Social commerce development in emerging markets

Gibreel, Omer; AlOtaibi, Dhari A.; Altmann, Jörn

doi:10.1016/j.elerap.2017.12.008

Cited by 137 publications

(137 citation statements)

References 25 publications

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“…Social commerce is a new development in e-commerce and many scholars have carried out research on this topic (Curty & Zhang, 2011;Gibreel, AlOtaibi, & Altmann, 2018;Kim & Park, 2013). Social commerce can be defined as word-of-mouth in e-commerce (Dennison, Bourdagebraun, & Chetuparambi, 2009) or as taking advantage of relationships in a social network to gain commercial benefits (Liang et al, 2011).…”

Section: Mobile Social Commercementioning

confidence: 99%

“…From the perspective of trust, Hajli (2017) studied the effect of trust on buying intentions on social networking sites. Some scholars found that familiarity and trust play a major role in mediating exchanges between sellers and buyers and have a positive effect on buyers' perceived usefulness of a social commerce platform (Gibreel et al, 2018). Cheng, Gu and Shen (2019) discussed two types of trust (i.e., particularised trust and system trust) that promote trust formation in social commerce.…”

Section: Mobile Social Commercementioning

confidence: 99%

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How Social Support Motivates Trust and Purchase Intentions in Mobile Social Commerce

Liu¹,

Su²,

Du³

et al. 2019

RBGN

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Purpose-This study refers to the social support and trust theories to explore what kind of factors affect the users' intention to adopt MSC. Design/methodology/approach-A total of 207 subjects who had previously experienced or known about MSC were identified and partial least squares were performed to analyse the measurement and structural models. Findings-The results indicated that MSC purchase intentions are affected significantly by four other factors (social support, perceived usefulness, subjective norm, and trust) and trust, in turn, is affected by three factors (social support, perceived usefulness, and subjective norms). Originality/value-First, this study examines the mechanism of trust formation and actions in the MSC context. Second, this study contributes by confirming the important role of social factors in the MSC context. Third, the study expands the social support theory. Finally, our research provides empirical evidence for enterprises that have adopted MSC to support their marketing strategy and programmes.

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Section: Mobile Social Commercementioning

confidence: 99%

Section: Mobile Social Commercementioning

confidence: 99%

How Social Support Motivates Trust and Purchase Intentions in Mobile Social Commerce

Liu¹,

Su²,

Du³

et al. 2019

RBGN

View full text Add to dashboard Cite

show abstract

“…[22] Knowledgeableness and confidence are instrumental in facilitating transactions between salespersons and purchasers. [11] Consumers' approaches in relation to social commerce sites are considerably shaped by diverse kinds of perception (of accessibility, of purchase gratification, and of buying risk). [24] Relational offline determinants notably impact online repurchase intentions, while WoM do not alter e-WoM.…”

Section: Discussionmentioning

confidence: 99%

“…[1] WoM is instrumental in building trust [5][6][7][8][9][10] and is decisive in accelerating purchasers' predisposition and purpose to try to find goods on social commerce sites. [11] Online consumer-to-consumer data transfers affect attitude [12][13][14][15][16] which, in succession, notably impacts WoM and eWoM repurchase purposes. [17] WoM content, positive and negative WoM behavior, and monitoring other consumers' acquisitions considerably shape consumers' purpose to purchase a product [18][19][20][21], consequently maximizing the prospect of concrete shopping and sharing item data on social commerce platforms.…”

Section: Introductionmentioning

confidence: 99%

The role of electronic word-of-mouth in influencing consumer repurchase intention in social commerce

Lăzăroiu¹,

Popescu²,

Nica³

2020

SHS Web Conf.

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This research investigates the way consumer repurchase intention in social commerce is shaped by electronic word-of-mouth at global scale. Building our argument by drawing on data collected from Deloitte, eMarketer, MarketingCharts, Marketing Insider Group, McKinsey, Mediakix, Nielsen, Smart Insights, and Statista, we performed analyses and made estimates regarding trusted information sources according to consumers, products/services that Internet users have personally recommended to others, channels through which Internet users would like to receive recommendations from others, leading social media marketing tactics among marketers/media agency professionals, sources that have influenced a recent purchase decision, what are retail digital buyers most concerned about when choosing a social e-commerce platform, important information in online reviews that influence purchase decisions according to Internet users, % of respondents who made a purchase influenced by electronic word-of-mouth channels, leading social media trends that will have the biggest effect on their advertising and marketing strategies, Internet users who agree that online reviews have an important influence on purchase decisions (by age), effective content formats for influencer marketing, and factors that influence whether a product is considered at each stage of the consumer decision journey. The data for this research were collected through an online survey questionnaire and were analyzed via structural equation modeling on a sample of 4,200 respondents.

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“…Menurut [4], komentar di Instagram sangat berharga, informatif, dan sangat membantu. Bagi penjual komentar adalah fitur yang menunjukkan respons pengguna Instagram terhadap produk yang ditawarkan.…”

Section: Pendahuluanunclassified

Analisis Komentar Potensial pada Social Commerce Instagram Menggunakan TF-IDF

Musyarofah¹,

Utami²,

Raharjo³

2020

eksplora

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Komentar di Instagram sangat berharga, informatif dan sangat membantu. Bagi penjual komentar adalah fitur yang menunjukkan respons pengguna Instagram terhadap produk yang ditawarkan, dan melalui fitur komentar penjual dapat menemukan pelanggan yang potensial. Manfaat tersebut diperoleh apabila penjual melakukan analisis pada komentar di toko Instagram-nya. Sangat dimungkinkan untuk menganalisis secara manual apabila data komentar pada tokonya berjumlah sedikit namun apabila komentar yang dimiliki banyak maka akan lebih cepat apabila menggunakan sistem. Banyaknya spam dapat mengganggu informasi yang ada pada komentar, sehingga tidak menjamin banyaknya komentar pada sebuah posting-an maka banyak pula yang ingin membeli produk tersebut. Oleh karena itu dibutuhkan sistem yang bisa memfilter komentar agar penjual dapat menemukan pelanggan yang tepat untuk produknya. Penelitian ini menggunakan algoritma TF-IDF untuk mengklasifikasikan komentar ke dalam 2 kelas (potensial dan tidak potensial) dan memperoleh akurasi sebesar 80%, presisi 0,76 dan recall 0,94. Berdasarkan hasil penelitian pada 294 komentar, 27% di antaranya adalah komentar tidak potensial. Kata yang menunjukkan minat beli seseorang adalah “berapa”, ”kak”, ”ada”, dan ”tidak”, sedangkan kata dominan pada komentar tidak potensial adalah kata “mention” yang menunjukkan aktivitas mention.

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Social commerce development in emerging markets

Cited by 137 publications

References 25 publications

How Social Support Motivates Trust and Purchase Intentions in Mobile Social Commerce

How Social Support Motivates Trust and Purchase Intentions in Mobile Social Commerce

The role of electronic word-of-mouth in influencing consumer repurchase intention in social commerce

Analisis Komentar Potensial pada Social Commerce Instagram Menggunakan TF-IDF

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