User:Caj94/Elaboration likelihood model

From Wikipedia, the free encyclopedia

The elaboration likelihood model is the level of elaboration that occurs in the thinking of someone who receives a certain message. This is based on the motivation to be invested and the behaviors that occur because of it. [1] There are two types of processing within the elaboration likelihood model: central processing and peripheral processing. When a message is processed through peripheral routes, the receiver does not have to think elaborately when making a decision. However, when a message is processed through central routes, receivers are more engaged and they use critical thinking skills when making a decision. [1]

In healthcare[edit]

Healthcare

Recent research has been conducted to apply the ELM to the healthcare field. In 2009, Angst and Agarwal published a research article, "Adoption of Electronic Health Records in the Presence of Privacy Concerns: the Elaboration Likelihood Model and Individual Persuasion".[2] This research studies electronic health records (EHRs), (an individual's) concern for information privacy (CFIP) and the elaboration likelihood model (ELM). The two researchers aimed to investigate the question, "Can individuals be persuaded to change their attitudes and opt-in behavioral intentions toward EHRs, and allow their medical information to be digitized even in the presence of significant privacy concerns?"[3]

When the COVID-19 pandemic first broke out in 2020, authorities relied heavily on persuasion tactics in order for the population to adopt behavior changes so that the outbreak could be controlled. A study was published in the European Review of Social Psychology that described the use of the elaboration likelihood model to organize persuasion tactics used in order to conclude which ones led to behavior change based on level of interest and motivation to respond.[4] The study described the theory as, "useful" when using it to analyze persuasion amidst the pandemic "as it allows health communicators to identify variables that are likely to lead to the greatest amount of persuasion depending on whether recipients are likely to process the message deeply." [5]

Another study looked specifically into the discourse surrounding vaccines on twitter by using the elaboration likelihood model to study the differences between pro-vaccine messages and anti-vaccine messages.[6] The study, published in the Journal of Health Communications, found that pro-vaccine messages relied heavily on the central processing route while anti-vaccine messages used the peripheral processing route more, but the difference wasn't as drastic.[7]

Since the ELM model provides an understanding how to influence attitudes, the said model could be leveraged to alter perceptions and attitudes regarding adoption and adaptation of change.

Findings of the research included:

  • "Issue involvement and argument framing interact to influence attitude change, and that concern for information privacy further moderates the effects of these variables."
  • "Likelihood of adoption is driven by concern for information privacy and attitude."
  • "An individual's CFIP interacts with argument framing and issue involvement to affect attitudes toward EHR use and CFIP directly influence opt-in behavioral intentions."
  • "Even people who have high concerns for privacy, their attitudes can be positively altered with appropriate message framing."


"In Media:"

The emergence of new and popular social media allow for the elaboration likelihood model to be used to analyze how users are persuaded and how users persuade others through different platforms.

"In marketing"

Another study published in January 2023 was conducted to evaluate the use of the ELM and its central and peripheral routes of processing to evaluate viral advertisements between 2009 and 2019. Sigal Segev and Juliana Fernandes found that the viral video ads contained more peripheral cues rather than central cues. [8] Similarly, Piao Pan and Hao Zhang published "Research on Social Media Advertising Persuasion Based on the Elaboration Likelihood Model" and found that when advertisements tried to be more persuasive, consumers were more likely to take the central processing route and become more involved in the content. [9] On the other hand, when advertisements tried to be more informative, consumers were more likely to take the peripheral processing route and be less involved. [9]

In advertising and marketing[edit][edit]

Advertising The elaboration likelihood model can be applied to advertising and marketing.

In 1983, Petty, Cacioppo and Schumann conducted a study to examine source effects in advertising. It was a product advertisement about a new disposable razor. The authors purposefully made one group of subjects highly involved with the product, by telling them the product would be test marketed soon in the local area and by the end of the experiment they would be given a chance to get a disposable razor. Whereas, the authors made another group of subjects have low involvement with the product by telling them that the product would be test marketed in a distant city and by the end of the experiment they would have the chance to get a toothpaste. In addition to varying involvement, the authors also varied source and message characteristics by showing a group of the subjects ads featuring popular athletes, whereas showing other subjects ads featuring average citizens; showing some subjects ads with strong arguments and others ads with weak arguments. This experiment shows that when the elaboration likelihood was low, featuring famous athletes in the advertisement would lead to more favorable product attitudes, regardless of the strength of the product attributes presented. Whereas when elaboration likelihood was high, only the argument strength would manipulate affected attitudes. Lee et al. supported the studies on that product involvement strengthens the effects of "endorser–product congruence on consumer responses" when the endorsers expertise is well related with product to create source credibility. Lee's finding also helps to understand celebrity endorsement as not only a peripheral cue but also a motivation for central route.

Later in 1985, Bitner, Mary J., and Carl Obermiller expand this model theoretically in the field of marketing. They proposed in the marketing context, the determinant of routes is more complex, involving variables of situation, person, and product categories.

It is widely acknowledged that effects of ads are not only limited to the information contained in the ad alone but are also a function of the different appeals used in the ads (like use celebrities or non-celebrities as endorsers). In a study conducted by Rollins and Bhutada in 2013, ELM theory was the framework used to understand and evaluate the underlying mechanisms describing the relationships between endorser type, disease state involvement and consumer response to direct-to-consumer advertisements (DTCA). The finding showed while endorser type did not significantly affect consumer attitudes, behavioral intentions and information search behavior; level of disease state involvement, though, did. More highly involved consumers had more positive attitudes, behavioral intentions and greater information search behavior.

Since social media become a popular marketing platform as well, some scholars also use the ELM to examine how purchase intentions, brand attitudes, and advertising attitudes could be affected by interactivity and source authority on social media platforms. Ott et al. conducted an experiment by presenting participants with Facebook posts from a fictitious company and analyzing their attitude change. The results shows that high and medium interactivity (which means numbers of responses from company representatives on social media posts would: 1) enhance the perceived informativeness (consumers can get useful information from advertising), and then strengthen positive attitudes and purchase intentions; Or 2) increase perceived dialogues, which led to increasing perceived informativeness and then positive attitudes and purchase intentions. However, high interactivity without the perceived informativeness would generate negative attitudes and low purchase intentions. This study has suggested that to some extent companies should engage audience in a systematic processing way in social media advertisings, as consumers elaborate along central route will generate more positive attitudes and higher purchase intentions.

In 2021, an article was published documenting a study done to find the effectiveness of films, sports, mascots, and celebrities in regard to advertisements for consumers. The studies showed none of the metrics listed above had a significant affect on the purchasing habits of "educated working consumers", which means they either have graduate or post graduate degrees. However, if the content was perceived as good by the consumer, and the figure advertising the product was credible in the eyes of the consumer, they would be more likely to purchase the product.[10]

Relations to Other Theories[edit]

  • Social judgment theory – emphasizes the distance in opinions, and whether it is in the "acceptance latitude" or "rejection latitude" or in the intermediate zone. This concept relates to the peripheral processing route because when a person already has a strong opinion on the concept (an anchor), they are more likely to take the peripheral route and not become involved in being persuaded.
  • Social impact theory – emphasizes the number, strength and immediacy of the people trying to influence a person to change their mind. If the number, strength and immediacy of the people trying to influence a person is high, the person being persuaded is more likely to take the central processing route.
  • Heuristic-systematic model - is very similar to the ELM because it is also a two-way model that explores how people gather and dissect persuasive messages. [11]
  • Extended transportation-imagery model
  1. ^ a b WOODWARD, GARY C. (2018). PERSUASION AND INFLUENCE IN AMERICAN LIFE (8TH ED ed.). LONG GROVE: WAVELAND PRESS. ISBN 1-4786-3612-2. OCLC 1037296115.
  2. ^ Angst, Corey; Agarwal, Ritu (2009). "Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion". MIS Quarterly. 33 (2): 339–370. doi:10.2307/20650295. JSTOR 20650295.
  3. ^ Angst, Corey; Agarwal, Ritu (2009). "Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion". MIS Quarterly. 33 (2): 339. doi:10.2307/20650295. JSTOR 20650295.
  4. ^ Susmann, Mark W.; Xu, Mengran; Clark, Jason K.; Wallace, Laura E.; Blankenship, Kevin L.; Philipp-Muller, Aviva Z.; Luttrell, Andrew; Wegener, Duane T.; Petty, Richard E. (2022-07-03). "Persuasion amidst a pandemic: Insights from the Elaboration Likelihood Model". European Review of Social Psychology. 33 (2): 323–359. doi:10.1080/10463283.2021.1964744. ISSN 1046-3283.
  5. ^ Susmann, Mark W.; Xu, Mengran; Clark, Jason K.; Wallace, Laura E.; Blankenship, Kevin L.; Philipp-Muller, Aviva Z.; Luttrell, Andrew; Wegener, Duane T.; Petty, Richard E. (2022-07-03). "Persuasion amidst a pandemic: Insights from the Elaboration Likelihood Model". European Review of Social Psychology. 33 (2): 323–359. doi:10.1080/10463283.2021.1964744. ISSN 1046-3283.
  6. ^ Scannell, Denise; Desens, Linda; Guadagno, Marie; Tra, Yolande; Acker, Emily; Sheridan, Kate; Rosner, Margo; Mathieu, Jennifer; Fulk, Mike (2021-07-03). "COVID-19 Vaccine Discourse on Twitter: A Content Analysis of Persuasion Techniques, Sentiment and Mis/Disinformation". Journal of Health Communication. 26 (7): 443–459. doi:10.1080/10810730.2021.1955050. ISSN 1081-0730. PMID 34346288.
  7. ^ Scannell, Denise; Desens, Linda; Guadagno, Marie; Tra, Yolande; Acker, Emily; Sheridan, Kate; Rosner, Margo; Mathieu, Jennifer; Fulk, Mike (2021-07-03). "COVID-19 Vaccine Discourse on Twitter: A Content Analysis of Persuasion Techniques, Sentiment and Mis/Disinformation". Journal of Health Communication. 26 (7): 443–459. doi:10.1080/10810730.2021.1955050. ISSN 1081-0730. PMID 34346288.
  8. ^ Segev, Sigal; Fernandes, Juliana (2023-01-02). "The Anatomy of Viral Advertising: A Content Analysis of Viral Advertising from the Elaboration Likelihood Model Perspective". Journal of Promotion Management. 29 (1): 125–154. doi:10.1080/10496491.2022.2108189. ISSN 1049-6491.
  9. ^ a b Pan, Piao; Zhang, Hao (2023). "Research on Social Media Advertising Persuasion Based on the Elaboration Likelihood Model". SHS Web of Conferences. 154: 03024. doi:10.1051/shsconf/202315403024. ISSN 2261-2424.
  10. ^ Srivastava, R. K. (2021-07-29). "Effectiveness of Films, Sport, Celebrity or Mascot to Content in the Advertising – A Dilemma for Global Brands". Journal of Promotion Management. 27 (5): 716–739. doi:10.1080/10496491.2021.1880518. ISSN 1049-6491.
  11. ^ Chaiken, Shelly (1980). "Heuristic versus systematic information processing and the use of source versus message cues in persuasion". Journal of Personality and Social Psychology. 39 (5): 752–766. doi:10.1037/0022-3514.39.5.752. ISSN 1939-1315.

Another study published in January 2023 was conducted to evaluate the use of the ELM and its central and peripheral routes of processing to evaluate viral advertisements between 2009 and 2019. Sigal Segev and Juliana Fernandes found that the viral video ads contained more peripheral cues rather than central cues.

Article Draft[edit]

Lead[edit]

Article body[edit]

References[edit]