Virtual Social Influencer Scale Development and Validation: An Algorithmic Approach

Patrick van Esch et al.

Australasian Marketing Journal2026https://doi.org/10.1177/14413582261423028article
AJG 1ABDC A
Weight
0.50

Abstract

Influencer marketing has emerged as a prominent strategy for brands to engage with consumers in the digital age, with virtual social influencers playing a pivotal role in shaping consumer perceptions and behaviors. To effectively evaluate influencer effectiveness and optimize marketing strategies, there is a need for a standardized measure. We introduce a virtual social influencer scale, which provides a comprehensive framework for assessing their performance across the dimensions of trustworthiness and connectedness. Drawing on insights from socio-technical systems theory, the use of an algorithmic approach in scale development spawns from the psychology literature. This research provides the foundation of its introduction into the marketing discipline. Future research directions include refining the scale, addressing algorithmic limitations, and exploring cross-cultural variations to enhance the scale’s validity and applicability in diverse contexts. The virtual social influencer scale represents a significant advancement in influencer marketing research, offering valuable insights to marketers, researchers, and practitioners in the digital marketing landscape.

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https://doi.org/https://doi.org/10.1177/14413582261423028

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@article{patrick2026,
  title        = {{Virtual Social Influencer Scale Development and Validation: An Algorithmic Approach}},
  author       = {Patrick van Esch et al.},
  journal      = {Australasian Marketing Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14413582261423028},
}

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Virtual Social Influencer Scale Development and Validation: An Algorithmic Approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.