social media allow me to limit the visibility of information I post to a small group). These items were created to capture social media’s ability to engage users and facilitate action. Several items were also revised for the current study context, including the ability to facilitate formation of social groups beyond geographic boundaries. A main difference between the original conceptualization and this factor structure

was that Filtering and Ownness did not load into distinct factors. Most of the Ownness items had high cross-loadings and were removed to improve the factor solution. However, OWNN 3 (my friends have their own ways of using social media) loaded into Filtering with a reasonable value and very low cross-loadings. This item was retained despite its low loading, as it helps operationalize the construct with at least three items and had a higher loading than other Filtering items. This is not a limitation, as it guides further discussion on differences between Filtering and Ownness. As with Coolness and Novelty, it is difficult to distinguish between Filtering and Ownness. Filtering may result in a sense of Ownness, as the facility to sort through information (FILT3) and limit the visibility of information (FILT4) can result in unique uses that make users feel like their friends have their own ways of using social media (OWNN3). While most of the items in the interactivity-based uses and gratification factor

model were either adopted or adapted from Sundar and Limperos (2013), three new items—ACTV1: I can perform a number of tasks on social media, ACTV4: I get to do a lot of things on social media, and DYNM2: social media give me more control over information I post—had high loadings to support the factor structure. In contrast to the original four-factor conceptualization, this model suggested three latent factors to operationalize interactivity-related uses and gratifications of social media. Activity had clear and reasonably high loadings that make it a distinct factor in the model. However, the model did not support identifying Responsiveness and Dynamic Control as two separate factors. This does not undermine the validity of the model, as it is reasonable to use both to operationalize a single factor since Responsiveness is a necessary condition for Dynamic Control. Responsiveness of social media to commands (RESP2) and requests (RESP3) can trigger the perception that users are in charge (DYNM1), have more control over information (DYNM2), and are able to control the interaction (DYNM3).

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Uses and gratifications related to Navigability cover Browsing, Scaffolding/ Navigation Aids, and Play/Fun aspects. While the first two aspects are related, Play shows a different dimension of navigation. Results reflect those nuances in that respondents’ evaluation of the two is not different, as Browsing/Variety Seeking and Scaffolding/Navigation Aids converged into one factor. This is not conceptually counter-intuitive, as Scaffolding can be part of Browsing. For instance, the ability of social media to link users to sites that have different types of information (BROW2) and other pieces of information (SCAF2) are very similar. Visual aids available on social media (SCAF3) help users to obtain a variety of information (BROW1). Due to the highly related nature of these two concepts, and with the support of factor loadings given in Table 1, they can be conceptualized as a single factor. Play, being a distinct dimension, loaded into a different factor. Consequently, the solution was a two-factor solution that was different from the hypothesized three-factor solution. Three out of nine items in the model (BROW2: social media can link me to sites that have different types of information, PLAY4: social media provide more entertaining information than other media, and PLAY5: social media offer more entertaining features than other media) were newly introduced, and these items focus on accessibility of information, entertainment provided by information and social media features. A correlation analysis showed that, except for four relationships (Realism and

Coolness, Realism and Filtering, Realism and Interaction, and Realism and Browsing), latent constructs identified by the separate factor analyses significantly correlated with each other. These correlations, however, were low to moderate, ranging from 0.120 to 0.568. This shows that the constructs have reasonable con- vergent validity in terms of representing the broad class of affordance hypothesized for measurement. Low to moderate correlations between constructs also support discriminant validity, as they are not overly correlated. Significant correlations among constructs across classes of affordances also indicate that the constructs and items can represent a single scale that has multiple dimensions to measure social media uses and gratifications. This supports conducting an all-inclusive factor analysis to support the validity of the model.

Assessment of the Latent Factor Structure

The above EFA analysis supports, subject to the revisions discussed above, Sundar and Limperos’ (2013) conceptualization of uses and gratifications under each class of affordance. Although the results did not distinguish between several factors, no items loaded into factors that represent a different class of affordance. This indicates that, while there is some room to refine the measures within each affordance, the distinc- tion between affordances can be supported. As this was not accomplished in the separate EFAs, further analysis was necessary to test the validity of the whole model. Therefore, the revised conceptual framework was used to test several CFA models to evaluate goodness-of-fit of the latent factor structure.

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The latent factor structure summarized in Table 2 was evaluated in two steps using three CFA models: 1) social media uses and gratifications constructs as a first-order factor structure, 2) the MAIN dimensions as a second-order factor structure, and 3) a model that tests the resultant second-order factor structure with the second sample. The first model situates the results of the four separate EFAs discussed above in a single factor structure—to support the validity of an all-inclusive model and show the suitability of individual items—to operationalize each construct regardless of the affordance they represent. The second model tests perhaps the most important contribution made by the original model (i.e., that uses and gratifications can represent four main types of affordances), in terms of the suitability of each construct suggested by Sundar and Limperos (2013). Results of the EFAs were used to build an all-inclusive first-order confirmatory factor model and a second-order factor model. Three criteria were used to further improve the model fit: 1) standardized regression weights, 2) standardized residuals, and 3) modification indices. Several items with high standardized residuals and low standardized regression weights were dropped and covariances were drawn between two adjacent items to further improve the model fit. Subject to modifications both CFA models showed acceptable results— standardized regression weights ranged between plus and minus one, standard errors were greater than zero and less than 0.20 for almost all parameters, and all para- meters in both models were statistically significant (p ≤ 0.05). According to Hu and Bentler (1999), cut-off values close to .95 for the Tucker

Lewis Index (TLI) and Comparative Fit Index (CFI) and .06 for the Root Mean Square Error Approximation (RMSEA) indicate a reasonable fit. The first CFA model, testing a single-order factor structure and including 33 items, indicated a weak but acceptable fit: root mean square residual (RMR): 0.049, Goodness-of-Fit Index (GFI): 0.878, Adjusted Goodness-of-Fit Index (AGFI): 0.844, Incremental Fit Index (IFI): 0.910, TLI: 0.892, CFI: 0.910, RMSEA: 0.052. The model had high standardized regression weights for each item. These results support using the factors identified in the four separate EFAs above as distinct constructs in an all-inclusive model. Given the acceptable fit of the first CFA model, a second CFA was conducted to examine the suitability of the constructs to represent each affordance in the MAIN model. Items used in the first CFA were used to develop this model, and second-order constructs were introduced to operationalize the MAIN dimensions. The items with high

Table 2 Latent Factor Structure

Modality Agency Interactivity Navigability

Realism Agency-enhancement Interaction Browsing Being there Community building Activity Play

Bandwagon Responsiveness Filtering

382 Journal of Broadcasting & Electronic Media/September 2018

standardized residuals and low standardized regression weights were dropped to further improve the model fit. This model also consisted of 33 items and showed a weak but acceptable fit (RMR: 0.056, GFI: 0.854, AGFI:0.828, IFI: 0.888, TLI: 0.875, CFI: 0.887, RMSEA: 0.056). This indicates that Sundar and Limperos’s (2013) con- ceptualization of affordance-based uses and gratifications is generally valid as a second-order factor structure. It is notable that there are no major differences in regression weights between the two models. However, one weak item in this model (INTR1) negatively affected the overall model fit. This item was retained in the EFA and in this CFA, as operationalizing Interaction with at least three items could help future studies that use this scale to further refine the items that represent the construct. For each model, all but two relationships had weights lower than 0.50. Most of the

effects in the models had weights higher than 0.60. In general, standardized regres- sion weights and the model-fit indices show that conceptualizing social media uses and gratifications under the four types of affordances suggested by Sundar and Limperos (2013) does not jeopardize the statistical validity of the measure. Coolness and Novelty items were excluded from the model, as Coolness included two Novelty items and users may not perceive many of the social media platforms as novel anymore. This can be particularly true with college students that are generally viewed as heavy social media users. The resultant factor structure was further tested with the second sample to ensure that it can be applied for different populations. We excluded Interaction from the final model to maximize the validity of the model. This, however, does not mean that this construct should be excluded in future applications of the scale, as larger samples and refined items can help improve the construct. The revised model (see Figure 1) showed slight improvement (RMSEA: 0.054, RMR: 0.048, GFI: 0.852, AGFI: 0.822, IFI: 0.931, TLI: 0.922, CFI: 0.931), indicating that the measure suggested can produce similar results when tested with a different, and more diverse, sample. Table 1 shows means, standard deviations, and Cronbach’s alpha values for each

construct (based on the first sample). The results indicate that, while respondents disagree that social media is similar to real life (M = 2.03 on a 5-item Likert scale), their perception of other uses and gratifications constructs range between 3 (“neither agree nor disagree”) and 4 (“agree”). However, the fact that the mean values of these responses gravitate towards 4 shows that the revised items capture uses and gratifica- tions relevant to social media. Moreover, alpha values above 0.64 indicate that all items have minimum internal consistency. A Multivariate Analysis of Variance (MANOVA) was conducted using the first

sample to provide a perspective on the effects of demographic variables. This test examined the effects of gender, age, ethnicity, and education of respondents on their perception of uses and gratifications. The results showed that, while gender, age, and ethnicity have no significant effect, the perception of social media uses and gratifications can be affected by education level (Pillai’s Trace: 0.223, F: 1.431, p ≤ 0.05). The between-subjects effects showed that perception of Agency and Filtering can depend on education level (Agency: F: 3.35, p ≤ 0.05, and

Rathnayake and Winter/CARRYING FORWARD THE USES AND GRATS 2.0 AGENDA 383

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384 Journal of Broadcasting & Electronic Media/September 2018

Filtering: F: 4.328, p ≤ 0.05). This is possible, given the presence of graduate students in the sample. The 2 interaction effects tested in the model (Age × Education and Age × Ethnicity)

were significant, indicating that the impact of age on perception of social media uses and gratifications depends on the education level (Pillai’s Trace: 0.603, F: 1.659, p ≤ 0.05) and ethnicity of respondents (Pillai’s Trace: 0.386, F: 1.248, p ≤ 0.05). According to between-subjects effects, the impact of age on several uses and gratifications constructs (e.g., Being There: F = 2.30, p ≤ 0.05, Agency: F = 3.43, p ≤ 0.05, Filtering: F = 2.55, p ≤ 0.05, Responsiveness: F = 2.13, p ≤ 0.05, Play: F = 1.83, p ≤ 0.05) also depends on education level. The impact of age on two dependent variables (Realism: F = 2.07, p ≤ 0.05, Agency: F = 2.54: p ≤ 0.05) was also found to depend on respondent ethnicity. The fact that basic demographic variables have no direct effect on uses and

gratifications indicates that the perception of constructs is consistent within the sample. However, the results show that there can be nuances in the perception of social media uses and gratifications. For instance, significant interaction effects show that the impact of age on perception of uses and gratifications depends on the educational level and ethnicity of respondents for some constructs. This leaves room for further examination of social media uses and gratifications in different settings with special attention on effects of demographic variables on specific constructs.

Discussion

This study tested an affordance-driven measure of social media uses and gratifica- tions using the conceptual framework and items suggested by Sundar and Limperos (2013), using two different samples. The results of this study support the conceptual accuracy of the MAIN model, as they indicate that conceptualizing social media uses and gratifications as a second-order factor structure—by classifying constructs into four different types of affordances—does not jeopardize the statistical validity of the measure. The distinction between the two factor structures is mainly theoretical, as the objective this study is to add the notion of social media affordances to uses and gratifications studies. This does not mean that the scale suggested should not be used as first-order factor structure. Such use still provides a more comprehensive measure of uses and gratifications, as it has been developed based on social media affordances. Using this scale as a second-order structure allows for more advanced analysis that includes affordances as second-order factors. Rather than treating the results of the present study as a mere validation of the

items suggested by Sundar and Limperos (2013), the factor structure and the mea- sures tested can be considered an improved scale to measure social media uses and gratifications. This is appropriate for several reasons: 1) the original items suggested by Sundar and Limperos (2013) were not targeted towards social media; 2) the original items were subject to substantive revisions in this study to tailor them to

Rathnayake and Winter/CARRYING FORWARD THE USES AND GRATS 2.0 AGENDA 385

social media users; 3) a reasonable number of new items were added, and 10 of them were included in the final 30-item scale; and 4) several constructs suggested by the original study were combined to create new constructs, making the present framework a reduced version of the original conceptualization. Therefore, the scale validated in this study should be considered as a contribution towards advan- cing the Uses & Grats 2.0 agenda put forward by Sundar and Limperos (2013), by situating the original items in a more strictly defined social media context. The revised model has some deviations from the original framework. For instance, the

results did not display several gratifications as distinct factors, suggesting the need to combine some factors. However, the combined factors did not include items from other affordances, supporting the conceptual accuracy of the original model. The question of operationalizing Coolness and Novelty emerges as a main topic for future investigation, as this study did not identify distinct factors for these two constructs. There are studies (e.g., Sundar et al., 2014) that show that Coolness is a complex construct that includes several dimensions. Possibly, a multi-dimensional Coolness measure can be integrated to the present model. Moreover, there is a lack of conceptualization of the meaning of Novelty of social media. Accordingly, there is a need for a precise definition of Novelty and empirical work that can establish differences between Coolness and Novelty. Although Coolness and Novelty were excluded from the final scale, based on the argument that social media may not be novel anymore, these constructs can be applied in other settings. For instance, future work that examines uses and gratifications of new social media platforms as well as applications that integrate social media with technol- ogies that users may perceive novel (e.g., virtual and augmented reality) can use Coolness and Novelty items as part of the scale. A similar issue arises with regard to Browsing and Scaffolding, two constructs that

this study did not differentiate between. However, as the focus of the scale here is to test an all-inclusive model, conceptualizing Coolness and Novelty, and Browsing and Scaffolding/Navigation Aids, as single constructs do not jeopardize the value of the conceptualization. The fact that Ownness and Filtering were combined to form a single factor requires further study, as it is possible that these two constructs may form distinct factors if the measures are further refined. The scale can also benefit from further work that adds new constructs to the measure. For instance, device- based affordances, such as mobility, can be added to the conceptual framework, as mobile devices are frequently used to access social media. This can lead to unique gratifications, such as immediacy, and broaden the scope of the scale. Moreover, new constructs, such as social surveillance, can be added to the scale to capture the full potential of social media. Despite the contribution this study makes by building on Sundar and Limperos’s

(2013) work, Cronbach’s Alpha values lower than 0.7 for Bandwagon and Filtering indicate that these constructs should be tested further with different populations and larger samples to improve the internal consistency. Moreover, the items removed in the data reduction process can be revised and tested with different samples. However, removal of these items does not affect the quality of the measure, as each latent construct was represented by at least three items. Items that measure

386 Journal of Broadcasting & Electronic Media/September 2018

Interaction, INTR1 and INTR3 in particular, should be subject to further revision and testing for two reasons. First, Interaction is a main characteristic of social media and ignoring this construct may result in the scale not capturing the gratification of interaction. Further, two out of three items in the measure suggested indicated need for improvement (INTR1 low regression weight the in the CFA model, and INTR low factor loading). We have included Interaction items in the EFA and reported results of the first two CFAs with Interaction as it could provide a starting point for future researchers to refine the construct. This study can form the foundation for a number of other studies. Future work

should focus on differences in perception of social media uses and gratifications across different actors, such as political actors, and organizations. Similarly, effects of attributes, such as efficacy, tolerance, community engagement, openness, sociability, political orientation, and cynicism on social media uses and gratifications can be examined. Moreover, moderating effects of constructs such as Internet skills, social media adoption, perceived Internet controls, social surveillance, and privacy con- cerns can be tested to uncover nuances in relationships between actors, attributes, and social media uses and gratifications.

Conclusion

Future studies that examine social media uses and gratifications also need to consider differences between platforms. The current study, as mentioned before, situates uses and gratifications in the context of social media that include several types of platforms. This is appropriate, as the MAIN framework considers new media as a single category that offers unique affordances that can lead to unique gratifications. Future studies can examine how platform type affects those gratifications. For instance, some social net- work sites may providemore community-building and interactivity-related gratifications than others. Moreover, the measure tested in this study can be further refined to develop uses and gratifications measures tailored to different types of platforms.

ORCID

Chamil Rathnayake http://orcid.org/0000-0003-1964-2639

Jenifer Sunrise Winter http://orcid.org/0000-0002-6393-6637

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