# Social Commerce Adoption Predictors: A Review and Weight Analysis

Prianka Sarker, Laurie Hughe, Yogesh K. Dwivedi, Nripendra P. Rana

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Prianka Sarker, Laurie Hughe, Yogesh K. Dwivedi, Nripendra P. Rana. Social Commerce Adoption Predictors: A Review and Weight Analysis. 19th Conference on e-Business, e-Services and e-Society (I3E), Apr 2020, Skukuza, South Africa. pp.176-191, 10.1007/978-3-030-44999-5_15. hal-03222856

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# Social commerce adoption predictors: A review and weight analysis

Prianka Sarker¹, Dr Laurie Hughe², Prof Yogesh K. Dwivedi³, Nripendra P. Rana⁴
Emerging Markets Research Centre (EMaRC), School of Management,
Swansea University Bay Campus, Swansea, SA1 8EN, UK
{937449, d.l.hughes, y.k.dwivedi@swansea.ac.uk, ykdwivedi@gmail.com,
Nrananp@gmail.com}

**Abstract:** Social commerce is a rapidly growing platform of e-commerce that utilises social media and online social interaction to build brand awareness and increase sales. Buying and selling through social media can create a reliable and sustainable platform for buyers and vendors, offering an alternative platform to traditional online approaches. Research on social commerce began to achieve traction in 2006 and has grown since with a significant focus from academics who have offered new insight to many of the key topics. This study seeks to offer an additional contribution to the literature by analysing the predictors of consumer adoption of social commerce from existing studies by employing a weight analysis technique. The analysis considered seven dependent variables (along with their best and worst predictors) that are most frequently examined and are relevant to consumer adoption. The review presented in this study suggests that the intention to purchase is the most frequently examined dependent variable and that *trust* in the social commerce context is a key factor.

**Keywords:** Social commerce, Weight analysis, literature review

## 1.Introduction

Development and growth of social media platforms (such as Facebook, Twitter, LinkedIn and Instagram) have given rise to a new business model for e-commerce, frequently known as social commerce. Social commerce utilises web 2.0 technology and a specially designed infrastructure to support online communications and user contributions to assist in the acquisition of products and services [1]. Social commerce technologies are not only delivering a platform for communication between consumers to vendors as well as consumers to consumers but also creating significant challenges for scholars that have led to the development and validation of new models and theories. According to e-commerce marketing statistics, 74% of online consumers are relying on social media to guide their purchases, and 60% of businesses have gained new customers through social media within the US [2]. This highlights the importance of social media for facilitating information diffusion and augmenting towards further growth of e-commerce. Social commerce has proved to be an essential platform for online shoppers where consumers can view the product, read reviews, analyse key information and browse special offers [3]. The use of social commerce drives an active engagement that regularly presents relevant product content within the consumer's news feed and social media interactions. In this way, consumers can interact with others using likes, comments and tagging posts within their friend network. Moreover, social commerce can help to generate loyal and sustainable customers via word of mouth and by supporting other customers to make timely buying decisions [4]. There are six dimensions of social commerce that create a sustainable social commerce platform: social shopping; rating and review; recommendation and referrals; forums and communities; social media optimisation; social ads and applications [5]. Due to emerging social commerce applications and increasing interest in this topic, researchers have conducted the number of studies to offer an additional contribution and facilitate
wider adoption of social commerce platforms. However, in this study, we analyse the
role of several different antecedents of social commerce intention and adoption that
have been examined within the literature. The analysis revealed that the effect of such
antecedents has been inconsistent across different studies in terms of significance and
coefficient relationships between independent and dependent variables. No study has
yet to conduct a consolidated view of the effect of various antecedents of social
commerce intention, use behaviour and other related dependent variables. Also, no
study has an attempt to understand the value of the relationships of social commerce
adoption. The research model is one of the essential parts of the research. Therefore, it
is also essential to understand various independent and dependent variables. However,
this study based on social commerce, the research focuses on independent/ dependent
variables and relationship that influence social commerce adoption. The study is
providing a robust view of the variables that will be supportive of creating a concrete
research model. The objectives of this study are to summarise the relationships and
analyse the weight of the relationship using the weight analysis technique. This study
to gain new insight into the various predictors of social commerce related dependent
variables including *intention, trust, satisfaction, attitude and urge to buy impulsively*.
To develop the objectives, this study undertook the following steps: (1) Identify
empirical studies that utilised different models, and associated antecedents (predictors)
for understanding consumer adoption of social commerce, (2) Conduct a weight
analysis using results from exiting studies to determine the importance of various
predictors. This paper is structured as follows. Section 2 of this paper briefly describes
the research method employed to conduct this study. Section 3 then presents the results
from the weight analysis, and the paper is concluded within section 4.

**2. Research Method**

The literature has highlighted several types of review studies for a deeper understanding of this area. This review studies were attempt weight analysis method, investigated the theories and model and conducted adoption researches. This studies based on: specific journals e.g. [6], [7], [8,9]; methods e.g. [10]; theories and models e.g. [11], [12], [13], [14,15] and topics e.g.[16], [17], [18,19,20], [21], [22], [23,24], [25-27], [28] [29]. Searches were undertaken using Scopus database the following set of keywords: "Social commerce" S-Commerce" OR "F-Commerce" AND title ABS Key "Adoption" OR "Acceptance" OR "Usage" OR "Use Behaviour" OR "Intention" OR Purchase". We make sure the keywords are included in abstracts or title or keywords of the journal paper. This search returned 166 articles. Therefore, we eliminated the conference paper, internet and newspaper blogs and only considered journal articles. Then we separated qualitative and quantitative studies and used quantitative studies for applying weight analysis technique. However, 68 studies were not related to social commerce adoption. Finally, we focus on 73 journal articles which are related to social commerce adoption and published from 2006 to 2019.

**3. Weight Analysis**

Weight analysis is a practical approach to calculate the importance of predictors.
Weight analysis determines the inductive and predictive power of an independent
variable over the dependent variable. This technique helps to rank the variables to
understand the most important and least important relationships. Also, this technique
supports to calculate each relationship, significant level and the predictors' weight. In
this study, the weight analysis technique finds out different relationships that influence
social commerce adoption. Therefore, the independent and dependent variables, which
are the most important aspects of developing a perfect adoption model. Using weight
analysis, we summarised all the relationships and segregated based on significant and
non-significant relationship. Therefore, the weight result indicated the value of the
relationships. Therefore, all the relationships, variables and weight values will be
helpful for the researcher to choose appropriate variables to develop a suitable research
model for further studies. The weight analysis approach employed within this study
was adopted from Jeyaraj et al. [30] and Tamilmani et al. [31] where the analysis of the
weights of each relationship is developed from the dependent and independent
variables. This study has selected the following most frequently examined dependent
variables: Intention to purchase, Trust, Social commerce intention, Behavioural
intention, Urge to buy impulsively, Attitude and Satisfaction. Each of the listed tables
that present the weight analysis calculates the antecedents of a specific dependent
variable, the total number of times a particular relationship has been examined and how
many times each relationship is found to be significant and non-significant. The weight
columns present the weight analysis of each of the relationships. The weight analysis
provides four different values:(a) "+1" indicate the significant relationships between
independent and dependent variables and hypothesised in positive direction, (b) "-1"
indicate the non-significant relationships between independent and dependent variables
and hypothesised in negative direction, (c) "0" suggest that the relationship of
independent and dependent variable is insignificant, (d) "Blank" when the relationships
were not examined [31]. Of the 73 articles examined, 251 are unique and can be
described as exhibiting significant relationships and 32 were categorised as non-
significant relationships. These relationships were aligned to the following seven
dependent variables: *intention to purchase* (table 1), *trust* (Table 2), *behavioural
intention* (Table 3), *social commerce intention* (Table 4), *satisfaction* (table 5), *urge to
buy impulsively* (table 6) and *attitude* (Table 7).

**3.1. Intention to Purchase**

Table 1 lists 32 out of 73 studies and highlights 108 individual relationships. The study found 63 independent variables which were aligned to the dependent variable *- Intention to purchase*. However, identical relationships that were examined in five or more studies are considered as strong utilised relationships and independent variables and were considered to be the best predictor of the dependent variable. Additionally, less than four of the relationships were considered as experimental variables with a weight of 0.80 or above., Independent variables could be considered as a promising predictor when used in less the five studies and have the perfect weight of 1 [31]. In this study, the best predictor found *trust* as an independent variable utilised maximum time with *purchase intention* (examined eight times) with all studies finding a significant effect. *Familiarity* has been utilised in four instances and all have found significant. Five independent variables occur in two *instances with buying intention, and those are Recommendations and referrals, Trust toward member, brand trust, social presence and Swift Guanxi* with weight "0". Additionally, 74 independent variables have occurred with a weight of "1". *Recommendations and referrals* have been utilised three times and found to be significant in two studies and non-significant
in one study with a weight "0.66". Informational support, perceived commerce risk, social commerce construct and usefulness analysis with intention to buy found two non-significant relationships. The weight result found "0.50" in three of those relationships. Finally, ten relationships found non-significant with weight "0" (See Table 1).

<table><caption>Table 1: Weight Analysis Summary for Intention to Purchase</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Trust</td><td>8</td><td>Kim and park [32]; Hajli et al. [33]; Makmor et al. [34]; Adwan [35]; Hajli et al. [36]; Faratin and Rodríguez [37]; Lee and Choi [38]; Zhao et al. [39].</td><td>0</td><td></td><td>8</td><td>1</td></tr><tr><td>Familiarity</td><td>4</td><td>Adwan [35]; Hajli et al. [36]; Ng [40]; Gibreel et al. [41].</td><td>0</td><td></td><td>4</td><td>1</td></tr><tr><td>Recommendations and referrals</td><td>2</td><td>Makmor and alam [42]; Mikale f et al. [43].</td><td>1</td><td>Li et al. [51]</td><td>3</td><td>0.66</td></tr><tr><td>Trust toward member</td><td>2</td><td>Farivar et al. [44]; Chen and Shen [45]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Brand trust</td><td>2</td><td>Zhao et al. [39]; Erdoğmuş [46].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Social Presence</td><td>2</td><td>Adwan [35]; Hajli et al. [36];</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Swift guanxi</td><td>2</td><td>Lin et al. [47]; Yang [48]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Familiarity</td><td>2</td><td>Hajli et al. [36]; Gibreel et al. [41].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Informational support</td><td>1</td><td>Makmor and Alam [42]</td><td>1</td><td>Li et al. [51]</td><td>2</td><td>0.5</td></tr><tr><td>Perceived commerce risk</td><td>0</td><td></td><td>2</td><td>Farivar et al. [44]; Gan et al. [50]</td><td>2</td><td>1</td></tr><tr><td>Social commerce constructs</td><td>1</td><td>Hajli et al. [49]</td><td>1</td><td>Li et al. [51]</td><td>2</td><td>0.5</td></tr><tr><td>Usefulness</td><td>1</td><td>Lee and Choi [38]</td><td>1</td><td>Gibreel et al. [41]</td><td>2</td><td>.50</td></tr></tbody></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Trust toward website; Mutual understanding; Relationship Harmony; Reciprocal favour; Trust in social network community; Closeness; WOM Content; Observe Consumer Purchase; Positive Valence WOM; Negative valence WOM; Social Desire; Commercial Desire; Good Friend; Simple friend; Non-reportable stranger; Re-routable stranger; Community commitment; Trust towards community; Trust in product recommendation; Brand engagement; Form factor; Intention to search; Para social interaction; EWOM information; Intuitive evaluation; Social identity; Rating and Reviews; Socializing; Product selection; Trust in sellers; Usage behaviour; System trust towards social commerce apps; Utilitarian value; Social value; Hedonic value; Social commerce information seeking; Innovativeness; Guanxi elements; Perceived value; Customers' experience; Discount rate; Social media product browsing; Positive; Observing consumer purchasing; Heuristic factors; Systemic factors; Attitude; Trust in sellers; Forums and Communities; Attitude towards eWOM; EWOM engagement; Social commerce information seeking

The following constructs had been tested by only one study and their effect was nonsignificant with resulting weight of 0: Perceived ease of use; Income; Emotional support; Information availability; Customer loyalty; Particularized trust towards social commerce members; Electronic inventiveness; Scarcity of time; Scarcity of quality; Provisional coupon
## 3.2. Trust

Table 2 represents 24 studies and 52 relationships on the subject of *trust*. The study found six significant relationships among *information quality* and *trust*. Additionally, five studies found significant associations between *relationship quality* and *trust*. However, three independent variables: *emotional support*, *social presence*, *familiarity* have occurred four times with *trust* and found significant relationships. The weighted analysis of the above relationships results “1” and were found as best predictors. Four hypotheses (Reputation, communication, size and WOM referrals) appeared twice and found significant relationships with weight “1”. The product price has found one significant and one non-significant relationship with the weight result “0.50”. The 19 independent variables found highlighted a relationship towards *trust* and found a significant correlation with weight “1”. Finally, two more relationships found to be non-significant with a weight result “0” which are the worst predictors in the study.

<table><caption>Table 2: Weight Analysis Summary for Trust</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Information quality</td><td>6</td><td>Lu et al. [52]; Faratin and Rodríguez [37]; Lin et al. [24]; Kim and Noh [34]; Shanmugam et al. [32]; Li et al. [51].</td><td>0</td><td></td><td>6</td><td>1</td></tr><tr><td>Relationship Quality</td><td>5</td><td>Liang and Turban [1]; Zhang et al. [54]; Hajli [55]; Sheikh et al. [56]; Tajvidi et al. [57].</td><td>0</td><td></td><td>5</td><td>1</td></tr><tr><td>Emotional Support</td><td>4</td><td>Makmor et al. [34]; Lin et al. [47]; Shanmugam et al. [53]; Li [51].</td><td>0</td><td></td><td>4</td><td>1</td></tr><tr><td>Social presence</td><td>4</td><td>Adwan [35]; Li et al. [51]; Hajli [58]; Lu et al. [52].</td><td>0</td><td></td><td>4</td><td>1</td></tr><tr><td>Familiarity</td><td>4</td><td>Gibreel et al. [41]; Hajli [32]; Adwan [35]; Li [51].</td><td>0</td><td></td><td>4</td><td>1</td></tr><tr><td>Reputation</td><td>2</td><td>Lu et al. [52]; Kim and Noh [59].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Communication</td><td>2</td><td>Lu et al. [52]; Kim and Noh [59].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Size</td><td>2</td><td>Lu et al. [52]; Kim and Noh [59].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Word-of-Mouth Referrals</td><td>2</td><td>Lu et al. [52]; Gibreel et al. [41].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>product price</td><td>1</td><td>Yahia et al. [60].</td><td>1</td><td>Gibreel et al. [41].</td><td>2</td><td>.50</td></tr></tbody></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Internal similarity, External similarity, Closeness, Social presence of interaction with sellers, Perception of others, Trust in sellers, Susceptibility reviews, General credibility, Perceived Security, E-WOM information, Perceived security, Perceived ease of use, Trust in product, Symbolic value, Feedback, Interactivity, Social Commerce Constructs, Transaction Safety.

The following constructs had been tested by only one study and their effect was nonsignificant with resulting weight of 0: Persuasiveness, Economic Feasibility

## 3.3. Behavioural intention

Table 3 presents 11 studies on *behavioural intention* as the dependent variable. The literature analysis found 26 significant relationships among various independent
variables with *behavioural intention* and six non-significant relationships. The best predictor is perceived usefulness which was appeared in six studies with the weight “1”. Perceived ease of use hypnosis in four studies with significant relationship and one study found non-significant relationship that weight “.080”. Risk appeared in three studies and weight “0.66”. Three studies quantified the factors - effort expectancy, facilitating conditions and social influence with *behavioural intention* as significant and defined one as non-significant with weight result “0.50”. The worst relationship found among Perceived connective affordances and *behavioural intention* with weight result “0”.

<table><caption>Table 3: Weight Analysis Summary for Behavioural intention</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Perceived usefulness</td><td>6</td><td>Williams [61]; Biucky et al. [62]; Hajli et al. [58]; Featherman (2016); Shin et al. (2012); Tello et al. (2018)</td><td>0</td><td></td><td>6</td><td>1</td></tr><tr><td>Perceived ease of use</td><td>4</td><td>Kim and Noh [59]; Hajli [58]; Biucky et al [62]; Featherman and Hajli [63]</td><td>1</td><td>Williams [61]</td><td>5</td><td>.80</td></tr><tr><td>Risk</td><td>2</td><td>Biucky et al. [62]; Tello et al. [64].</td><td>1</td><td>Featherman and Hajli [63]</td><td>3</td><td>.66</td></tr><tr><td>Effort expectancy</td><td>1</td><td>Gatautis and Medziausiene [65]</td><td>1</td><td>Sheikh et al. [68]</td><td>2</td><td>.50</td></tr><tr><td>Facilitating conditions</td><td>1</td><td>Gatautis and Medziausiene [65]</td><td>1</td><td>Sheikh et al. [68]</td><td>2</td><td>.50</td></tr><tr><td>Social influence</td><td>1</td><td>Gatautis and Medziausiene [65]</td><td>1</td><td>Sheikh et al. [68]</td><td>2</td><td>.50</td></tr><tr><td>Perceived enjoyment</td><td>2</td><td>Shin [66]; Akman and Mishra [67]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Subjective norm</td><td>2</td><td>Featherman and Hajli [63]; Shin [66].</td><td>0</td><td></td><td>2</td><td>1</td></tr></tbody></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Learning and training, perceived hedonic affordances, perceived utilitarian affordance, Innovativeness, Convenience, Perceived Social pressure, Perceived satisfaction, Perceived awareness, perceived Ethics, Trust perception person, The website's reputation, Website visual and design, Attitude, Social commerce constructs, Price saving orientation, Hedonic motivations, Habit, Performance expectancy

The following constructs had been tested by only one study and their effect was non-significant with resulting weight of 0: Perceived connective affordances

### 3.4. Social commerce intention

*Social commerce intention* quantified as dependent variables appeared in 11 studies with 40 relationships. However, 35 relationships were found to be significant, and five relationships defined as non-significant. Table 4 lists various independent variables with the relationship between *social commerce intentions*. Social support with *social commerce intention* was found to be the best predictors. The relationships have appeared in five studies with weight “1”. Therefore, Flow, relationship quality, website quality and social presence appeared three times with significant relationships. Informational support, trust towards community and emotional support found twice with significant relationships as well. However, all the relationships are weight “1”
which is found as best predictors. Finally, the worst predictors are Habit, forums and communities, recommendations and referrals, perceived interactivity and perceived personalization with *social commerce intention* found the negative relationship and weight “0.”

<table><caption>Table 4: Weight Analysis Summary for Social commerce intention</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Social support</td><td>5</td><td>Liang and Turban [1]; Hajli [55]; Zhang et al. [54]; sheikh et al. [28]; Hajli and Sims [74]; Hajli [55]; Liang et al. [69]; Zhang et al. [54] sheikh et al. [56]; Hajli and Sims [70].</td><td>0</td><td></td><td>5</td><td>1</td></tr><tr><td>Flow</td><td>3</td><td>Zhang et al. [71]; Molinillo et al. [72]; Bhat and Singh [73]</td><td>0</td><td></td><td>3</td><td>1</td></tr><tr><td>Relationship Quality</td><td>3</td><td>Hajli [55]; Liang et al. [69]; sheikh et al. [56].</td><td>0</td><td></td><td>3</td><td>1</td></tr><tr><td>Web site quality</td><td>3</td><td>Liang and Turban. [1]; Molinillo et al. [72]; Bhat and Singh [43]</td><td>0</td><td></td><td>3</td><td>1</td></tr><tr><td>Social presence</td><td>3</td><td>Zhang et al. [71]; Molinillo et al. [72]; Bhat and Singh [43].</td><td>0</td><td></td><td>3</td><td>1</td></tr><tr><td>Informational support</td><td>2</td><td>Molinillo et al. [72]; Lal [74].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Trust towards community</td><td>2</td><td>Lal [74]; Goraya et al. [75].</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Emotional support</td><td>2</td><td>Molinillo et al. [72]; Bhat and Singh [43].</td><td>0</td><td></td><td>2</td><td>1</td></tr></tbody></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Social commerce constructs, Rating and reviews, Community commitment, Trust towards Members, Service quality, Perceived Sociability, Trust in Platform, Ease of navigation, Hedonic motivation, Perceived ease of use, facilitating conditions, Trust in the s-vendor.

The following constructs had been tested by only one study and their effect was non-significant with resulting weight of 0: Perceived personalization, Perceived interactivity, Recommendations and Referrals, Forums and Communities, Habit

### 3.5. Satisfaction

Nine studies used satisfaction as dependent variables and are presented in table 5. In this study we found 19 significant relationships and one non-significant relationship. The study found the best predictors are Social support, utilitarian, hedonic, relationship quality and confirmation that appeared twice. However, the relationships are found significant with weight “1”. Information quality with satisfaction appeared once as a significant and once as a non-significant predictor and found the weight “0.50”. Moreover, Perceived usefulness, trust, service quality, system quality, physical environment quality, outcome quality, interaction quality, perceived risk has relationship with satisfaction and result significant with weight “1”. This study did not find any non-significant relationships within the literature.

<table><caption>Table 5: Weight Analysis Summary for Satisfaction</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Social support</td><td>2</td><td>Gan et al. [50]; Osatuyi et al. [76]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Utilitarian</td><td>2</td><td>Gan et al. [50]; Osatuyi et al. [76]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Hedonic</td><td>2</td><td>Gan et al. [50]; Osatuyi et al. [76]</td><td>0</td><td></td><td>2</td><td>1</td></tr></tbody></table>
<table><tr><td>Relationship Quality</td><td>2</td><td>Liang and Turban [1]; Zhang et al. [54]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Confirmation</td><td>2</td><td>Hew et al. [77,78]</td><td>0</td><td></td><td>2</td><td>1</td></tr><tr><td>Information quality</td><td>1</td><td>Vongsraluang and Bhatiasevi [79]</td><td>1</td><td>Cho and Son [80]</td><td>2</td><td>0.5</td></tr></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Perceived usefulness, trust, Service quality, system quality, physical environment quality, outcome quality, interaction quality, perceived risk

### 3.6. Urge to buy impulsively

Urge to buy impulsively was referenced in six different studies with 12 significant relationships and one non-significant relationship. Table 6 presents the urge to buy impulsively as dependent variables with Hedonic shopping value and impulsiveness as independent variables. Both of the relationships appeared twice and found significant predictors with weight “1”. However, the independent variables such as *consumer attitude*, *arousal*, *pleasure*, *affective trust in recommender*, *serendipity information*, *scarcity*, *para social interaction* and *perceived enjoyment* relationship with the *urge to buy impulsively* found significant with weight “1”. Therefore, the worst predictor is *utilitarian shopping* value with weight “0”.

<table><caption>Table 6: Weight Analysis Summary for Urge to buy impulsively</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Hedonic Shopping value</td><td>2</td><td>Chung et al. [81]; Xiang et al. [82]</td><td></td><td></td><td>2</td><td>1</td></tr><tr><td>Impulsiveness</td><td>2</td><td>Chung et al. [81]; Xiang et al. [82]</td><td></td><td></td><td>2</td><td>1</td></tr></tbody></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Consumer attitude, arousal, Pleasure, Affective trust in recommender, Serendipity information, Scarcity, Para social interaction, Perceived enjoyment.

The following constructs had been tested by only one study and their effect was non-significant with resulting weight of 0: Utilitarian shopping value

### 3.7. Attitude

Table 7 has listed consumer *attitude* towards social commerce. Five studies used *attitude* as a dependent variable. In this study, the best predictor is perceived enjoyment with *attitude* that appeared in two studies and found significant relationships. However, *Usefulness*, *Ease of use*, *Social interaction*, *Vendor trust*, *Social networking site trust*, *Systemic factors*, *Heuristic factors*, *Perceived benefit*, *Trust in the initiator*, *Peer norm*, *Trustworthiness* and *Attractiveness with attitude* weight “1”.

<table><caption>Table 7: Weight Analysis Summary for Attitude</caption><thead><tr><th>IV</th><th>Sig</th><th>Citation</th><th>Non-Sig</th><th>Citation</th><th>Total</th><th>Weight</th></tr></thead><tbody><tr><td>Enjoyment</td><td>2</td><td>Shin [66]; Cho and Son [80]</td><td>0</td><td></td><td>2</td><td>1</td></tr></tbody></table>

The following constructs had been tested by only one study and their effect was significant with resulting weight of 1: Usefulness, Ease of use, Social interaction, Vendor trust, Social networking site trust, Systemic factors, Heuristic factors, Perceived benefit, Trust in the initiator, Peer norm, Trustworthiness, Attractiveness.

## 4. Discussion and Conclusion

This study conducted a weight analysis technique to determine the importance of various predictors of consumer adoption of social commerce. However, it is essential
that a robust research model needs reliable variables, and this study provided a
summary of those variables. The results revealed the most important independent and
dependent variables that influence social commerce adoption. Using weight analysis
technique, the study finds out the different value of the predictors. The study found
Trust, behavioural intention, social commerce intention, urge to buy impulsively,
satisfaction and attitude that used in maximum studies that influence social commerce.
The weight analysis has identified the best, moderate and worst predictors of consumer
adoption for social commerce. The analysis of this study found that Trust to purchase
intention, information quality with Trust, social support with social commerce intention
and satisfaction, informational quality with behavioural intention, perceived enjoyment
with attitude and hedonic shopping value with the urge to buy are the best predictors.
There is no study without limitation. However, this study points out some limitation
and recommend future step for the scholar to take forward this study. Firstly, the study
has considered journal papers. Therefore, future research could involve conference
papers to minimise publication bias. Secondly, the study did not include any control
variables. However, future research can separately analyse them and can showcase their
impact on the independent/dependent variables. Thirdly, this study considers the
essential variables and relationship that influence social commerce adoption. However,
some other variables appeared in less study. A future study could involve those for a
better view. Lastly, the study considered the weight analysis method. Therefore, Future
research can extend this study further using a different method such as Meta-analysis
with the combination of weight analysis.

References

1. Liang, T. P., Ho, Y. T., Li, Y. W., and Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. *International journal of electronic commerce, 16*(2), 69-90.

2. Oragui, D. (2019). *Social Commerce: 8 Benefits for Your Business*. [online] Blog.salesandorders.com. Available at blog.salesandorders.com/social-commerce-benefits 30(2), 1-3.

3. Search Engine Journal. (2019). *15 Must-Have Features for Ecommerce Sites*. [online] Available at searchenginejournal.com/ecommerce-guide/must-have-website-features

4. Stephen, A. T., & Toubia, O. (2010). Deriving value from social commerce networks. *Journal of marketing research, 47*(2), 215-228.

5. Shadkam, M., and O'Hara, J. (1970). Social commerce dimensions: The potential leverage for marketers. *The Journal of Internet Banking and Commerce, 18*(1), 1-14.

6. Avison, D. E., Dwivedi, Y. K., Fitzgerald, G., & Powell, P. (2008). The beginnings of a new era: time to reflect on 17 years of the ISJ. *Information Systems Journal, 18*(1), 5-21.

7. Dwivedi, Y. K., & Kuljis, J. (2008). Profile of IS research published in the European Journal of Information Systems. European Journal of Information Systems, 17(6), 678-693.

8. Dwivedi, Y. K., Lal, B., Mustafee, N., & Williams, M. D. (2009). Profiling a decade of Information Systems Frontiers' research. *Information Systems Frontiers, 11*(1), 87.

9. Dwivedi, Y. K., Kiang, M., Williams, M. D., & Lal, B. (2008). Profiling electronic commerce research published in the Journal of Electronic Commerce Research. *Journal of Electronic Commerce Research, 9*(2), 77-91.

10. Choudrie, J., & Dwivedi, Y. K. (2005). Investigating the research approaches for examining technology adoption issues. *Journal of Research Practice, 1*(1), 1

11. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. *Information Systems Frontiers, 21*(3), 719-734.

12. Sarker, P., Kizgin, H., Rana, N. P., & Dwivedi, Y. K. (2019, September). Review of Theoretical Models and Limitations of Social Commerce Adoption Literature. In Conference on e-Business, e-Services and e-Society (pp. 3-12). Springer, Cham.

13. Weerakkody, V., Dwivedi, Y. K., & Irani, Z. (2009). The diffusion and use of institutional theory: a cross-disciplinary longitudinal literature survey. *Journal of information technology, 24*(4), 354-368.
14. Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. *Journal of Enterprise Information Management*, 28(3), 443-488.

15. Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. *Journal of Information Technology*, 24(1), 1-10.

16. Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. *Telematics and Informatics*, 34(7), 1177-1190.

17. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda. *International Journal of Information Management*, 48, 63-71.

18. Dwivedi, Y. K., Rana, N. P., Tajvidi, M., Lal, B., Sahu, G. P., & Gupta, A. (2017, March). Exploring the role of social media in e-government: an analysis of emerging literature. In *Proceedings of the 10th International Conference on Theory and Practice of Electronic Governance* (pp. 97-106). ACM.

19. Dwivedi, Y. K., Kapoor, K. K., & Chen, H. (2015). Social media marketing and advertising. *The Marketing Review*, 15(3), 289-309.

20. Dwivedi, Y. K., Venkitachalam, K., Sharif, A. M., Al-Karaghouli, W., & Weerakkody, V. (2011). Research trends in knowledge management: Analyzing the past and predicting the future. *Information Systems Management*, 28(1), 43-56.

21. Hughes, L., Dwivedi, Y. K., Misra, S. K., Rana, N. P., Raghavan, V., & Akella, V. (2019). Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. *International Journal of Information Management*, 49, 114-129.

22. Irani, Z., Gunasekaran, A., & Dwivedi, Y. K. (2010). Radio frequency identification (RFID): research trends and framework. *International Journal of Production Research*, 48(9), 2485-2511.

23. Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2019a). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. *Journal of Retailing and Consumer Services*. DoI: https://doi.org/10.1016/j.jretconser.2019.01.005

24. Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019b). Smart cities: Advances in research—An information systems perspective. *International Journal of Information Management*, 47, 88-100.

25. Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: past, present and future. *Information Systems Frontiers*, 20(3), 531-558.

26. Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2014a). Rogers' innovation adoption attributes: A systematic review and synthesis of existing research. *Information Systems Management*, 31(1), 74-91.

27. Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2014b). Innovation adoption attributes: a review and synthesis of research findings. *European Journal of Innovation Management*, 17(3), 327-348.

28. Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2015). A meta-analysis of existing research on citizen adoption of e-government. *Information Systems Frontiers*, 17(3), 547-563.

29. Shiau, W. L., Dwivedi, Y. K., & Lai, H. H. (2018). Examining the core knowledge on facebook. *International Journal of Information Management*, 43, 52-63.

30. Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. *Journal of information technology*, 21(1), 1-23.

31. Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2018, October). Mobile Application Adoption Predictors: Systematic Review of UTAUT2 Studies Using Weight Analysis. In *Conference on e-Business, e-Services and e-Society* (pp. 1-12). Springer, Cham.

32. Kim, S., and Park, H. (2013). Effects of various characteristics of social commerce (s-commerce) on consumers' trust and trust performance. *International Journal of Information Management*, 33(2), 318-332.

33. Hajli, N., Shanmugam, M., Powell, P., and Love, P. E. (2015). A study on the continuance participation in on-line communities with social commerce perspective. *Technological Forecasting and Social Change* 96(2), 232-241.

34. Makmor, N., Alam, S. S., and Aziz, N. A. (2018). Social Support, Trust and Purchase Intention in Social Commerce Era. *International Journal of Supply Chain Management*, 7(5), 572-581.

35. Al-adwan, a.s. (2018). Novel research framework for social commerce purchase intentions. *Journal of Theoretical and Applied Information Technology* 96(14), 28-29.

36. Hajli, N., Sims, J., Zadeh, A. H., and Richard, M. O. (2017). A social commerce investigation of the role of trust in a social networking site on purchase intentions. *Journal of Business Research* 71(1), 133-141.

37. Faratin, P., & Rodríguez-Aguilar, J. A. (2006). Agent-Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems.

38. Lee, H., and Choi, J. (2014). Why do people visit social commerce sites but do not buy? The role of the scarcity heuristic as a momentary characteristic. *KSII Transactions on Internet and Information Systems*, 8(7), 125-127.
39. Zhao, J. D., Huang, J. S., and Su, S. (2019). The effects of trust on consumers' continuous purchase intentions in C2C social commerce: A trust transfer perspective. *Journal of Retailing and Consumer Services*, 50(2), 42-49.

40. Ng, C. S. P. (2013). Intention to purchase on social commerce websites across cultures: A cross-regional study. *Information & management*, 50(8), 609-620.

41. Gibreel, O., AlOtaibi, D. A., and Altmann, J. (2018). Social commerce development in emerging markets. *Electronic Commerce Research and Applications* 27(2), 152-162.

42. Makmor, N. B., and Alam, S. S. (2017). Attitude towards Social Commerce: A Conceptual Model Regarding Consumer Purchase Intention and its Determinants. *International Journal of Economic Research*, 14(15), 431-441.

43. Mikalef, P., Giannakos, M. N., and Pappas, I. O. (2017). Designing social commerce platforms based on consumers' intentions. *Behaviour and Information Technology*, 36(12), 1308-1327.

44. Farivar, S., Turel, O., and Yuan, Y. (2017). A trust-risk perspective on social commerce use: An examination of the biasing role of habit. *Internet Research* 27(3), 586-607.

45. Chen, J., and Shen, X. L. (2015). Consumers' decisions in social commerce context: An empirical investigation. *Decision Support Systems* 79(1), 55-64.

46. Erdoğmuş, İ. E., & Tatar, Ş. B. (2015). Drivers of social commerce through brand engagement. *Procedia-Social and Behavioral Sciences*, 207, 189-195.

47. Lin, J., Yan, Y., and Chen, S. (2017). Understanding the impact of social commerce website technical features on repurchase intention: a Chinese guanxi perspective. *Journal of Electronic Commerce Research, 18*(3), 225-226.

48. Yang, X. (2019). Consumers' decisions in social commerce: the role of guanxi elements. *Asia Pacific Journal of Marketing and Logistics*, 25(2), 243-245.

49. Hajli, N. (2015). Social commerce constructs and consumer's intention to buy. *International Journal of Information Management* 35(2), 183-191.

50. Gan, C., and Wang, W. (2017). The influence of perceived value on purchase intention in social commerce context. *Internet Research* 27(4), 772-785.

51. Li, C. Y. (2017). How social commerce constructs influence customers' social shopping intention? An empirical study of a social commerce website. *Technological Forecasting and Social Change*, 45(2), 342-345.

52. Lu, B., Fan, W., and Zhou, M. (2016). Social presence, trust, and social commerce purchase intention: An empirical research. *Computers in Human Behaviour*, 56(2), 225-237.

53. Shanmugam, M., Sun, S., Amidi, A., Khani, F., and Khani, F. (2016). The applications of social commerce constructs. *International Journal of Information Management*, 36(3), 425-432.

54. Zhang, K. Z., Benyoucef, M., & Zhao, S. J. (2016). Building brand loyalty in social commerce: The case of brand microblogs. *Electronic Commerce Research and Applications*, 15(1), 14-25.

55. Hajli, M. N. (2014). The role of social support on relationship quality and social commerce. *Technological Forecasting and Social Change*, 87(1), 17-27.

56. Sheikh, Z., Yezheng, L., Islam, T., Hameed, Z., & Khan, I. U. (2019). Impact of social commerce constructs and social support on social commerce intentions. *Information Technology & People*, 32(1), 68-93.

57. Tajvidi, M., Richard, M. O., Wang, Y., & Hajli, N. (2018). Brand co-creation through social commerce information sharing: The role of social media. *Journal of Business Research*.

58. Hajli, M. (2012). An integrated model for e-commerce adoption at the customer level with the impact of social commerce. *International Journal of Information Science and Management* 22(1), 77-97.

59. Kim, S., and Noh, M. J. (2012). Determinants influencing consumers' trust and trust performance of social commerce and moderating effect of experience. *Information technology journal*, 11(10), 1369-1380.

60. Yahia, I. B., Al-Neama, N., and Kerbache, L. (2018). Investigating the drivers for social commerce in social media platforms: Importance of trust, social support and the platform perceived usage. *Journal of Retailing and Consumer Services*, 41(2), 11-19.

61. Williams, M. D. (2018). Social commerce and the mobile platform: Payment and security perceptions of potential users. *Computers in Human Behavior*.

62. Biucky, S. T., and Harandi, S. R. (2017). The effects of perceived risk on social commerce adoption based on tam model. *International Journal of Electronic Commerce Studies* 8(2), 173-196.

63. Featherman, M. S., and Hajli, N. (2016). Self-service technologies and e-services risks in social commerce era. *Journal of Business Ethics* 139(2), 251-269.

64. Mendoza-Tello, J. C., Mora, H., Pujol-López, F. A., & Lytras, M. D. (2018). Social commerce as a driver to enhance trust and intention to use cryptocurrencies for electronic payments. *IEEE Access*, 6, 50737-50751.

65. Gatautis, R., & Medziausiene, A. (2014). Factors affecting social commerce acceptance in Lithuania. *Procedia-Social and Behavioral Sciences*, 110, 1235-1242.
66. Shin, D. H. (2013). User experience in social commerce: in friends we trust. *Behaviour and information technology, 32*(1), 52-67.

67. Akman, I., and Mishra, A. (2017). Factors influencing consumer intention in social commerce adoption. *Information Technology and People* 30(2), 356-370.

68. Sheikh, Z., Islam, T., Rana, S., Hameed, Z., & Saeed, U. (2017). Acceptance of social commerce framework in Saudi Arabia. *Telematics and Informatics, 34*(8), 1693-1708.

69. Li, Q., Liang, N., and Li, E. Y. (2018). Does friendship quality matter in social commerce? An experimental study of its effect on purchase intention. *Electronic Commerce Research, 18*(4), 693-717.

70. Hajli, N., and Sims, J. (2015). Social commerce: The transfer of power from sellers to buyers. *Technological Forecasting and Social Change* 94(1), 350-358.

71. Zhang, H., Lu, Y., Gupta, S., and Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. *Information and Management, 51*(8), 1017-1030.

72. Molinillo, S., Liébana-Cabanillas, F., and Anaya-Sánchez, R. (2018). A social commerce intention model for traditional e-commerce sites. *Journal of theoretical and applied electronic commerce research, 13*(2), 80-93.

73. Bhat, I. H., & Singh, S. (2018). Intention to Participate on Social Commerce Platform: A Study on E-Commerce Websites. *Academy of Marketing Studies Journal, 22*(4), 1-10.

74. Lal, P. (2017). Analyzing determinants influencing an individual's intention to use social commerce website. *Future Business Journal, 3*(1), 70-85.

75. Goraya, M. A. S., Jing, Z., Shareef, M. A., Imran, M., Malik, A., and Akram, M. S. (2019). An investigation of the drivers of social commerce and e-word-of-mouth intentions: Elucidating the role of social commerce in e-business. *Electronic Markets* 34(3), 1-15.

76. Osatuyi, B., and Qin, H. (2018). How vital is the role of effect on post-adoption behaviours? An examination of social commerce users. *International Journal of Information Management, 40*(2), 175-185.

77. Hew, J. J., Lee, V. H., Ooi, K. B., and Lin, B. (2016). Mobile social commerce: The booster for brand loyalty? *Computers in Human Behaviour* 59(2), 142-154.

78. Hew, J. J., Leong, L. Y., Tan, G. W. H., Ooi, K. B., and Lee, V. H. (2017). The age of mobile social commerce: An artificial neural network analysis on its resistances. *Technological Forecasting and Social Change* 32(2), 45-46.

79. Vongsraluang, N., and Bhatiasevi, V. (2017). The determinants of social commerce system success for SMEs in Thailand. *Information Development, 33*(1), 80-96.

80. Cho, E., and Son, J. (2019). The effect of social connectedness on consumer adoption of social commerce in apparel shopping. *Fashion and Textiles* 6(1), 14-15.

81. Chung, N., Song, H. G., and Lee, H. (2017). Consumers' impulsive buying behavior of restaurant products in social commerce. *International Journal of Contemporary Hospitality Management* 29(2), 709-731.

82. Xiang, L., Zheng, X., Lee, M. K., and Zhao, D. (2016). Exploring consumers' impulse buying behavior on social commerce platform: The role of parasocial interaction. *International Journal of Information Management, 36*(3), 333-347.

83. Ali, s. y., Hussin, a. r. c., and Ghazali, m. (2018). Antecedents and consequences of ewom in social commerce. *Journal of Theoretical and Applied Information Technology* 96(1), 22-24.

84. Bai, Y., Yao, Z., and Dou, Y. F. (2015). Effect of social commerce factors on user purchase behaviour: An empirical investigation from renren. com. *International Journal of Information Management* 35(5), 538-550.

85. Braojos, J., Benitez, J., and Llorens, J. (2019). How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence. *Information and Management* 56(2), 155-171.

86. Ng, C. S. P. (2013). Intention to purchase on social commerce websites across cultures: A cross-regional study. *Information and management, 50*(8), 609-620.

87. Wang, Y., and Yu, C. (2017). Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning. *International Journal of Information Management, 37*(3), 179-189.

88. Ko, H. C. (2018). Social desire or commercial desire? The factors driving social sharing and shopping intentions on social commerce platforms. *Electronic Commerce Research and Applications, 28*(2), 1-15.

89. Cheng, X., Gu, Y., and Shen, J. (2019). An integrated view of particularized trust in social commerce: An empirical investigation. *International Journal of Information Management* 45(3), 1-12.

90. Yang, X. (2019). Consumers' decisions in social commerce: the role of guanxi elements. *Asia Pacific Journal of Marketing and Logistics, 25*(2), 243-245.

91. Hassan, M., Iqbal, Z., and Khanum, B. (2018). The role of trust and social presence in social commerce purchase intention. *Pakistan Journal of Commerce and Social Sciences* 12(1), 111-135.
92. Yeon, J., Park, I., and Lee, D. (2019). What creates trust and who gets loyalty in social commerce? *Journal of Retailing and Consumer Services*, 50(2), 138-144.

93. Yusuf, A. S., Che Hussin, A. R., and Busalim, A. H. (2018). Influence of e-WOM engagement on consumer purchase intention in social commerce. *Journal of Services Marketing*, 32(4), 493-504.

94. Fu, S., Xu, Y., and Yan, Q. (2017). Enhancing the parasocial interaction relationship between consumers through similarity effects in the context of social commerce: Evidence from social commerce platforms in China. *Journal of Strategic Marketing* 27(2), 100-118.

95. Tarmedi, E., Sulastri Sumiyati, S., and Dirgantari, P. D. (2018). Factors Affecting Customer Trust and Their Impact on Customer Behavioural Intention: A Study of Social Commerce in Indonesia. *Pertanika Journal of Social Sciences and Humanities*, 23(2), 35-36.

96. Tang, J., & Zhang, P. (2018). The impact of atmospheric cues on consumers' approach and avoidance behavioral intentions in social commerce websites. *Computers in Human Behavior*.

97. Amelina, D., & Zhu, Y. Q. (2016, June). Investigating Effectiveness of Source Credibility Elements on Social Commerce Endorsement: The Case of Instagram in Indonesia. In *PACIS* (p. 232).

98. Lin, C. S., and Wu, S. (2015). Exploring antecedents of online group-buying: Social commerce perspective. *Human Systems Management*, 34(2), 133-147.

99. Tariq, A., Wang, C., Tanveer, Y., Akram, U., and Akram, Z. (2019). Organic food consumerism through social commerce in China. *Asia Pacific Journal of Marketing and Logistics*, 31(1), 202-222.

100. Ju, J., and Ahn, J. H. (2016). The effect of social and ambient factors on impulse purchasing behaviour in social commerce. *Journal of Organizational Computing and Electronic Commerce*, 26(4), 285-306.