Skip to main content

A model of purchase intention of complementary and alternative medicines: the role of social media influencers’ endorsements

Abstract

Background

Social Media Influencers (SMIs) are a fashionable way of marketing products by creating electronic word-of-mouth (e-WOM) on social media. The marketing of complementary and alternative medicines (CAMs) by SMIs is becoming increasingly popular and gaining credibility within consumers on social media platforms. Nonetheless, advising about healthcare products on social media should be examined as it is different from endorsing other kinds of commercial products. The aim of this study is to develop a model that provides the underlying mechanisms of the stimuli of SMIs on social media towards consumers’ purchase intention of CAMs.

Methods

This study used best fit framework synthesis methods to develop the model. A priori theory selection was conducted by identifying a BeHEMoTh strategy (Behavior of Interest, Health context, Exclusions and Models or Theories) to systematically approach identifying relevant models and theories relative to the research aim. Further evidence derived from primary research studies that describe the behavior identified is coded against selected a priori theory to develop the model.

Results

This study presents a novel model for understanding the purchase behavior of CAMs using SMIs as a marketing strategy. The model included two well-known theories (theory of planned behaviour theory and source credibility theory) as well as extensive existing research from a multidisciplinary perspective. The model is exclusively designed to help identify elements affecting perceived source credibility and factors that have an influence over consumers’ preferences to purchase CAMs by taking into consideration SMIs’ endorsements.

Conclusions

This study provides unique insights introducing new research areas to health literature and offers, new roles for healthcare professionals in this digital era by gaining new skills and competencies required to provide more credible and accurate information about CAMs. The study also highlights the new marketing era of online health-related product endorsements and recommends that policymakers and researchers carefully evaluate the impact of SMI’s on the use of CAMs, as well as to regulate the content of these promotional materials.

Peer Review reports

Background

Consumers are increasingly using social media to gather information on which to base their decisions to purchase products. There are people on social media platforms called social media influencers/opinion leaders (SMI’s) who are known to hold a certain level of influence over other people [1, 2]. Brand marketers have accelerated to approach (SMIs) to e promote and endorse their products [3]. This has given rise to; a new trend in marketing commonly called “influencer marketing” by generating e-WOM (electronic word-of-mouth) [4].

e-WOM is a behavior through which the message is diffused among the consumers, whereas viral marketing is a technique creating “viral infection” or “buzz” used by companies [5, 6]. Consumers who spread awareness about product or service offerings among their social networks by using e-WOM behavior, highlight pros and cons to assist other consumers with buying decisions [7, 8]. Conventionally, traditional marketing focused on using famous celebrities, TV and film stars, for marketing their brands, because people like and trust the advice, of people whom they like [9]. With the social media era, the brands and marketers realized that SMI’s on commonly used social media platforms [10] such as Meta, Instagram, twitter, tik-Tok also have a lot of people following them because they are considered influential members of the community. SMI’s usually create an “influence” over consumers by creating e-WOM messages using textual, verbal as well as video content [11].

Despite the fact that existing literature has explored the effects of SMIs on various commercial products and services [1,2,3, 12,13,14,15], there remains a noticeable gap in the literature concerning their impact on health-related products, particularly CAMs [16]. CAMs cover a diverse array of natural and herbal products, each offering unique modalities and practices. Over the years, interest in and use of CAMs have gained popularity among individuals seeking complementary or alternative treatments to conventional medicine [17]. With the emergence of social media, there is now a medium for attaining health information as well as purchasing products and services relative to CAMs. Although existing literature provides insights into the motivations behind CAM usage [18, 19], the role of SMIs in shaping consumer decision-making behavior regarding CAMs has remained largely unexplored. While some studies touch upon the marketing of health products through social media channels [8, 12, 15, 20, 21], a comprehensive understanding of the influencing factors that drive consumers to purchase CAMs through SMIs’ endorsements is yet to be established.

Advising about health-related products, including OTC medicines, medical devices, and complementary and alternative medicines (CAMs), is far different than marketing or endorsing travel destinations or convincing people to buy a box of toys. Nevertheless, for example, the pharmaceutical and nutraceuticals industry have recognized the potential power of Instagram as a social media marketing platform by assigning SMIs to market their healthcare products, creating e-WOM, which has had the tendency to increase their product sales and generate business [13]. To that said, studies exploring influencers’ narrative strategies for various brands and industries are expanding for the advancement of social media marketing [22,23,24]. However, the development of knowledge regarding the strategies for influencers to attract consumers to be used by the pharmaceutical industry is still in need.

Taking all this into account, there is both a managerial and an academic need to better understand the role played by SMIs in the pharmaceutical industry, especially for CAMs. To bridge the existing research gap between the motivational factors (cognitive process, emotions, and individual factors) behind CAM usage and the influence of SMI stimuli on consumers’ purchase behavior concerning CAMs, the present study seeks to develop a comprehensive model. This model will shed light on the intricate interplay between these two critical aspects, providing valuable insights into consumers’ decision-making behavior regarding CAMs on social media platforms. Our research aims to model the underlying mechanisms that shape consumer choices by providing insights into SMIs’ endorsements and motivational factors of CAM usage. Through this holistic approach, we hope to contribute significant knowledge that empowers both scholars and marketers to better understand the combined impact of motivational factors and SMI stimuli on consumers’ CAM purchase behavior in the digital age.

Methods

This study used “best fit” framework synthesis approach [25, 26] for evidence synthesis to develop a Complementary and Alternative Medicine Purchase Intention Model (CAMPIM). This approach enables the authors to systematically identify relevant frameworks, models, or theories for selecting a priori theories with the “best fit” to the topic. Further evidence derived from primary research studies that describe the influence of SMI on consumers’ purchase behavior of CAM and the factors affecting people purchasing CAM was coded against selected a-priori theories. An overview of methods used in this study is shown in Fig. 1.

Fig. 1
figure 1

Overview of methodology [26]

Developing a search strategy for a priori theory

The framework synthesis included identifying a BeHEMoTh strategy [26] (Behavior of Interest, Health context, Exclusions and Models or Theories) to systematically approach identifying relevant models and theories, relative to the research aims as shown in Table 1. Databases included were MEDLINE (OVID), Embase (OVID), PsycINFO (Ovid), CINAHL (EBSCO), Science Direct, Emerald, JSTOR, Business Source Ultimate, ProQuest. All study designs, including theories and models to describe the behavior presented in Table 1, were included in this study, however, no grey literature was included. The bibliography of selected papers was also reviewed systematically, as well as Google Scholar searches to find relevant information in similar studies.

Table 1 BeHEMoTh strategy for framework analysis

Selection of theory

Two independent authors (Author 1 and 2) searched and identified relevant theories relating to the research aims. After their initial assessments, both authors discussed the merits and demerits of each selected theory via regular meetings via Microsoft Teams (USA) after being retrieved from the databases described in the previous section. Consensus was reached on theories to be used as a-priori theory representing the core ideas of the research aim.

Search strategy for primary research studies

To identify relevant primary research studies, clear research questions were developed as shown below:

  • What kind of information people look up on social media about CAMs?

  • What motivates people to use CAMs and purchase CAMs online?

  • What do people look for/in SMIs? Why do people trust SMIs?

  • On what criteria do people select influencers and then adhere to their information?

  • What type of strategies do SMIs use while marketing CAM products or other products?

These questions led to the development of individual search strategies using relevant taxonomy and keywords appropriate to each question (Appendix 1) and involved conducting searches using selected databases. MEDLINE (OVID), Embase (OVID), PsycINFO (Ovid), CINAHL (EBSCO), Science Direct, Emerald, JSTOR, Business Source Ultimate, ProQuest were searched by three authors. Author 1, 2, and 3 undertook an independent search using an assigned research question, respectively (Fig. 2). All study designs were included in this study; however, no grey literature was included. The bibliography of selected papers was also reviewed systematically, andGoogle Scholar searches were done to find relevant information in similar studies. Backward citation tracking was also used to trace more studies. The search was limited to articles available in the English language or English translation. The studies included the use, or intention to purchase CAMs and SMI influences only in adult population. The inclusion and exclusion criteria of inclusion of this study are also provided in Table 1. The final inclusion of primary research studies was discussed and approved by all authors. Relevant studies were exported to Endnote X9 (Clarivate Analytics, PA, USA), and duplicates were removed.

Fig. 2
figure 2

Search results for primary research studies

Data analysis and synthesis

The approach described by Carroll et al. [26] was adopted for the data analysis and synthesis. After the studies were selected, a data extraction form was developed that included the main constructs of the a priori theories [27]. In this way, the data were coded using the data extraction sheet following a deductive approach. The data in the studies were extracted from the methods, results, and discussion sections of the selected studies. Two researchers (Authors 1 and 2) independently coded two studies to agree on the final form. Relevant data from primary research were coded into themes (sub-constructs of the model) and sub-themes [28]. This initial coding was then supplemented by secondary thematic analysis to capture the remaining evidence that did not fit the data extraction form. The themes were then discussed by the research team, and when necessary, new themes were developed that fit the codes uncovered by the a priori theories. Finally, the author team agreed upon the final list of themes and sub-themes.

Model development

Relationships between the themes of the model were recreated or generated based on the evidence from the primary research studies included in this study by focusing on social explanations developed from comparative understanding. This also involved the process of translation [29], which helped bring together themes from different studies to become representative of each other. This step helped provide a robust testing process and implying transferability or inferential generalization. The final model development involved multiple synthesis and refinement cycles as well as developing a consensus on the taxonomy of constructs used within the model by all authors.

Stimulus-Organism-Response (SOR) model was used to help visualize and provide the connectivity in two selected a priori theories in Fig. 3. The SOR model is a psychological framework used to understand the process of human response to stimuli in a given environment [30]. It recognizes that human behavior is not solely determined by external stimuli but also by internal factors such as cognitive processes, emotions, and individual differences. Thus, the part of the model derived from the Source Credibility theory modeling the factors of how SMIs influence consumers to purchase CAMs represents the stimulus, whereas the part derived from the TPB that explains the cognitive, emotional, and individual factors of why consumers use CAMs refers to the organism part of the SOR. Finally, response refers to the intention to purchase CAM in the model (see Fig. 3).

Fig. 3
figure 3

The complementary and alternative medicine purchase intention model

Quality assessment of the studies and sensitivity analysis

Author 1, 2, and 3 conducted independent quality assessments of the included studies. The focus was on the reporting of basic methods and not potentially subjective judgments regarding studies’ validity or reliability. Although the presence of uncertainty in the quality of a study’s execution is recognized, providing a clear description of the authors’ methodology, including approach, sampling, data collection, and analysis methods, can enhance the strength and reliability of the study’s outcomes [31]. This does not preclude the possibility that an “inadequately-reported” study has actually been well conducted, but it does form a reasonable basis for making a quality assessment. Studies were deemed adequately reported if they offered comprehensive information on two or more criteria. A sensitivity analysis would be performed in the event of the inclusion of “inadequately-reported” studies [31, 32]. A sensitivity analysis [32] aims to investigate whether the results were significantly impacted by inadequately-reported studies or other specific characteristics. In other words, whether any of the themes generated in the data analysis were lost because of the exclusion of these studies would be evaluated.

Results

Study selection for a-priori theories, primary research studies and quality assessment

The BeHEMoTh search approach focusing on theories regarding consumers’ purchase behavior of CAMs and the impact of SMI stimuli on consumers’ purchase behavior resulted in 44 studies (Fig. 2). Twelve eligible theories and models were identified, but the theories with rich information and concepts reflecting major aspects of the research aim were focused on. Two theories were chosen as a priori theories for this study. Theory of Planned Behavior (TPB) [33,34,35,36] and Source Credibility theory [37, 38]. The TPB is a cognitive theory developed by Azjen (1985) which proposes and provides explanations to an individual’s decision to engage in a specific behavior. TPB operates within three main constructs: behavioral beliefs, normative beliefs, control beliefs [36]. This theory provided key components for the initial framework to understand the intention drivers for behavior to purchase CAMs. Source Credibility theory developed by Hovland et.al (1940) states that people are more likely to be persuaded when the source presents itself as credible [37, 38]. The credibility of all communication, regardless of the format it is being delivered in, has been found to be heavily influenced by the perceived credibility of the source of that communication. This theory was selected because it helped inform source credibility of SMIs using social media sites. Details of other relevant theories and models identified can be found in Appendix 2.

For primary research studies, 128 studies were used to develop the model by combining all the searches by three authors (Author 1, 2, and 3) as shown in Fig. 2. No study failed to describe clearly at least two of the following: the question and study design, and the methods of sampling, data collection or analysis. Hence, a sensitivity analysis was not performed.

The following section now presents an overview of the main themes of the model, the dotted boxes depicted in Fig. 3 are the sub-themes of stimulus part (derived from source credibility theory) and organism part (derived from TPB) of the model and are explained in Tables 2 and 3 respectively.

Table 2 Conceptual definitions of sub-themes developed in the stimulus part of the model
Table 3 Conceptual definitions of sub-themes developed in the organism part of the model

Development of the complementary and alternative medicine purchase intention model

Figure 3 shows the CAMPIM model. Each concept (boxes in the Fig. 3) is now described along with its identified determinants and relationships. The “stimulus” part of the model depicted in Fig. 3 illustrates the themes and sub-themes showing the influence of SMIs on consumers’ purchase behavior of CAMs by using a-priori theory of Source Credibility, and the “organism” part of the model refers to the themes and sub-themes mapped onto TPB showing the factors affecting people to use CAMs directing them to adopt an intention to purchase CAMs.

The stimulus part of the model derived from a-priori theory of source credibility

Perceived source credibility

Source credibility is a concept that means the more credible the source appears to seem, the more chances are the message or phenomenon will be accepted [38]. The credibility of the message source shows how much the recipient believes in the sender [76]. In the context of social media, source credibility is the measure to which content producers or SMI’s are perceived as trustable, having knowledge, and are considered ‘credible’ [41]. Source credibility was a significant factor found in studies that were seeking information about CAMs and the use of CAMs, was linked with the credibility of source (SMIs), advertising, selling, or giving advice to use CAMs [77]. Credible and perceived ‘accurate’ information provided by SMIs about CAMs are linked with how they approach these products and adjust and adhere to them.

Perceived SMI credibility and brand credibility are the two dimensions of source credibility generated as main themes in the model (Fig. 3) [2, 78] SMI’s own credibility refers to the extent to which to convey an audio or video endorsement about a particular product [2]. The reputation and credibility of the manufacturer or brand developing the product were also found linked with source credibility [78]. Source credibility has been conceptualized as having two main dimensions—trustworthiness and expertise [37]. These main dimensions are used for both SMI and brand credibility in the model. In addition, two additional themes developed from primary research studies were inserted in the model for SMI credibility (Fig. 3). The following section presents an overview of perceived SMI and brand credibility and their developed sub-themes.

Perceived brand credibility

Brand credibility derived from brand signaling theory proposes that markets are full of products and random information about these products. The information may be symmetric, asymmetric, focused, unfocused, company-created, marketeer-created, or the public sharing their experiences and perceptions of products [79, 80]. Amongst all this chaos in information, brands serve as signals of products based on their strong credibility of being authentic, and reliable, which in turns makes their products appear more credible [81]. Brands use different marketing and promotional techniques to create signals [80]. However, the credibility of brands comes when they build on their past successes and proven, promised products, and this is commonly known as ‘reputation’ in the economics literature [80].

Brand credibility thus broadly requires two core components: trustworthiness and expertise [80]. Brand credibility is defined as how effectively the product information is conveyed by the brand signal, which requires that consumers perceive that the brand has the ability (i.e., expertise) and willingness (i.e., trustworthiness) to deliver what has been promised continuously [82]. Credibility is linked to the confidence of consumers in that product or brand that they have the ability (i.e., expertise) and willingness (i.e., trustworthiness) to continuously deliver what has been promised [81]. Customers looking for CAM information or to be able to purchase CAM, and look for credibility of both the brand, company, manufacturer, distributor who are providing these products [83]. For example, consumers may tend to believe that a well-known brand will provide the promised level of effectiveness and safety of a CAM, whereas an equivalent claim by a less-known brand may be less credible.

Perceived SMI credibility

Perceived SMIs’ credibility is one of the factors determining the influencer endorsement effectiveness [84]. In the context of CAMs, this means that the more people who perceive an SMI is credible, the higher the chances that they will agree to the endorsement content and may continue to make the purchase [37, 38]. Studies reported that SMIs’ credibility consists of several dimensions –expertise, trustworthiness, and attractiveness [38, 84]. When consumers perceive the SMI to be an expert, trustworthy, and willing to provide accurate information, they might forgo the thinking process and, without thinking, accept their message as reliable and credible [8]. In terms of CAMs, consumers prefer sources that are more credible when they try to access the details of the product before making the purchase [85]. Moreover, emotional attachment is the other dimension added to the model [86]. Credible or even new SMIs (without established credibility but have some similarities with the followers) might develop and deliver content related to diseases and conditions, which might give people confidence and enable them to purchase of CAMs.

The below part presents the explanations of the main constructs of perceived SMI credibility (expertise, trustworthiness, attractiveness, and emotional attachment) in the model. In addition, Table 2 shows all the developed sub-themes shown in dotted boxes in Fig. 3 of perceived SMI credibility in the model and briefly provides the conceptual definitions of these sub-themes.

Expertise

Expertise refers to the knowledge, skills, or experience of a SMI. However, expertise in marketing literature examining influencers’ effect on decision-making process does not mean professional expertise, but rather, the expertise of the SMI to target their followers and people on social media to perceive them as experts [37, 38]. SMIs building their content on their actual expertise, or, for example, projecting themselves as experts on a specific genre, were found to be linked with more follower satisfaction [87]. A message coming from a perceived expert has a higher chance of producing a behavior change in consumers and leads to the purchase of products [9, 38]. Thus, the expertise of an SMI that is shown as CAM expertise in the model was anticipated to impact the purchasing of products. Social media studies report that the higher the informativeness [44], argument quality [39,40,41,42], and comprehensiveness [42] of the endorsement content provided by SMIs, the more people perceive them to be experts and believe in their endorsements as being credible. Hence, three sub-themes are developed for CAM expertise, defined in Table 2.

In addition to providing high-quality information about CAMs indicating CAM expertise, SMIs have some characteristics indicating their competencies that impact the consumers’ perceptions [47] that lead them to be considered experts. Table 2 presents conceptual definitions of every developed sub-theme of various competencies of SMIs in the model.

Trustworthiness

The motivation for including trustworthiness, the second theme of SMI credibility, in CAMPIM comes from the efforts of CAM consumers to acquire trustworthy advice [70]. As previously mentioned, studies reported that the use of CAM and adherence to CAM were linked to the lack of trust in prescribers due to their lack of empathy towards them [18, 61,62,63]. Social media studies reported that trustworthiness was a considerable criterion for people to place value on the influencer or endorser [4, 88, 89]. Having trust and belief that an SMI might project honesty, empathy, and demonstrate integrity in their information might inspire trustworthiness, which is directly linked to acceptance of their content, to be true [2]. Studies also reported that marketers and brands also invest in SMIs who are valued as trustworthy members of the audience [2, 90]. The overview of the sub-themes of trustworthiness in the model can be seen in Table 2.

Emotional attachment

Emotional attachment was found to be a strong factor in influencing people’s behavior [91, 92]. Emotional attachment, the third theme of SMI credibility in the model, means a bond between people and the SMIs. In social media, emotional attachment toward SMIs positively affects people to purchase a product, the SMI was endorsing [92]. Emotional attachment involves two aspects in the model: homophily [58] and wishful identification (for the definitions of the sub-themes, see Table 2) [93]. Emotional attachment with SMIs is usually inspired by content creators by building their ‘similarities’ to their target audience of the intended products usually by using similar beliefs, social status, interests, benefits, value addition and convenience.

Emotional attachment was also a construct identified in research regarding CAM, where people suffering from the same diseases and conditions feel emotionally attached and supported by using similar CAM products and forming beliefs (homophily in the model) [64]. The sense of emotional attachment was found to be a substantial factor in convincing others to influence CAM purchases [94]. Consumers might purchase CAMs because they desire to be like the other person they admire (wishful identification in the model), a SMI, where they already have developed emotional attachments. Thus, emotional attachment and perceived subjective norms can have a relationship in terms of whether consumers follow the suggestions of those similar to them within their social networks.

Attractiveness

Attractiveness in social media studies shows that an SMI, might be considered attractive if they are perceived by the followers as classy, sensual, and beautiful [1] and was found as another factor impacting consumer buying behavior [95]. In terms of the product content, the attractiveness of an SMI means that the information and endorsements can be trusted and agreed upon as credible if the SMIs themselves look attractive while consuming/applying/using them [89]. However, attractiveness in CAM studies was found to vary in context, where people who got better by using CAMs and returned to either everyday lives or manageable lives or even reporting improved quality of life were perceived as having a normal lifestyle, which seemed ‘attractive’ to people and people were attracted to. Although medical-related CAMs are not the beauty or cosmetic products that require physical attractiveness of an SMI to be purchased on social media, consumers could adopt SMIs’ advice when some unique traits, such as charisma, have been identified. This could lead to a purchase intention. Therefore, attractiveness is the fourth theme of SMI credibility developed.

The organism part of the model derived from a-priori theory of TPB

This part explains the developed themes from primary research studies mapped onto the constructs of TPB: attitudes, subjective norms, and perceived behavioral control [34, 36]. Table 3 explains all the sub-themes depicted in dotted boxes in Fig. 3 corresponding to the organism part of the model.

Perceived subjective norm

What other people think and do is one concept that shapes our behavior [33]. The perceived subjective norm can be defined as “beliefs about the normative expectations of others” [35]. Normative beliefs thus include both; when an individual performs the behavior by observing (intrinsic drive) their social networks that leads them to perform the behavior in a particular ‘conventional’ way similar to others or the individual’s social networks directly and indirectly (extrinsic drive) prompting them to perform the particular behavior [19, 96].

Subjective norm in the model includes two themes that were found to impact intention. This part represents an overview of these themes and relationships with the model.

Having traditional practices

One of the roles that society assigns individuals is seeking and accessing healthcare services and/or products responsibly when they get ill [97]. Ideally, conventional medicine exists to help individuals to fulfill this task with scientifically proven treatments [98]. When this task cannot be met by conventional medicine due to several factors, such the existence of an incurable disease, lack of compliance with treatment, lack of communication, accessibility-availability-affordability problems [99], individuals begin to find the necessary treatment from other sources rather than conventional medicine such as CAMs. In addition, having been pre-exposed to cultural and traditional practices about using CAMs [18, 61, 66,67,68, 71, 100,101,102,103,104] are seen as influencing factors that drive the intention to purchase CAM.

Family, friends, and healthcare professionals’ recommendations

Studies reported that family and friends [18, 61, 65, 67,68,69,70,71, 73, 100, 102,103,104], physician recommendations [18, 61, 65, 70, 102, 103], are also external factors affecting individuals to purchase CAMs, which are the developed themes included in the model. In social media, posts about health-related experiences and searching for health information on the internet are increasing day by day [105]. It is inevitable for individuals to have subjective norms in social media, where there is a high level of interaction and messages open to others’ access [106].

Attitude towards CAM purchase

Attitude towards behavior refers to the degree to which a person has made an evaluation (can be favorable or unfavorable) of a particular behavior. According to TPB, behavioral beliefs and attitudes toward the ‘behavior’ are significantly linked. Each individual’s belief is related from the outset to achieving the certain behavioral outcome(s). These were found to influence an individual’s interest in believing and developing a positive attitude towards the behavior [33].

Regarding social media studies, people purchase products because other people are reporting to have purchased them [107]. Nowadays, specially Instagram is mostly being used for marketing purposes that allows people to leave their comments based on their evaluations and outcomes after using the products [15]. Hence, consumers could develop positive or negative attitudes and behaviours that help them decide whether to purchase the same products or look for different ones [44]. By investigating the literature, different perspectives were grouped under developing positive or negative attitudes for using CAM in the model, which could drive the behavior to purchase CAMs.

Positive attitudes toward CAM

The first sub-theme of positive attitudes toward CAM was the satisfaction and dissatisfaction of people with conventional medicines [18]. When people are satisfied with CAM usage and dissatisfied with conventional medicines for several reasons generated as sub-themes, they may be more likely to adopt positive attitudes toward CAM. This may drive their attitude and beliefs towards the purchase of CAMs. The detailed explanation of sub-themes of positive attitudes toward CAM can be seen in Table 3.

Negative attitudes toward CAM

The developed sub-themes of negative attitudes toward CAM in the model are the opposite cases of the positive attitudes (see Fig. 3). Satisfaction with conventional medicines and dissatisfaction with CAMs are seen as the reasons for adopting negative attitudes toward the intention of purchasing CAMs. The detailed explanation about sub-themes of positive attitudes toward CAM can be seen in Table 3.

Perceived behavioural control

Three main themes developed in the model refer to an individual’s perceived ease or difficulty of using or purchasing CAMs (see Fig. 3) [33, 35].

Perceived autonomy over health

Many CAM users seek these products because these products offer autonomy to them for decision-making and taking control of their health [63, 65, 70,71,72, 102, 108]. Purchasing CAMs by reflecting on SMIs’ endorsements could cause customers to think they have autonomy in selecting health products by themselves without any healthcare professionals’ advice.

Availability, affordability, and accessibility of CAM

Availability-accessibility-affordability of CAM [18, 65, 71, 74, 100] were considered as easing factors of using or purchasing CAMs in the literature. The ease of accessing health information [105], through SMIs could impact people’s intention to purchase these products by affecting consumers’ perceptions of availability and accessibility of CAMs. SMIs play a significant role in informing and contextualizing social media content for their followers. They do this by explaining the ease of use of these products and the overall simplicity of accessibility of CAMs by clicking and buying these products. Moreover, SMIs could show the relative benefit of CAMs over money to persuade consumers to believe these products are affordable [18].

Availability, affordability, and accessibility of conventional medicines

In most cases, availability-accessibility-affordability of conventional medicines [65, 70, 71, 73, 100, 101] could be more complex than CAMs due to several reasons [70]. In most countries conventional medicines are distributed with prescriptions that could be a hindering factor for the availability and accessibility of conventional medicines. In addition, high costs prevent the use of prescribed medicines, leading to many patients purchasing CAMs [65, 75]. Therefore, difficulties in availability-accessibility-affordability of conventional medicines could be driving forces that influence consumers to purchase CAMs.

Discussion

This study introduces a novel model that used the best fit framework synthesis approach to comprehensively comprehend consumers’ purchase behavior of CAMs influenced by SMIs. Notably, to the best of the authors’ knowledge, this research represents the first empirical evidence illustrating the significant impact of SMI endorsements on consumers’ purchase intentions of CAMs. CAMPIM has several strengths. Firstly, the model provides a holistic overview and synthesis of the SMI endorsements (stimuli) that influence consumers’ psychological and cognitive processes (organism), leading to their intentions to purchase CAMs (response). Secondly, the development of the CAMPIM model involves a synergistic integration of two relevant theories, TPB and Source Credibility Theory, amalgamating existing research from diverse disciplines, thus yielding unique insights into the underlying mechanisms and deep perspectives governing consumers’ intentional behavior towards CAM purchases under the influence of SMIs. CAMPIM offers a more comprehensive understanding of the dynamic interplay between SMIs, consumers, and purchase intentions in the context of CAMs, paving the way for future research and evidence-based strategies in influencer marketing and CAM consumption.

Legislation around CAM use differs across different countries; where some countries regulate the manufacturing, distribution, and sale of CAMs like pharmaceutical products [109], but most governments do not regulate CAMs as medical or health products [110]. This lack of regulation allows manufacturers of CAMs to promote their products without a scientific check on the promotional content [111]. The endorsement of unregulated CAM products by SMIs on social media platforms raises concerns regarding potential harm to consumers. Using unregulated CAMs as adjunct or supplementary therapies could reduce efficacy, interactions, or side effects when combined with conventional medicines [112,113,114], posing risks to patients’ well-being. Despite the potential adverse effects of CAMs reported in numerous studies [115,116,117,118,119], the lack of statutory legislation for CAMs enables their endorsement on social media, similar to commercial products, without adhering to regulations related to health and well-being. Given the varied and ambiguous statutory legislation of CAMs worldwide, the use of SMIs to endorse these products on social media platforms has the potential to pose threats to public health. Policymakers urgently need to introduce legislation specifically addressing the marketing of CAMs on social media platforms. Evaluating the content of CAM endorsements should be the responsibility of governments and policymakers, ensuring the protection of consumers’ health and well-being.

Effectively utilizing the insights gathered from the CAMPIM model necessitates a comprehensive and targeted marketing approach to promote CAMs. As marketers in the healthcare industry embrace the use of SMIs to endorse CAMs on various social media platforms, it becomes paramount to establish clear and stringent guidelines governing their promotional activities [111]. A crucial finding of this study highlights the need for marketers to conduct rigorous evaluations of SMI-generated content and claims before endorsement. Marketers can ensure alignment with established regulatory standards and evidence-based practices by inspecting the accuracy, validity, and scientific basis of the information presented. This thorough vetting process is paramount to safeguarding public health by preventing the dissemination of misleading or false information that could potentially harm consumers. This combined approach ensures that the promotion of CAMs through SMIs is conducted responsibly and ethically, fostering consumer trust and confidence in these products.

In implementing a successful marketing approach for healthcare products, including CAMs, prioritizing transparency and disclosure are essential. Marketers must be explicit about any affiliations or relationships between SMIs and the CAM products they promote, as honesty and integrity are crucial to building and retaining consumer trust. In today’s discerning environment, authenticity and transparency are pivotal in fostering consumer confidence.

Considering the increasing trend of people using the internet for health-related information, including medicines, products, and CAMs [120,121,122], people tend to perceive unlicensed, non-professional SMIs as credible as licensed healthcare professionals when seeking health-related information [123]. Information provided by SMIs with no education about healthcare can jeopardize the health of those who follow them on social media. This opens up a novel role for healthcare professionals to leverage their expertise in the realm of social media. By providing knowledge, guidance, and counseling on CAMs and Over-the-Counter (OTC) medicines available for online purchase, healthcare professionals can optimize medicine usage and effectively manage diseases. Collaborating with reputable healthcare professionals and organizations further enhances the credibility of promotional efforts. Involving licensed practitioners and CAM experts in marketing campaigns strengthens the authenticity and trustworthiness of endorsements. Testimonials and endorsements from healthcare professionals, especially pharmacists, who vouch for the effectiveness and safety of CAMs wield significant persuasive power in building consumer confidence. This collaborative approach bridges the gap in credible health information on social media and empowers consumers to make informed decisions about their health and well-being. To capitalize on this opportunity, professional organizations should explore the development of new roles as healthcare SMIs. By leveraging their expertise, these professionals can deliver reliable health information about diseases, health-related products, medical devices, and CAMs. Implementing regulated digital services for providing health-related information ensures uniformity of content and the dissemination of accurate knowledge. This approach enhances consumer trust and empowers individuals to make informed decisions about their health and adhere to medical advice, contributing to improved public health outcomes.

Moreover, the involvement of healthcare professionals in mainstream media to deliver public health messages is not a new concept [124, 125]. However, in the era of digitalized health, new opportunities arise, such as designing services with healthcare professionals as SMIs. The COVID-19 pandemic has accelerated the development and implementation of digitalized healthcare worldwide, including remote digital health services, which have shown improvements in public health benefits [126,127,128]. With a growing interest in digitalized health services, understanding the factors influencing consumers’ perceptions of CAMs on digital platforms, as presented in CAMPIM, becomes crucial for implementing e-health services. Monitoring and managing medication therapy, counseling patients, and making informed decisions about treatment can be enhanced through these digital avenues, fostering better health outcomes for consumers.

Further scope of the study

The medical research council (MRC) framework of complex interventions [129] could be used to explore contextual factors. Implementation science could be used to inform the development and implementation of patient counselling services by healthcare professionals in a digitalized format using social media platforms. Another advantage of this CAMPIM model is that it can give policy makers a baseline, for the required competencies of healthcare professionals, to deliver these kinds of services.

Considering its breadth, the CAMPIM may also be relevant and useful not just for enhancing new roles of healthcare professionals for policy makers to enable them to be more active on social media to protect public health, but also laying the foundations for marketers in the pharmaceutical sector to understand the complexity of SMIs’ influence on consumers’ intention to purchase CAMs. For example, if the aim is to decrease the risk of irrational use of CAMs, professional bodies could adopt new ways to reach consumers to influence their beliefs and attitudes on social media by increasing the argument quality of healthcare professional opinion leaders who are rather selected due to their similar background and values with consumers. Furthermore, pharmaceutical companies could tune to reach target audiences genuinely interested in exploring alternative healthcare options by effectively utilizing the insights captured from the CAMPIM.

Limitations of the study

The CAMPIM has some limitations. One limitation is that the relationships among the constructs included in the model does not apply linearity in principle. Many constructs can affect and impact other constructs simultaneously and can positively or negatively impact the other constructs and may affect the behavior to purchase CAMs. It is also important to highlight that it is impossible to understand the correlations of these constructs within the model; for example, the reciprocal relationship between trustworthiness and expertise could not be shown in the model, if any. The authors thus propose future studies to evaluate the impact of constructs, mediators, and factors with each other and their overall impact on the purchase of CAM.

Another limitation was that as there was a lack of studies reporting the influence of SMIs on the purchasing behavior of CAM thus, the model relied on studies from different fields and was constructed by triangulating the findings. Although the model uses theory to understand how the constructs lead to the purchase intention, it differs widely from a realist evaluation and does not create any program theories. The model thus lacks the power to infer causality.

The model might need to undergo a rigorous overview by an expert panel of relevant stakeholders, including but not limited to consumers, current SMIs, and policy makers to validate and clarify concepts and to determine causal pathways among constructs, which authors are already planning to design a further study to evaluate the constructs of CAMPIM using stakeholders and expert panel with mixed methods. The model has the strength to be flexible in predicting the behavior of the purchase of CAM across different social media platforms. Still, the dynamics of each social media need to be accounted for in terms of producing an impact. Another feature of interest is that the sub-construct of ‘visuality’ in the model can be differently presented in various social media platforms. For example, the visuality of SMI would be presented differently than on Twitter, but the impact of visuality remains true in influencing the purchase behavior towards CAM. The dynamics of each platform may differ, and the model should be carefully examined and contextualized for developing new roles of health SMI’s across different platforms and implementing successful marketing approaches.

Conclusions

This study presents a new and comprehensive model to understand factors influencing consumers’ purchase intention of CAMs endorsed by SMIs on social media platforms. The CAMPIM is a promising model that provides a holistic overview of social media factors influencing the purchase of CAMs. During the development of the CAMPIM model, it became evident that a versatile marketing approach that prioritizes transparency, targeted messaging, and collaboration with healthcare professionals is crucial for the responsible promotion of CAMs through SMIs. By adopting these evidence-based strategies, marketers can enhance consumer trust, empower informed decision-making, and promote the safe and effective use of CAMs in the digital era. However, the lack of regulation around CAMs highlights the need for urgent policy intervention to protect consumers from potential harm caused by unregulated endorsements. Implementing evidence-based strategies for enhancing new roles for healthcare professionals as SMIs will empower consumers to make informed decisions and promote the safe and responsible use of CAMs through social media platforms.

Availability of data and materials

All relevant data are included in this manuscript. Furthermore, data set used in this study will be available upon request from the correspondence author.

References

  1. Weismueller J, Harrigan P, Wang S, Soutar GN. Influencer endorsements: how advertising disclosure and source credibility affect consumer purchase intention on social media. Australas Mark J. 2021;28(4):160–70.

    Article  Google Scholar 

  2. Lou C, Yuan S. Influencer marketing: how message value and credibility affect consumer trust of branded content on social media. J Interact Advert. 2019;19(1):58–73.

    Article  Google Scholar 

  3. Jiménez-Castillo D, RaquelSánchez-Fernández. The role of digital influencers in brand recommendation: examining their impact on engagement, expected value and purchase intention. Int J Inf Manag. 2019;49:366–76.

    Article  Google Scholar 

  4. López M, Sicilia M. eWOM as source of influence: the impact of participation in eWOM and perceived source trustworthiness on decision making. J Interact Advert. 2014;14(2):86–97.

    Article  Google Scholar 

  5. Sharma RR, Kaur B. E-mail viral marketing: modeling the determinants of creation of “viral infection”. Manag Decis. 2020;58(1):112–28.

    Article  Google Scholar 

  6. Kaur B, Sharma RR. Impact of viral advertising on product promotion: an experimental study. Indian J Mark. 2018;48(6):57–68.

    Article  Google Scholar 

  7. Di Virgilio F, Antonelli G. Consumer behavior, trust, and electronic word-of-mouth communication. In: Virgilio FD, editor. Social Media for Knowledge Management Applications in modern organizations. USA: USA: IGI Global; 2018. p. 58–80.

    Chapter  Google Scholar 

  8. Djafarova E, Rushworth C. Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users. Comput Hum Behav. 2017;68:1–7.

    Article  Google Scholar 

  9. Erdogan BZ. Celebrity endorsement: A literature review. J Mark Manag. 1999;15(4):291–314.

    Article  Google Scholar 

  10. Veirman MD, Hudders L, Nelson MR. What is influencer marketing and how does it target children? A review and direction for future research. Front Psychol. 2019;10:2685.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ki C-W, Cuevas LM, Chong SM, Lim H. Influencer marketing: social media influencers as human brands attaching to followers and yielding positive marketing results by fulfilling needs. J Retail Consum Serv. 2020;55:102133.

    Article  Google Scholar 

  12. Fakhreddin F, Foroudi P. Instagram influencers: the role of opinion leadership in consumers’ purchase behavior. J Promot Manag. 2021;28:1–31.

    Google Scholar 

  13. Folkvord F, Roes E, Bevelander K. Promoting healthy foods in the new digital era on Instagram: an experimental study on the effect of a popular real versus fictitious fit influencer on brand attitude and purchase intentions. BMC Public Health. 2020;20(1):1677.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Mangan RM, Flaherty GT. The advent of social media influencer tourism: travel health risks and opportunities. J Travel Med. 2021;28(8):taab140.

    Article  PubMed  Google Scholar 

  15. Djafarova E, Bowes T. ‘Instagram made me buy it’: generation Z impulse purchases in fashion industry. J Retail Consum Serv. 2021;59:102345.

    Article  Google Scholar 

  16. Willis E, Delbaere M. Patient influencers: the next frontier in direct-to-consumer pharmaceutical marketing. J Med Internet Res. 2022;24(3):e29422.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mertz M. Complementary and alternative medicine: the challenges of ethical justification. A philosophical analysis and evaluation of ethical reasons for the offer, use and promotion of complementary and alternative medicine. Med Health Care Phil. 2007;10(3):329–45.

    Article  Google Scholar 

  18. Tangkiatkumjai M, Boardman H, Walker D-M. Potential factors that influence usage of complementary and alternative medicine worldwide: a systematic review. BMC Complement Med Ther. 2020;20(1):363.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sirois FM, Salamonsen A, Kristoffersen AE. Reasons for continuing use of complementary and alternative medicine (CAM) in students: a consumer commitment model. BMC Complement Altern Med. 2016;16:75.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pilgrim K, Bohnet-Joschko S. Selling health and happiness how influencers communicate on Instagram about dieting and exercise: mixed methods research. BMC Public Health. 2019;19(1):1054.

    Article  PubMed  PubMed Central  Google Scholar 

  21. <Decision-Making and Problem-Solving Approaches in Pharmacy Education.pdf>.

  22. Zhou S, Barnes L, McCormick H, Blazquez CM. Social media influencers’ narrative strategies to create eWOM: A theoretical contribution. Int J Inf Manag. 2021;59:102293.

    Article  Google Scholar 

  23. Uzunoğlu E, Misci KS. Brand communication through digital influencers: leveraging blogger engagement. Int J Inf Manag. 2014;34(5):592–602.

    Article  Google Scholar 

  24. Tafesse W, Wood BP. Followers' engagement with instagram influencers: the role of influencers’ content and engagement strategy. J Retail Consum Serv. 2021;58:102303.

    Article  Google Scholar 

  25. Flemming K, Noyes J. Qualitative evidence synthesis: where are we at? Int J Qual Methods. 2021;20:1609406921993276.

    Article  Google Scholar 

  26. Carroll C, Booth A, Leaviss J, Rick J. “Best fit” framework synthesis: refining the method. BMC Med Res Methodol. 2013;13(1):37.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Weber P, Birkholz L, Kohler S, Helsper N, Dippon L, Ruetten A, et al. Development of a framework for scaling up community-based health promotion: A best fit framework synthesis. Int J Environ Res Public Health. 2022;19(8):4773.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Srivastava A, Thomson SB. Framework analysis: A qualitative methodology for applied policy research. J Manag Gov. 2009;72:72–9.

    Google Scholar 

  29. Atkins S, Lewin S, Smith H, Engel M, Fretheim A, Volmink J. Conducting a meta-ethnography of qualitative literature: lessons learnt. BMC Med Res Methodol. 2008;8:21.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mehrabian A, Russell JA. An approach to environmental psychology. The MIT Press; 1974.

    Google Scholar 

  31. Carroll C, Booth A, Cooper K. A worked example of “best fit” framework synthesis: A systematic review of views concerning the taking of some potential chemopreventive agents. BMC Med Res Methodol. 2011;11:29.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Carroll C, Booth A, Lloyd-Jones M. Should we exclude inadequately reported studies from qualitative systematic reviews? An evaluation of sensitivity analyses in two case study reviews. Qual Health Res. 2012;22(10):1425–34.

    Article  PubMed  Google Scholar 

  33. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211.

    Article  Google Scholar 

  34. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior: Englewood cliffs. NJ: Prentice-Hall; 1980.

    Google Scholar 

  35. Ajzen I. From intentions to actions: a theory of planned behavior. In: Kuhl J, Beckmann J, editors. Action control: from cognition to. Behavior: Springer; 1985.

    Google Scholar 

  36. Ajzen I. The theory of planned behaviour: reactions and reflections. Psychol Health. 2011;26(9):1113–27.

    Article  PubMed  Google Scholar 

  37. Hovland CI, Janis IL, Kelley HH. Communication and persuasion: New Haven. Yale University Press; 1953.

    Google Scholar 

  38. Ohanian R. Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. J Advertising. 1990;19(3):39–52.

    Article  Google Scholar 

  39. Teng S, Khong KW, Goh WW, Chong AYL. Examining the antecedents of persuasive eWOM messages in social media. Online Inf Rev. 2014;38(6):746–68.

    Article  Google Scholar 

  40. Cheung MY, Luo C, Sia CL, Chen H. Credibility of electronic word-of-mouth: informational and normative determinants of on-line consumer recommendations. Int J Electron Commer. 2014;13(4):9–38.

    Article  Google Scholar 

  41. Cheung CMK, Lee MKO, Rabjohn N. The impact of electronic word-of-mouth: the adoption of online opinions in online customer communities. Internet Res. 2008;18(3):229–47.

    Article  Google Scholar 

  42. Wixom BH, Todd PA. A theoretical integration of user satisfaction and technology acceptance. Inf Syst Res. 2005;16(1):85–102.

    Article  Google Scholar 

  43. Cheunga CMK, Thadani DR. The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decis Support Syst. 2012;54(1):461–70.

    Article  Google Scholar 

  44. Dwidienawati D, Tjahjana D, Abdinagoro SB, Gandasari D, Munawaroh. Customer review or influencer endorsement: which one influences purchase intention more? Heliyon. 2020;6(11):e05543.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Applbaum RF, Anatol KWE. The factor structure of source credibility as a function of the speaking situation. Speech Monogr. 1972;39(3):216–22.

    Article  Google Scholar 

  46. Maslach C, Stapp J, Santee RT. Individuation: conceptual analysis and assessment. J Pers Soc Psychol. 1985;49(3):729–38.

    Article  Google Scholar 

  47. Casaló LV, Flavián C, Ibáñez-Sánchez S. Influencers on Instagram: antecedents and consequences of opinion leadership. J Bus Res. 2020;117:510–9.

    Article  Google Scholar 

  48. Gentina E, Shrum LJ, Lowrey TM. Teen attitudes toward luxury fashion brands from a social identity perspective: A cross-cultural study of French and U.S. teenagers. J Bus Res. 2016;69(12):5785–92.

    Article  Google Scholar 

  49. Veirman MD, Cauberghe V, Hudders L. Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. Int J Advert. 2017;36(5):798–828.

    Article  Google Scholar 

  50. Phuaa J, Jin SV, Kim JJ. Gratifications of using Facebook, twitter, Instagram, or snapchat to follow brands: the moderating effect of social comparison, trust, tie strength, and network homophily on brand identification, brand engagement, brand commitment, and membership intention. Telemat Inform. 2017;34(1):412–24.

    Article  Google Scholar 

  51. Hwang Y, Jeong S-H. “This is a sponsored blog post, but all opinions are my own”: the effects of sponsorship disclosure on responses to sponsored blog posts. Comput Hum Behav. 2016;62:528–35.

    Article  Google Scholar 

  52. Boermana SC, Willemsen LM, Aa EPVD. “This post is sponsored” effects of sponsorship disclosure on persuasion knowledge and electronic word of mouth in the context of Facebook. J Interact Mark. 2017;38:82–92.

    Google Scholar 

  53. Mikulincer M, Nachshon O. Attachment styles and patterns of self-disclosure. J Pers Soc Psychol. 1991;61(2):321–31.

    Article  Google Scholar 

  54. Reuter K, Wilson ML, Moran M, Le N, Angyan P, Majmundar A, et al. General audience engagement with antismoking public health messages across multiple social media sites: comparative analysis. JMIR Public Health Surveill. 2021;7(2):e24429.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Martensen A, Brockenhuus-Schack S, Zahid AL. How citizen influencers persuade their followers. J Fash Mark Manag. 2018;22(3):335–53.

    Google Scholar 

  56. Ruef M, Aldrich HE, Carter NM. The structure of founding teams: Homophily, strong ties, and isolation among U.S. entrepreneurs. Am Sociol Rev. 2003;68(2):195–222.

    Article  Google Scholar 

  57. Schouten AP, Janssen L, Verspaget M. Celebrity vs. influencer endorsements in advertising: the role of identification, credibility, and product-endorser fit. Int J Advert. 2019;39(2):258–81.

    Article  Google Scholar 

  58. Ladhari R, Massaa E, Skandrani H. YouTube vloggers’ popularity and influence: the roles of homophily, emotional attachment, and expertise. J Retail Consum Serv. 2020;54:102027.

    Article  Google Scholar 

  59. Bu Y, Parkinson J, Thaichon P. Influencer marketing: Homophily, customer value co-creation behaviour and purchase intention. J Retail Consum Serv. 2022;66:102904.

    Article  Google Scholar 

  60. Hoffner C, Buchanan M. Young adults' wishful identification with television characters: the role of perceived similarity and character attributes. Media Psychol. 2005;7(4):325–51.

    Article  Google Scholar 

  61. Wanchai A, Armer JM, Stewart BR. Complementary and alternative medicine use among women with breast cancer: a systematic review. Clin J Oncol Nurs. 2010;14(4):E45–55.

    Article  PubMed  Google Scholar 

  62. Grant SJ, Bin YS, Kiat H, Chang DH-T. The use of complementary and alternative medicine by people with cardiovascular disease: A systematic review. BMC Public Health. 2012;12:299.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Reid R, Steel A, Wardle J, Trubody A, Adams J. Complementary medicine use by the Australian population: a critical mixed studies systematic review of utilisation, perceptions and factors associated with use. BMC Complement Altern Med. 2016;16:176.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Keim-Malpass J, Albrecht TA, Steeves RH, Danhauer SC. Young women's experiences with complementary therapies during cancer described through illness blogs. West J Nurs Res. 2013;35(10):1309–24.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Alzahrani AS, Greenfield SM, Paudyal V. Factors affecting complementary and alternative medicine (CAM) use by adult diabetic patients: A systematic review using the theoretical domains framework (TDF). Res Social Adm Pharm. 2022;18(8):3312–22.

    Article  PubMed  Google Scholar 

  66. Pallivalappila AR, Stewart D, Shetty A, Pande B, McLay JS. Complementary and alternative medicines use during pregnancy: A systematic review of pregnant women and healthcare professional views and experiences. Evid Based Complement Alternat Med. 2013;2013:205639.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Agu JC, Hee-Jeon Y, Steel A, Adams J. A systematic review of traditional, complementary and alternative medicine use amongst ethnic minority populations: A focus upon prevalence, drivers, integrative use, health outcomes, referrals and use of information sources. J Immigr Minor Health. 2019;21(5):1137–56.

    Article  PubMed  Google Scholar 

  68. Chung VCH, Ma PHX, Lau CH, Wong SYS, Yeoh EK, Griffiths SM. Views on traditional Chinese medicine amongst Chinese population: a systematic review of qualitative and quantitative studies. Health Expect. 2014;17(5):622–36.

    Article  PubMed  Google Scholar 

  69. Verhoef MJ, Balneaves LG, Boon HS, Vroegindewey A. Reasons for and characteristics associated with complementary and alternative medicine use among adult cancer patients: a systematic review. Integr Cancer Ther. 2005;4(4):274–86.

    Article  PubMed  Google Scholar 

  70. Willcox M, Donovan E, Hu X-Y, Elboray S, Jerrard N, Roberts N, et al. Views regarding use of complementary therapies for acute respiratory infections: systematic review of qualitative studies. Complement Ther Med. 2020;50:102382.

    Article  PubMed  Google Scholar 

  71. James PB, Wardle J, Steel A, Adams J. Traditional, complementary and alternative medicine use in sub-Saharan Africa: a systematic review. BMJ Glob Health. 2018;3(5):e000895.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Bishop FL, Yardley L, Lewith GT. A systematic review of beliefs involved in the use of complementary and alternative medicine. J Health Psychol. 2007;12(6):851–67.

    Article  PubMed  Google Scholar 

  73. Ryan A, Wilson S, Taylor A, Greenfield S. Factors associated with self-care activities among adults in the United Kingdom: a systematic review. BMC Public Health. 2009;9:96.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Chali BU, Hasho A, Koricha NB. Preference and practice of traditional medicine and associated factors in Jimma town, Southwest Ethiopia. Evid Based Complement Alternat Med. 2021;2021:9962892.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Ventola CL. Current issues regarding complementary and alternative medicine (CAM) in the United States: part 1: the widespread use of CAM and the need for better-informed health care professionals to provide patient counseling. PT. 2010;35(8):461–8.

    Google Scholar 

  76. Wu PCS, Wang Y-C. The influences of electronic word-of-mouth message appeal and message source credibility on brand attitude. Asia Pac J Mark Logist. 2011;23(4):448–72.

    Article  Google Scholar 

  77. Sharma V, Holmes J, Sarkar IN. Identifying complementary and alternative medicine usage information from internet resources: A systematic review. Methods Inf Med. 2016;55(4):322–32.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Goldsmith RE, Lafferty BA, Newell SJ. The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands. J Advertising. 2000;29(3):43–54.

    Article  Google Scholar 

  79. Erdem T, Swait J, Louviere J. The impact of brand credibility on consumer price sensitivity. Int J Res Mark. 2002;19:1–19.

    Article  Google Scholar 

  80. Erdem T, Swait J. Brand credibility, brand consideration, and choice. J Consum Res. 2004;31(1):191–8.

    Article  Google Scholar 

  81. Erdem T, Swait J. Brand equity as a signaling phenomenon. J Consum Psychol. 1998;7(2):131–57.

    Article  Google Scholar 

  82. Spry A, Pappu R, Cornwell TB. Celebrity endorsement, brand credibility and brand equity. Eur J Mark. 2011;45(6):882–909.

    Article  Google Scholar 

  83. Oppong PK. The effect of brand credibility, brand image and customer satisfaction on behavioural intentions in traditional medicine market. J Soc Dev Sci. 2021;11(4(S)):15–25.

    Google Scholar 

  84. Reinikainen H, Munnukka J, Maity D, Luoma-aho V. ‘You really are a great big sister’ – parasocial relationships, credibility, and the moderating role of audience comments in influencer marketing. J Mark Manag. 2020;36(3–4):279–98.

    Article  Google Scholar 

  85. Thongruang C. Consumer purchasing behavior for herbal medicine in drugstore in Bangkok. Naresuan Univ J. 2008;16(3):195–202.

    Google Scholar 

  86. Granovetter MS. The strength of weak ties. Am J Sociol. 1973;78(6):1360–80.

    Article  Google Scholar 

  87. Belanche D, Casaló LV, Flavián M, Ibáñez-Sánchez S. Understanding influencer marketing: the role of congruence between influencers, products and consumers. J Bus Res. 2021;132:186–95.

    Article  Google Scholar 

  88. Metzger MJ, Flanagin AJ. Credibility and trust of information in online environments: the use of cognitive heuristics. J Pragmat. 2013;59:210–20.

    Article  Google Scholar 

  89. Onu CA, Nwaulune J, Adegbola EA, Nnorom G. The effect of celebrity physical attractiveness and trustworthiness on consumer purchase intentions: A study on Nigerian consumers. Manag Sci Lett. 2019;9:1965–76.

    Article  Google Scholar 

  90. Rehman SU, Bhatti A, Mohamed R, Ayoup H. The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan. J Glob Entrep Res. 2019;9(1):1–25.

    Article  Google Scholar 

  91. Sánchez-Fernández R, Jiménez-Castillo D. How social media influencers affect behavioural intentions towards recommended brands: the role of emotional attachment and information value. J Mark Manag. 2021;37(11–12):1123–47.

    Article  Google Scholar 

  92. Kowalczyk CM, Pounders KR. Transforming celebrities through social media: the role of authenticity and emotional attachment. J Prod Brand Manag. 2016;25(4):345–56.

    Article  Google Scholar 

  93. Lim JS, Choe M-J, Zhang J, Noh G-Y. The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: A social cognitive theory perspective. Comput Hum Behav. 2020;108:106327.

    Article  Google Scholar 

  94. Jang A, Kang D-H, Kim DU. Complementary and alternative medicine use and its association with emotional status and quality of life in patients with a solid tumor: A cross-sectional study. J Altern Complement Med. 2017;23(5):362–9.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Till BD, Busler M. The match-up hypothesis: physical attractiveness, expertise, and the role of fit on brand attitude, purchase intent and brand beliefs. J Advertising. 2000;29(3):1–13.

    Article  Google Scholar 

  96. Ajzen I. Constructing a theory of planned behavior questionnaire 2006 [https://www.researchgate.net/publication/235913732_Constructing_a_Theory_of_Planned_Behavior_Questionnaire. Accessed March 23, 2023.

  97. Evans HM. Do patients have duties? J Med Ethics. 2007;33(12):689–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Jacobson GM, Cain JM. Ethical issues related to patient use of complementary and alternative medicine. J Oncol Pract. 2009;5(3):124–6.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Jin J, Sklar GE, Oh VMS, Li SC. Factors affecting therapeutic compliance: A review from the patient's perspective. Ther Clin Risk Manag. 2008;4(1):269–86.

    PubMed  PubMed Central  Google Scholar 

  100. Shewamene Z, Dune T, Smith CA. The use of traditional medicine in maternity care among African women in Africa and the diaspora: a systematic review. BMC Complement Altern Med. 2017;17(1):382.

    Article  PubMed  PubMed Central  Google Scholar 

  101. George M, Topaz M. A systematic review of complementary and alternative medicine for asthma self-management. Nurs Clin N Am. 2013;48(1):53–149.

    Article  Google Scholar 

  102. Truant TL, Porcino AJ, Ross BC, Wong ME, Hilario CT. Complementary and alternative medicine (CAM) use in advanced cancer: a systematic review. J Support Oncol. 2013;11(3):105–13.

    Article  PubMed  Google Scholar 

  103. Keene MR, Heslop IM, Sabesan SS, Glass BD. Complementary and alternative medicine use in cancer: A systematic review. Complement Ther Clin Pract. 2019;35:33–47.

    Article  PubMed  Google Scholar 

  104. Liwa AC, Smart LR, Frumkin A, Epstein H-AB, Fitzgerald DW, Peck RN. Traditional herbal medicine use among hypertensive patients in sub-Saharan Africa: a systematic review. Curr Hypertens Rep. 2014;16(6):437.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Zhao Y, Zhang J. Consumer health information seeking in social media: a literature review. Health Info Libr J. 2017;34(4):268–83.

    Article  PubMed  Google Scholar 

  106. Kim S, Lee J, Yoon D. Norms in social media: the application of theory of reasoned action and personal norms in predicting interactions with Facebook page like ads. Commun Res Rep. 2015;32(4):322–31.

    Article  Google Scholar 

  107. Munnukka J, Maity D, Reinikainen H, VilmaLuoma-aho. “Thanks for watching”. The effectiveness of YouTube vlogendorsements. Comput Hum Behav. 2019;93:226–34.

    Article  Google Scholar 

  108. Galbraith N, Moss T, Galbraith V, Purewal S. A systematic review of the traits and cognitions associated with use of and belief in complementary and alternative medicine (CAM). Psychol Health Med. 2018;23(7):854–69.

    Article  PubMed  Google Scholar 

  109. Wiesenera S, Falkenberg T, Hegyid G, Hök J, Sarsinae PR, Fønnebø V. Legal status and regulation of complementary and alternative medicine in Europe. Forschende Komplementärmedizin. 2012;19(Suppl 2):29–36.

    Google Scholar 

  110. Liang Z, Hu H, Li J, Yao D, Wang Y, Ung COL. Advancing the regulation of traditional and complementary medicine products: A comparison of five regulatory systems on traditional medicines with a long history of use. Evid Based Complement Alternat Med. 2021;2021:5833945.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Plachkinova M, Kettering V, Chatterjee S. Increasing exposure to complementary and alternative medicine treatment options through the design of a social media tool. Health Systems (Basingstoke). 2019;8(2):99–116.

    Article  Google Scholar 

  112. Satija A, Bhatnagar S. Complementary therapies for symptom management in cancer patients. Indian J Palliat Care. 2017;23(4):468–79.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Greenlee H, DuPont-Reyes MJ, Balneaves LG, Carlson LE, Cohen MR, Deng G, et al. Clinical practice guidelines on the evidence-based use of integrative therapies during and after breast cancer treatment. CA Cancer J Clin. 2017;67(3):194–232.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Mansky PJ, Wallerstedt DB. Complementary medicine in palliative care and cancer symptom management. The Cancer J. 2006;12(5):425–31.

    Article  PubMed  Google Scholar 

  115. Jiang X, Williams KM, Liauw WS, Ammit AJ, Roufogalis BD, Duke CC, et al. Effect of ginkgo and ginger on the pharmacokinetics and pharmacodynamics of warfarin in healthy subjects. Br J Clin Pharmacol. 2005;59(4):425–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Jiang X, Williams KM, Liauw WS, Ammit AJ, Roufogalis BD, Duke CC, et al. Effect of St John's wort and ginseng on the pharmacokinetics and pharmacodynamics of warfarin in healthy subjects. Br J Clin Pharmacol. 2004;57(5):592–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Piscitelli SC, Burstein AH, Welden N, Gallicano KD, Falloon J. The effect of garlic supplements on the pharmacokinetics of saquinavir. Clin Infect Dis. 2002;34(2):234–8.

    Article  PubMed  Google Scholar 

  118. Engelsen J, Nielsen JD, Hansen KFW. Effect of coenzyme Q10 and Ginkgo biloba on warfarin dosage in patients on long-term warfarin treatment. A randomized, double-blind, placebo-controlled cross-over trial. Ugeskr Laeger. 2003;165(18):1868–71.

    PubMed  Google Scholar 

  119. Gurley BJ, Gardner SF, Hubbard MA, Williams DK, Gentry WB, Cui Y, et al. Clinical assessment of effects of botanical supplementation on cytochrome P450 phenotypes in the elderly: St John's wort, garlic oil, Panax ginseng and Ginkgo biloba. Drugs Aging. 2005;22(6):525–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Tan SS-L, Goonawardene N. Internet health information seeking and the patient-physician relationship: A systematic review. J Med Internet Res. 2017;19(1):e9-e.

    Article  Google Scholar 

  121. Bujnowska-Fedak MM, Węgierek P. The impact of online health information on patient health behaviours and making decisions concerning health. Int J Environ Res Public Health. 2020;17(3):880.

    Article  PubMed  PubMed Central  Google Scholar 

  122. Jia X, Pang Y, Liu LS. Online health information seeking behavior: A systematic review. Healthcare (Basel). 2021;9(12):1740.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Gierth L, Bromme R. Attacking science on social media: how user comments affect perceived trustworthiness and credibility. Public Underst Sci. 2020;29(2):230–47.

    Article  PubMed  Google Scholar 

  124. Soubra R, Hasan I, Ftouni L, Saab A, Shaarani I. Future healthcare providers and professionalism on social media: a cross-sectional study. BMC Med Ethics. 2022;23(1):4.

    Article  PubMed  PubMed Central  Google Scholar 

  125. Herrera-Peco I, Jimenez-Gomez B, Pena Deudero JJ, Benitez De Gracia E, Ruiz-Nunez C. Healthcare Professionals' role in social media public health campaigns: analysis of Spanish pro vaccination campaign on twitter. Healthcare (Basel). 2021;9(6):662.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Gunasekeran DV, Tseng RMWW, Tham Y-C, Wong TY. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med. 2021;4(1):40.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health. 2020;20(1):1193.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Unni EJ, Patel K, Beazer IR, Hung M. Telepharmacy during COVID-19: A scoping review. Pharmacy (Basel). 2021;9(4):183.

    Article  PubMed  PubMed Central  Google Scholar 

  129. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M, et al. Developing and evaluating complex interventions: the new Medical Research Council guidance. Br Med J. 2008;337:a1655.

    Article  Google Scholar 

Download references

Acknowledgements

The authors of this study extend their appreciation to the Research Supporting Project, King Saud University, Saudi Arabia, for supporting this study (RSP-2023/378) and for funding this work.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Research Supporting Project, King Saud University, Saudi Arabia, (RSP-2023/378) who provided funding for this work.

Author information

Authors and Affiliations

Authors

Contributions

GG: designed and conceptualized the study, collected and analyzed data, drafted the manuscript, critically revised the manuscript, and gave final approval of the version to be published.

MBU: designed and conceptualized the study, co-drafted the manuscript, collected and analyzed data, critically revised the manuscript, and gave final approval of the version to be published.

AI: designed and conceptualized the study, co-drafted the manuscript, visualized the figures, critically revised the manuscript, and gave final approval of the version to be published.

CA: designed and conceptualized the study, critically revised the manuscript and gave final approval of the version to be published.

WS: critically revised the manuscript and gave final approval of the version to be published and provided funding.

MBAA-R: critically revised the manuscript and gave final approval of the version to be published provided funding.

Corresponding authors

Correspondence to Ayesha Iqbal or Wajid Syed.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gülpınar, G., Uzun, M.B., Iqbal, A. et al. A model of purchase intention of complementary and alternative medicines: the role of social media influencers’ endorsements. BMC Complement Med Ther 23, 439 (2023). https://doi.org/10.1186/s12906-023-04285-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12906-023-04285-1

Keywords