Journal of Business Research 89 (2018) 455–461
Contents lists available at ScienceDirect
Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
The pursuit of virtual happiness: Exploring the social media experience
across generations
Orie Berezana,1, Anjala S. Krishenc,
⁎,1
T
, Shaurya Agarwalb, Pushkin Kachrooc
a
California State University, Dominguez Hills, 1000 E. Victoria Street, Carson, CA 90747, USA
California State University, Los Angeles, 5151 State University Drive, Los Angeles, CA 90032, USA
c
University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV 89154, USA
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Social media networking
Affect balance
Virtual happiness
Self-determination theory
Generational cohorts
FsQCA
Social media environments can transform and reinforce life experiences, influencing self-concept and providing
happiness. The goal of this research is to examine social media networking as an experiential phenomenon,
wherein consumers pursue virtual happiness by satisfying the self-determination theory (SDT) needs of relatedness, competence, and autonomy. Beginning with the memory connection to self-concept, the study proposes
an experiential outcome circle for social media to virtual happiness. A circle depicts the idea that self-concept
motivates social media behavior, which influences the self-concept. Happiness, or affect balance, is a potential
outcome of this connection. The study analyzes n = 504 social media networking participants using generational
cohorts with fuzzy set qualitative comparative analysis (fsQCA). This study suggests the metaphors for each
generation based on the following SDT recipes: (1) “we” for generation Y with relatedness and competence, (2)
“me” for generation X, with autonomy and competence, and (3) “be” for baby boomers with competence.
1. Introduction
Happiness can be an endless and often relentless goal for individuals
in society. As consumers, individuals often traverse the marketplace,
acquiring goods and services, on the path to happiness. Guevarra and
Howell (2015) indicate that experiences or experiential products, such
as travel, video games, or electronic devices, offer greater happiness
than material ones, such as jewelry or clothing. Because the consumption of experiences versus material goods can lead to greater selfdefinition, these experiences may also result in higher levels of happiness (Bhattacharjee & Mogilner, 2014). In effect, social media platforms
are also experiential products. Social media can transform and continually reinforce life experiences, both positively and negatively
(Scheinbaum, 2017). For example, many social media platforms allow
members to share their consumption experiences, enabling self-reflection, growth, and learning, potentially affecting their happiness
(Bosangit & Demangeot, 2016). By facilitating social media experiences
that positively affect the happiness and subjective well-being of target
markets, marketers can more effectively engage with consumers.
When an experience leads to a memory, it is more likely to shape an
individual's self-concept (Carter & Gilovich, 2012). Such experiences,
whether they are ordinary (common and frequent) or extraordinary
⁎
1
(beyond everyday life), determine and affect a consumer's happiness
(Bhattacharjee & Mogilner, 2014). Social media networking involves
the active consumption of an experience because it allows individuals
to share moments that later become a set of memories, carefully timelined and linked to virtual others throughout cyberspace. The enjoyment and happiness resulting from such shared experiences are influenced by motivations to belong and confidence in social information
(Raghunathan & Corfman, 2006).
As people age and traverse through different life stages, not only
does their definition of happiness itself shift (Mogilner, Kamvar, &
Aaker, 2011), but also their view of their lifespan, also known as the
future perspective (Strough et al., 2016). Differences in the meaning of
happiness stem from several types of arousal; whereas happiness for
younger people can result from high states of arousal such as excitement, older people can achieve happiness from low states of arousal
such as peacefulness. In a meta-analytic study of subjective well-being,
Pinquart (2001) finds that as age increases, overall positive affect decreases, and high and low arousal and emotional states decline. Social
media experiences enable both empowerment activities as well as social
interactions. Yuksel, Milne, and Miller (2016) empirically validate
differences in younger versus older consumers. They find that collegeaged consumers gain more value from socialization than older
Corresponding author.
E-mail addresses: oberezan@csudh.edu (O. Berezan), anjala.krishen@unlv.edu (A.S. Krishen), pushkin@unlv.edu (P. Kachroo).
First two authors contributed equally.
https://doi.org/10.1016/j.jbusres.2017.11.038
Received 17 June 2017; Received in revised form 25 November 2017; Accepted 28 November 2017
Available online 06 December 2017
0148-2963/ © 2017 Elsevier Inc. All rights reserved.
Journal of Business Research 89 (2018) 455–461
O. Berezan et al.
Fig. 1. Conceptual framework.
allow individuals to be shaped by their physical and virtual experiences. The idea of a malleable self-concept has relevance when individuals feel that they are adapting to self-descriptive groups that have
high psychological utility (Sim, Goyle, McKedy, Eidelman, & Correll,
2014). As such, even the process of aging itself is considered a socially
constructed subjective phenomenon that results from positioning in
society and an acceptance of the identity labels associated with “elderly” or “old age” (Barnhart & Peñaloza, 2013).
In a study on the social media networking practices of college-aged
users, Krasnova, Widjaja, Buxmann, Wenninger, and Benbasat (2015)
indicate that the consumption of social information can lead to increases in envy and self-enhancement strategies and ultimately lower
cognitive and affective well-being. According to a study on the happiest
10% of college students, Diener and Seligman (2004) conclude that
while students need positive social relationships for their well-being,
these relationships alone are not sufficient. Because linkages exist between studies on aging and generational cohorts, these are two separate
but intertwined concepts. The formation of self-concept can be strongly
tied to social identity, especially during younger years because it is
malleable and needs further development. At the same time, generational cohorts share social, cultural, and technological experiences, that
can lead to similar identity structures.
consumers. Yuksel and colleagues surmise that generation effects could
be the reason for this difference, and the recent research also indicates
that social media provides different cognitive responses (in terms of
satisfaction) with a basis in various motivational antecedents per generation (Krishen, Berezan, Agarwal, & Kachroo, 2016).
The goal of the present research is to examine social media networking as an experiential phenomenon wherein consumers pursue
virtual happiness in their everyday exchanges. The present study views
happiness as a potential affective outcome of social media experiences
and builds on the research regarding age and generational differences.
First, the study offers a conceptual framework that combines the definition of self-concept, self-determination theory (SDT), and happiness
(see Fig. 1). Then, through a quasi-convenience sample of 504 subjects,
the study explores various motivation recipes according to SDT for each
generation, using fuzzy set qualitative comparative analysis (fsQCA).
Finally, the study presents conclusions, implications, and limitations to
offer a broader understanding of social media networking, generational
motivations, and SDT.
2. Conceptual framework
2.1. Self-concept definition and generations
Because teenagers are still forming their self-identity, their activity
on social media networks provides insights into their identity construction and self-enhancement (Doster, 2013). In their qualitative
study of generation Y consumers, Noble, Haytko, and Phillips (2009)
identify the socialization issues of freedom and self-identity (finding
oneself) as their key purchasing motivations. Individuals can change
their beliefs and behaviors through implicit experience-taking when
they spontaneously assume the mindset of a character in a fictional
novel (Kaufman & Libby, 2012). To do this, they must temporarily set
aside their self-concept and allow themselves to adopt the internal traits
of the narrative character. Social media is an experiential phenomenon
wherein consumers identify with the characteristics of others in virtual
space.
Several theories, including self-verification theory, self-determination theory, and uniqueness theory, discuss identity construction as
having a set of six key motives: self-esteem, continuity, distinctiveness,
belonging, efficacy, and meaning. Identity structure can be delineated
into cognitive and affective dimensions, with happiness serving as the
strongest predictor of self-esteem, efficacy, and belonging (Vignoles,
Regalia, Manzi, Golledge, & Scabini, 2006). Another interesting aspect
of self-concept is the self-synchronization process that occurs as part of
the construction of a socially adaptive identity, which Kawakami et al.
(2012) find can enhance survival likelihood. This socially adaptive
identity will change over time and through different experiences that
2.2. Self-determination theory, social media networking behavior, and
happiness
Self-determination theory posits that three basic psychological
needs drive behavior and lead to growth, development, and well-being.
They are relatedness, autonomy, and competence (Ryan & Deci, 2000;
Sheldon, Abad, & Hinsch, 2011). Relatedness provides a feeling of belongingness, namely the need to connect with and develop close and
affectionate relationships with others (Baumeister & Leary, 1995). The
need for autonomy involves a sense of freedom and the ability to
control one's own life in a way that enhances one's sense of identity
(Deci & Ryan, 1985). Lastly, the need for competence refers to feelings
of being able to control one's environment and the results of one's actions (Ryan & Deci, 2000). According to SDT, people must satisfy all
three of these needs to experience the height of well-being, or happiness
(Deci & Ryan, 2000). Indeed, the research shows that the satisfaction of
these psychological needs may be a predictor of well-being, relationship
quality, and happiness (Deci & Ryan, 2008; Sapmaz, Doğan, Sapmaz,
Temizel, & Tel, 2012). To some degree, each of these needs motivates
behaviors in daily experiences, including virtual lives. In terms of social
media behavior, the research primarily evaluates SDT as a predictor of
satisfaction, a cognitive outcome (Krishen et al., 2016). Ong, Chang,
and Lee (2015) were among the first to explore factors that influence
website-related emotions in their study on Facebook users. They stress
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O. Berezan et al.
arguably enhances its validity. As it takes a larger span of time into
account, SPANE is considered to be less limiting than previous scales
such as ABS and PANAS that measure affective aspects of subjective
well-being (Jovanovic, 2015; Li, Bai, & Wang, 2013). The present study
uses SPANE to evaluate subjective affect balance (positive minus negative reported feelings) of participants in relation to their social media
behaviors over their most recent four weeks, thereby providing a
measure of happiness.
Fig. 1 presents a conceptual framework that combines self-concept
and generations, social media networking, SDT, and affect balance, or
happiness. This framework identifies the generational cohorts of gen Y,
gen X, and baby boomers as salient groups to study. As the literature
proposes, individuals in older generations can form memories more
readily from either extraordinary or ordinary experiences whereas
those from younger generations normally require extraordinary experiences to form meaningful memories. These memories then translate
into a self-concept. Beginning with the memory connection to selfconcept, the present study proposes an experiential outcome circle for
social media to virtual happiness. A circle depicts the idea that selfconcept motivates social media behavior, which in turn influences one's
self-concept. Happiness, or affect balance, is a potential outcome of this
connection.
Table 1
Construct items.
Construct
Autonomy
Relatedness
Competence
SPANE-P
SPANE-N
SPANE-B
Items
1.
2.
3.
4.
5.
I feel like I can be myself.
I often feel like I have to follow other member's commands.
If I could choose, I would do things differently.
The tasks I have to do are in line with what I really want to do.
I feel free to use social media networks in the way I think they
can best be used.
6. I feel forced to do things I do not want to do.
1. I don't really feel connected with other people.
2. I feel part of a group.
3. I don't really mix with other people.
4. I can communicate with people about things that really matter to
me.
5. I often feel alone when I am communicating with people.
6. Some people I communicate with are close friends of mine.
1. I really master my social media networking tasks.
2. I feel competent using them.
3. I am good at the things I do on them.
4. I have the feeling that I can even accomplish the most difficult
social media networking tasks.
1. Positive
2. Good
3. Pleasant
4. Happy
5. Joyful
6. Contended
1. Negative
2. Bad
3. Unpleasant
4. Sad
5. Afraid
6. Angry
Fuzzy construct derived from SPANE-P and SPANE-N
3. fsQCA study
3.1. Sampling and measures
Data collection involves an online survey that uses a snowball collection method (n = 504); subjects are screened based on identification
of their generational cohort and their membership in social networks.
Snowball sampling is a quasi-convenience data collection methodology
that allows for specified sampling frames (such as generational cohorts), while providing an acceptable level of bias and reducing potential estimation problems (Chen, Chen, & Xiao, 2013). Previous research utilizes this technique, for example when exploring online
preferences including privacy issues (Krishen, Raschke, Close, &
Kachroo, 2017), service employee affect (Rayburn, 2014), or when
targeting specific demographic cohorts (Berezan, Raab, Krishen, &
Love, 2015).
As the present study explores social media networking, online
sampling is an appropriate collection technique; while the generational
cohorts are not the exact same size, they do provide acceptable ecological validity in the social media environment. All measures involved in
this study are existing and validated scales, and all of the constructs
have acceptable reliabilities of 0.7 or higher. Table 1 contains the scale
details, and Table 2 provides the respondent demographics.
the fact that emotions (affect) are as important as satisfaction (cognition) in determining happiness, both of which can ultimately motivate
users to increase their behavioral commitment to a website. The current
study answers their call for research by evaluating social media networking from the lens of a generational cohort as it relates to the definition of self-concept in determining one's sense of well-being, or
happiness.
2.3. Affect balance and happiness
No single definition exists for the term happiness, and every individual has their own perspective of what makes them happy. When
asked to describe what makes them happy, people respond with vastly
different answers, from sunshine to dancing, health to wealth, and
successful children to a long-term love relationship. Researchers use
subjective well-being (SWB) to recognize the fact that happiness is
subjective and therefore individuals evaluate their own life based on
their own standards (Deci & Ryan, 2008; Diener, Nickerson, Lucas, &
Sandvik, 2002). Ryan and Deci (2001) recognize both hedonic (pleasure-related) and eudaimonic (meaning-related) aspects of SWB and
identify hedonic well-being as focusing on happiness. Subjective wellbeing incorporates both positive and negative affect and considers the
balance between the two as an aspect of happiness in measuring mood
and emotion based on what is occurring in one's life. Bradburn's (1968)
affect balance scale (ABS) is one of the most widely established in
measuring SWB. The positive and negative affect scale is empirically
validated, and the most commonly used scale in literature (PANAS;
Diener et al., 2010). The hedonic balance scale evaluates positive and
negative affect with three items each (HBS; Schimmack, Diener, &
Oishi, 2002). A newer measure of affective well-being is the scale of
positive and negative experience (SPANE; Diener et al., 2010). SPANE
evaluates six self-reported positive and six self-reported negative feelings based on the participants' recall of the past four weeks, which
3.2. Procedure and analysis
The survey uses a set of Likert scales to gather data as well as a set of
demographic questions. To analyze the data, fsQCA allows for the
identification of recipes for happiness based on generational cohorts
(Emmenegger, Schraff, & Walter, 2014). The initial step in fsQCA is
data calibration; or the mapping of the original values for all conditions
into membership scores that range from zero to one. The survey data is
divided into the following generational cohorts as per previous research: baby boomers from 1946 to 1964, gen X from 1965 to 1983, and
gen Y or millennials from 1984 to 2002 (Elmore, 2014; Markert, 2004).
Originally the study received 565 survey responses. Removing 61
responses due to incomplete data yielded a final data set consisting of
n = 504 respondents. In the final data set, each generational cohort has
an acceptable sample size for fsQCA with n = 46 for baby boomers,
n = 165 for gen X, and n = 293 for gen Y. Tables 3, 4, and 5 provide
the multiplicative construct scores and fuzzy-set thresholds for each of
the generations, respectively.
SPANE is used to assess the positive, negative, and overall affect
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Table 2
Sample demographics.
Baby boomers
Gender
Highest level of education
Employment status
Male
Female
Other
Total
Less than high school degree
High school
Some college but no degree
Associate degree in college (2 years)
Bachelor's degree in college
Master's Degree
Doctorate
Professional Degree
Total
Student only
Employee for 20 h or more per week plus student
Employee for over 30 h per week or full-time employee
Unemployed or retired
Total
Reliability
Autonomy
Relatedness
Competence
SPANE-P
SPANE-N
Range of
construct
score
Threshold
for full non
membership
Maximum
point of
ambiguity
Threshold
for full
membership
1–117,649
1–117,649
1–2401
1–117,649
1–117,649
1400
1080
12
4096
1
13,400
12,096
256
21,600
288
67,228
72,030
864
74,088
5400
Reliability
Range of
construct
score
Threshold
for full non
membership
Maximum
point of
ambiguity
Threshold
for full
membership
Autonomy
Relatedness
Competence
SPANE-P
SPANE-N
1–117,649
1–117,649
1–2401
1–117,649
1–117,649
1764
784
16
3072
1
16,464
12,500
432
18,000
256
84,035
86,436
2401
74,088
11,250
Autonomy
Relatedness
Competence
SPANE-P
SPANE-N
Reliability
Frequency
Percentage
Frequency
Percentage
N
%
N
%
N
%
22
24
47.83
52.17
0.00
100.00
4.35
17.39
32.61
13.04
19.57
8.70
0.00
4.35
100.00
0.00
6.52
58.70
34.78
100.00
53
110
2
165
5
18
48
32
46
15
32.12
66.67
1.21
100.00
3.03
10.91
29.09
19.39
27.88
9.09
0.00
0.61
100.00
6.06
7.88
78.18
7.88
100.00
113
177
3
293
1
34
104
112
34
7
38.57
60.41
1.02
100.00
0.34
11.60
35.49
38.23
11.60
2.39
0.00
0.34
100.00
24.57
45.73
29.01
0.68
100.00
3
27
16
46
1
165
10
13
129
13
165
1
293
72
134
85
2
293
scale. First, the two positive and negative constructs for affect are calculated and calibrated by using the standard fsQCA procedure: SPANEP and SPANE-N. The second step is to subtract the negative feelings
score (SPANE-N) from the positive feelings score (SPANE-P); the resultant difference score can vary from − 1 (unhappiest) to +1 (happiest). In essence, a respondent with a score of + 1 indicates that s/he
very rarely or never experiences any negative feelings and very often or
always has all positive feelings. Because fsQCA requires the constructs
to be the fuzzy scale of zero to one, we further process the resulting
difference to determine the final score of the affect balance (SPANE-B).
The absolute value of the minimum negative term is added to the difference (shifting) followed by the division of the maximum resulting
number (scaling) to obtain the new construct SPANE-B.
3.3. Estimating complex causal statements (recipes)
The sample is split into two subsamples, the first is a training sample
(n = 23 for baby boomers, n = 83 for gen X, and n = 147 for gen Y),
with the second subsample (n = 23 for baby boomers, n = 82 for gen
X, and n = 146 for gen Y) providing validation for the results.
4. Results
4.1. Using original calibrated constructs
Table 5
Construct descriptions: gen Y.
Construct
Percentage
2
46
Table 4
Construct descriptions: gen X.
Construct
GenY
Frequency
46
2
8
15
6
9
4
Table 3
Construct descriptions: baby boomers.
Construct
GenX
Range of
construct
score
Threshold
for full non
membership
Maximum
point of
ambiguity
Threshold
for full
membership
1–117,649
1–117,649
1–2401
1–117,649
1–117,649
1728
686
64
1920
15
13,125
8000
625
18,000
1000
67,228
72,030
2401
63,504
15,625
The outcome construct (SPANE-B) is measured in terms of the three
antecedent variables of interest, which are autonomy, relatedness, and
competence. Tables 6, 7, and 8 show the recipes in the complex, parsimonious, and intermediate solutions for the three age groups. The
frequency cutoff used for the three generational cohorts is 4, 13, and 19
respectively, and the consistency cutoff of 0.80 is used for the analysis.
Many of the solutions qualify with high individual consistency scores
that are above 0.8, along with the raw coverages meeting the desired
values that fall in the range of 0.25 to 0.65 as suggested by Woodside
(2013). Tables 6, 7 and 8 highlight solutions meeting the desired cutoff
criterion for consistency and raw coverage.
balance of the survey participants over the last four weeks (Diener
et al., 2010). SPANE can derive an overall affect balance score, but can
also be divided into positive and negative scales for feelings. Since the
constructs in fsQCA are calculated by multiplying the corresponding
items rather than adding/subtracting, the present study identifies a
suitable method to derive the calibrated affect balance using a fuzzy
4.2. Testing for predictive validity
To test for predictive validity, the sample randomly assigns cases to
the training subsample (n = 23 for baby boomers, n = 83 for gen X,
and n = 147 for gen Y) and to the testing subsample (n = 23 for baby
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O. Berezan et al.
Table 6
Solutions for baby boomers group.
Table 7
Solutions for gen X group.
Complex solution
Frequency cutoff: 4.00
Consistency cutoff:
0.811242
~ Competence
Solution coverage: 0.711116
Solution consistency:
0.696637
Parsimonious solution
Frequency cutoff: 4.00
Consistency cutoff:
0.811242
~ Competence
Solution coverage: 0.711116
Solution consistency:
0.696637
Intermediate solution
Frequency cutoff: 4.00
Consistency cutoff:
0.811242
~ Competence
Solution coverage: 0.711116
Solution consistency:
0.696637
Complex solution
Raw
coverage
Unique
coverage
Consistency
0.711116
0.711116
0.696637
Raw
coverage
Unique
coverage
Consistency
0.711116
0.711116
0.696637
Raw
coverage
Unique
coverage
Consistency
0.711116
0.711116
0.696637
Frequency cutoff: 13.00
Consistency cutoff:
0.838187
Relatedness
Competence ∗ ~autonomy
~Competence ∗ autonomy
Solution coverage: 0.813097
Solution consistency: 0.726422
Parsimonious solution
Frequency cutoff: 13.00
Consistency cutoff:
0.838187
Competence
Relatedness
Autonomy
Solution coverage: 0.864556
Solution consistency: 0.699646
Intermediate solution
frequency cutoff: 13.00
consistency cutoff:
0.838187
relatedness
Autonomy ∗ ~ competence
~Autonomy ∗ competence
Solution coverage: 0.813097
Solution consistency: 0.726422
Predictive validity testing
Training sample (n = 23)
Overall solution
Overall solution
consistency
coverage
0.600191
0.710061
Validity sample (n = 23)
Overall solution
Overall solution
consistency
coverage
0.610170
0.705925
Raw coverage
Unique
coverage
Consistency
0.630011
0.420490
0.434671
0.185470
0.067722
0.074225
0.759688
0.804320
0.822092
Raw coverage
Unique
coverage
Consistency
0.641298
0.630011
0.654995
0.074847
0.048862
0.083956
0.759221
0.759688
0.770014
Raw coverage
Unique
coverage
Consistency
0.630011
0.434671
0.420490
0.185470
0.074225
0.067722
0.759688
0.822092
0.804320
Predictive validity testing
Training sample (n = 83)
Overall solution
Overall solution
consistency
coverage
0.750772
0.796130
boomers, n = 82 for gen X, and n = 146 for gen Y). The test then
performs a series of fsQCA to examine the recipes for SPANE-B (Wu,
Yeh, & Woodside, 2014) in relation to the seven antecedents. Next, the
overall solutions from the training and validation samples are compared for their ability to predict the same recipes. The last sections of
Tables 6, 7 and 8 summarize the results from these analyses. The results
show similar consistency and coverage for both the testing and training
data sets. Overall, findings indicate that social media networking ingredients for happiness differ according to generational cohorts as follows: (1) for gen Y, relatedness and competence, (2) for gen X, autonomy and competence, and (3) for baby boomers, competence.
Validity sample (n = 82)
Overall solution
Overall solution
consistency
coverage
0.764440
0.771703
present study, overlaying the generations and their social media experiences with SDT.
Self-definition is malleable at a younger age. As individuals age,
their self-definition becomes more defined. At an early age (gen Y and
millennials in the present study), social media can allow individuals to
help themselves in society. Through interacting with others, this group
meets their need for relatedness and develops their individual sense of
self. This is where social influence and social acceptance play a key role
in the pursuit of virtual happiness for millennials. They use posting,
liking, tagging, sharing, and other behaviors to help create their selfidentities (Raab, Berezan, Krishen, & Tanford, 2016). In essence, they
choose social media as a way of pursuing an online identity that can
lead to virtual happiness. The present study refers to gen Y as the “we”
cohort wherein social influence can potentially play a key role, and
therefore a balance of relatedness and competence is the recipe for
virtual happiness. Hence, a key motivation for gen Y individuals when
using social media is relatedness. However, because countless entities
are often involved in satisfying the need of relatedness at this “we”
stage, individuals have less control over the social influences that act
upon their sense of well-being. This is consistent with the literature on
the impact of ordinary and extraordinary events on happiness
(Bhattacharjee & Mogilner, 2014). Their research shows that when individuals are younger, extraordinary experiences define one's self-concept more; whereas as they age, extraordinary experiences influence
one's self-concept less and ordinary experiences have more influence.
Applying this idea to the present study, gen Y individuals' need for
relatedness may typically be met by more extraordinary experiences
such as meeting and interacting with new and interesting people.
Specific to social media, this need may include being innovative in the
use of multiple social media platforms, including ones such as Instagram, Snapchat, and YouTube and finding ways to increase
5. Conclusions and implications
The present research explores subjective well-being and the role
that psychological needs play as motivating factors in many aspects of
the virtual experience. Existing research connects happiness and aging
and argues that aging impacts the preoccupation with negative life
events (Strough et al., 2016). This preoccupation leads to lower overall
positive emotional well-being and affect balance (Pinquart, 2001). To
extend the research on social media and aging (Krishen et al., 2016),
the present research explores the overlap of psychological needs with
the social networking experience. It focuses on participants' affect
balance according to their past four weeks of social media networking.
By calibrating affect balance using a fuzzy SPANE, the present study
proposes a new qualitative mechanism to evaluate subjective wellbeing.
This study applies SDT and SPANE to the social media experience
and assesses the positive, negative, and overall affect balance of participants, thereby giving an indication of which set of ingredients can
lead to virtual happiness. Findings indicate that the pursuit of virtual
happiness varies with different generations, yet competence spans all of
them. Fig. 2 depicts the emergent metaphor from the findings of the
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O. Berezan et al.
and move away from the need to be as high in relatedness. In general,
gen X is more independent than gen Y (Howe & Strauss, 2007; Pew
Research, 2010). At this stage, the need for autonomy becomes paramount to self-concept and happiness. The present research metaphorically suggests that gen X individuals may look into a mirror and evaluate who they are and whether the person in the mirror is in sync with
their self-concept; hence, it is the “me” or “Is this me?” stage. Rather
than pursuing social media behaviors that result in and create social
influence, this cohort may seek to ensure that their social networking
behaviors accurately reflect who they are as individuals and portrays
them as independent.
Finally, further development due to age and life experiences (baby
boomers) brings individuals to a place where they have little to prove to
others or to themselves. Therefore, the present research conceptualizes
the metaphor for baby boomers to be – “I am who I am” or “Let it be” –
the “be” stage. People at this stage may feel little social pressure to be
anything other than what and who they perceive themselves to be. The
recipe for virtual happiness at the “be” stage mainly relies on the need
for competence. Baby boomers focus on personal growth and self-fulfillment but are not necessarily as comfortable with new technology
and platforms (such as social media networking) as the younger generations (Littrell, Ma, & Halepete, 2005; Obal & Kunz, 2013). Since they
have less social pressure, their recipe for virtual happiness is much
simpler. This group must satisfy the need for competence, or in this
study, have the knowledge and ability to effectively use social media.
Table 8
Solutions for gen Y group.
Complex solution
Frequency cutoff: 19.00
Consistency cutoff:
0.842207
Autonomy
Competence ∗ relatedness
Solution coverage: 0.730945
Solution consistency: 0.737053
Parsimonious solution
Frequency cutoff: 19.00
Consistency cutoff:
0.842207
Relatedness
Autonomy
Solution coverage: 0.779532
Solution consistency:
0.717298
Intermediate solution
Frequency cutoff: 19.00
Consistency cutoff:
0.842207
Autonomy
competence ∗ relatedness
Solution coverage: 0.730945
Solution consistency: 0.737053
Raw coverage
Unique
coverage
Consistency
0.640082
0.497912
0.233033
0.090863
0.750415
0.805499
Raw coverage
Unique
coverage
Consistency
0.649212
0.640082
0.139450
0.130320
0.749425
0.750415
Raw coverage
Unique
coverage
Consistency
0.640082
0.497912
0.233033
0.090863
0.750415
0.805499
6. Limitations and future research
Predictive validity testing
Training sample (n = 147)
Overall solution
Overall solution
consistency
coverage
0.723481
0.700231
Validity sample (n = 146)
Overall solution
Overall solution
consistency
coverage
0.728661
0.703811
The present study offers insights into the behavioral motivations for
social media use across generations from a SDT perspective. However,
this study also has limitations that lay the groundwork for future research. One such limitation centers on the differences between age and
generational cohorts. Whereas individuals within each generational
cohort share experiences and motivations that make them similar in
some ways, the aging process itself also shifts the individual definitions
of, and thereby the recipes for, happiness. Therefore, future research
can conduct longitudinal studies that overlay both generational cohorts
and aging as a process to determine the antecedents to virtual happiness
in social media behavior. The present study is limited due to the
grouping of multiple social media networking platforms into one
complex entity per generation. Instead, a future study could investigate
relatedness in these platforms. This cohort actively seeks to develop
their self-concept through highly active social networking activity and
platforms. It is also important to consider that millennials in general are
group-oriented and have developed in a more connected world than
previous generations (Obal & Kunz, 2013), which may influence their
higher need for relatedness in social networking to achieve virtual
happiness.
As people gain more life experiences, such as in gen X, they develop
Fig. 2. Self-concept, social media, and motivations.
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Journal of Business Research 89 (2018) 455–461
O. Berezan et al.
individual platforms of interest as a variable itself and delineate differences in platform preferences across generations. Another potential
limitation of this study is its method and sample characteristics; future
research can use other qualitative and quantitative methodologies and
gather larger samples to further validate these findings. Building on the
current findings as well as others including Berezan, Krishen, Tanford,
and Raab (2017), Krishen et al. (2016), and Noble et al. (2009), additional quantitative designs can uncover direct relationships and test
theoretical models to triangulate these qualitative findings. Future research can also explore the impact of cross-cultural differences on social
media antecedents to happiness. Finally, a further understanding of
social media motivations and behaviors through the overlay of both
emotional and cognitive outcomes presents another future opportunity
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