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Navigating Purchase Intentions: The Influence of Reviewers’ Comments on Consumer Behavior

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13 May 2024

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14 May 2024

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Abstract
For most people, using social media to research products has become common. Despite previous research, there is a gap in understanding reviewers' product-related comments and how they lead to purchase intentions. In terms of contributions, this study analyses people's dependence on social media for product information in purchase intention while considering the moderating impact of risk and trust on the link between purchase intention and social media. The data were collected quantitatively using surveys. The sample consisted of 384 students. The study hypotheses were tested using the PROCESS macro after exploratory factor analysis and correlation analysis were conducted. Among those surveyed, it was confirmed that most people sought product-related comments on social media when seeking information about a product they might be interested in purchasing. It was also observed that the risk factor reduced the impact of other variables in the model presented in this study. In conclusion, this investigation is more reliable, and its outcomes benefit scholars, supervisors, merchants, and webmasters, for instance, in establishing a study for future research efforts and providing practical guidance that could boost promotional efforts and marketing activities, especially in this era of artificial intelligence.
Keywords: 
Reviewers' comments
;  
social media
;  
purchase intention
;  
risk
;  
trust
Subject: 
Social Sciences  -   Behavior Sciences

supplementary.zip (94.64KB )

1. Introduction

Social media has influenced significant aspects of human life [1]. With masses of people present on social media, it has become an "online world" in which people live [2,3]. This new life on social media has become so natural that most people make significant decisions based on the information they receive or see on social media, including judgments on purchases [4]. Therefore, social media is now considered a significant factor influencing online purchase decisions [5].
Purchase intention is one of the final stages before an individual makes a purchase, and it occurs when an individual decides to patronize a product or service and is willing to part ways with their money in exchange for the product or service [6]. In deciding to purchase, several factors come into play. Sometimes, people do not even know that they need a product or service until they are exposed to information about it [7]. This is because social media exposes users to information that affects their willingness to purchase and facilitates their decision-making process [5,8].
Most marketers use social media as a tool to drive customers’ purchase intentions [9] through paid advertisements, as well as unpaid or organic advertisements. [6] explained that social media has become a means of advertising to target populations and influencing purchase intentions. Most organizations also use social media influencers to advertise their products and share information that they intend to spread to their existing and potential customers [10]. The justification for organizations’ use of social media influencers and celebrities’ social media influencers and celebrities in advertisements is that the followers of these celebrities and media influencers tend to relate more to them, and as such, they believe that these celebrities and media influencers are trustworthy [11]. As [12] advanced, credibility is the most important factor in regard to online advertisements.
Marketers are mostly concerned about the perceptions of their existing and target customers of the credibility of the messages, information, and advertisements they share [12]. Recently, marketers have recognized the importance of customer reviews and word-of-mouth referrals in improving the credibility of their products, services, or brands. Several studies have investigated the usefulness of online reviews in influencing purchase intentions, especially when those reviews are considered trustworthy or credible [13,14]. One study that looked at this topic was [15], whose study concluded that hotels received a greater number of bookings when there were existing and positive online reviews about the hotel.
With the constant increase in technology usage, it is not unexpected that most people seek information about products and services through social media rather than traditional media [5,6,8]. Existing studies have shown that as people constantly seek these products and services on social media, they develop a dependency on social media [5,16,17,18]. In other words, constantly seeking product and service information on social media leads to dependency on social media for product information; this dependency on social media affects purchase intentions [6,8,18].
This study intends to examine how risks and trust moderate the relationship between dependency on social media for reviewers’ comments and purchase intentions. This research article makes several contributions. This is the first study to focus on the subtle consequences of social media reviewer comments on purchase intention. The majority of research that is currently available aggregates all social media interactions without differentiating between the unique effects of perceived risk and trustworthiness related to “individual reviewer comments”. Additionally, the focus of earlier research on social media dependence was on people's addictions to platforms rather than their reliance on social media for product information. This study addresses social media dependence as the reliance on social media, rather than an addiction to social media. Furthermore, this study was conducted with diverse participants, from different countries, and continents of the world, stationed in a region that has not been explored in previous research. This study therefore provides extensive information, on the social media influence dynamics worldwide, as well as its unique impact in an area that has received less research attention, providing insightful information on regional differences in consumer behavior and the decision-making process. This research makes a distinctive contribution to our understanding of how diverse cultural and socioeconomic circumstances impact the effectiveness and reception of social media evaluations by including participants from a range of backgrounds in North Cyprus. Furthermore, a varied participant pool improves the generalizability of the results by offering a more comprehensive understanding of how risk and trust are viewed in relation to social media-driven purchase intentions across various demographic groups. In view of this, the research question guiding this study is as follows: do trust and perceived risk of reviewer comments on social media moderate the relationship between consumers' dependence on social media for product information and their subsequent purchase intentions?

1.1. Literature Review

1.1.1. Seeking Product-Related Reviews on Social Media and Purchase Intentions

The opinion or feedback of customers regarding a particular product or brand constitutes a product or brand review [19]. A growing number of businesses are advertising their products and services through social media [20]. It is rare for internet users to read web pages in full detail. Instead, they scan the page and retrieve only the needed information [14,21]. Searching for information should be quick and easy, and people should put little effort into finding what they need [14]. As social networking media elements have gained popularity and enhanced, customers now have more options for accumulating product-specific information, which provides numerous avenues for consumers to contribute their consumption-related suggestions through engagement with online communities in the form of reviews [22]. As such, a reviewer is any person who provides feedback, criticism, or information about a product or service.
The growth of online consumer reviews has made them an important marketing communication tool because many consumers use them as the first step in their shopping process. Ratings and reviews have a considerable impact on social presence, proximity, familiarity, and informational support, but they do not significantly affect emotional support or purchase intentions, according to a study by [23] concerning ratings and reviews. This, however, runs counter to the conclusions made by [5].
People connect with content on social media by watching, reading, or hearing it and creating their content [24]. As a result, it is an excellent resource for information for many people. Social media has been hailed as one of the most essential sources for purchasing and influencing consumer decision-making [23]. Most individuals actively look for product information on social media.
Online customer reviews simplify customer decision-making by reducing customer mental strain while simultaneously increasing purchase intention [25]. The decision-making process is also heavily influenced by the nature of the product and the traits of the users. Compared with products with negative reviews on eWOM platforms, positive reviews receive more recommendations from friends. Instead of depending only on advertisements, buyers frequently read prior customer reviews before deciding what to buy [25]. Consumers can gain more confidence in their purchase decisions by reviewing online comments. Hence, the following hypothesis is proposed:
H1- 
Seeking reviewers' product-related comments on social media affect purchase intentions.

1.1.2. Dependency on Social Media for Reviewer's Comments on Products

Dependency is defined by [26] as a relationship where one party depends on the resources of another to satisfy needs or to reach goals. The distinctive and essential conceptualization of media dependence relations is the foundation for MSD, which explains the reasons behind and effects of people's media use [16]. This study examines how social media use can alter customers' views about buying products using the well-known media system dependence theory (MSD) as a theoretical framework.
Media system dependency theory: According to [5], a dependence relationship is one in which the resources of one person assist the other in meeting their needs, hence preserving the relationship. [16] reported that when people’s needs are met any time they interact with media, they become dependent on media. The media has a significant impact if people rely on them whenever they need to obtain a piece of information [16,27]. Audience dependency is determined by how long a medium is used [18]. In addition, an individual's greater dependency on a medium may be due to the medium meeting their needs [16].
When one party needs another's resources to achieve its goals, the party repeats the procedure or is likely to repeat the process each time it needs that resource, reinforcing dependency [28]. People frequently seek information on social media and depend on it for resources and information to accomplish a goal: decision-making [16,29]. Therefore, using social media to research products and make decisions leads to dependency on social media [30].
Individuals are more prone to forming dependent connections with the media depending on how well it satisfies their expectations and wants, which affects how they utilize it [17]. According to [16], when social media gives people the information they need, they become utterly dependent on it. Based on their findings, [18] further confirmed that people's time in the media determines their dependency. Additionally, the literature has demonstrated that using social media for informational purposes makes one dependent on it. Additionally, the literature has shown that people increasingly rely on social media to research products (Ben Abdelaziz et al., 2015; Kheiravar, 2018; Li et al., 2019; Luqiu & Kang, 2021). Based on this, the following hypotheses are proposed:
H2- 
Seeking reviewers' product-related comments has a positive impact on dependency on social media.
H3: 
Social media dependency mediates the relationship between seeking reviewers' product-related comments and purchase intentions.

1.1.3. Risks of Buying a Product

Risk in the context of this research refers to the fear of a product not meeting the expectations or needs of the customer, a product not being sustainable, or a product that can cause reputational damage to the user. When a review of a product provides negative information about the product, consumers perceive the product to be risky [31]. There is ample evidence that perceived risk negatively impacts purchase intentions [15,32]. [15] demonstrated that consumers' perceptions of the possibility of product malfunction have a negative impact on their intention to purchase a product.
[5] state that people who utilize social media and believe that products with negative comments are detrimental are more likely to be aware of the negative aspects of the products and to adopt the negative opinions of other users about the products. Risk harms consumers and will likely reduce their purchase intentions [31]. If seeking product information on social media makes people dependent on social media and a review that portrays the risk of a product possesses the ability to influence purchase intention, then the following hypothesis is proposed:
H4- 
Risk moderates the relationship between dependency on social media reviews and purchase intentions.

1.1.4. Trust in the Reviewer's Comments

Trust in the context of this research refers to the belief that reviews about a product are trustworthy and that the product will meet the customer's expectations. The findings of a study published by [7] suggest that customers depend on expert sources, popularity indications, and two-sided reviews to inform their choice of services. In other words, believing that a product or service's performance and quality are the same as or comparable to those of prior users' reviews is the basis for trusting product reviews on social media.
In online business transactions, trust is the most critical factor [23,33]. Recently, marketing managers have recognized that online reviews play an important role in the decision-making process of customers. Research indicates that online consumer reviews might be a more reliable information resource than information created by sellers (Jain et al., 2021). Due to the growing impact of online consumer reviews (OCRs) on consumers' decision-making processes, online vendors have started incorporating OCRs into their advertising. Compared with ads, reviews are more trustworthy in online shops. Purchase intentions are greater among potential consumers when OCRs are perceived as more trustworthy [34]. For this reason, trust in reviews influences purchase intentions [23,35]. If people become dependent on social media as a result of seeking product information through social media and their trust in product information on social media affects their purchase intentions, then the following hypothesis is proposed:
H5- 
Trust moderates the relationship between dependency on social media reviews and purchase intentions.

1.1.5. Dependency on Social Media and Purchase Intentions

It has been empirically demonstrated that media dependency positively influences purchase intentions because media provides sufficient product information. Consumers are hesitant to purchase products if they believe they lack the information necessary to make an informed decision [36]. The lack of specific product information or the difficulty in locating it through traditional offline communication channels confuses customers [36,37]. However, user-generated information offered by social media platforms can significantly impact consumers' purchase decisions because it is more enriched and more accessible to retrieve [38].
Making informed decisions during purchase is difficult for consumers who lack knowledge about a particular product [13]. As a result, consumers may delay or even stop making purchases to avoid cognitive strain, thus influencing their product choice. Social media benefits customers by offering thorough information from several sources, making them dependent on social media for product knowledge [25]. If using social media to research items makes people dependent on it and if consumers rely on reviewers' comments on social media to obtain product information to make well-informed purchasing decisions, then we propose the following hypothesis:
H6 
- Dependency on social media for reviewers' comments on products influences purchase intention.

1.2. Conceptual Model

This research proposed a conceptual model based on the literature, as shown in Figure 1.
The moderating role of trust and risk on the relationship between purchase intention and dependence on social media reviews is examined in this study, which establishes a precedent in this field of inquiry. Previous research has examined how social media reviews affect consumers' intentions to make purchases. Previous research has also concentrated on how trust in social media reviews influences purchase intentions. This study introduces dependency on social media in this debate. Rather than focusing solely on the association between reviews and purchase intentions, as other research has done, this study also examines trust and risk as moderators of the relationship between dependence on social media reviews and purchase intentions.

2. Materials and Methods

This section provides information about the methodology used for this research. This study aimed to examine how trust and risk moderate the association between purchase intention and dependence on social media reviews. The research conducted in the present study is a quantitative study that analyses quantitative data using quantitative analysis techniques. For this study's results to be objective and generalizable, a significant volume of data must be gathered. Objectivity was also a reason for choosing the quantitative research method. As [39] showed in their study, quantitative studies facilitate the collection and analysis of larger datasets, which can reduce the subjectivity of research results. The details of the materials and methods are explained in the following paragraphs.

2.1. Participants

The participants for this study were selected from North Cyprus Universities. There are students from many continents and countries in North Cyprus with various cultures, ideologies, and overall perspectives on life, making the sample more diverse and representative. Compared with nonstudents, students have an online presence, which is why students were chosen as the target population. Additionally, considering how active students are online, they are exposed to more reviewers’ comments from North Cyprus and their countries of residence.
Four of North Cyprus's most populous universities provided the study sample. The selected schools are Near East University, which is based in Nicosia and has a population of 27,000 [40]. Cyprus International University, based in Haspolat, has a population of more than 22,000 [41]. Girne American University, based in Girne, has an 18,000 population [42], and Eastern Mediterranean University, based in Famagusta), with an 18,000 population [43]. Table 1 below shows the participants from the student population based on available information as of 2024.
The sample size for this study included 384 students selected through the convenience sampling technique. The sample sizes of previous studies that examined related topics to this study ranged between 150 and 300 [45,46]. The sample size of 200 does not adequately represent the number of university students in North Cyprus, so this study considered a sample size of 384 since [44] estimates the number of university students to be 108,295—more than 78% of all students in North Cyprus study at the universities selected for this study. That is,
85,000/108,295 * 100%= 78%
A sample size of 384 participants was determined by employing a sample size calculator available online [47]. In addition, the 384-sample size is acceptable for this study, according to [48].

2.1.1. The Respondents' Demographic Characteristics

Gender, age, and educational attainment are the three demographic attributes of the respondents that were recorded for this research. An overview of the respondents' demographics is provided in Table 2. The study's findings indicate that 54.4% were male, 46.6% were more than 30 years old, and 58.9% had earned an undergraduate degree.

2.2. Data Collection Instrument

The study conducted a survey using a questionnaire developed by the researchers, using previous measurement variables and guided by theories. The survey comprised a 5-point Likert scale adapted to align with the study's themes. The data were collected both online, through Google Forms, and in person by handing the forms directly to the participants in their schools.
After collecting resources for this research from previous related studies and settling on the aim and questions for this study, the questionnaires for this research were prepared. The questionnaire included four demographic questions, seven questions for variable 1 (dependency on social media for reviews' comments on products), six questions for variable 2 (purchase intention), five questions for variable 3 (trust), six questions for variable 4 (seeking reviewers' product-related comments on social media), and four questions for variable 5 (risk).
For each of the different factors (variables of measurement) in the questionnaire, the researchers calculated the factor loadings and Cronbach's alpha using exploratory factor analysis. After the initial loading, two attitude statements were identified as irrelevant to the study. Hence, two questions were removed from variable 5 (risk). The Cronbach's alpha was then calculated again for variable 5 (factor 5). For factor 1, the Cronbach's alpha was 0.874, and for factor 2, it was 0.865. Factor 3 was 0.867, factor 4 was 0.779, factor 5 was 0.737, and the Cronbach’s alpha for all the variables or factors was 0.920, which indicated a high level of reliability for the variables of measurement. The convergent validity test, which is analyzed using factor loading, was used by the researchers to evaluate the study's validity.

2.3. Data Analysis Technique

IBM SPSS version 25 was used in this study to analyze the collected data. To identify the relationships between the observed variables, exploratory factor analysis (EFA) was used. A correlation analysis was performed to ensure that the variables were related. To test the study hypotheses, the researchers utilized SPSS version 4.2's PROCESS macro.

3. Results

This study distributed 400 questionnaires to students who fell within the sample frame: university students at Cyprus International University, Eastern Mediterranean University, Girne American University, and Near East University. There were, however, 384 completed questionnaires. This indicates that the realization rate for the survey was 96%.

3.1. Factor Analysis

Researchers break down the observed variables into smaller groups and determine how they are related using exploratory factor analysis [49]. The factors were extracted using Promax with the Kaiser normalization rotation method, a principal component analysis (PCA) approach. Following [50], only items with a loading value of at least 0.4 were included in this study. [51] specification of the requisite sample value is effectively met by the KMO of 0.903 and Bartlett's test significance level of (P 0.05).
The results of an exploratory factor analysis revealed that there are five distinct factors that account for 61.38% of the overall variance. With seven items and a loading range of 0.469 to 0.893, DSM accounted for 34.59% of the variance in the total. The PI loaded six items between 0.591 and 0.876, and 9.81% of the overall variance was explained by this factor. Five items in the T construct, which accounted for 7.23% of the variance overall, had loadings between 0.638 and 0.880. The loadings of six items in the SRP ranged between 0.480 and 0.837, accounting for 5.01% of the variance in total. Two of the four elements that made up R's initial construct were removed; a total of 4.74% of the variance was explained by the final two items, which loaded between 0.725 and 0.856. The results of the exploratory factor analysis are reported in Table 3.
CFA, which relies on the existence of a single dimension underpinning a set of measures, was employed to guarantee the unidimensionality of construct identification. AMOS version 24 was utilized for this purpose. Using the convergent validity test, the researchers examined the validity of this investigation. Factor loading is one way to analyze convergent validity, according to [50]. Also, to attain validity, the composite reliability (CR) should be greater than or equal to 0.6, and the average variance extracted (AVE) should be 0.5 or greater. The confirmatory factor analysis results are summarized in Table 4, which also demonstrates that all the constructs satisfy the validity requirements and are dependable.
Figure 2 captures the CFA
Furthermore, as Table 4 illustrates, [52] identified six indicators of the quality of the model fit: the comparative fit index (CFI), the normative fit index (NFI), the root mean square error of approximation (RMSEA), the incremental fit index (IFI), the standardized root mean square residual (SRMR), and the chi-square/degree of freedom (CMIN/DF). The study's CMIN/DF value, which was 2.189, fully satisfied [53] fewer than three criteria. Additionally, the CFI, NFI, and IFI values were 0.936, 0.890, and 0.937, respectively. The criteria of [54], [55], and [56] were all met by these indicator values, which were all very close to 0.9. Additionally, the values of RMSEA (0.056) and SRMR (0.054) met the benchmarks set by [56] and [57]. It is possible to conclude that the model fits the data sufficiently when taking into account the outcomes of these fit indices (Table 5). Thus, it is possible to perform the following analysis.

3.2. Mean Scores of the Study Variables

The mean scores for the research variables are displayed in Table 6. For every variable, the respondents' mean score exceeded the 3.00 midpoint level. According to these results, most participants agreed that they depend on social media for product reviews; most participants agreed that social media product reviews have an impact on their decision to buy; the majority of the participants trusted reviewers' product-related comments on social media; the majority of the participants agreed to seek reviewers' product-related remarks on social media; and most participants agreed that negatively reviewed products are risky purchases.
Table 7 displays the correlation analysis results, which reveal that all seven constructs had a positive correlation with one another at a significance level of 0.01.

3.3. Hypothesis Testing

To test the study hypotheses, the researchers utilized SPSS version 4.2's PROCESS macro. The hypothesis details are compiled in Table 8. The findings of this investigation demonstrated that the hypotheses produced statistically significant results following [58] criteria. The results showed that H1 (Seeking reviewers' product-related comments on social media affect purchase intentions) has a very weak relationship with the SRP and PI (R2=0.2201, p=0.000). H2 (Seeking reviewers' product-related comments positively impact dependency on social media) demonstrated that the SRP has a very weak impact on DSM (R2=0.2758, p=0.000). H3 (Dependency on social media mediates the relationship between Seeking Reviewers' product-related comments and purchase intentions) DSM mediates the relationship between the SRP and PI and has a moderate and positive influence on both (R2=0.5259, p=0.000). H4 (Risk moderates the relationship between seeking reviewers' product information, dependency on social media reviews, and purchase intentions) demonstrated that R moderates the relationship between DSM and PI and has a weak impact on the relationship between DSM and PI (R2=0.4462, p=0.070). H5 was rejected. H6 (Dependency on social media for reviewers' comments on products influences purchase intentions) indicated that DSM has a positive and very weak impact on PI (R2=0.2150, p=0.000). Thus, except for H5, which was rejected, all of the hypotheses had statistically significant results and were accepted.
This study used Process Macro Model 14 to examine the relationships between the dependent variable (TPI), the moderator (TR), and the mediator (TDSM). According to Figure 3 below, as risk increases, purchase intention decreases.

4. Discussion

The relationship between dependence on social media reviews and purchase intentions was explored in this study to determine how risks and trust affect purchase intentions. In other words, do risk and trust make customers perceive social media reviews differently, and does the dependency on social media reviews make customers see the product or service negatively or positively? With the help of a survey of 384 students studying at North Cyprus universities, this study achieved its aim.
The relationship between purchase intentions and Seeking Reviewers' product-related comments on social media was 22%. The results imply a weak relationship between purchase intentions and seeking reviewers' product-related comments on social media. One reason for the poor correlation between the SRP and IP is the excessive number of reviews. As numerous reviews and comments are available, individuals may find it difficult to sift through the data and draw a direct link between those reviews and their purchasing decisions. Ensuring that reviews are up to par can improve the overall usefulness of information [13,14,25]. This study recommends the integration of reviews with expert perspectives. This can give consumers a more balanced perspective by combining the perspectives of industry professionals and regular users.
The relationship between the SRP and DSM was 27.6%. The results indicated a weak relationship between the SRP and DSM. Social media is a useful tool for seeking information, and people become dependent on social media when they constantly seek information on social media [29]; this finding has been confirmed in several studies, including that of [16]. The current study also confirms that people depend on social media for product information. The weak relationship between the SRP and DSM, however, can be explained by the lack of consensus among social media reviews, where there are differing opinions, information overload, and inconsistent information; as a result, consumers find it difficult to make educated decisions based solely on social media reviews, especially in the absence of expert opinions. Likewise, dependency on social media depends on how well social media satisfies the need for user information [17]. Therefore, to increase the relationship between the SRP and DSM, users must find beneficial information each time they use social media. This study recommends that organizations indicate reviews from customers who have bought and utilized their products. This can solve the effect of inconsistent information that may deter the use of social media. Additionally, the availability of expert opinions can curb the effect of information overload.
DSM mediates the relationship between the SRP and PI by 52.3%. The results imply that DSM is an effective mediator in the relationship between the SRP and PI. Marketing and advertising campaigns may influence the association between the SRP and the PI. The mediating effect of DSM may be mitigated by powerful and widespread marketing messaging. Similarly, there could be a moderate mediating effect if consumers regard social media reviews as just one information source among many. The degree to which social media content is perceived as more dependable and of higher quality than information from other sources may affect the effectiveness of such mediation. Organizations must collaborate with influencers and industry experts to create credible social media reviews. Having well-known people involved might increase the impact of comments made on social media and make users more reliant on the site. The study also recommends including components of social proof in the review display, including the number of views, likes, and shares. Users' perceptions of social media remarks can be improved by social evidence, which makes them seem more valuable.
The interaction between SRP, DSM, R, and PI was 44.5%. This implies that there was a weak relationship between the SRP, DSM, and PI, with R as the moderator. R in this study’s analysis consisted of the following attitude statements: “I would face negative consequences if I use this product/brand because of social or environmental harm”, which addressed sustainability issues, and “using the product/brand would damage my reputation or image as a person”, which addresses societal concern and identity and the need to maintain a good reputation in society. The weak relationship can therefore be attributed to users' worries about sustainability issues, societal concerns and identity, and reputational issues. [31], explained that consumers perceive a product to be risky if a review provides negative information about that product. Therefore, the researchers recommend that organizations monitor social media reviews and address any issues raised, especially issues regarding sustainability and reputational damage. Negative reviews about products should also be discussed extensively to clarify the doubts of potential customers who may depend on reviews to make a purchase decision. Resolving problems related to perceived risks can build DSM.
The relationship between dependency on social media for reviewers' comments on products and purchase intentions was 21.5%. This implies that people are dependent on social media to make purchase decisions. Based on the results of this study, dependency on social media significantly changes the relationship between seeking reviewers' product-related comments on social media and purchase intent. According to previous literature and the findings of this study, it is clear that as people continuously seek information from social media, they become dependent upon it for the feedback of reviewers [16,18]. People will continue to seek information on social media if they find important or interesting information. This implies that if people find essential product information on social media, they will continue to seek important information on social media, and hence, they will become dependent on social media, which will influence purchase intentions. This analogy could explain why the dependency on social media in this study did not improve the relationship between the SRP and PI. Thus, if people are unable to find engaging product information on social media regarding a product they wish to purchase, they may cease seeking information this way; thus, they may not be dependent on social media.
Similarly, if the information is engaging and leads to dependency on social media but is not useful for analyzing the worth of a product, it may not be able to influence a purchase. The researchers believe that the information available must be engaging and useful for DSM to mediate the SRP and PI effectively. As a result, it is recommended that reviews about products are designed to address the pertinent concerns most customers have about the product. This would require organizations to actively involve themselves in review sites, provide information, clarify misconceptions, and correct product or service mistakes. This can be achieved with social media listening tools.

5. Conclusions

In this study, we explored how reviews and people's dependency on social media for product information contribute to purchase intentions while also considering how risk and trust moderate the relationship between dependency on social media and purchase intent. This study succeeded in filling these research gaps. This study also introduced an additional variable (DSM) in the relationship between S and PI that has not been extensively researched in previous studies. The focus of earlier research on social media dependence was on people's addictions to platforms rather than their reliance on them for product information. This study focused on the dependency on social media concerning the dependence on social media for product information.
According to the results of this study, negatively reviewed products are risky purchases. As such, the presence of risk reduces purchase intention. Risk in this research was first defined as the fear of a product not meeting the expectations or needs of the customer, a product not being sustainable, or a product that can cause reputational damage to the user. After the analysis of the study, however, this study redefined risk in this study as the fear of a product not being sustainable and a product that can cause reputational damage to the user. The reason the study researchers redefined risk after the data analysis was because the first two items in the survey questionnaire that addressed risk as the fear of a product not meeting the expectations or needs of the customer were deleted after the initial factor loading since they were deemed irrelevant to the study. The remaining items that were considered relevant to the study addressed risk as the fear of a product not being sustainable and a product that can cause reputational damage to the user. As the study results revealed, people avoid products that have been negatively reviewed. What does an organization do if its products are negatively reviewed on social media? The researchers recommend that organizations that face negative reviews on social media be strategic in handling them. This may include addressing the source of concern for the Reviewer. If a product is flawed, the company must fix it and respond to the review with a revised version. The goal is to turn the majority of all negative reviews in favor of the organization. Organizations may need to go the extra mile to address harmful reviews on their social media pages; this must be done with caution because, if not adequately handled, it may aggravate the organization's dented image. This is the reason most organizations avoid addressing negative reviews. However, organizations that strategically address negative reviews have salvaged their images.
Considering the managerial and practical implications, based on the results of the study, most participants use social media to seek product-related comments from reviewers, and most depend on social media for product-related information. The influences that the SRP, DSM, and PI have on each other are significant, although their relationships are weak. Webmasters, supervisors, merchants, and social media marketers can strategically manipulate reviews to their advantage by making pertinent information easily accessible, offering professional reviews, and producing captivating content online. This will strengthen the correlation between the variables, thus enhancing purchase intention. Organizations may also benefit from DSM influenced by another organization or Reviewer. This is because as people find engaging product information on social media, they become dependent on social media for product information. However, suppose an organization fails to answer a question or concern about a product that a potential client is interested in buying. In that case, this potential client may purchase a similar product from another organization that provides the information they need, at least to some extent. Regardless of the source of a review, organizations must ensure that the information provided is complete to prevent their clients from moving to substitute or related products.
Additionally, regarding managerial implications, the digital landscape is evolving, which has affected how consumers seek information on social media. With the use of artificial intelligence (AI), most organizations are providing tools that make it easier and more effective for consumers to find their product or service information. For an organization to thrive, increase, or maintain its market share, it is essential to leverage AI tools and keep up with the quickly changing digital scene. AI-powered social listening tools can be used by organizations to retrieve customer reviews that can aid in sentiment analysis. Managing a brand's reputation effectively enables companies to address any unfavorable comments or concerns promptly and to maintain a positive reputation among the general public.
By analyzing social media data, artificial intelligence can identify new trends, predict consumer preferences, and estimate the demand for particular products. Artificial intelligence (AI) algorithms can be used to protect an organization's reputation on social media. This can be accomplished by identifying and reducing spam, fraudulent activity, and fake reviews. Coupled with the ability to utilize social media data to discover insights about competitors' products, customer sentiment, and marketing strategies, this can help an organization stay competitive. Therefore, for businesses to benefit from people’s dependency on social media for product information, prioritizing investments in AI technology might help.
This study also provides recommendations for future researchers. This study concluded that DSM mediates the SRP and PI. However, different moderators can significantly improve or reduce purchase intentions, even in the presence of DSM. According to the study, risk reduces PI, and trust is insignificant. If risk reduces purchase intention and trust is insignificant in the relationship, then what increases the mediating effect of DSM on the relationship between the SRP and PI? This study, therefore, recommends that future researchers conduct extensive research on factors that improve dependency on social media for product information that could influence purchase intentions. In light of this study's findings, it would be useful to conduct a qualitative study to gather additional information. This study also recommends that future researchers introduce "user engagement" as a moderator of the relationship between the SRP and DSM and "usefulness of information" as a moderator between DSM and PI. Another area of research that could build on the findings of this study would be to examine people's opinions about artificial intelligence (AI) and whether they are open to using AI-powered tools to obtain product-related information from social media sites.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org.

Author Contributions

All the authors contributed equally to this work.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and ethical approval was obtained from the ethics committee of the anonymized university before the study was conducted. Ethical approval for this study was given, with the application number xxxxxxxxx.

Informed Consent Statement

The participants were given access to a written consent form explaining the purpose of the study and requesting their approval to participate (available in the supplementary file). The identities of the participants were not revealed, and they remained anonymous throughout the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Confirmatory factor analysis.
Figure 2. Confirmatory factor analysis.
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Figure 3. Model representation between the variables.
Figure 3. Model representation between the variables.
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Table 1. Population of students from which the participants were drawn.
Table 1. Population of students from which the participants were drawn.
Name of institution Location of school Population of students Source of information
Near East University Nicosia, North Cyprus 27,000 [40]
Cyprus International University Haspolat, North Cyprus 22,000 [41]
Girne American University Girne, North Cyprus 18,000 [42]
Eastern Mediterranean University Famagusta, North Cyprus 18, 000 [43]
Total population of students in selected institutions 85,000
Total population of students in North Cyprus 108,295 [44]
Table 2. Respondents' demographic characteristics.
Table 2. Respondents' demographic characteristics.
Gender
Frequency Per cent
Valid Male 209 54.4
Female 175 45.6
Total 384 100.0
Age
Frequency Per cent
Valid 18-24 91 23.7
25-30 114 29.7
30+ 179 46.6
Total 384 100.0
Education
Frequency Per cent
Valid Undergraduate 226 58.9
Graduate 158 41.1
Total 384 100.0
Table 3. Exploratory Factor Analysis Results.
Table 3. Exploratory Factor Analysis Results.
Factors Items Factor Loading Cronbach's alpha Variance Explained %
DSM (Factor 1) 7 0.469 - 0.893 0.874 34.59%
PI (Factor 2) 6 0.591 – 0.876 0.865 9.81%
T (Factor 3) 5 0.638 – 0.880 0.867 7.23%
SRP (Factor 4) 6 0.480 – 0.837 0.779 5.01%
R (Factor 5) 2 0.725 – 0.856 0.737 4.74%
Total 26 0.920 61.38%
Note:DSM (dependency on social media), PI (purchase intention), T (trust), SRP (seeking reviewers' product-related information), R (risk)
Table 4. Confirmatory Factor Analysis Results.
Table 4. Confirmatory Factor Analysis Results.
Construct Items Factor Loading CR AVE
DSM (Factor 1) 7 0.660 - 0.833 0.888 0.53
PI (Factor 2) 6 0.641 – 0.775 0.860 0.51
T (Factor 3) 5 0.730 – 0.792 0.870 0.57
SRP (Factor 4) 6 0.662 – 0.870 0.878 0.55
R (Factor 5) 2 0.626 – 0.937 0.770 0.63
Note:DSM (dependency on social media), PI (purchase intention), T (trust), SRP (seeking reviewers' product-related information), R (risk)
Table 5. Fit indicators for the CFA Model.
Table 5. Fit indicators for the CFA Model.
Model CMIN DF P CMIN/DF CFI NFI IFI RMSEA SRMR
558.251 255 0.000 2.189 0.936 0.890 0.937 0.056 0.054
Table 6. Mean Scores of the Study Variables.
Table 6. Mean Scores of the Study Variables.
Variable Name Number of Items Means Standard Deviation
DSM 7 4.11 0.829
PI 6 3.90 0.965
T 5 3.89 0.810
SRP 6 3.74 0.902
R 4 3.49 0.997
Note:DSM (dependency on social media), PI (purchase intention), T (trust), SRP (seeking reviewers' product-related information), R (risk)
Table 7. Correlations between Variables.
Table 7. Correlations between Variables.
TSRP TDSM TR TT TPI
TSRP 1
TDSM .525** 1
TR .415** .389** 1
TT .361** .492** .576** 1
TPI .469** .464** .590** .598** 1
N=384
**. Correlation is significant at the 0.01 level (2-tailed).
Note: TSRP (Total seeking reviewers' product-related information), TDSM (Total Dependency on social media), TR (Total Risk), TT (Total Trust), TPI (Total Purchase intention)
Table 8. Results of hypothesis testing.
Table 8. Results of hypothesis testing.
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