An increasing amount of organizations are incorporating video-mediated communication (VMC) into their website. VMC can include communication methods such as video conferences, video blogs, and video press releases. Their rising popularity indicates that corporations need to know the secret to making their VMC strategies successful.
As with all content, audience connection is key. Without an engaged audience, any type of content, including VMC will be ineffectual. Audience engagement is dependent upon how the audience cognitively processes the content.
So, the most important question to answer is how an audience processes a video message.
Regardless of mode of communication, audience engagement is key to persuasion and conversion. According to the Elaboration Likelihood Model (ELM), a prominent theory of persuasion, humans inherently strive to think as little as possible, and will unconsciously adjust their information processing methods to fit the cognitive needs of the situation. The theory states that an audience will process a persuasive message (i.e. video blog, video conference) in one of two ways: via central processing or peripheral processing. And cognitive load, or the amount a person has to think, determines which of these processing routes they take. [1]
- Central Processing: Central processing is characterized by intense scrutiny of the credibility of the source and the content of their argument. Central processing is common in settings where knowledge is shared in face-to-face communication, such as a business conference or presentation.
- Peripheral Processing: When an audience uses peripheral processing to process a message, they are more affected by the likeability and attractiveness of the source, as opposed to the validity of their argument. This type of processing occurs when the cognitive load is too heavy for central processing. Multiple factors can activate peripheral processing; sometimes the audience is simply not interested in the topic, or they are distracted by other stimuli.
Corporations that are adopting VMC often ask whether VMC is processed via centralized processing (i.e. face-to-face interaction) or via peripheral processing. This is an important question, as the answer determines how to structure VMC website content.
Some believe that VMC behaves just like face-to-face communication and is thus processed via central processing. Interestingly however, this is not the case. Research into VMC reveals that people use peripheral processing when viewing a videoconference as opposed to attending the conference in person. This means they are more influenced by speaker’s likeability than the quality of the speaker’s message.[2] The researchers highlight the role of cognitive load. In regards to knowledge sharing, levels of cognitive load seem to be higher for VMC than face-to-face communication. In other words, people have to think more when processing communication in video form as opposed to listening to a speaker in person.
Research mentions the possible role of distractions that are unique to video media as opposed to face-to-face communication. Background noise, interruptions, time pressure, and visual distractions that do not typically occur during face-to-face knowledge transfer are common in the settings where people will watch a video blog or videoconference (i.e. busy office setting)
Therefore, it seems that a likeable speaker is essential to any VMC strategy. However, if an organization wishes to emphasize their message over the likeability of their speaker, research suggests they should minimize distractions and use tactics that highlight informational cues, such as turn-taking or audio-location indicators.
VMC adds interest and variety to any corporate website. It also increases the amount of content available to an online audience, thus increasing exposure and the likelihood of conversion. While VMC is an excellent and personalized strategy to engage an audience, corporations should carefully plan how it is presented.
References
[2]Ferran, C & Watts, S: Videoconferencing in the field: A heuristic processing model
