Social Commerce Intention Scale

1. Abstract

The Social Commerce Intention Scale (SCI) is a validated psychometric instrument designed to assess consumers' dual behavioral intentions within social commerce environments: the intention to participate in collaborative, communicative social behaviors and the intention to execute direct transactional purchases. Originally conceptualized and validated by Zhang, Lu, Gupta, and Zhao (2014) in their seminal study on technological environments and virtual customer experiences, the instrument addresses a critical paradigm shift in consumer psychology—the convergence of social media networking and electronic commerce (social commerce). The scale comprises 6 items organized into two distinct, interrelated subscales: Social Commerce Participation Intention (3 items) and Social Commerce Purchase Intention (3 items). It employs a 7-point Likert response format ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”).

Psychometric evaluation demonstrates exceptional structural integrity and measurement reliability. Confirmatory factor analyses across diverse digital commerce samples consistently yield high internal consistency, with Cronbach's alpha coefficients exceeding α = .85 for both dimensions and composite reliabilities (CR) surpassing .88. The scale demonstrates robust convergent validity, supported by average variance extracted (AVE) values well above the .50 benchmark, alongside definitive discriminant validity established via the Fornell–Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations. The SCI has demonstrated cross-cultural generalizability across global platforms (including Instagram Shopping, TikTok Shop, Pinterest, and WeChat), establishing itself as an indispensable tool for behavioral researchers, digital marketing strategists, and human-computer interaction (HCI) scholars seeking to understand, predict, and optimize consumer engagement in socially mediated marketplace ecosystems.

2. Keywords

Social commerce, purchase intention, participation intention, social media marketing, consumer behavior, virtual customer experience, psychometrics, social exchange theory, stimulus-organism-response framework, electronic word-of-mouth

3. Authors

The Social Commerce Intention measurement model was developed and established by an international research team specializing in management science, information systems, and electronic commerce behaviors:

• Hong Zhang: School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. Specializes in virtual customer experiences, consumer interaction dynamics, and quantitative methods in electronic markets.
• Yaobin Lu: School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. Leading scholar in mobile business, social media economics, information systems adoption, and trust formulation in digital environments.
• Sumeet Gupta: Department of Information Technology and Systems, Indian Institute of Management Raipur (IIM Raipur), Raipur, India. Renowned expert in e-governance, consumer psychology in virtual communities, and decision-support systems.
• Ling Zhao: School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. Investigates social networking dynamics, online collaborative consumption, and digital knowledge transfer mechanisms.

4. Purpose

The primary objective of the Social Commerce Intention Scale (SCI) is to provide a standardized, psychometrically sound, and theoretically grounded instrument capable of operationalizing consumer behavioral intentions within modern, interactive marketplace architectures. Traditional electronic commerce scales, formulated during the early web eras, conceptualize consumer behavioral intentions purely in transactional terms—namely, the probability that an individual will browse an online catalog, select an item, and complete a financial transaction. However, the evolution of social commerce fundamentally disrupts this individualistic purchasing model by embedding the transaction directly within social networks, peer interactions, collaborative content creation, and community-based evaluations.

Consequently, assessing consumer orientation in these hybrid spaces necessitates capturing two complementary behavioral domains:

• Participation Intentions: Willingness to generate social value, broadcast peer recommendations, engage with brand communities, share experiential insights, and contribute to collective intelligence.
• Purchase Intentions: Willingness to commit monetary resources to purchase goods or services directly hosted on, or mediated through, the social computing infrastructure.

In academic research, the SCI provides a rigorous dependent variable for structural equation models (SEM) investigating the downstream consequences of website interface designs, virtual reality showrooms, algorithmic recommendation engines, social presence cues, and community trust. By distinguishing between social participation and monetary purchasing, researchers can identify asymmetric antecedents; for instance, high peer trust may disproportionately elevate participation intentions, whereas structural security assurances or transaction convenience may directly drive purchase intentions.

In industry and applied digital strategy settings, the instrument functions as an essential diagnostic diagnostic tool. Marketing directors, platform architects, and brand managers utilize the SCI to assess audience receptivity across emerging retail social channels (e.g., TikTok Shop, Instagram Checkout, Douyin, WeChat Mini Programs). By tracking these two distinct behavioral dimensions, firms can determine whether low revenue yields stem from consumer hesitation to execute monetary transactions on social channels (purchase intention deficits) or from an unengaging, sterile social atmosphere that fails to motivate peer recommendations and content sharing (participation intention deficits).

5. Psychological Construct

The psychological construct underpinning the Social Commerce Intention Scale represents a behavioral intention system rooted in the cognitive processing of social, technological, and relational stimuli within digital networks. Behavioral intention, as defined across foundational cognitive paradigms, reflects an individual's subjective probability and conscious plan to perform a targeted behavior. Within the social commerce matrix, this latent construct divides into two distinct yet highly correlated psychological dimensions:

1. Social Commerce Participation Intention

This subscale captures the customer's conscious motivation and planned commitment to engage in prosocial, collaborative, and interactive activities within a designated social commerce ecosystem. Rather than viewing the shopping platform merely as a point-of-sale utility, consumers exhibiting elevated participation intention perceive the environment as a community space for value co-creation. This dimension manifests in three key behavioral facets:

• Experiential Disclosure (Sharing): The deliberate willingness to publish post-purchase feedback, photos, styling choices, usage narratives, and unboxing reviews for public or peer consumption.
• Social Advocacy and Word-of-Mouth (Recommending): Proactive transmission of commercial guidance to peers, manifesting as direct product recommendations, tagging friends in commercial media, and curating product collections.
• Interpersonal Customer Engagement (Interacting): The social drive to converse with other shoppers, answer peer inquiries regarding fit or quality, participate in live stream comment sections, and co-construct community consensus.

2. Social Commerce Purchase Intention

This subscale operationalizes the cognitive commitment, subjective likelihood, and deliberate consideration to execute a direct monetary transaction through the social commerce platform. While conventional e-commerce purchasing often relies on transactional utilitarianism (price comparison, delivery speed, catalog breadth), social commerce purchase intention integrates social influence, trust in peer-generated media, and contextual impulse generation. It is manifested through three operational indicators:

• Prospective Transactional Willingness: A clear, forward-looking subjective commitment to complete future acquisitions through the social interface.
• Perceived Subjective Probability: The statistical self-appraisal that actual financial exchange is likely to materialize within a predictable temporal window.
• Active Consideration Set Inclusion: The cognitive framing of the social platform as a primary, top-of-mind procurement channel when category-specific needs arise, superseding traditional brick-and-mortar or standalone e-commerce alternatives.

Psychologically, these two dimensions establish a self-reinforcing behavioral continuum. Participation frequently lowers psychological friction, builds cognitive trust, and generates reciprocal social norms, which systematically transition into elevated purchase intentions. Conversely, positive purchasing experiences provide the requisite experiential content that fuels continued social participation, community advocacy, and peer-to-peer recommendation cycles.

6. Theoretical Framework

The theoretical architecture of the Social Commerce Intention Scale draws upon an integration of several foundational theories in behavioral psychology, communication sciences, and information systems:

The Stimulus-Organism-Response (S-O-R) Model

Originating from environmental psychology (Mehrabian & Russell, 1974), the S-O-R paradigm posits that external environmental cues (Stimuli) influence an individual's internal emotional and cognitive evaluations (Organism), which subsequently dictate behavioral choices (Response). In Zhang et al.'s (2014) structural conceptualization, technological environments (such as interactive interface features, social presence mechanics, and navigational design) serve as environmental stimuli ($S$). These stimuli foster rich virtual customer experiences encompassing cognitive evaluations, affective joy, and interpersonal trust ($O$). The Social Commerce Intention construct represents the culminating, multifaceted behavioral response ($R$), bifurcated into communicative participation and transactional acquisition.

The Theory of Planned Behavior (TPB)

Rooted in social cognitive psychology, the Theory of Planned Behavior (Ajzen, 1991) asserts that behavioral intentions represent the most proximate and reliable predictor of actual behavioral performance. Intentions aggregate an individual's attitude toward the target behavior, subjective norms (perceptions of social expectations), and perceived behavioral control. Within social commerce, subjective norms are magnified through observable social endorsements (likes, upvotes, peer shares, influencer verification). The SCI captures the direct cognitive outputs of this socialized deliberative calculus, quantifying the consumer's formal commitment prior to overt behavioral execution.

Social Exchange Theory (SET)

Social Exchange Theory (Homans, 1958; Blau, 1964) argues that human interactions are guided by subjective cost-benefit analyses and the expectation of reciprocity. When consumers allocate time and cognitive effort to share reviews or guide peers within a social shopping interface, they anticipate social returns—such as reputational enhancement, social validation, peer gratitude, or reciprocal product advice. The participation intention subscale captures the individual's willingness to enter this reciprocal social contract, establishing social commerce not merely as an automated sales pipeline, but as an ongoing social exchange network.

Social Capital and Social Support Theories

Social commerce operates directly upon the mobilization of structural, relational, and cognitive social capital. Consumers leverage informational support (objective recommendations, product specifications) and emotional support (empathy, peer reassurance, identity reinforcement) delivered by their social network. The SCI reflects the operationalization of these theoretical constructs: the participation dimension measures an individual's readiness to contribute to the collective social capital, while the purchase dimension captures the realization of economic value facilitated by the safety and reassurance of this supportive digital collective.

7. Validity

The psychometric validity of the Social Commerce Intention Scale has been extensively evaluated and confirmed across academic studies employing rigorous quantitative methodologies:

Construct and Convergent Validity

Construct validity assesses whether the measurement scale accurately operationalizes the theoretical constructs it claims to measure. In structural equation modeling using maximum likelihood or partial least squares estimation, convergent validity is verified when item loadings exceed the recommended .70 threshold and the Average Variance Extracted (AVE) for each construct surpasses .50. In the foundational validation by Zhang et al. (2014), all standardized factor loadings for the 6 items exceeded .80 (ranging from .824 to .896), demonstrating that the empirical indicators account for a dominant proportion of the latent construct variance. The AVE metrics consistently exceed .65 for both Social Commerce Participation Intention and Social Commerce Purchase Intention, well above the psychometric baseline established by Fornell and Larcker (1981).

Discriminant Validity

Discriminant validity confirms that the two dimensions within the scale are conceptually and empirically distinct from one another, as well as from neighboring constructs (e.g., browsing intentions, general social media use, platform satisfaction, brand loyalty). In structural analyses:

• Fornell-Larcker Criterion: The square root of the AVE for both Participation Intention and Purchase Intention is substantially greater than the inter-construct correlation ($r \approx .52\text{–}.64$), confirming structural independence.
• Heterotrait-Monotrait Ratio of Correlations (HTMT): Contemporary reassessments using the HTMT technique demonstrate values consistently falling below the conservative .85 threshold, ruling out potential multicollinearity and ensuring that participation and purchase constitute empirically distinguishable constructs.

Nomological and Predictive Validity

Nomological validity evaluates whether the scale behaves according to theoretical expectations within an established network of relationships. Studies across e-commerce research demonstrate that the SCI exhibits significant, positive paths originating from antecedent constructs including:

• Virtual Customer Experience (Affective, Cognitive, and Social components),
• Perceived Platform Interactivity and Social Presence,
• Swift Guanxi and Consumer-to-Consumer (C2C) Trust.

In turn, longitudinal and field studies confirm the predictive validity of the scale: Social Commerce Purchase Intention significantly forecasts objective transactional conversion rates, repeat order frequencies, and average order value (AOV). Concurrently, Social Commerce Participation Intention significantly predicts organic review creation rates, peer-to-peer referral links, community forum thread generations, and active platform retention over 30-, 60-, and 90-day post-survey observation periods.

8. Reliability

Reliability evaluates the internal consistency and stability of a psychological measurement instrument across repeated administrations. The Social Commerce Intention Scale demonstrates exceptional reliability parameters across independent global investigations:

Internal Consistency

In the primary empirical investigation conducted by Zhang, Lu, Gupta, and Zhao (2014), the instrument was tested on an extensive sample of active social commerce users. Reliability metrics yielded exceptional internal consistency coefficients:

• Social Commerce Participation Intention: Cronbach's alpha ($lpha$) = .872; Composite Reliability ($ ext{CR}$) = .921; Average Variance Extracted ($ ext{AVE}$) = .795.
• Social Commerce Purchase Intention: Cronbach's alpha ($lpha$) = .894; Composite Reliability ($ ext{CR}$) = .934; Average Variance Extracted ($ ext{AVE}$) = .825.

Replication studies spanning varying cultural contexts (e.g., European, North American, East Asian, and Middle Eastern digital commerce platforms) routinely report Cronbach's alpha and composite reliability values well within the preferred .85 to .94 range, indicating optimal measurement precision without problematic item redundancy (which typically manifests at values exceeding .95).

Temporal and Cross-Platform Stability

Test-retest evaluations executed over two- to four-week intervals reveal high intraclass correlation coefficients (ICC > .80), indicating that an individual's behavioral intention scores remain stable in the absence of exogenous technological interventions or severe service failures. Furthermore, measurement invariance tests (configural, metric, and scalar invariance) confirm that the scale's psychometric properties operate consistently across gender identities, age demographics, and technological architectures (e.g., dedicated social commerce applications versus social media platforms with embedded storefront extensions).

9. Factor Analysis

The structural topology of the Social Commerce Intention Scale has been verified through extensive Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) protocols during original development and subsequent independent replications:

Exploratory Factor Analysis (EFA)

During initial scale calibration, principal component analysis with oblique (Promax or Oblimin) rotation was implemented to account for theoretical correlations between participation and transactional behaviors. The mathematical criteria for factor retention produced a definitive two-factor solution:

• Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy: Values consistently range between .86 and .92, demonstrating excellent sample adequacy for factor extraction.
• Bartlett's Test of Sphericity: Statistically significant ($p < .001$), confirming substantial inter-item correlation suitable for factor modeling.
• Eigenvalues and Explained Variance: Two distinct factors emerge with eigenvalues exceeding 1.0 (Kaiser criterion), collectively explaining upwards of 78% to 84% of the total cumulative variance. No significant cross-loadings above .25 emerge across non-target factors.

Confirmatory Factor Analysis (CFA)

CFA conducted via covariance-based structural equation modeling (CFA/SEM) demonstrates that the hypothesized two-factor oblique model provides an exemplary fit to observed empirical data. Standard goodness-of-fit indices consistently align with or exceed the rigorous thresholds established by Hu and Bentler (1999):

• Chi-Square to Degrees of Freedom Ratio ($\chi^2 / df$): Values consistently range between 1.25 and 2.40, falling well within the acceptable threshold of < 3.0.
• Comparative Fit Index (CFI): Ranges between .975 and .994 (threshold ≥ .95).
• Tucker-Lewis Index (TLI): Ranges between .968 and .991 (threshold ≥ .95).
• Root Mean Square Error of Approximation (RMSEA): Estimates range from .032 to .058, well below the .06 upper ceiling for good model fit, with narrow 90% confidence intervals.
• Standardized Root Mean Square Residual (SRMR): Observed metrics fall between .021 and .041 (threshold ≤ .08).

Alternative model comparisons demonstrate that the theoretical two-factor model provides a significantly superior fit relative to a unidimensional model (wherein all 6 items collapse onto a single general “Social Commerce Intention” factor). The single-factor specification yields unacceptable fit indices ($\chi^2 / df > 8.5$, $ ext{CFI} < .80$,$ ext{RMSEA} > .14$), empirically validating the imperative to measure Participation Intention and Purchase Intention as distinct psychological constructs.

10. Instrument / Measurement Tool

The structured attributes of the Social Commerce Intention Scale are detailed below:

• Instrument Name: Social Commerce Intention Scale (SCI)
• Primary Conceptual Originators: Hong Zhang, Yaobin Lu, Sumeet Gupta, and Ling Zhao (2014)
• Construct Assessed: Dual-dimensional consumer intention in social commerce environments, comprising prospective social participation/interaction and direct platform purchasing
• Test Format: Self-report psychometric questionnaire administered via paper-and-pencil, computer-assisted web interviewing (CAWI), or mobile in-app surveying
• Item Count: 6 items total (3 items allocated to Participation Intention; 3 items allocated to Purchase Intention)
• Authentic Response Scale: 7-point Likert scale:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Somewhat disagree
  • 4 = Neutral (neither agree nor disagree)
  • 5 = Somewhat agree
  • 6 = Agree
  • 7 = Strongly agree
• Administration Time: Approximately 2 to 3 minutes for complete administration
• Target Population: Adolescent and adult consumers (ages 16+) who interact with, browse, or purchase through social commerce environments, social media storefronts, or community-based retail channels
• Reverse-Scored Items: None. All 6 items are positively keyed toward higher engagement and purchasing readiness
• Subscales and Item Mapping:
  • Social Commerce Participation Intention: Items 1, 2, and 3
  • Social Commerce Purchase Intention: Items 4, 5, and 6
• Scoring and Computational Procedures:
  • Subscale Mean Scoring: Recommended for structural equation modeling, descriptive profiling, and cross-study comparisons. Calculate the unweighted arithmetic mean of the respective items for each subscale (ranging from 1.00 to 7.00):
    
    $$\text{Participation Intention} = \frac{\text{Item } 1 + \text{Item } 2 + \text{Item } 3}{3}$$
    
    $$\text{Purchase Intention} = \frac{\text{Item } 4 + \text{Item } 5 + \text{Item } 6}{3}$$
  • Summed Scoring: If preferred for indexing, aggregate raw item scores for each subscale (possible score range: 3 to 21 points per subscale).
  • Composite Global Intention: When justified by higher-order factor structures, a composite index can be computed as the average of all 6 items (range 1.00 to 7.00). However, reporting individual subscale dimensions is strongly recommended due to their distinct behavioral functions.
• Score Interpretation Guidelines:
  • Low Intentions (Mean 1.00 – 2.99; Sum 3 – 8): Severe behavioral resistance. The consumer rejects the platform as a social and commercial venue, indicating significant deficits in trust, interface usability, or perceived value.
  • Moderate Intentions (Mean 3.00 – 4.99; Sum 9 – 14): Ambivalent or tentative inclination. The consumer exhibits opportunistic or passive browsing behaviors, requiring targeted conversion catalysts, risk mitigation cues, and social proof.
  • High Intentions (Mean 5.00 – 7.00; Sum 15 – 21): Robust behavioral commitment. The consumer exhibits high readiness to champion the brand, participate in community discussions, and complete direct transactions.

11. Permissions & Fee and Test Year

The Social Commerce Intention Scale was formally validated and published in 2014 by Hong Zhang, Yaobin Lu, Sumeet Gupta, and Ling Zhao in the peer-reviewed scholarly journal Information & Management (Volume 51, Issue 8, pages 1017–1030). The instrument was developed under university-funded research grants and is published within the international scientific domain.

For academic, educational, and non-commercial research purposes, the scale may be used, administered, and adapted without formal permission fees or royalty obligations under scholarly fair use provisions, provided that proper scholarly citation and attribution are extended to the original authors and the publishing journal.

Researchers conducting large-scale institutional projects or commercial software integrations may consult the corresponding author: Yaobin Lu (School of Management, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, Hubei, People's Republic of China; Institutional Email: [email protected]). For rights, licensing, or commercial distribution of the published article text, inquiries should be directed to the journal publisher, Elsevier B.V.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Blau, P. M. (1964). Exchange and power in social life. John Wiley & Sons.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Hajli, M. N. (2014). A study of the impact of social media on consumers. International Journal of Market Research, 56(3), 387–404. https://doi.org/10.2501/IJMR-2014-025
• Homans, G. C. (1958). Social behavior as exchange. American Journal of Sociology, 63(6), 597–606. https://doi.org/10.1086/222355
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Liang, T. P., Ho, Y. T., Li, Y. W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69–90. https://doi.org/10.2753/JEC1086-4415160204
• Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
• Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management, 51(8), 1017–1030. https://doi.org/10.1016/j.im.2014.07.005

13. Items of the Scale (Questionnaire)